JP7803301B2 - Virtual flow rate calculation device, virtual flow rate calculation method, and virtual flow rate calculation program - Google Patents

Description

The present invention relates to a virtual flow rate calculation device, a virtual flow rate calculation method, and a virtual flow rate calculation program.

Patent Document 1 states, "We provide a flowmeter design support system that can improve the measurement accuracy of differential pressure flowmeters and reduce the risk of non-negligible errors in flow rate indication values occurring in actual equipment at the design stage."
[Prior art documents]
[Patent Documents]
[Patent Document 1] JP 2020-144662
[Patent Document 2] JP 2019-194424
[Patent Document 3] JP 2012-132797 A
[Patent Document 4] JP 2009-014726 A

A first aspect of the present invention provides a virtual flow rate calculation device. The virtual flow rate calculation device includes an environmental information storage unit that stores environmental information indicating the environment of the real space in which a flow sensor is installed; a physical property information storage unit that stores physical property information indicating the physical properties of the fluid to be measured; a simulation unit that uses the environmental information and the physical property information to simulate the flow phenomenon of a multiphase flow in the fluid in a virtual space that reproduces the real space; and a calculation unit that calculates a virtual flow rate that estimates the actual flow rate measured by the flow sensor based on the results of the simulation.

In the virtual flow rate calculation device, the flow rate sensor may be at least one of a Coriolis flow meter, an ultrasonic flow meter, an electromagnetic flow meter, or a vortex flow meter.

In any of the virtual flow rate calculation devices, the flow rate sensor may be a Coriolis flowmeter, and the calculation unit may estimate at least one of the density of the fluid or the fraction of the multiphase flow based on the resonant frequency of the flow tube.

In any of the virtual flow rate calculation devices, the flow rate sensor may be a Coriolis flowmeter, and the calculation unit may estimate the multiphase flow fraction based on a drive current applied to an oscillator to vibrate a flow tube.

In any of the virtual flow rate calculation devices, the flow rate sensor may be an ultrasonic flowmeter, and the calculation unit may estimate the distribution state of particles dispersed in the fluid based on the results of analyzing the received ultrasonic signal strength for each frequency band.

In any of the virtual flow rate calculation devices, the flow rate sensor may be an ultrasonic flowmeter, and the calculation unit may estimate the uneven distribution state of particles dispersed in the fluid based on the difference in received signal strength of opposing ultrasonic waves.

In any of the virtual flow rate calculation devices, the flow rate sensor may be an electromagnetic flowmeter, and the calculation unit may estimate the amount of flow noise generated at the electrodes based on the electromotive force effect of electrical insulators dispersed in the fluid.

In any of the virtual flow rate calculation devices, the flow rate sensor may be an electromagnetic flow meter, and the calculation unit may estimate the multiphase flow fraction based on the results of a parametric calculation of the simulated results.

In any of the virtual flow rate calculation devices, the flow rate sensor may be a vortex flowmeter, and the calculation unit may estimate the fraction of the multiphase flow based on the amplitude strength of the voltage generated in the piezoelectric element.

In any of the virtual flow rate calculation devices, the flow rate sensor may be a vortex flowmeter, and the calculation unit may estimate the multiphase flow fraction based on noise generated in a piezoelectric element.

In any of the virtual flow rate calculation devices, if the fluid is in a gas-liquid two-phase multiphase state, the calculation unit may calculate the virtual flow rate of the gas phase alone based on the fraction of the multiphase flow.

Any of the virtual flow rate calculation devices may further include a diagnostic unit that issues an alert if the parameters estimated by the calculation unit do not meet predetermined criteria.

In any of the virtual flow rate calculation devices, the diagnostic unit may further issue an alert if the difference between the actual flow rate and the virtual flow rate does not satisfy a predetermined standard.

In any of the virtual flow rate calculation devices, the simulation unit may use an analytical model selected according to at least one of the phase combinations or flow patterns in the multiphase flow.

In any of the above virtual flow rate calculation devices, the analytical model may be a continuous phase model or a dispersed phase model.

Any of the virtual flow rate calculation devices may be provided by a cloud server.

A second aspect of the present invention provides a virtual flow rate calculation method. The virtual flow rate calculation method includes a computer storing environmental information indicating the environment of the real space in which a flow sensor is installed, storing physical property information indicating the physical properties of the fluid to be measured, simulating a multiphase flow phenomenon in the fluid in a virtual space that reproduces the real space using the environmental information and the physical property information, and calculating a virtual flow rate that estimates the actual flow rate measured by the flow sensor based on the results of the simulation.

A third aspect of the present invention provides a virtual flow rate calculation program. The virtual flow rate calculation program is executed by a computer and causes the computer to function as: an environmental information storage unit that stores environmental information indicating the environment of the real space in which the flow sensor is installed; a physical property information storage unit that stores physical property information indicating the physical properties of the fluid to be measured; a simulation unit that uses the environmental information and the physical property information to simulate the flow phenomenon of a multiphase flow in the fluid in a virtual space that reproduces the real space; and a calculation unit that calculates a virtual flow rate that estimates the actual flow rate measured by the flow sensor based on the results of the simulation.

Note that the above summary of the invention does not list all of the features of the present invention. Subcombinations of these features may also constitute inventions.

An example of a block diagram of a virtual flow rate calculation device 100 according to this embodiment is shown together with a sensor module 10. 1 shows an example of a flowchart of a virtual flow rate calculation method executed by a virtual flow rate calculation device 100 according to the present embodiment. 1 shows an example of a block diagram of a virtual flow rate calculation device 100 that functions as a virtual Coriolis flowmeter. 1 shows an example of a block diagram of a virtual flow rate calculation device 100 that functions as a virtual ultrasonic flow meter. 1 shows an example of a block diagram of a virtual flow rate calculation device 100 that functions as a virtual electromagnetic flow meter. 1 shows an example of a block diagram of a virtual flow rate calculation device 100 that functions as a virtual vortex flowmeter. An example of a block diagram of a virtual flow rate calculation device 100 according to a first modified example is shown together with a sensor module 10. An example of a block diagram of a virtual flow rate calculation device 100 according to a second modified example is shown together with a sensor module 10. An example of a multiphase flow pattern is shown below. FIG. 10 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual Coriolis flowmeter. FIG. 10 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual ultrasonic flow meter. FIG. 10 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual electromagnetic flow meter. FIG. 10 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual vortex flowmeter. 99 illustrates an example computer 9900 in which aspects of the present invention may be embodied, in whole or in part.

The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention as claimed. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

Figure 1 shows an example of a block diagram of a virtual flow rate calculation device 100 according to this embodiment, along with a sensor module 10. Note that these blocks are functionally separated functional blocks and do not necessarily correspond to the actual device configuration. In other words, just because something is shown as a single block in this diagram does not necessarily mean that it is made up of a single device. Also, just because something is shown as separate blocks in this diagram does not necessarily mean that it is made up of separate devices. The same applies to the block diagrams that follow.

Sensor modules 10 are installed at various locations in the facility, measure the physical quantities of the measurement target, and transmit the measurement data to other devices. Such facilities may be, for example, a device (or devices) that manufacture products from raw materials. As an example, the facility may be a plant. Examples of plants include industrial plants such as chemical and biotech plants, plants that manage and control wellheads and surrounding areas of gas and oil fields, plants that manage and control hydroelectric, thermal, and nuclear power generation, plants that manage and control solar and wind energy generation, and plants that manage and control water supply and sewage systems and dams. The sensor module 10 comprises a flow sensor 20, a processing unit 30, and a sensor-side communication unit 40.

The flow sensor 20 is a measuring instrument that is installed in a real space (such as the piping of a plant) and measures the amount of the fluid to be measured (liquid, gas, steam, powder, or a mixed phase of these) flowing through the pipeline per unit time. Examples of such flow sensors 20 include various flow meters with different sensing principles depending on various conditions, such as the purpose of the measurement, the measurement location, the type of fluid, or the state of the fluid. As an example, the flow sensor 20 may be at least one of a Coriolis flow meter, an ultrasonic flow meter, an electromagnetic flow meter, or a vortex flow meter.

A Coriolis flowmeter is a flowmeter that utilizes the physical phenomenon of Coriolis force. When a fluid passes through a flow tube that operates at a resonant frequency, inertia causes the flow tube to twist, resulting in a phase shift in the detection signal from the vibration detection sensors attached to the inlet and outlet sides of the flow tube. In a Coriolis flowmeter, for example, this phase shift is detected and multiplied by a coefficient to output the flow rate.

An ultrasonic flowmeter is a flowmeter that utilizes the difference in propagation time of ultrasonic waves. When ultrasonic waves are transmitted and received alternately across a fluid in a pipe at an angle, the ultrasonic waves travel slower when they travel against the flow of the fluid and travel faster when they travel with the flow. In an ultrasonic flowmeter, the flow velocity is calculated using the difference in propagation time of two such ultrasonic waves, corrected to the average flow velocity over the surface using a flow correction coefficient, and then multiplied by the cross-sectional area of the pipe to output the flow rate.

An electromagnetic flowmeter is a flowmeter that uses Faraday's electromagnetic induction. A magnetic field is created by an electromagnet, and when a conductive fluid passes through the magnetic field, an electromotive force proportional to the flow rate is generated in a direction perpendicular to both the direction of the magnetic field and the direction of the fluid flow. In an electromagnetic flowmeter, for example, the magnitude of this electromotive force is detected and multiplied by the cross-sectional area of the pipe to output the flow rate.

A vortex flowmeter is a flowmeter that uses Karman vortices. When a cylindrical obstacle (vortex generator) is present in a flowing fluid, Karman vortices are generated downstream of it. In this case, the flow velocity of the fluid and the vortex frequency of the Karman vortices are proportional to each other. In a vortex flowmeter, for example, the vortex frequency of such Karman vortices is used to calculate the flow velocity, and the flow rate is output by multiplying this by the cross-sectional area of the pipe.

