JP4107501B2 - A-priori / In-situ Hybrid pressure-sensitive paint data processing technique - Google Patents

Description

  The present invention relates to a method for measuring the pressure distribution and temperature distribution on the surface of an object, and a pressure distribution for calculating the surface pressure distribution of a general device used in a fluid environment, for example, a rocket, an aircraft, an automobile, etc. The present invention relates to an image measurement system.

Conventionally, a number of calculation methods have been announced as data processing methods for calculating pressure from the amount of emitted light using a pressure sensitive paint (PSP). The major issue for improving the accuracy of this PSP data processing is that the emission intensity of the pressure-sensitive paint changes not only with the pressure but also with the temperature. It is to do well, and how to appropriately cope with various error factors that affect the pressure-sensitive paint measurement data such as the change in the light quantity of the pressure-sensitive paint excitation light source and the light quantity deterioration of the pressure-sensitive paint.
For example, Non-Patent Document 1 describes three types of data processing methods: a-priori method, in-situ method, and K-fit method. The a-priori method obtains the characteristics of pressure-sensitive paint in advance using temperature and pressure as parameters, obtains temperature information separately by some detection means, and uses this to perform temperature compensation from pressure-sensitive paint data to calculate pressure It is. In the in-situ method, the data obtained simultaneously from the pressure sensor arranged at a predetermined position is correlated with the pressure-sensitive paint data of the portion, and the pressure is calculated using this as a calibration value. Furthermore, the K-fit method
This is a method represented by the following calculation formula. Here, K represents Iref as the emission intensity under the setting conditions (Pref, Tref) for the pressure P and temperature T,
It is represented by In addition, the coefficients C ′ ₁ , C ′ ₂ , and C ′ ₃ in (1) are

It is a coefficient of the pressure sensitive paint characteristic formula used on the assumption that it does not depend on temperature inherently.
Non-Patent Document 2 and Non-Patent Document 3 also basically present data processing techniques similar to these data processing techniques.
However, the a-priori method does not include measures against the light amount change of the pressure-sensitive paint excitation light source or the light amount deterioration of the pressure-sensitive paint, and these error factors cannot be corrected. For the in-situ method and the K-fit method, the effect of correcting these error factors by comparison with the pressure sensor data has been introduced. It can be applied only when the temperature is high, and empirically, it can be extended when there is a correlation between the temperature and the pressure being steady at the adiabatic wall temperature. In a place where it exists, it is not possible to deal with it uniformly. As for the K-fit method, non-patent documents 1 to 3 use the assumption that the temperature on the model is uniform, and further, coefficients C ′ ₁ , C ′ ₂ , and C ′ _{3 in} Equation (1). As can be seen from the comparison with equation (3), strictly, it can be applied only to pressure-sensitive paints that do not change with temperature. For paint, an error occurs in the measurement result.

  In Non-Patent Document 4, not only pressure-sensitive paint, but also a method of applying temperature-sensitive paint to another part of the model to measure the temperature distribution on the model and correcting the temperature assuming the same temperature distribution. It is adopted. However, as a data processing method, it is an extension of the a-priori method, and it is not possible to deal with error factors other than temperature dependency associated with pressure-sensitive paint measurement. Non-Patent Document 1 also mentions binarypaint as a means of measuring the temperature distribution on the model, but the data processing method is based on the a-priori method, and it is based on pressure-sensitive paint measurement data processing. Although it is possible to cope with changes in temperature dependency and excitation light intensity, it is impossible to cope with error factors of other components such as deterioration of pressure-sensitive paint components.

In Non-Patent Document 5, as well as Non-Patent Document 4, not only the pressure-sensitive paint but also the temperature distribution on the model is assumed. It is carried out. In-situ method is combined to deal with error factors other than temperature dependence. That is, a step of acquiring temperature distribution data from each temperature-sensitive paint data for each divided region of the model surface in which pressure holes are provided at a plurality of locations and pressure-sensitive paint is applied to the surface, and pressure-sensitive paint based on the temperature distribution data Correcting the change in emission intensity due to temperature, obtaining the emission intensity distribution of pressure-sensitive paint as a function of pressure alone, corrected pressure-sensitive paint emission intensity around the pressure hole, and pressure detected by other pressure gauges A calibration step by an in situ method for correlating the pore data, and a step for converting the emission intensity distribution of the pressure-sensitive paint as a function of only the pressure into a pressure distribution based on the calibration characteristics by the in situ method. However, the area between the pressure holes is interpolated by interpolation, and it is necessary to use a high-order fitting equation to accurately describe non-linear pressure-sensitive paint characteristics. If data distributed over a sufficient pressure range cannot be used as data, there is a risk of performing data processing that deviates from the original characteristics in the extrapolation part of the fitting formula.
JH Bell, ET Schairer, LA Hand, and RD Mehta, "Surface Pressure Measurements Using Luminescent Coatings," Annual Review of Fluid Mechanics, 2001. 33: 155-206. MA Woodmansee and JCDutton, "Treating temperature-sensitivity effects of pressure-sensitive paint", Experiments in Fluids, Vol. 24, pp. 163-174, 1998. Mebarki, Y and Cooper KR, "Aerodynamic Testing of a Generic Automotive Model with Pressure SensitivePaint", The 10th International Symposium on Flow Visualization, F0120, August 26-29, Kyoto, Japan, 2002. 6.2 Choice of in-situ calibration order and 6.3 Summary of data reduction methods Y. Shimbo, K. Asai, H. Kanda, Y. Iijima, M. Komatsu, S. Kita and M. Ishiguro, "Evaluation of Several calibration techniques for Pressure-Sensitive Paint in Transonic Testing," AIAA Paper 98-2502, 1998 . M. Kurita, K. Nakakita, K. Mitsuo, and S. Watanabe, "Data Processing of Pressure-Sensitive Paint for Industrial Wind Tunnel Testing," AIAA-2004-2189, 24th AIAA Aerodynamic Measurement Technology and Ground Testing Conference, June 28 -July 1, Portland, Oregon, USA, 2004.