The flow sensor 20 may be, for example, at least one of a Coriolis flow meter, an ultrasonic flow meter, an electromagnetic flow meter, or a vortex flow meter. The sensor module 10 may further include other sensors (not shown) capable of measuring physical quantities different from those of the flow sensor 20. For example, the sensor module 10 may further include other sensors such as a pressure gauge, a thermometer, a viscometer, a pH meter, a conductivity meter, or a slurry concentration meter.

The processing unit 30 processes the output signals from the flow sensor 20 and other sensors. The processing unit 30 may supply measurement data obtained by signal processing the output signals from the sensors to the sensor-side communication unit 40. Such measurement data may be data indicating at least the actual flow rate measured by the flow sensor 20.

The sensor-side communication unit 40 is equipped with a communication stack (including a data link layer and application layer) and a communication driver (including a physical layer) for communicating with the virtual flow rate calculation device 100 in accordance with a communication protocol. The sensor-side communication unit 40 may transmit the measurement data supplied from the processing unit 30 to the virtual flow rate calculation device 100 via a network.

Generally, flow sensors 20 are calibrated under standard operating conditions in facilities that are traceable to national standards. For example, the standard operating conditions are: fluid = water, fluid temperature = room temperature ±α, ambient temperature = room temperature ±α, and upstream and downstream straight pipe lengths = sufficiently long. When such a flow sensor 20 is installed in a real space, the operating conditions, such as fluid type, fluid temperature, ambient temperature, or upstream and downstream straight pipe lengths, will differ from the standard operating conditions at the time of calibration, resulting in a discrepancy with the calibrated value.

Naturally, suppliers of flow sensors 20 anticipate these differences in usage conditions and design their products to minimize the effects of usage environment and fluid properties to a certain level. However, when there are changes in the instrument due to long-term use or changes in the actual flow equipment, it is necessary to estimate the actual flow rate based on output fluctuations and internal status information of the flow sensor 20 installed in the actual space. Furthermore, to ensure the integrity of the flow sensor 20, it is ultimately necessary to recalibrate it in equipment that is traceable to national standards.

The virtual flow rate calculation device 100 according to this embodiment performs a simulation related to fluid measurement in a virtual space, assuming the usage conditions, such as the usage environment and fluid properties, of a flow rate sensor 20 installed in real space, and calculates a virtual flow rate that estimates the above-mentioned actual flow rate based on the simulation results. The virtual flow rate calculation device 100 according to this embodiment includes an environmental information storage unit 110, a physical property information storage unit 120, a device-side communication unit 130, a simulation unit 140, a calculation unit 150, and a diagnosis unit 160.

The environmental information storage unit 110 stores environmental information that indicates the environment of the real space in which the flow sensor 20 is installed. For example, the environmental information storage unit 110 may be a database, and may store environmental information obtained via user input, various memory devices, or a network, etc., so that it can be accessed by the calculation unit 150.

The physical property information storage unit 120 stores physical property information indicating the physical properties of the fluid to be measured. For example, the physical property information storage unit 120 may be a database, and may store physical property information obtained via user input, various memory devices, or a network, etc., so that it can be accessed by the calculation unit 150.

The device-side communication unit 130 includes a communication stack and a communication driver for communicating with the sensor module 10 in accordance with a communication protocol. For example, the device-side communication unit 130 may communicate with the sensor module 10 via a network and acquire measurement data from the sensor module 10. As described above, such measurement data may be data indicating at least the actual flow rate measured by the flow sensor 20. The device-side communication unit 130 may supply the acquired measurement data to the diagnosis unit 160.

The simulation unit 140 uses the environmental information and physical property information to perform a simulation related to fluid measurement in a virtual space that recreates real space. For example, the simulation unit 140 may perform a simulation related to fluid measurement in a virtual space using the environmental information stored in the environmental information storage unit 110 and the physical property information stored in the physical property information storage unit 120 in accordance with instructions from the calculation unit 150. The simulation unit 140 may supply the simulation results to the calculation unit 150.

The calculation unit 150 calculates a virtual flow rate that estimates the actual flow rate measured by the flow sensor 20 based on the simulation results. For example, the calculation unit 150 may obtain the simulation results from the simulation unit 140 and calculate a virtual flow rate that estimates the actual flow rate measured by the flow sensor 20 based on the simulation results. The calculation unit 150 may notify the diagnosis unit 160 of the calculated virtual flow rate.

The diagnostic unit 160 diagnoses the flow sensor 20 based on the actual flow rate and the virtual flow rate. For example, the diagnostic unit 160 may diagnose the flow sensor 20 by comparing the actual flow rate indicated by the measurement data supplied from the device-side communication unit 130 with the virtual flow rate notified by the calculation unit 150.

The virtual flow rate calculation device 100 equipped with such functional units may be a computer such as a PC (personal computer), tablet computer, smartphone, workstation, server computer, or general-purpose computer, or may be a computer system in which multiple computers are connected. Such computer systems are also considered computers in a broad sense. The virtual flow rate calculation device 100 may also be implemented using a virtual computer environment in which one or more programs can be executed within the computer. Alternatively, the virtual flow rate calculation device 100 may be a dedicated computer designed for calculating virtual flow rates, or may be dedicated hardware realized using dedicated circuits. Furthermore, if the device is capable of connecting to the Internet, the virtual flow rate calculation device 100 may be realized using cloud computing. In particular, it is preferable that the virtual flow rate calculation device 100 be provided by a cloud server from the standpoint of processing power and memory capacity.

Furthermore, such a computer may include a memory that stores a virtual flow rate calculation program and a processor that executes the virtual flow rate calculation program, and the processor may execute the virtual flow rate calculation program to implement the functions of the virtual flow rate calculation device 100. That is, a virtual flow rate calculation program may be provided that is executed by a computer and causes the computer to function as an environmental information storage unit 110 that stores environmental information indicating the environment of the real space in which the flow sensor 20 is installed, a physical property information storage unit 120 that stores physical property information indicating the physical properties of the fluid to be measured, a simulation unit 140 that uses the environmental information and physical property information to perform a simulation related to fluid measurement in a virtual space that recreates the real space, and a calculation unit 150 that calculates a virtual flow rate that estimates the actual flow rate measured by the flow sensor 20 based on the results of the simulation.

Figure 2 shows an example of a flow diagram of a virtual flow rate calculation method executed by the virtual flow rate calculation device 100 according to this embodiment. Each step in the virtual flow rate calculation method may be performed by a computer. However, as long as the computer is the overall performer of each step, some non-major parts may be performed by something other than a computer.

In step S210, the computer stores environmental information. For example, the environmental information storage unit 110 may store environmental information indicating the environment of the real space in which the flow sensor 20 is installed so that it can be accessed by the calculation unit 150. In this case, the environmental information storage unit 110 may store, as environmental information, information on the piping attached to the outside of the flow sensor 20 (straight pipe length, the state of the upstream and downstream elbows, etc.), information on the mounting gasket, information on the fluid (liquid type, concentration, presence or absence of multiphase, expected temperature, expected pressure, etc.), or information on peripheral equipment. Note that some of this environmental information, such as piping information, may be extracted from CAD (Computer Aided Design) data, aerial photography data, etc.

Here, if the usage environment changes over time, the environmental information storage unit 110 may store, as environmental information, measurement data from other sensors, such as a pressure gauge, thermometer, viscometer, pH meter, conductivity meter, or slurry concentration meter, that are installed in the real space in the same way as the flow sensor 20. The environmental information storage unit 110 may also store, as environmental information, measurement data from the flow sensor 20, i.e., the actual flow rate measured by the flow sensor 20. In such cases, the environmental information storage unit 110 may store the measurement data in chronological order, assuming that the data will change over time.

In step S220, the computer stores physical property information. For example, the physical property information storage unit 120 may store physical property information indicating the physical properties of the fluid to be measured so that it can be accessed by the calculation unit 150. In this case, the physical property information storage unit 120 may store, as physical property information, information such as density, viscosity, conductivity, electrical resistivity, dielectric constant, or acoustic properties, including the temperature-pressure characteristics of the fluid to be measured. In the above description, the physical property information storage unit 120 stores only physical property information indicating the physical properties of the fluid to be measured, but this is not limited to this. The physical property information storage unit 120 may also store physical property information indicating the physical properties of various substances required for simulations other than the fluid to be measured. In this case, the physical property information storage unit 120 may store various information (e.g., resistivity of metals or temperature characteristics of mechanical properties) listed in so-called scientific chronologies or various basic physical property databases as physical property information.

In step S230, the computer acquires measurement data. For example, the device-side communication unit 130 may acquire the measurement data from the sensor module 10 via a network. As described above, such measurement data may be data indicating at least the actual flow rate measured by the flow sensor 20.

At this time, the environmental information storage unit 110 may update the environmental information stored in step S210 by adding the acquired measurement data (measurement data from the flow sensor 20 and measurement data from other sensors) in chronological order.

In step S240, the computer executes a simulation. For example, the calculation unit 150 may access the environmental information storage unit 110 to acquire environmental information required for the simulation. The calculation unit 150 may also access the physical property information storage unit 120 to acquire physical property information required for the simulation. As described above, such physical property information may include information indicating the physical properties of the fluid to be measured, or may include information indicating the physical properties of substances other than the fluid to be measured. The calculation unit 150 may then supply this information to the simulation unit 140 and instruct it to execute a simulation. In response, the simulation unit 140 may use the environmental information and physical property information to execute a simulation related to fluid measurement in a virtual space that recreates real space, for example, in a digital twin.

As an example, the simulation unit 140 may perform a simulation using a known numerical analysis method such as the finite element method (FEM) or the finite difference method (FDM). In this case, the simulation unit 140 may simulate at least one of stress, fluid, electromagnetic field, and ultrasonic waves in a virtual space.

For example, in a stress simulation, the simulation unit 140 may simulate the vibration state that is transmitted from a vibration source such as a pump through piping and applied to the flow sensor 20. The simulation unit 140 may also simulate the fluid pressure applied to the flow sensor 20 based on information from a pressure gauge, level gauge, etc. The simulation unit 140 may also simulate the stress distribution on piping due to bolt fastening between piping, stanchions, etc.

Furthermore, the simulation unit 140 may input basic information, such as pump head, fluid, and pressure loss throughout the entire piping system, to simulate the flow rate of the flow meter portion used in the real world. In this case, performing a fluid simulation using a full model allows for a direct comparison between the actual flow rate and the virtual flow rate. The advantage of this method is that it allows for knowledge of the flow rate distribution across the flow meter cross section, which changes depending on the flow rate and fluid viscosity. While Coriolis flow meters, ultrasonic flow meters, electromagnetic flow meters, and vortex flow meters are susceptible to the influence of flow rate distribution, it is possible to estimate whether their output is dependent on the flow rate distribution. On the other hand, if a fluid simulation that considers the entire piping of a plant or other facility is difficult due to the performance limitations of the computing device, the simulation unit 140 may omit some elements and perform the simulation. In this case, discrepancies between the actual flow rate and the virtual flow rate may occur. Therefore, in such cases, the simulation unit 140 may use the setpoint flow rate set by PID (Proportional Integral Differential) control in a control system used in a plant or the like as the initial value, or the actual flow rate value during a period considered to be normal operation as the initial value. The simulation unit 140 can reduce the calculation load by partially performing a fluid simulation, but in this case, it will look at the difference between the virtual flow rate value considered to be in a normal operating state and the actual flow rate value.

Furthermore, the simulation unit 140 may, for example, in an electromagnetic field simulation, provide the applied current values of the two coils provided within the flow sensor 20 as initial values and simulate the magnetic flux density distribution at the cross section of the pipeline.

Furthermore, the simulation unit 140 may, for example, simulate ultrasonic propagation time and ultrasonic signal attenuation based on fluid properties, plant piping parameters, and environmental temperature in an ultrasonic propagation simulation.

In this way, the simulation unit 140 may simulate at least one of stress, fluid, electromagnetic field, or ultrasound, preferably a combination of these, in a virtual space.

In step S250, the computer calculates a virtual flow rate. For example, the calculation unit 150 may calculate a virtual flow rate that estimates the actual flow rate measured by the flow sensor 20 based on the results of the simulation performed in step S240. Generally, flow velocity distribution and fluid properties (fluid pressure, density, viscosity, etc.) vary depending on piping conditions, fluid conditions, etc. However, the calculation unit 150 calculates a virtual flow rate based on the results of a simulation using environmental information and physical property information. This allows the calculation unit 150 to reflect the actual usage environment and fluid properties in the calculation, thereby calculating a virtual flow rate that more accurately estimates the actual flow rate. Specific calculations of the virtual flow rate will be described in detail below for each sensing principle of the flow meter.

The calculation unit 150 may determine the process for calculating the virtual flow rate based on the actual flow rate and the virtual flow rate during a period when the flow sensor 20 is considered to be operating normally. More specifically, if there is a difference between the virtual flow rate calculated during a period when the flow sensor 20 is considered to be operating normally when installed in the real space and the actual flow rate, the calculation unit 150 may multiply the calculated virtual flow rate by a ratio corresponding to the difference as a correction value and use the result as the initial result of the virtual flow rate. This allows the virtual flow meter to be priced at a correct initial flow rate (a flow rate closer to the actual flow rate without any aging error) by adjusting the calculation result of the virtual flow rate to the actual flow rate at an early stage, such as immediately after installing the flow sensor 20 in the real space, when no aging error is present. The calculation unit 150 may also add calculation conditions for the virtual flow rate (e.g., by using measured data from the real space for the ambient temperature or setting a smaller convergence value for the simulation) to iteratively approximate the virtual flow rate to the actual flow rate. Furthermore, when calculating a virtual flow rate based on the results of a fluid simulation, the calculation unit 150 may calculate a virtual flow rate that is even closer to the actual flow rate by repeating the procedure of calculating a virtual flow rate based on the results of a fluid simulation using an actual flow rate, and then calculating a virtual flow rate using a fluid simulation using a value that is closer to the actual flow rate.

In step S260, the computer compares the actual flow rate with the virtual flow rate. For example, the diagnosis unit 160 may compare the actual flow rate indicated by the measurement data acquired in step S230 with the virtual flow rate calculated in step S250.

In step S270, the computer determines whether the difference satisfies a criterion. For example, the diagnosis unit 160 may determine whether the difference between the actual flow rate and the virtual flow rate satisfies a predetermined criterion as a result of the comparison in step S260. If it is determined that the difference satisfies the criterion (Yes) (e.g., the difference is less than a threshold), the virtual flow rate calculation device 100 may return the process to step S230 and continue the flow. On the other hand, if it is determined that the difference does not satisfy the criterion (No) (e.g., the difference is equal to or greater than the threshold), the virtual flow rate calculation device 100 may proceed to step S280.

In step S280, the computer issues an alert. For example, the diagnostic unit 160 may output a message on a monitor, a sound, a printout, or a signal indicating that the difference does not meet the criteria. In this way, the diagnostic unit 160 may issue an alert if the difference between the actual flow rate and the virtual flow rate does not meet a predetermined criterion. This allows the diagnostic unit 160 to diagnose the flow sensor 20 based on the actual flow rate and the virtual flow rate.

The virtual flow calculation device 100 then terminates this flow. The virtual flow calculation device 100 can dynamically perform these calculations and diagnoses. For example, the virtual flow calculation device 100 may continuously calculate the virtual flow rate and diagnose the flow sensor 20 at a period multiple times the measurement period of the actual flow rate. The virtual flow calculation device 100 may also calculate the virtual flow rate and diagnose the flow sensor 20 at a timing specified by the user (e.g., every hour, every day, or at a timing dependent on an event such as the start of operation). The virtual flow calculation device 100 may also calculate the virtual flow rate and diagnose the flow sensor 20 when the fluctuation in the actual flow rate reaches a value specified by the user or the system (e.g., 5% of the measurement span), when the conditions input into the environmental information or physical property information are changed, or when the change exceeds a value specified by the user or the system (e.g., 5%).

As explained using this flow chart, a virtual flow rate calculation method may be provided in which a computer stores environmental information indicating the environment of the real space in which the flow sensor 20 is installed, stores physical property information indicating the physical properties of the fluid to be measured, uses the environmental information and physical property information to perform a simulation related to fluid measurement in a virtual space that recreates the real space, and calculates a virtual flow rate that estimates the actual flow rate actually measured by the flow sensor 20 based on the results of the simulation. Specific virtual flow rate calculations will now be described in detail for each type of flow meter.

Figure 3 shows an example block diagram of a virtual flow rate calculation device 100 functioning as a virtual Coriolis flow meter. The virtual flow rate calculation device 100 may function as a virtual Coriolis flow meter. When functioning as a virtual Coriolis flow meter, the simulation unit 140 may include, for example, a fluid simulation unit 141, a stress simulation unit 142, and an electromagnetic field simulation unit 143. The virtual flow rate calculation device 100 may then estimate the output value of a Coriolis flow meter used in real space using, for example, the fluid simulation unit 141, the stress simulation unit 142, and the electromagnetic field simulation unit 143.

More specifically, if SK is a coefficient that is a function of structural features such as the shape of the flow tube and the position of the vibration detection sensor, and τ is a phase time difference calculated from the phase difference between vibrations occurring upstream and downstream of the flow tube, the calculation unit 150 may calculate the virtual flow rate Q using the following equation. That is, the calculation unit 150 may calculate the virtual flow rate Q as the product of the coefficient SK and the phase time difference τ. Here, the phase time difference τ is the phase difference φ occurring in the vibration detection sensor divided by the excitation frequency fr of the oscillator. Therefore, the virtual flow rate Q can also be expressed as the product of the coefficient SK and the phase difference φ divided by the excitation frequency fr.

In this case, in relation to the coefficient SK, the simulation unit 140 may perform a stress simulation using the stress simulation unit 142. As an example, the stress simulation unit 142 may input a 3D model of the flow tube, oscillator, and vibration detection sensor at normal temperature and pressure, as well as the temperature and pressure obtained from the instrument in real space, and output the flow tube shape of a virtual Coriolis flowmeter. Furthermore, the stress simulation unit 142 may input the flow tube shape and the Young's modulus under the same conditions, and output the resonant frequency and moment of inertia of the flow tube required to calculate the coefficient SK.

The calculation unit 150 may then calculate characteristic quantities such as the natural angular frequency from the results of a stress simulation that takes into account the temperature and pressure applied to the flow tube and the excitation force of the oscillator that powers the flow tube's vibration, and calculate the coefficient SK by substituting the calculated characteristic quantities into a function that uses the previously derived characteristic quantities as variables.

Furthermore, in relation to the phase time difference τ, the simulation unit 140 may perform a coupled simulation using the fluid simulation unit 141, stress simulation unit 142, and electromagnetic field simulation unit 143. The calculation unit 150 may then calculate the phase time difference τ by calculating the phase difference φ occurring in the vibration detection sensor from the results of the coupled simulation and dividing this by the excitation frequency fr of the oscillator.

Figure 4 shows an example block diagram of a virtual flow calculation device 100 that functions as a virtual ultrasonic flow meter. The virtual flow calculation device 100 may function as a virtual ultrasonic flow meter. When functioning as a virtual ultrasonic flow meter, the simulation unit 140 may include, for example, a fluid simulation unit 141 and an ultrasonic propagation simulation unit 144. The virtual flow calculation device 100 may then estimate the output value of an ultrasonic flow meter used in real space, for example, using the fluid simulation unit 141 and the ultrasonic propagation simulation unit 144.