  The present invention relates to data processing of pressure-sensitive paint, has a temperature distribution on the measurement object, and the pressure-sensitive paint measurement system also includes error components such as light quantity change and light deterioration of the pressure-sensitive paint. This is intended for a measurement test using a general pressure-sensitive paint, such that there is no data over a sufficiently wide pressure range as comparative pressure sensor data for correcting the paint data. When such a measurement target is used, as described above, the problem of error based on the temperature dependence of the pressure sensitive paint due to its temperature distribution, the problem when some error component is superimposed on the pressure sensitive paint measurement data, and this This is accompanied by a problem of reduced accuracy due to extrapolation interpolation that occurs because the pressure sensor data for comparison does not cover a sufficiently wide pressure range.

  Under such circumstances, the problem to be solved by the present invention is to correct the temperature dependence of the pressure-sensitive paint, correct other factor errors superimposed on the light emission amount, and eliminate the influence of extrapolation in the interpolation calculation. It is to present a data processing technique with high accuracy of pressure sensitive paint in a measurement test using pressure sensitive paint.

The A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention is the pressure measurement using pressure-sensitive paint measurement data, and the pressure-sensitive paint measurement data and the sensitivity acquired in advance by a-priori calibration. Pressure paint characteristic data, temperature measurement data reflecting temperature distribution on the model, a step of obtaining pressure data at a predetermined position measured by a pressure sensor as basic data , the pressure sensor data for the predetermined position, and the a correction coefficient C _PSP Ru the resulting step is the ratio based on both the pressure-sensitive paint measurement data, the systematic errors in the pressure-sensitive paint measurement by various factors by multiplying the correction coefficient C _PSP in the sense圧塗charge measurement data and correcting, to calculate the pressure from the temperature measurement data that reflects the temperature distribution on the said sensitive圧塗charge measurement data model by using the a-priori calibration data It consisted of a step.

Further, another A-priori / In-situ Hybrid pressure-sensitive paint data measurement processing method of the present invention is a test using the pressure-sensitive paint measurement data and a-priori calibration in pressure measurement using pressure-sensitive paint measurement data. Acquires pressure-sensitive paint characteristic data and temperature-sensitive paint characteristic data acquired in advance, measurement data by temperature-sensitive paint applied on the model, and pressure data at a predetermined position measured by the pressure sensor as basic data . a step, and obtaining a comparison temperature data T _TSP from said sense圧塗charge measurement data and the temperature sensitive paint measurement data and the sensitive圧塗charge characteristic data and the temperature sensitive paint characteristic data corresponding to the predetermined position, the comparison Pressure sensor data converted into comparative data constructed from the temperature data for pressure and the pressure sensor measurement data at the predetermined position using the pressure-sensitive paint characteristic data by the a-priori calibration, and the predetermined A step of obtaining an in-situ correction coefficient C _PSP of the pressure sensitive paint measurement data from the ratio with the pressure sensitive paint measurement data of the position, and multiplying the pressure sensitive paint measurement data by the pressure sensitive paint measurement data in the pressure sensitive paint measurement due to various factors And calculating the pressure using the a-priori calibration data after correcting the systematic error .