More specifically, if the angle between the measurement pipe axis and the ultrasonic propagation axis is θ, the distance the ultrasonic waves propagate is L, the propagation time of the ultrasonic waves from the upstream side to the downstream side is t1, and the propagation time of the ultrasonic waves from the downstream side to the upstream side is t2, the calculation unit 150 may calculate the flow velocity v using the following formula: That is, the calculation unit 150 may calculate the flow velocity v using a function of the reciprocal difference (frequency difference) of the propagation times.

Then, assuming that the flow rate correction coefficient is k and the cross-sectional area of the pipeline is A, the calculation unit 150 may calculate the virtual flow rate Q using the following formula: That is, the calculation unit 150 may calculate the virtual flow rate by correcting the flow velocity v to the average flow velocity of the cross section through which the fluid flows using the flow rate correction coefficient k, and then multiplying this by the cross-sectional area A of the pipeline.

At this time, the simulation unit 140 may use the fluid simulation unit 141 to perform a fluid simulation in relation to the propagation times t1 and t2. As an example, the fluid simulation unit 141 may calculate the three-dimensional flow velocity distribution within the measurement pipe, taking into account factors such as the upstream and downstream straight pipe lengths, upstream and downstream elbows, and fluid viscosity. At this time, the actual flow rate (flow velocity) output value of the flow sensor 20 installed in the real space may be used, if necessary.

The simulation unit 140 may also perform an ultrasonic propagation simulation using the ultrasonic propagation simulation unit 144. As an example, the ultrasonic propagation simulation unit 144 may calculate the propagation time of ultrasonic waves emitted from the piezoelectric element in the upstream-to-downstream direction and the downstream-to-upstream direction at the upstream and downstream sensor installation positions when the flow rate is zero. The ultrasonic propagation simulation unit 144 may then simulate the propagation of ultrasonic waves by calculating the propagation time taking into account piping parameters such as pipe thickness and scale, and temperature (ambient temperature, fluid temperature).

The calculation unit 150 may then calculate the propagation time t1 from the upstream side to the downstream side and the propagation time t2 from the downstream side to the upstream side by performing a coupled analysis of the propagation time based on the results of a simulation of the three-dimensional flow velocity distribution in the measurement pipe and ultrasonic propagation. The coupled analysis can calculate the propagation times t1 and t2 by combining the propagation time calculated by the ultrasonic propagation simulation with the three-dimensional flow velocity distribution calculated by the fluid simulation.

Figure 5 shows an example block diagram of a virtual flow rate calculation device 100 that functions as a virtual electromagnetic flow meter. The virtual flow rate calculation device 100 may function as a virtual electromagnetic flow meter. When functioning as a virtual electromagnetic flow meter, the simulation unit 140 may include, for example, a fluid simulation unit 141 and an electromagnetic field simulation unit 143. The virtual flow rate calculation device 100 may then estimate the output value of an electromagnetic flow meter used in real space, for example, using the fluid simulation unit 141 and the electromagnetic field simulation unit 143.

More specifically, the calculation unit 150 may calculate the electromotive force e generated in the electrode using the following equation: That is, the calculation unit 150 may calculate the electromotive force e by multiplying and integrating the weighting function w, the magnetic flux density B, and the flow velocity v.

The calculation unit 150 may then calculate the virtual flow rate Q using the following equation: That is, the calculation unit 150 may calculate the virtual flow rate Q using the calculated electromotive force e, the pipe inner diameter D, and the constant K.

Here, in relation to the magnetic flux density B, the simulation unit 140 may use the electromagnetic field simulation unit 143 to perform an electromagnetic field simulation. As an example, the electromagnetic field simulation unit 143 may input the dimensions of the coil and magnetic material of the electromagnetic flowmeter and the physical properties of the magnetic material to calculate the magnetic flux density distribution inside the measurement pipe. In this case, for the magnetic flux density, the magnetic flux density distribution inside the measurement pipe that has actually been measured may be stored as a database. This method can be used when design information for at least one of the coil, magnetic material dimensions, or magnetic material physical properties of other companies' products is not available.

Furthermore, in relation to the flow velocity v, the simulation unit 140 may perform a fluid simulation using the fluid simulation unit 141. As an example, the fluid simulation unit 141 may calculate the flow velocity distribution at the cross section of the measurement pipeline, taking into account factors such as the upstream and downstream straight pipe lengths, upstream and downstream elbows, and fluid viscosity. In this case, the actual flow rate (flow velocity) output value of the flow sensor 20 installed in the real space may be used, if necessary.

The weighting function w is a function of the electric field (magnetic flux density x flow velocity) generated at each point in the measurement pipe and the distance between the electrodes. For example, a weighting function described in JIS B 7554 or a weighting function based on the electrode shape and placement position may be used.

The calculation unit 150 may then calculate the electromotive force e generated at the electrode by multiplying the electric field (magnetic flux density B x flow velocity v) generated at each point in the measurement pipe by a weighting function w and integrating the result. In this case, since material properties may have a certain range, the calculated electromotive force e may be multiplied by a certain coefficient.

Figure 6 shows an example block diagram of a virtual flow rate calculation device 100 functioning as a virtual vortex flowmeter. The virtual flow rate calculation device 100 may function as a virtual vortex flowmeter. When functioning as a virtual vortex flowmeter, the simulation unit 140 may include, for example, a fluid simulation unit 141. The virtual flow rate calculation device 100 may then use, for example, the fluid simulation unit 141 to estimate the output value of a vortex flowmeter used in real space.

More specifically, the calculation unit 150 may calculate the virtual flow rate Q using the following equation: That is, the calculation unit 150 may calculate the virtual flow rate Q by multiplying the vortex frequency f by the cross-sectional area A of the pipe and the width d of the vortex shedder, and dividing the result by the Strouhal number St. Note that the Strouhal number St is a dimensionless number determined by the shape and dimensions of the vortex shedder.

Here, in relation to the vortex frequency f, the simulation unit 140 may use the fluid simulation unit 141 to perform a fluid simulation. As an example, the fluid simulation unit 141 may input the physical properties of the fluid, the shape of the vortex bars, and the piping conditions (straight pipe length, steps, etc.) to simulate the generation of vortices in the measurement pipeline. In this case, the actual flow rate (flow velocity) output value of the flow sensor 20 installed in the real space may be used, if necessary.

The simulation unit 140 may also use the fluid simulation unit 141 to calculate the pressure distribution and temperature distribution in the measurement pipeline. Then, the calculation unit 150 may correct the virtual flow rate Q for the calculated pressure and temperature distribution in the measurement pipeline, particularly when the measured fluid is a gas.

In this way, the virtual flow rate calculation device 100 according to this embodiment can function as at least one of a virtual Coriolis flowmeter, a virtual ultrasonic flowmeter, a virtual electromagnetic flowmeter, or a virtual vortex flowmeter.

Conventional technology does not take into account external environmental factors such as the flow in the flow meter section and pipe vibration in the user's environment, and does not simulate flow meter output in real time. Furthermore, when measuring flow rates, the influence of the flow velocity distribution in the measurement pipe changes depending on the usage environment, and the fluid properties themselves change depending on the multiphase state, all of which affect measurement accuracy. However, it has been extremely difficult to estimate the actual flow rate of the flow meter output while taking these factors into account.

In contrast, the virtual flow rate calculation device 100 according to this embodiment performs a simulation related to fluid measurement in a virtual space, assuming the usage conditions, such as the usage environment and fluid properties, of the flow sensor 20 installed in real space, and calculates a virtual flow rate based on the simulation results. As a result, the virtual flow rate calculation device 100 according to this embodiment can accurately estimate the actual flow rate measured by the flow sensor 20 in accordance with the user's actual usage environment and usage conditions, such as fluid properties. Therefore, the virtual flow rate calculation device 100 according to this embodiment supports the operation of flow rate measurements by the flow sensor 20, which is installed in real space and is affected by usage conditions, and ultimately leads to stable operation of the instrumentation system.

The virtual flow rate calculation device 100 according to this embodiment may also diagnose the flow sensor 20 based on the actual flow rate and the virtual flow rate. In this case, the virtual flow rate calculation device 100 according to this embodiment may issue an alert if the difference between the actual flow rate and the virtual flow rate does not meet a standard. As a result, the virtual flow rate calculation device 100 according to this embodiment can diagnose whether the flow sensor 20 itself is functioning correctly or whether the flow sensor 20 is correctly instrumented, depending on whether the calculated virtual flow rate is the intended value in light of the actual flow rate, and can notify the user if an abnormality is suspected (something unexpected may be happening to the flow sensor 20).

Furthermore, the virtual flow rate calculation device 100 according to this embodiment may determine the processing to be performed by the calculation unit 150 based on the actual flow rate and virtual flow rate during a period in which the operation of the flow sensor 20 is considered normal. As a result, the virtual flow rate calculation device 100 according to this embodiment can learn the calculation processing algorithm so that the calculated virtual flow rate approaches the actual flow rate during a normal period.

The virtual flow rate calculation device 100 according to this embodiment also functions as at least one of a virtual Coriolis flowmeter, a virtual ultrasonic flowmeter, a virtual electromagnetic flowmeter, or a virtual vortex flowmeter, based on the results of at least one of a stress simulation, a fluid simulation, an electromagnetic field simulation, or an ultrasonic simulation, or preferably a coupled simulation consisting of a combination of these, executed in virtual space. As a result, the virtual flow rate calculation device 100 according to this embodiment calculates a virtual flow rate based on the results of various simulations, making it possible to calculate a virtual flow rate that conforms to the sensing principles of the flowmeter with high accuracy.

Here, estimating flow meter output while taking into account the effects of the usage environment and multiphase conditions requires a high-speed calculation unit and large-capacity memory, but it has been extremely difficult to provide these inside the flow meter. In contrast, the virtual flow calculation device 100 according to this embodiment may be provided by a cloud server. As a result, the virtual flow calculation device 100 according to this embodiment makes it possible to remove constraints such as the processing power, memory capacity, and power consumption of the processor installed in the flow meter in real space. Therefore, the virtual flow calculation device 100 according to this embodiment increases the amount of data and the degree of freedom in calculation, making it possible to obtain a flow output that incorporates the various instrumentation conditions of the flow meter.

Figure 7 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a first modified example, together with the sensor module 10. In the above-described embodiment, the virtual flow rate calculation device 100 performs a simulation each time it calculates a virtual flow rate. However, in this modified example, the virtual flow rate calculation device 100 reuses at least a portion of the simulation results.

The virtual flow rate calculation device 100 according to this modification further includes a simulation result storage unit 710. In this modification, the simulation unit 140 adds the simulation results to the calculation unit 150 and supplies them to the simulation result storage unit 710.

The simulation result storage unit 710 stores the simulated results. For example, the simulation result storage unit 710 may be a database, and may store the results simulated by the simulation unit 140 so that they can be accessed by the calculation unit 150.

The calculation unit 150 may access the simulation result storage unit 710 and obtain the stored results. The calculation unit 150 may then reuse at least a portion of the stored results when calculating the virtual flow rate.

The virtual flow rate calculation device 100 according to this modified example may store the results of previously executed simulations and reuse at least a portion of those simulation results. As a result, the virtual flow rate calculation device 100 according to this modified example stores the results of a previous calculation, eliminating the need to re-simulate under conditions that have already been executed, thereby reducing the calculation load and enabling analysis that takes multiple conditions into account using a learning calculation module (AI analysis). Furthermore, the virtual flow rate calculation device 100 according to this modified example does not necessarily need to perform full-model simulations including stress, fluid, electromagnetic field, and ultrasonic propagation simulations in real time each time. Instead, by storing simulation results under various conditions in advance and recursively deriving simulation results from the stored results, the amount of calculations executed in real time can be significantly reduced.

Figure 8 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second modified example, together with the sensor module 10. In the above-described embodiment, an example was shown in which the virtual flow rate calculation device 100 diagnoses the flow sensor 20 based on the virtual flow rate. However, in this modified example, the virtual flow rate calculation device 100 identifies the fluctuation trend of the virtual flow rate.

The virtual flow rate calculation device 100 according to this modified example further includes a trend identification unit 810, a notification unit 820, and a recommendation unit 830.

For example, the calculation unit 150 may calculate a virtual flow rate when at least one variable of the environmental information or the physical property information is varied. As an example, the calculation unit 150 may calculate a virtual flow rate when the fluid temperature is varied within a range of ±10°C. The calculation unit 150 may supply the virtual flow rates calculated under different conditions in this way to the trend identification unit 810, along with the conditions under which the virtual flow rates were calculated.

The trend identification unit 810 can obtain the trend between the varied variable (e.g., fluid temperature) and the virtual flow rate based on the information supplied from the calculation unit 150. In this way, for example, the trend identification unit 810 can identify the trend of the virtual flow rate when at least one variable of the environmental information or physical property information is varied.

Among the environmental information or physical property information, some variables are insensitive to the virtual flow rate, while others have a certain trend (e.g., monotonically increasing, monotonically decreasing, or a trend based on a function). If a variable with a certain trend is discovered, the trend identification unit 810 may provide information about the trend to the notification unit 820. In this case, the trend identification unit 810 may, for example, derive multiple curves by approximating the data of a scatter plot of the variables and the virtual flow rate in various ways (e.g., linear approximation, exponential approximation, logarithmic approximation, polynomial approximation, power approximation, etc.), and select the curve with the largest squared correlation coefficient (the closer to 1, the stronger the correlation; the closer to 0, the weaker the correlation) between the data of the scatter plot and each curve as the approximation curve. The trend identification unit 810 may also create a mathematical formula for the selected approximation curve and calculate a correction function from the formula. The trend identification unit 810 may, for example, provide information indicating such an approximation curve or correction function to the notification unit 820 as trend information.

The notification unit 820 may then notify the sensor module 10 of information regarding the trend via the device-side communication unit 130. In this way, for example, the notification unit 820 can notify the sensor module 10 equipped with the flow sensor 20 of information regarding the trend.

Therefore, in this modified example, the sensor module 10 may further include a trend characteristic storage unit 50. The trend characteristic storage unit 50 may store information related to the trend notified by the virtual flow rate calculation device 100 as a trend characteristic. The processing unit 30 may then perform signal processing on the output signal from the sensor based on the trend characteristic stored in the trend characteristic storage unit 50. As an example, the processing unit 30 may perform correction processing on the output signal from the sensor using a correction function stored in the trend characteristic storage unit 50.

If there are multiple flow sensors 20 for which the virtual flow rate is to be calculated, the trend identification unit 810 can also identify the trend of the virtual flow rate for each flow sensor 20 when at least one variable is varied. These multiple flow sensors 20 may differ from each other in terms of at least one of the supplier and sensing principle. In such a case, the trend identification unit 810 may supply information regarding the respective trends identified for each flow sensor 20 to the recommendation unit 830.

The recommendation unit 830 may then determine which flow sensor 20 to recommend from among the multiple flow sensors 20 based on the trends of each flow sensor 20. For example, the recommendation unit 830 may compare the magnitude of fluctuations in the virtual flow rate for one or more selected variables (e.g., temperature, viscosity, or Reynolds number) between the multiple flow sensors 20, and determine the flow sensor 20 with the smallest fluctuation as the recommended flow sensor 20. In this way, for example, the virtual flow calculation device 100 according to this modified example can compare the fluctuation characteristics of multiple flow sensors 20 of various types from various suppliers, and recommend to the user the flow sensor 20 that is optimal for the usage environment.

Up to this point, we have assumed that the fluid being measured is a single-phase flow, but in reality, the presence of multiple substances (including unintended substances) can result in a multi-phase flow.

Here, "multiphase flow" refers to a fluid that is a mixture of multiple substances with different physical properties, and includes, depending on the combination of phases (gas, liquid, and solid), for example, gas-liquid two-phase flow, solid-gas two-phase flow, solid-liquid two-phase flow, and solid-gas-liquid three-phase flow. Furthermore, "multiphase flow" is not necessarily limited to flows with multiple phases, but can also be interpreted as including flows with a single phase, such as a fluid that is a mixture of multiple substances that separate like water and oil, i.e., liquid-liquid two-phase flow.

Figure 9 shows an example of a multiphase flow pattern. When a fluid is a single-phase flow, the flow state can be broadly classified as laminar flow or turbulent flow. In contrast, when a fluid is a multiphase flow, a variety of flow states can exist depending on the physical properties of the mixed materials, the flow velocity, the shape of the pipeline, and other factors. Flow patterns are classifications that capture the characteristics of these flow states.

The upper left of this diagram shows a liquid-phase-based dispersed flow. For example, gas (such as air bubbles) or solids (such as sand or slurry) may flow dispersedly within a fluid that is primarily liquid. Also, other liquids with different physical properties (such as droplets) may flow dispersedly without being completely mixed into the primary liquid (such as a trace amount of oil flowing dispersedly in water, or a trace amount of water flowing dispersedly in oil). In this way, a liquid-phase-based dispersed flow can occur when particles such as gas, solids, or liquids flow dispersedly within the liquid phase.

The upper right of this figure shows a gas-phase-based dispersed flow. For example, solids and liquids may be dispersed and flow within a fluid that is primarily gas. In this way, a gas-phase-based dispersed flow can occur when solid or liquid particles are dispersed and flow within the gas phase.

The lower left of this diagram shows stratified flow in a gas-liquid two-phase flow. For example, if a pipeline extends in a roughly horizontal direction, liquid may flow in the lower layer (bottom layer) of the pipeline, and gas may flow in the upper layer. In this way, stratified flow can occur when multiple components (multiple phases) with different properties flow in layers.

The bottom right of this diagram shows an annular flow in a gas-liquid two-phase flow. For example, if a pipeline extends in a roughly vertical direction, the liquid may flow concentrically in the outer layer (on the wall) of the pipeline, while the gas may flow in the inner layer (center) of the pipeline. In this way, an annular flow can occur when multiple components (multiple phases) with different properties flow in a circular pattern.

As such, a variety of phase combinations and flow patterns exist in multiphase flows. When the fluid to be measured becomes a multiphase flow, a mismatch occurs between the actual flow rate measured by the flow sensor 20 and the virtual flow rate due to a decrease or fluctuation in the sensed signal, but the impact of such a mismatch varies depending on the state of the multiphase flow. Therefore, in the second embodiment, the virtual flow rate calculation device 100 also takes into account the multiphase state of the fluid to be measured when calculating the virtual flow rate.

In the second embodiment, commonalities with the above-described embodiments will not be described and only differences will be described, but the virtual flow rate calculation device 100 according to the second embodiment may also be able to provide the same functions as the virtual flow rate calculation device 100 according to the above-described embodiments. Furthermore, the virtual flow rate calculation device 100 according to the second embodiment may also be modified in the same way as the virtual flow rate calculation device 100 according to the above-described embodiments (for example, as in the first modified example or the second modified example).

In the virtual flow rate calculation device 100 according to the second embodiment, the simulation unit 140 may further include a multiphase fluid simulation unit 145 that executes a multiphase fluid simulation. The multiphase fluid simulation unit 145 may use environmental information and physical property information to simulate the multiphase flow phenomenon of a fluid in a virtual space that replicates the real space. The calculation unit 150 may then calculate a virtual flow rate based on the results of this simulation.

That is, a virtual flow rate calculation device 100 may be provided that includes an environmental information storage unit 110 that stores environmental information indicating the environment of the real space in which the flow sensor 20 is installed; a physical property information storage unit 120 that stores physical property information indicating the physical properties of the fluid to be measured; a simulation unit 140 that uses the environmental information and physical property information to simulate the flow phenomenon of a multiphase flow in the fluid in a virtual space that recreates the real space; and a calculation unit 150 that calculates a virtual flow rate that estimates the actual flow rate actually measured by the flow sensor 20 based on the simulation results. Similar to the virtual flow rate calculation device 100 according to the above embodiment, this virtual flow rate calculation device 100 is preferably provided by a cloud server.