Further, another A-priori / In-situ Hybrid pressure-sensitive paint measurement data processing method of the present invention is a test using the pressure-sensitive paint measurement data and a-priori calibration in pressure measurement using pressure-sensitive paint measurement data. Acquires pressure-sensitive paint characteristic data and temperature-sensitive paint characteristic data acquired in advance, measurement data by temperature-sensitive paint applied on the model, and pressure data at a predetermined position measured by the pressure sensor as basic data . a step, and obtaining a comparison temperature data T _TSP from said sense圧塗charge measurement data and the temperature sensitive paint measurement data and the sensitive圧塗charge characteristic data and the temperature sensitive paint characteristic data corresponding to the predetermined position, the comparison Pressure sensor data that has been converted into comparative data, which is constructed using pressure-sensitive paint characteristic data obtained by the a-priori calibration from the temperature data for the predetermined position and the pressure sensor measurement data at the predetermined position. A step of obtaining an in-situ correction coefficient C _PSP of the pressure sensitive paint measurement data from the ratio with the pressure sensitive paint measurement data at a predetermined position, and applying this to the pressure sensitive paint measurement data, the pressure sensitive paint due to various factors It consisted of a step of calculating the pressure by using the a-priori calibration data after correcting systematic errors in the measurement.

The A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention uses a-priori calibration characteristics, and in-pressure using pressure measurement data with high precision at specific locations in addition to pressure-sensitive paint measurement data. Although in-situ calibration is performed, the corresponding data is matched as in the past, and the data between them is not processed by the interpolation method, but pressure-sensitive paint measurement data and accurate pressure measurement Since the in-situ correction coefficient C _PSP is obtained from the data and applied to the pressure-sensitive paint measurement data, the system error in the pressure-sensitive paint measurement due to various factors can be effectively corrected. Even if the pressure sensor data for comparison of pressure-sensitive paint data does not have data over a sufficiently wide pressure range, even a general pressure-sensitive paint measurement situation, a-priori of pressure-sensitive paint with a wide coverage Calibration data It is intended to eliminate the influence of extrapolation by using the data. According to the present invention, it is possible to perform pressure calculation with ensuring measurement accuracy under any pressure sensitive paint measurement environment.
Further, the other A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention is the above method, and the temperature distribution measurement data is not provided at a direct temperature but is obtained as temperature-sensitive paint data. But, the a-priori calibration data of the temperature-sensitive paint used, by performing a pressure sensitive paint measurement data and complementary data processing, similarly to the in-situ correction coefficient C _PSP pressure sensitive paint measurement data to calculate the The pressure can be obtained while the measurement accuracy is ensured by correcting the pressure measurement data based thereon.
Further, the A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention uses an accurate in-situ correction coefficient using temperature measurement data at a specific location in addition to temperature-sensitive paint measurement data for temperature. C _TSP is obtained and corrected by multiplying the value obtained from the a-priori calibration characteristic, so that a more accurate temperature distribution can be obtained. As a result, pressure based on pressure-sensitive paint measurement data can be obtained based on this information. Measurement data can be corrected more accurately.

In pressure measurement using pressure-sensitive paint, it is common that 1) there is a temperature distribution on the model, and 2) there is a possibility of including error factors such as fluctuations in the amount of light from the excitation light source and light deterioration of the pressure-sensitive paint. These measures are necessary for sufficiently quantitative measurement. In addition, it is assumed that the pressure-sensitive paint used in the present invention contains any of a porphyrin series, transition metal complex, polycyclic aromatic compound, rare earth complex, and phthalocyanine series.
With regard to 1), since the light emission amount of the pressure-sensitive paint has not only pressure sensitivity but also temperature dependence, in the present invention, pressure-sensitive paint measurement is performed using a technique such as a temperature-sensitive paint, an infrared camera, or a pressure / temperature-sensitive binary paint. The temperature distribution in the region is measured, and the temperature dependence correction of the pressure sensitive paint is performed for each location.
For 2), these error factors appear as offset components in the measurement result, so in the present invention, other high-precision measurements acquired simultaneously with pressure-sensitive paint measurement data such as pressure hole measurement data connected to the pressure sensor. The data is used in combination, and the error factor is corrected by making in-situ corrections by comparing the pressure-sensitive paint measurement results with these highly accurate measurement data at the representative points.
3) However, as in the existing in-situ method for pressure-sensitive paint measurement, creating a correlation equation from the purely pressure-sensitive paint measurement results and high-precision measurement data is a common technique for pressure-sensitive paint measurement. In cases where high-precision measurement data for comparison, which is an attribute, may not have data over a sufficiently wide pressure range, there is an error in the extrapolated part outside the pressure range of the measurement data for comparison. Therefore, in the present invention, the influence of such extrapolation is avoided by using a-priori calibration data of a pressure-sensitive paint pre-calibrated over a sufficiently wide pressure and temperature range as a base. .
Therefore, 2) 3) As a method corresponding to both, a-priori calibration data is used as a base, and other high-precision measurement data is used in combination. An in-situ correction factor is introduced by comparing measurement data.
The outline of the method which is the basis of the present invention is schematically as shown in FIG. 1, and the temperature distribution information obtained by the pressure-sensitive paint measurement data, the a-priori calibration data, and the measurement using the temperature-sensitive paint. Thus, the correction coefficient C is first determined, and the pressure distribution information is obtained after the coefficient is applied to the entire pressure-sensitive paint measurement image. The correction coefficient C is calculated in-situ by taking representative information at a specific point from pressure-sensitive paint measurement data and comparing it with data obtained from accurate pressure values detected by other measurement means. Can be determined.
When formulating the means corresponding to these, as the most basic expression
Can be written. Here, P is a pressure to be measured to be calculated, Pref is a pressure in a known reference state, I is a measured light emission amount of the pressure sensitive paint, Iref is a light emission amount of the pressure sensitive paint in a known reference state, C _PSP is a correction coefficient which is in-situ manner calculated by comparing the high measurement data of pressure sensitive paint measurement results and other precision representative points. As the light emission characteristic data of the pressure-sensitive paint, a characteristic surface obtained as a-priori calibration data between P, T, and Iref / I as illustrated in FIG. 2 is used.