A virtual flow rate calculation method may also be provided, in which a computer stores environmental information indicating the environment of the real space in which the flow sensor 20 is installed, stores physical property information indicating the physical properties of the fluid to be measured, simulates the flow phenomenon of a multiphase flow in the fluid in a virtual space that reproduces the real space using the environmental information and the physical property information, and calculates a virtual flow rate that estimates the actual flow rate measured by the flow sensor 20 based on the results of the simulation.

A virtual flow rate calculation program may also be provided that is executed by a computer and causes the computer to function as an environmental information storage unit 110 that stores environmental information indicating the environment of the real space in which the flow sensor 20 is installed, a physical property information storage unit 120 that stores physical property information indicating the physical properties of the fluid to be measured, a simulation unit 140 that uses the environmental information and physical property information to simulate the flow phenomenon of multiphase flow in the fluid in a virtual space that reproduces the real space, and a calculation unit 150 that calculates a virtual flow rate that estimates the actual flow rate actually measured by the flow sensor 20 based on the results of the simulation.

When performing the multiphase fluid simulation, the simulation unit 140 may use an analytical model selected according to at least one of the phase combinations in the multiphase flow and the flow pattern. Such an analytical model may be a physical model of the multiphase flow, such as a continuous phase model or a dispersed phase model. The analytical model may be selected manually, or the simulation unit 140 may automatically select it according to predetermined rules.

As an example, when analyzing a multiphase flow macroscopically and roughly analyzing the flow, the simulation unit 140 may use a continuous phase model such as the Euler-Euler method. In a continuous phase model, all phases are treated as continuous phases in an Euler-like manner.

On the other hand, when viewing a multiphase flow at a microscopic level and tracking individual particles, the simulation unit 140 may use a dispersed phase model such as the Euler-Lagrangian method. In a dispersed phase model, particles dispersed in a continuous phase are treated individually in a Lagrangian manner.

The specific calculation of virtual flow rates when performing such multiphase fluid simulations will be explained in detail for each flow meter sensing principle.

Figure 10 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual Coriolis flowmeter. In this figure, components having the same functions and configurations as those in Figure 3 are denoted by the same reference numerals, and descriptions thereof will be omitted hereinafter except for differences. In the virtual flow rate calculation device 100 according to the second embodiment, the simulation unit 140 may further include a multiphase fluid simulation unit 145 in addition to the fluid simulation unit 141, stress simulation unit 142, and electromagnetic field simulation unit 143. In the second embodiment, the virtual flow rate calculation device 100 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to estimate the output value of a Coriolis flowmeter used in real space.

As described above, in a virtual Coriolis flowmeter, the virtual flow rate Q can be calculated from the phase time difference τ. However, for example, if the fluid to be measured is a gas-liquid two-phase flow, the Coriolis force acting on the flow tube changes depending on the flow velocity of each phase and their position within the flow tube, and the phase time difference τ also changes accordingly. Therefore, the simulation unit 140 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to calculate the flow velocity of each phase within the flow tube and the distribution position of bubbles. The calculation unit 150 may then calculate the phase difference φ occurring in the vibration detection sensor from the results of a coupled simulation of the multiphase fluid, stress, and electromagnetic field, and divide this by the excitation frequency fr of the oscillator to calculate the phase time difference τ.

In this case, the virtual flow rate calculation device 100 estimates the actual flow rate of the Coriolis flowmeter based on the simulation results, and can also estimate various other parameters by performing theoretical calculations using the simulation results.

For example, the virtual flow rate calculation device 100 can estimate the density of the fluid to be measured from the resonant frequency of the flow tube. As an example, the simulation unit 140 may perform a coupled simulation of a multiphase fluid and stress. The calculation unit 150 may calculate the resonant frequency fv of the entire flow tube including the fluid from the results of this simulation. The calculation unit 150 may then estimate the fluid density ρ using the following equation. That is, the calculation unit 150 may estimate the fluid density ρ by multiplying the squared value of the resonant frequency fl of the flow tube in a vacuum by the resonant frequency fv of the entire flow tube including the fluid by a coefficient C.

The calculation unit 150 can also estimate the fraction of multiphase flow based on the resonant frequency of the flow tube. For example, assume that the resonant frequency fref0 of the flow tube when the fluid is solely in the gas phase and the resonant frequency fref1 of the flow tube when the fluid is solely in the liquid phase are stored in advance. In this case, the calculation unit 150 may estimate the fraction of gas in the liquid by comparing the calculated resonant frequency fv of the flow tube with the known resonant frequencies fref0 and fref1 of the flow tube. In this way, when the flow sensor 20 is a Coriolis flowmeter, the calculation unit 150 may estimate at least either the density of the fluid or the fraction of multiphase flow based on the resonant frequency of the flow tube.

In a Coriolis flowmeter, the flow tube is vibrated by the excitation force of the oscillator. For example, if the liquid contains gas bubbles, the damping of the flow tube increases, and the drive current applied to the oscillator increases to compensate for the excitation. Therefore, the virtual flow calculation device 100 can estimate the multiphase flow fraction based on this drive current. As an example, the simulation unit 140 may perform a coupled simulation of multiphase fluid, stress, and electromagnetic fields. The calculation unit 150 may calculate the drive current applied to the oscillator from the results of this simulation. Assume that a data set showing the correlation between the drive current applied to the oscillator and the gas bubble fraction in the liquid is pre-stored. In this case, the calculation unit 150 may estimate the gas fraction in the liquid by comparing the calculated drive current with a known data set. In this way, when the flow sensor 20 is a Coriolis flowmeter, the calculation unit 150 may estimate the multiphase flow fraction based on the drive current applied to the oscillator to vibrate the flow tube.

The calculation unit 150 may notify the diagnosis unit 160 of the parameters estimated in this manner, for example. The diagnosis unit 160 may then issue an alert if the parameters notified from the calculation unit 150 do not satisfy predetermined criteria. As an example, assume that a certain amount of contaminants is known to be mixed into a fluid in a plant process. In this case, the diagnosis unit 160 may issue an alert if the parameters notified from the calculation unit 150 (e.g., fluid density, multiphase flow fraction, etc.) exceed the range expected from the known amount of contaminants. The diagnosis unit 160 can issue an alert, for example, if the parameters estimated by the calculation unit 150 in this manner do not satisfy predetermined criteria. Note that in the second embodiment, the diagnosis unit 160 may also have the functions described in the above embodiments. That is, the diagnosis unit 160 can also issue an alert if the difference between the actual flow rate and the virtual flow rate does not satisfy predetermined criteria.

Figure 11 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual ultrasonic flowmeter. In this figure, components having the same functions and configurations as those in Figure 4 are denoted by the same reference numerals, and descriptions thereof will be omitted hereinafter except for differences. In the virtual flow rate calculation device 100 according to the second embodiment, the simulation unit 140 may further include a multiphase fluid simulation unit 145 in addition to the fluid simulation unit 141 and the ultrasonic propagation simulation unit 144. In the second embodiment, the virtual flow rate calculation device 100 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to estimate the output value of an ultrasonic flowmeter used in real space.

As described above, in a virtual ultrasonic flowmeter, the virtual flow rate Q can be calculated from the propagation times t1 and t2 of the ultrasonic waves. However, for example, if the fluid to be measured is a dispersed flow in which particles such as bubbles or solids are dispersed in the liquid phase, the particles will cause the ultrasonic waves to be scattered and reflected. This can reduce the signal strength of the ultrasonic waves transmitted from the ultrasonic transmitter and received on the opposite side, or change the apparent propagation speed in the liquid. Therefore, the simulation unit 140 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to calculate the flow velocity distribution within the pipe. The calculation unit 150 may then calculate the propagation times t1 and t2 from the results of a coupled simulation of the multiphase fluid and the ultrasonic waves.

In this case, the virtual flow rate calculation device 100 estimates the actual flow rate of the ultrasonic flow meter based on the simulation results, and can also estimate various other parameters by performing theoretical calculations using the simulation results. The virtual flow rate calculation device 100 can also diagnose the amount of air bubbles and solid particles and estimate the impact on the flow rate output.

For example, the size (diameter) of particles such as bubbles and solids affects the magnitude and frequency components of scattered reflections. Therefore, the calculation unit 150 may analyze the received ultrasonic signal strength for each frequency band and estimate the particle distribution (size, etc.) by combining the analysis results with the simulation results of ultrasonic propagation and multiphase fluid. This allows the virtual flow rate calculation device 100 to diagnose fluid changes, etc., based on the particle distribution. In this way, when the flow sensor 20 is an ultrasonic flowmeter, the calculation unit 150 may estimate the distribution of particles dispersed in the fluid based on the results of analyzing the received ultrasonic signal strength for each frequency band.

Furthermore, when particles such as bubbles or solids flow unevenly, a difference occurs in the received signal strength of the opposing ultrasonic waves (i.e., the received signal strength of the ultrasonic waves from the upstream side to the downstream side, and the received signal strength of the ultrasonic waves from the downstream side to the upstream side). Therefore, the calculation unit 150 may calculate the difference in the received signal strength of the opposing ultrasonic waves, i.e., the degree of imbalance. Here, it is assumed that a data set showing the correlation between the difference in received signal strength and the uneven distribution state of particles is stored in advance. In this case, the calculation unit 150 may estimate the uneven distribution state of particles such as bubbles or solids by comparing the calculated difference in received signal strength with a known data set. In this way, when the flow sensor 20 is an ultrasonic flowmeter, the calculation unit 150 may estimate the uneven distribution state of particles dispersed in the fluid based on the difference in the received signal strength of the opposing ultrasonic waves.

The calculation unit 150 may, for example, notify the diagnosis unit 160 of the parameters estimated in this manner. The diagnosis unit 160 may then issue an alert if the parameters notified by the calculation unit 150 do not satisfy predetermined criteria. As an example, the diagnosis unit 160 may monitor the parameters notified by the calculation unit 150 (e.g., particle distribution state, particle uneven distribution state, etc.), and issue an alert if the fluctuation value of the parameter exceeds a predetermined range. The diagnosis unit 160 can issue an alert, for example, if the parameters estimated by the calculation unit 150 in this manner do not satisfy predetermined criteria.

Figure 12 shows an example block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual electromagnetic flowmeter. In this figure, components having the same functions and configurations as those in Figure 5 are denoted by the same reference numerals, and descriptions thereof will be omitted hereinafter except for differences. In the virtual flow rate calculation device 100 according to the second embodiment, the simulation unit 140 may further include a multiphase fluid simulation unit 145 in addition to the fluid simulation unit 141 and the electromagnetic field simulation unit 143. In the second embodiment, the virtual flow rate calculation device 100 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to estimate the output value of an electromagnetic flowmeter used in real space.

As described above, the virtual electromagnetic flowmeter can calculate the virtual flow rate Q from the electromotive force e generated at the electrodes. Here, the electromotive force e is calculated by multiplying and integrating the weighting function w, the magnetic flux density B, and the flow velocity v. However, if the fluid being measured is a dispersed flow in which electrically insulating particles (such as air bubbles, oil, sand, or slurry) are dispersed in an electrically conductive liquid phase (e.g., water), the electrically insulating particles do not contribute to the electromotive force detected by the electrodes. Therefore, the simulation unit 140 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to calculate the frequency and area of particle occurrence near the electrodes. The calculation unit 150 may then calculate the electromotive force e by incorporating signals from areas where particles are present into the integral calculation as zero. Naturally, since there are elements omitted in the simulation, the calculation unit 150 may incorporate predetermined coefficients that pre-associate the simulation results with the actual flow rate. In this way, the calculation unit 150 can estimate the actual flow rate of the electromagnetic flowmeter by reducing the electromotive force effect of electrical insulators dispersed in the fluid being measured.

In this case, the virtual flow rate calculation device 100 estimates the actual flow rate of the electromagnetic flow meter based on the simulation results, and can also estimate various other parameters by performing theoretical calculations using the simulation results.

For example, electrically insulating particles rubbing against the electrodes generates fluctuating electrical noise. Additionally, various flow noises are generated at the electrodes of an electromagnetic flowmeter depending on various fluid conditions. Such flow noises may be as described in Patent No. 6,229,852, and therefore will not be described here. In the second embodiment, the simulation unit 140 includes a multiphase fluid simulation unit 145, enabling the virtual flow calculation device 100 to correlate such flow noises generated at the electrodes of the electromagnetic flowmeter. For example, by determining the flow fluctuations and fluctuations of the multiphase flow near the electrodes, the calculation unit 150 can calculate the amount of fluctuation in the multiphase flow fraction at the measurement cross section of the measuring pipe or near the electrodes. By pre-correlating these calculation results with the actual flow rate of the electromagnetic flowmeter and setting a predetermined coefficient, it becomes possible to correlate the simulation results in virtual space with the flow noise diagnostic signal. This allows the calculation unit 150 to identify the electromotive force effect of the electrically insulating material in the dispersed phase and the amount of flow noise obtained from the multiphase fluid simulation. In this way, when the flow sensor 20 is an electromagnetic flowmeter, the calculation unit 150 may estimate the amount of flow noise generated at the electrodes based on the electromotive force effect of electrical insulators dispersed in the fluid.

Furthermore, by comparing the fluctuations in the electromagnetic flowmeter's process value and flow noise with the results of a parametric calculation of the multiphase state, the calculation unit 150 can estimate the multiphase state, such as determining the bubble fraction if the dispersed phase is gas, or the oil fraction if the dispersed phase is oil. In this way, when the flow sensor 20 is an electromagnetic flowmeter, the calculation unit 150 may estimate the multiphase flow fraction based on the results of a parametric calculation of the simulated results.

The calculation unit 150 may, for example, notify the diagnosis unit 160 of the parameters estimated in this manner. The diagnosis unit 160 may then issue an alert if the parameters notified by the calculation unit 150 do not satisfy predetermined criteria. As an example, the diagnosis unit 160 may monitor the parameters notified by the calculation unit 150 (e.g., flow noise amount or fraction), and issue an alert if the fluctuation value of the parameter exceeds a predetermined range. The diagnosis unit 160 can issue an alert, for example, if the parameters estimated by the calculation unit 150 in this manner do not satisfy predetermined criteria.

Figure 13 shows an example of a block diagram of a virtual flow rate calculation device 100 according to a second embodiment that functions as a virtual vortex flowmeter. In this figure, components having the same functions and configurations as those in Figure 6 are denoted by the same reference numerals, and descriptions thereof will be omitted hereinafter except for differences. In the virtual flow rate calculation device 100 according to the second embodiment, the simulation unit 140 may further include a stress simulation unit 142, an electromagnetic field simulation unit 143, and a multiphase fluid simulation unit 145 in addition to the fluid simulation unit 141. In the second embodiment, the virtual flow rate calculation device 100 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to estimate the output value of a vortex flowmeter used in real space.

As described above, in a virtual vortex flowmeter, the virtual flow rate Q can be calculated using the vortex frequency f and the pipe cross-sectional area A. However, for example, if the fluid to be measured is in a gas-liquid multiphase state such as a stratified flow or annular flow, the presence of a liquid phase on the pipe wall reduces the apparent pipe inner diameter and increases the flow rate. Therefore, the simulation unit 140 may perform a multiphase fluid simulation using the multiphase fluid simulation unit 145 to calculate the flow rate distribution within the pipe. The calculation unit 150 may then calculate the virtual flow rate Q of the gas phase alone from the results of the simulation.

In this case, the virtual flow rate calculation device 100 estimates the actual flow rate of the vortex flowmeter based on the simulation results, and can also estimate various other parameters by performing theoretical calculations using the simulation results. The virtual flow rate calculation device 100 can also be used to estimate dryness fraction during a steam process.

For example, the virtual flow rate calculation device 100 can estimate the fraction of multiphase flow from the amplitude and intensity of the voltage generated in the piezoelectric element. As an example, to detect the vortex frequency f, a method is used in which a piezoelectric element detects the stress generated by the alternating pressure applied to the vortex shedder when Kármán vortices are generated. However, if a liquid phase is present on the pipe wall, the alternating pressure applied to the vortex shedder due to the generation of Kármán vortices is lower than in the case of a gas phase alone. Therefore, the simulation unit 140 may perform a coupled simulation of multiphase fluid and stress. The calculation unit 150 may calculate the amplitude and intensity of the voltage generated in the piezoelectric element using the simulation results and a piezoelectric function, and estimate the liquid fraction in the gas phase from this amplitude and intensity. In this way, when the flow sensor 20 is a vortex flowmeter, the calculation unit 150 may estimate the fraction of multiphase flow based on the amplitude and intensity of the voltage generated in the piezoelectric element.

The virtual flow rate calculation device 100 can also estimate the multiphase flow fraction from noise (fluctuations and fluctuations in the flow of the multiphase fluid) generated in the piezoelectric element. For example, if a liquid phase is present on the pipe wall, the piezoelectric element in the vortex shedder will perceive vibrations caused by fluctuations in the liquid flow as noise. Furthermore, if droplets are present in the gas phase, the collision of the droplets with the vortex shedder will generate noise in the piezoelectric element. Therefore, the simulation unit 140 may perform a coupled simulation of the multiphase fluid, stress, and electromagnetic field. The calculation unit 150 may calculate the noise generated in the piezoelectric element based on the results of this simulation and estimate the liquid fraction in the gas phase, etc., from this noise. In this way, when the flow sensor 20 is a vortex flowmeter, the calculation unit 150 may estimate the multiphase flow fraction based on the noise generated in the piezoelectric element.

The simulation unit 140 may also use the multiphase flow fraction (e.g., the gas-liquid phase fraction) to perform a multiphase fluid simulation using the multiphase fluid simulation unit 145, and calculate the flow velocity distribution within the pipeline. The calculation unit 150 may then calculate the virtual flow rate Q of the gas phase alone from the results of the simulation. In this way, when the fluid is in a multiphase state of two phases, gas and liquid, the calculation unit 150 may calculate the virtual flow rate Q of the gas phase alone based on the multiphase flow fraction.

The calculation unit 150 may, for example, notify the diagnosis unit 160 of the parameters estimated in this manner. The diagnosis unit 160 may then issue an alert if the parameters notified by the calculation unit 150 do not satisfy predetermined criteria. As an example, the diagnosis unit 160 may monitor the parameters notified by the calculation unit 150 (e.g., the fraction of multiphase flow, etc.) and issue an alert if the fluctuation value of the parameter exceeds a predetermined range. The diagnosis unit 160 can issue an alert, for example, if the parameters estimated by the calculation unit 150 in this manner do not satisfy predetermined criteria.

As such, in the second embodiment, the flow sensor 20 may be at least one of a Coriolis flowmeter, an ultrasonic flowmeter, an electromagnetic flowmeter, or a vortex flowmeter, and the virtual flow calculation device 100 may function as at least one of a virtual Coriolis flowmeter, a virtual ultrasonic flowmeter, a virtual electromagnetic flowmeter, or a virtual vortex flowmeter.

When measuring flow rates, the multiphase state inside the measurement pipe affects measurement accuracy. Therefore, the virtual flow rate calculation device 100 according to the second embodiment uses environmental information and physical property information to simulate the multiphase flow phenomenon in the fluid to be measured, and calculates a virtual flow rate based on the simulation results. This allows the virtual flow rate calculation device 100 according to the second embodiment to further enhance the versatility of flow rate measurements and diagnosis related to flow rate measurements.