As basic data necessary for applying the A-priori / In-situ Hybrid pressure-sensitive paint data processing method according to the present invention,
a. Pressure-sensitive paint measurement data (I image: Image at measurement and Iref image: Image under reference pressure and reference temperature)
b. High-precision pressure measurement data at specific points by other pressure sensors (for in-situ calibration)
c. Temperature distribution data in the pressure sensitive paint measurement area
d. Four types of data are required: characteristic data from pressure-sensitive paint a-priori calibration.
As the pressure-sensitive paint measurement data for the I image and the Iref image, a single image may be used, or a plurality of images may be integrated and averaged and noise may be removed. Other highly accurate pressure measurement data includes pressure data for calibration to correct error components such as light intensity change and light degradation of pressure-sensitive paint that may be included in the pressure-sensitive paint measurement system. Use the one that can be regarded as having measured the same pressure field as the paint measurement data. As this data, it is possible to use a discrete pressure measurement method using a combination of a pressure hole on the measurement surface and a pressure transducer. In the present invention, the pressure measurement data does not necessarily need to be data over a wide pressure range including the pressure range included in the pressure-sensitive paint measurement region. In theory, there should be at least one calibration pressure measurement data point. It ’s fine. However, in practice, it is clear that the measurement accuracy is better when the least squares are calculated using as many calibration pressure measurement data points as possible.

As temperature distribution data, in addition to the temperature distribution in the pressure sensitive paint measurement area measured by temperature sensitive paint, infrared camera, pressure sensitive / temperature sensitive binary paint, etc., these temperature measurement methods are used, Although the temperature distribution in the pressure-sensitive paint measurement area is not directly measured, the pressure-sensitive paint measurement area assumes the symmetry of the flow field and temperature field, the identity of the flow field and temperature field in separate parts, etc. In some cases, it is used as data equivalent to the internal temperature distribution.
Characteristic data of pressure-sensitive paint by a-priori calibration includes measurement points in a matrix with temperature and pressure as independent parameters, pressure-sensitive paint luminescence corresponding to each point, and temperature and pressure as reference conditions The pressure-sensitive paint characteristics are calibrated based on the amount of light emitted from the pressure-sensitive paint. This a-priori calibration may be performed before the test, after the test, or may be performed in parallel with the test, and the a-priori calibration data is databased without performing the calibration test. In some cases, characteristic data is used. Examples of calibration test pieces include those using sample pieces that can be regarded as the same characteristics as the test model, and those that perform calibration using the model itself to obtain characteristic data.

FIG. 3 shows an example of a basic flowchart of pressure-sensitive paint data processing using the A-priori / In-situ Hybrid pressure-sensitive paint data processing method according to the present invention, and FIG. 4 shows an in-situ correction coefficient in FIG. A basic example of the details of the calculation routine will be shown.
In FIG. 3, dark image subtraction is performed to remove camera non-uniformity from the image to obtain a non-dimensional light quantity ratio image (Iref / I). The data supplied to the in-situ correction coefficient calculation routine in the figure is only the area that can be regarded as taking the same pressure as the pressure measurement data for comparison from the pressure-sensitive paint measurement data in the form of a light intensity ratio image (Iref / I) And the pressure-sensitive paint measurement data and the comparative pressure measurement data are used as a set in association with each other. There may be a plurality of these sets, and FIG. 4 shows an example in which there are N sets. The temperature data T around the pressure hole of the pressure gauge for comparison is acquired together with the pressure measurement data Ptap, and the emission ratio (Iref / I) tap of the pressure hole part is calculated from the a-priori calibration data of the pressure-sensitive paint. A ratio with the surrounding pressure sensitive paint measurement data (Iref / I) psp is taken, and a correction coefficient Cpsp, j obtained from the ratio is calculated. In this way, the in-situ correction coefficient C _PSP is calculated for each set of one pressure-sensitive paint measurement data and comparative pressure measurement data, and finally the average value of N points as a least square representative C _PSP. Is used. However, since there is a possibility that the data of these N points includes a point that is not logical due to the influence of an error factor, data selection may be performed at a stage prior to the average value calculation.