Furthermore, the virtual flow rate calculation device 100 according to the second embodiment is capable of estimating not only the actual flow rate but also various other parameters in accordance with the sensing principles of a flow meter, and can also issue an alert if the estimated parameters do not meet predetermined criteria. Furthermore, when performing a multiphase fluid simulation, the virtual flow rate calculation device 100 according to the second embodiment can use the optimal analysis model depending on the multiphase state, such as using a continuous phase model to reduce the calculation load when analyzing a rough flow, or using a dispersed phase model to obtain detailed simulation results when tracking individual particles.

Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry supplied with computer-readable instructions stored on a computer-readable medium, and/or a processor supplied with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and/or analog hardware circuitry and may include integrated circuits (ICs) and/or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry including logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), etc.

A computer-readable medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture, including instructions that can be executed to create means for performing the operations specified in the flowchart or block diagram. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, etc.

The computer-readable instructions may include either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages such as the "C" programming language or similar programming languages.

The computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, either locally or over a local area network (LAN), a wide area network (WAN) such as the Internet, etc., which executes the computer-readable instructions to create means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

Figure 14 shows an example of a computer 9900 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 9900 may cause the computer 9900 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and/or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 9912 to cause the computer 9900 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

The computer 9900 according to this embodiment includes a CPU 9912, RAM 9914, a graphics controller 9916, and a display device 9918, which are interconnected by a host controller 9910. The computer 9900 also includes input/output units such as a communication interface 9922, a hard disk drive 9924, a DVD drive 9926, and an IC card drive, which are connected to the host controller 9910 via an input/output controller 9920. The computer also includes legacy input/output units such as a ROM 9930 and a keyboard 9942, which are connected to the input/output controller 9920 via an input/output chip 9940.

The CPU 9912 operates according to programs stored in the ROM 9930 and RAM 9914, thereby controlling each unit. The graphics controller 9916 retrieves image data generated by the CPU 9912 into a frame buffer or the like provided in the RAM 9914 or into itself, and causes the image data to be displayed on the display device 9918.

The communication interface 9922 communicates with other electronic devices via a network. The hard disk drive 9924 stores programs and data used by the CPU 9912 in the computer 9900. The DVD drive 9926 reads programs or data from the DVD-ROM 9901 and provides the programs or data to the hard disk drive 9924 via the RAM 9914. The IC card drive reads programs and data from an IC card and/or writes programs and data to an IC card.

The ROM 9930 stores therein boot programs and the like that are executed by the computer 9900 upon activation, and/or programs that depend on the hardware of the computer 9900. The input/output chip 9940 may also connect various input/output units to the input/output controller 9920 via parallel ports, serial ports, keyboard ports, mouse ports, etc.

The programs are provided on a computer-readable medium such as a DVD-ROM 9901 or an IC card. The programs are read from the computer-readable medium, installed on the hard disk drive 9924, RAM 9914, or ROM 9930, which are also examples of computer-readable media, and executed by the CPU 9912. The information processing described in these programs is read by the computer 9900, resulting in cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by implementing information manipulation or processing in accordance with the use of the computer 9900.

For example, when communication is performed between the computer 9900 and an external device, the CPU 9912 may execute a communication program loaded into the RAM 9914 and instruct the communication interface 9922 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 9912, the communication interface 9922 reads transmission data stored in a transmission buffer processing area provided in the RAM 9914, hard disk drive 9924, DVD-ROM 9901, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes received data received from the network to a reception buffer processing area or the like provided on the recording medium.

The CPU 9912 may also read all or a necessary portion of a file or database stored on an external recording medium such as a hard disk drive 9924, a DVD drive 9926 (DVD-ROM 9901), an IC card, etc. into the RAM 9914, and perform various types of processing on the data in the RAM 9914. The CPU 9912 then writes the processed data back to the external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 9912 may perform various types of processing on data read from RAM 9914, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search/replacement, etc., as described throughout this disclosure and specified by the program's instruction sequence, and write the results back to RAM 9914. The CPU 9912 may also search for information in files, databases, etc. on the recording medium. For example, if multiple entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, are stored on the recording medium, the CPU 9912 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

The programs or software modules described above may be stored on computer-readable media on or near the computer 9900. Recording media such as a hard disk or RAM provided within a server system connected to a dedicated communications network or the Internet can also be used as computer-readable media, thereby providing the programs to the computer 9900 via the network.

The present invention has been described above using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be clear to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

The order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," or the like, and it should be noted that processes can be performed in any order, unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that it is necessary to perform the processes in that order.

10 Sensor module 20 Flow sensor 30 Processing unit 40 Sensor side communication unit 50 Trend characteristic storage unit 100 Virtual flow rate calculation device 110 Environmental information storage unit 120 Physical property information storage unit 130 Device side communication unit 140 Simulation unit 141 Fluid simulation unit 142 Stress simulation unit 143 Electromagnetic field simulation unit 144 Ultrasonic wave propagation simulation unit 145 Multiphase fluid simulation unit 150 Calculation unit 160 Diagnosis unit 710 Simulation result storage unit 810 Trend identification unit 820 Notification unit 830 Recommendation unit 9900 Computer 9901 DVD-ROM
9910 Host controller 9912 CPU
9914 RAM
9916 Graphics controller 9918 Display device 9920 Input/output controller 9922 Communication interface 9924 Hard disk drive 9926 DVD drive 9930 ROM
9940 Input/Output Chip 9942 Keyboard

Claims (18)

an environmental information storage unit that stores environmental information indicating an environment of a real space in which the flow sensor is installed;
a property information storage unit that stores property information indicating the property of the fluid to be measured;
a simulation unit that simulates a flow phenomenon of a multiphase flow in the fluid in a virtual space that reproduces the real space using the environmental information and the physical property information;
a calculation unit that calculates a virtual flow rate by estimating the actual flow rate actually measured by the flow rate sensor based on the simulation result;
A virtual flow rate calculation device comprising: The virtual flow rate calculation device according to claim 1, wherein the flow rate sensor is at least one of a Coriolis flow meter, an ultrasonic flow meter, an electromagnetic flow meter, and a vortex flow meter. the flow sensor is the Coriolis flow meter;
The virtual flow rate calculation device according to claim 2 , wherein the calculation unit estimates at least one of the density of the fluid and the fraction of the multiphase flow based on a resonance frequency of a flow tube. the flow sensor is the Coriolis flow meter;
The virtual flow rate calculation device according to claim 2 , wherein the calculation unit estimates the fraction of the multiphase flow based on a drive current applied to an oscillator to vibrate a flow tube. the flow sensor is the ultrasonic flow meter;
3. The virtual flow rate calculation device according to claim 2, wherein the calculation unit estimates the distribution state of particles dispersed in the fluid based on a result of analyzing the received signal strength of the ultrasonic waves for each frequency band. the flow sensor is the ultrasonic flow meter;
The virtual flow rate calculation device according to claim 2 , wherein the calculation unit estimates the uneven distribution state of particles dispersed in the fluid based on a difference in received signal strength of opposing ultrasonic waves. the flow sensor is an electromagnetic flow meter;
3. The virtual flow rate calculation device according to claim 2, wherein the calculation unit estimates the amount of flow noise generated at the electrodes based on the electromotive force effect of an electrical insulator dispersed in the fluid. the flow sensor is an electromagnetic flow meter;
The virtual flow rate calculation device according to claim 2 , wherein the calculation unit estimates the fraction of the multiphase flow based on a result of parametrically calculating the simulated result. the flow sensor is the vortex flowmeter;
The virtual flow rate calculation device according to claim 2 , wherein the calculation unit estimates the fraction of the multiphase flow based on the amplitude intensity of a voltage generated in a piezoelectric element. the flow sensor is the vortex flowmeter;
The virtual flow rate calculation device according to claim 2 , wherein the calculation unit estimates the fraction of the multiphase flow based on noise generated in a piezoelectric element. When the fluid is in a gas-liquid two-phase multiphase state,
The virtual flow rate calculation device according to claim 9 or 10, wherein the calculation unit calculates the virtual flow rate of the gas phase alone based on a fraction of the multiphase flow. The virtual flow rate calculation device described in any one of claims 3 to 10, further comprising a diagnostic unit that issues an alert if the parameters estimated by the calculation unit do not satisfy predetermined criteria. The virtual flow rate calculation device of claim 12, wherein the diagnostic unit further issues an alert if the difference between the actual flow rate and the virtual flow rate does not satisfy a predetermined standard. A virtual flow rate calculation device according to any one of claims 1 to 10, wherein the simulation unit uses an analytical model selected according to at least one of the phase combinations in the multiphase flow and the flow pattern. The virtual flow rate calculation device according to claim 14, wherein the analytical model is a continuous phase model or a dispersed phase model. The virtual flow rate calculation device described in any one of claims 1 to 10, provided by a cloud server. The computer
storing environmental information indicative of an environment of a real space in which the flow sensor is instrumented;
storing physical property information indicating the physical properties of the fluid to be measured;
simulating a flow phenomenon of a multiphase flow in the fluid in a virtual space that reproduces the real space using the environmental information and the physical property information;
calculating a virtual flow rate that estimates the actual flow rate actually measured by the flow sensor based on the simulation result;
A virtual flow rate calculation method comprising: The method is executed by a computer, causing the computer to:
an environmental information storage unit that stores environmental information indicating an environment of a real space in which the flow sensor is installed;
a property information storage unit that stores property information indicating the property of the fluid to be measured;
a simulation unit that simulates a flow phenomenon of a multiphase flow in the fluid in a virtual space that reproduces the real space using the environmental information and the physical property information;
a calculation unit that calculates a virtual flow rate by estimating the actual flow rate actually measured by the flow rate sensor based on the simulation result;
This is a virtual flow calculation program that functions as a virtual flow rate calculation program.