In addition, when using temperature-sensitive paint measurement data instead of direct temperature distribution data, the temperature-sensitive paint has a slight pressure dependency, so the temperature distribution can be calculated accurately only from the temperature-sensitive paint. Is not possible, and is an extension of equation (4) shown above
Can be used to perform data processing for simultaneously calculating pressure and temperature using pressure-sensitive paint measurement data and temperature-sensitive paint measurement data. The temperature distribution of a temperature-sensitive paint cannot always be calculated by itself. As described later, for the two inputs of pressure-sensitive paint data and temperature-sensitive paint data, the data processing is repeated until the calculation of the two outputs of pressure and temperature converges. After that, temperature distribution data can be obtained, but the temperature distribution data in the A-priori / In-situ Hybrid pressure-sensitive paint data processing method is the shadow obtained by combined data processing with other data in this way. It also includes typical temperature distribution data.

FIG. 5 introduces an in-situ correction coefficient for pressure-sensitive paint measurement data by considering the pressure dependence of temperature-sensitive paint and using pressure sensor data for comparison with pressure-sensitive paint measurement data. An outline of pressure-sensitive paint data processing using the A-priori / In-situ Hybrid pressure-sensitive paint data processing technique is schematically shown, and FIG. 6 shows an example of a flowchart thereof. Here, as an example, an Iref image and an I image are obtained by applying PSP to the left half of the airframe model and applying TSP to the right half. Here, since the left and right aircrafts are targets, the temperature distribution is also assumed on the premise that the temperature distribution is symmetrical about the axis. In addition, pressure holes are provided at specific N locations on the surface of the machine body, and pressure sensors are installed. The in-situ correction coefficient C _PSP is calculated from the pressure data Ptap of the pressure hole and the measurement data of the pressure-sensitive paint and temperature-sensitive paint of the part corresponding to the pressure hole, using the a-priori calibration data of the pressure-sensitive paint and temperature-sensitive paint. calculate. FIG. 7 is a diagram illustratively explaining the in-situ correction factor calculation routine part in FIG. In this case, as can be seen from FIG. 7, unlike the case where the temperature distribution is obtained as a fixed value separately as shown in FIGS. 2 and 3, there is no comparison temperature data corresponding to the comparison pressure sensor data, and the pressure sensitive paint. Temperature data corresponding to the temperature data for comparison must be calculated from the measurement data and the measurement data obtained from the temperature-sensitive paint. This is T _TSP in FIG. Since this _{T TSP} is whose value changes every time the input data _{[Iref / C PSP · I]} PSP is _updated, the iteration until _{C PSP} value also changes, the difference between the previous step is converged Need to repeat. That is, based on the obtained T _TSP and Ptap, the light emission amount ratio (Iref / I) tap of the pressure hole portion is calculated from the a-priori calibration data of the pressure-sensitive paint, and the pressure-sensitive paint measurement data (Iref / I) of that portion. _{C PSP,} to calculate the j taking the ratio of the _PSP. This is adopted as a new C _PSP , j of [Iref / C _PSP · I] _PSP of the pressure-sensitive paint measurement data around the original pressure hole and recalculated. The resulting C _{PSP, j} and the previous value C _'PSP, repeatedly performs calculation until difference between _j is within the threshold value [delta]. When the C _PSP , j value converges, the value is set as an in-situ correction coefficient C _{PSP, j} calculated from the data for the j point. The calculation taking the arithmetic mean is performed for data of the N pressure hole and in-situ correction coefficient C _PSP. Returning to FIG. 6, the obtained in-situ correction coefficient C _PSP is applied to pressure-sensitive paint measurement data (Iref / I) for correction, and a-priori calibration data for known pressure-sensitive paint and temperature-sensitive paint The pressure value and temperature will be determined from the a-priori calibration data. In this case, using the formula (5), the pressure-sensitive paint data corrected for each section area and the temperature-sensitive paint measurement data are input, and the process of obtaining pressure and temperature as output is performed. It is necessary to repeat the calculation until is converged. The above is the A-priori / In-situHybrid pressure-sensitive paint data processing method according to the present invention in consideration of the pressure dependence of the temperature-sensitive paint.

Equation (4) is further expanded to
Equation (6) may be used. This equation (6) introduces an in-situ correction coefficient of the temperature sensitive paint measurement data by using the temperature sensor data for comparison in the temperature sensitive paint measurement data as well as the pressure sensitive paint side. The previous equation (5) can be regarded as an example in the case of C _TSP = 1 in equation (6). As the temperature sensor data for comparison, data that can be regarded as having measured the same temperature field as the temperature-sensitive paint measurement data such as a resistance temperature detector, a thermocouple, or an infrared camera is used. Similar to the comparative pressure measurement data, this comparative temperature measurement data does not necessarily have to be data over the temperature range including the temperature range in the temperature-sensitive paint measurement region, and theoretically, at least one comparative temperature is required. It is sufficient if there are measurement data points. However, in practice, it is clear that the measurement accuracy is better if the calculation is performed in the least square manner using as many comparative temperature measurement data points as possible.

  In consideration of the pressure dependence of temperature-sensitive paints in Fig. 8 and using pressure sensor data for comparison with pressure-sensitive paint measurement data, an in-situ correction factor for pressure-sensitive paint measurement data is introduced, and temperature-sensitive paint measurement is performed. Pressure-sensitive paint using the A-priori / In-situHybrid pressure-sensitive paint data processing method according to the present invention, which also introduces in-situ correction coefficient of temperature-sensitive paint measurement data by using the temperature sensor data for comparison with data An outline of data processing is schematically shown, and FIG. 9 shows an example of a flowchart thereof. The difference from the embodiment of FIG. 6 is that the input information necessary for calculating the in-situ correction coefficient is not only the pressure-sensitive paint and temperature-sensitive paint measurement data around the pressure holes, but also the pressure-sensitive paint and temperature sensitivity around the temperature sensor. In addition to the fact that paint measurement data is required, temperature sensor data is required in addition to pressure sensor data for pressure holes. FIG. 10 is a diagram for exemplifying the in-situ correction coefficient calculation routine portion in FIG. 9. FIG. 10 shows an example in which there are M pieces of data as comparison temperature measurement data. ing.

In this case, the C _PSP is the same as that shown in FIG. First, a temporary in-situ correction coefficient C _PSP is calculated by this C _PSP calculation routine, and the C _TSP calculation routine is started. Using the temporary C _PSP , the PSP light quantity ratio data (Iref / I) _PSP around the temperature sensor is corrected and calculated. Using this and the temperature-sensitive paint measurement data (I / Iref) _TSP around the temperature sensor, the pressure data P _PSP for comparison is calculated from the pressure-sensitive paint and the a-priori calibration data of the temperature-sensitive paint, and this value and the temperature sensor Using the data Tsensor, the (I / Iref) sensor to be compared using the TSP characteristics is determined. A ratio between this value and the temperature sensitive paint data (I / Iref) _TSP is taken to obtain a temporary in-situ correction coefficient C _TSP . Thereafter, the process returns to the previous in-situ correction coefficient _CPSP calculation routine to obtain the correction value [C _TSP · I / Iref] _TSP of the temperature sensitive paint measurement data around the pressure hole. Based on this and initiate an in-situ correction coefficient C _PSP recalculation is performed until convergence. When converged, the temporary in-situ correction coefficient C _TSP already obtained is used to obtain TSP data around the new temperature sensor [C _TSP · I / Iref] _TSP , and the in-situ correction coefficient C _{TSP is} calculated. Run the routine. When the difference between the C _TSP and previous values C _'TSP calculated here exceeds the threshold δ is must be recalculated, it enters that value in re-calculation routine of in-situ correction coefficient C _PSP calculation. Thereafter, it recalculates until TSP data _[C TSP · I / _Iref] to give a _TSP in-situ correction coefficient _{C TSP} new temperature around the sensor using the updated in-situ correction coefficient _{C TSP} converges To do. At the time of convergence, the in-situ correction coefficient C _TSP is calculated for each set of one thermosensitive paint measurement data and comparative temperature measurement data. Each of M sets is calculated, and an average value of M points is used as a least square representative C _TSP . Within each routine of calculating the in-situ correction coefficient C _TSP from the M-point temperature-sensitive paint measurement data and the comparative temperature measurement data, a routine for calculating the pressure-sensitive paint side in-situ correction coefficient C _PSP is provided. In order to include, it will be comprised from the above double repetition calculation (iteration) processes. The final Examples for pressure sensitive paint side in-situ correction coefficient C _PSP, in-situ correction coefficients for the set of comparative pressure measurement data and pressure sensitive paint measurement data of N points calculated in the last iteration times _CPSP is used as the average value of. However, as for the in-situ correction coefficient C _TSP on the temperature-sensitive paint side, as in the case of the pressure-sensitive paint, these M point data may contain points that are not logical due to the influence of error factors. For this reason, data selection may be performed at a stage prior to the average value calculation.

Returning to Figure 9, the correction of the pressure sensitive paint measurement data obtained in-situ correction coefficient C _PSP perform _{[Iref / C PSP · I]} , resulting in-situ correction coefficient C _TSP temperature sensitive paint measurement data Correction [C _TSP · I / Iref] is performed, and the pressure value and temperature are calculated from a-priori calibration data of the known pressure-sensitive paint and temperature-sensitive paint. Here, using the equation (6), the pressure-sensitive paint measurement data corrected for each divided region and the corrected temperature-sensitive paint measurement data are input, and a process of obtaining pressure and temperature as output is performed. Similar to the above method, calculation is repeated until both values converge. The above is the A-priori / In-situHybrid pressure-sensitive paint data processing method according to the present invention in consideration of the pressure dependence of the temperature-sensitive paint and using an in-situ correction factor for TSP data in combination with another temperature sensor. It is.

  A pressure-sensitive paint test conducted using the ONERAM5 model, which is a standard model of the 2m x 2m transonic wind tunnel of the Japan Aerospace Exploration Agency wind tunnel technology development center, is shown as an example. Figure 11 shows the ONERA M5 model with TSP on the starboard and PSP on the starboard. As temperature distribution data in the pressure sensitive paint measurement area in this example, the flow field and the temperature field are assumed to be symmetric, and pressure sensitive paint data and pressure sensitive paint are used together by using pressure sensitive paint and pressure sensitive paint together. For the two inputs of the thermal paint data, the one that obtains the temperature distribution data after performing the data processing that returns two outputs of the pressure and the temperature is used. As other highly accurate pressure measurement data for correcting the error factor of the pressure sensitive paint, the pressure information acquired from the pressure holes discretely provided on the model surface is transmitted to the pressure converter through the tube. The pressure hole measurement method is used. A resistance temperature detector that can be used as temperature sensor data for comparison of temperature-sensitive paint measurement data is also installed inside the model, and temperature data is also measured. In this data processing, in-situ No correction factor has been introduced.

  Figure 12 shows the pressure-sensitive / temperature-sensitive paint measurement data, taking into account the pressure dependence of the temperature-sensitive paint, and using the pressure sensor data for comparison with the pressure-sensitive paint measurement data. The example of what displayed the result of the pressure distribution using the A-priori / In-situ Hybrid pressure sensitive paint data processing method which introduces a situ correction coefficient by the pressure coefficient Cp is shown. This pressure coefficient is expressed by [(object surface pressure−air flow static pressure) / air flow pressure]. The top and bottom surfaces of the model are the result of processing and arranging the data obtained by separate tests. From these results, it is understood that the detailed pressure distribution of the complicated flow field on the model can be measured quantitatively by using the pressure-sensitive and temperature-sensitive paint. FIG. 13 shows the result of the temperature distribution calculated simultaneously with the pressure by data processing. Also in this case, the model upper surface and the model lower surface are obtained by processing data obtained by separate tests and arranging them. In order to prioritize productivity, these tests were practicality-oriented tests that did not take enough time for the temperature distribution on the model to become small, but from these temperature distribution measurement results, in-situ It can be clearly seen that the temperature uniformity on the model that the method and the K-fit method are based on does not hold. In the processing of pressure-sensitive paint test data having such a temperature distribution, it is well understood that assuming a uniform temperature causes a large error, and that temperature correction considering the temperature distribution is necessary.

  FIG. 14 is a comparison of the a-priori method in the pressure hole array on the main wing and the A-priori / In-situ Hybrid pressure-sensitive paint data processing method and the pressure hole measurement method according to the present invention. The pressure-sensitive paint measurement data processing result on the upper surface is almost the same between the a-priori method and the A-priori / In-situ Hybrid pressure-sensitive paint data processing method. The A-priori / In-situ Hybrid pressure-sensitive paint data processing method has an in-situ correction factor, so that the pressure hole measurement data is generally consistent with each other. You can see that This effect is due to the difference between the pressure hole measurement data in FIG. 15 and the data of the a-priori method and A-priori / In-situ Hybrid pressure-sensitive paint data processing method. As a result, it can be seen that the matching with the pressure hole measurement data is improved by the correction effect of the A-priori / In-situ Hybrid pressure-sensitive paint data processing method. On the other hand, the correction effect of the A-priori / In-situ Hybrid pressure-sensitive paint data processing method is reduced for the data on the upper surface side that is consistent with the pressure hole measurement data even in the a-priori method result. Even in this case, it is possible to calculate a result while maintaining good agreement with the pressure hole measurement data.

  The technology itself for measuring the pressure distribution using pressure-sensitive paint can be widely used not limited to industries. The present invention is a technique for accurately calculating pressure from data measured by this pressure-sensitive paint technology, and a wind tunnel test using an aircraft model is described as an example as an example. The present invention can be applied as a pressure-sensitive paint data processing method in a wide range of applications to which the paint can be applied.

It is a conceptual diagram of the basic A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention. It is a figure which shows the example of the characteristic surface of the pressure-sensitive coating material used by this invention. It is an example of the flowchart of the basic A-priori / In-situ Hybrid pressure-sensitive paint data processing method of this invention. It is a figure which shows the flow of the in-situ correction coefficient calculation routine of the basic A-priori / In-situ Hybrid pressure-sensitive paint data processing method of this invention. It is a conceptual diagram of the A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention considering the pressure dependence of the temperature-sensitive paint. It is an example of the flowchart of the A-priori / In-situ Hybrid pressure-sensitive paint data processing method of the present invention considering the pressure dependence of the temperature-sensitive paint. It is a figure which shows the flow of the in-situ correction coefficient calculation routine in a previous figure. It is a conceptual diagram of the A-priori / In-situHybrid pressure-sensitive paint data processing method of the present invention in which an in-situ correction coefficient is introduced into temperature-sensitive paint measurement data in consideration of the pressure dependence of the temperature-sensitive paint. It is an example of a flowchart of the A-priori / In-situHybrid pressure-sensitive paint data processing method of the present invention in which an in-situ correction coefficient is introduced into temperature-sensitive paint measurement data in consideration of pressure dependence of the temperature-sensitive paint. It is a figure which shows the flow of the in-situ correction coefficient calculation routine of a previous figure. It is a photograph of the ONERA M5 model coated with PSP and TSP used in the experiment of the example. It is an example of the pressure distribution image by the data processing of the pressure sensitive paint test using the ONERA M5 model obtained in the experiment of the Example. (The top and bottom surfaces were measured separately) It is an example of the temperature distribution image by the data processing of the pressure sensitive paint test using the ONERA M5 model obtained in the experiment of the Example. (The top and bottom surfaces were measured separately) It is a graph which shows the comparative example of the pressure sensitive paint data of the pressure sensitive paint test using the ONERA M5 model obtained in the experiment of the Example, and the pressure hole measurement result. (The top and bottom surfaces were measured separately) It is a graph which shows the comparative example of the result by the A-priori method, and the result by the A-priori / In-situ Hybrid method of the pressure sensitive paint test data using the ONERA M5 model obtained in the experiment of the Example.

Explanation of symbols

PSP Pressure-Sensitive Paint
TSP Temperature-Sensitive Paint
C _PSP pressure-sensitive paint measurement data in-situ correction coefficient C _TSP temperature-sensitive paint measurement data in-situ correction coefficient I Pressure / temperature-sensitive paint test data or test image Iref Pressure / temperature-sensitive paint known pressure Reference data or reference image under known temperature T Temperature data for _TSP comparison P Pressure data for _PSP comparison

Claims (10)

In pressure measurement using pressure-sensitive paint measurement data, the pressure-sensitive paint measurement data, pressure-sensitive paint characteristic data obtained in advance by a-priori calibration, temperature measurement data reflecting the temperature distribution on the model, Ru resulting acquiring pressure data in a predetermined position measured by the pressure sensor as the basic data, the correction coefficient C _PSP with a ratio based on both the sense圧塗charge measurement data and the pressure sensor data for said predetermined position a step, a step of correcting the systematic errors in the pressure-sensitive paint measurement by various factors by multiplying the correction coefficient C _PSP in the sense圧塗charge measurement data, the temperature distribution on the said sensitive圧塗charge measurement data model A method of processing pressure-sensitive paint measurement data comprising a step of calculating pressure from the reflected temperature measurement data using the a-priori calibration data. As the correction coefficient C _PSP , pressure sensor data converted into comparative data constructed from pressure sensor data for a predetermined position and temperature data for the predetermined position using pressure sensitive paint characteristic data based on the a-priori calibration data, The data processing method according to claim 1, wherein a ratio with the pressure-sensitive paint measurement data at the predetermined position is used. In pressure measurement using pressure-sensitive paint measurement data, the pressure-sensitive paint measurement data, pressure-sensitive paint characteristic data and temperature-sensitive paint characteristic data obtained in advance by the a-priori calibration, and coating on the model and the measured data by the temperature sensitive paint, a step of acquiring pressure data in a predetermined position measured by the pressure sensor as the basic data, the temperature sensitive paint measurement data and the said sensitive圧塗charge measurement data corresponding to the predetermined position Step of obtaining comparison temperature data _TTSP from pressure sensitive paint characteristic data and temperature sensitive paint characteristic data, and pressure sensitive paint characteristic data by the a-priori calibration from the comparison temperature data and pressure sensor measurement data at the predetermined position In-situ correction of pressure-sensitive paint measurement data from the ratio of pressure sensor data converted to comparison data and pressure-sensitive paint measurement data at the predetermined position Calculating a pressure using a step of obtaining a number C _PSP, the a-priori calibration data after correcting systematic errors in pressure sensitive paint measurement by various factors by multiplying them in the sense of圧塗charge measurement data Processing method of pressure-sensitive paint measurement data consisting of The pressure sensitive paint measurement data processing method according to claim 3, further comprising a step of calculating a temperature using the temperature sensitive paint measurement data and the a-priori calibration data. The operation to obtain the in-situ correction coefficient C _PSP of the pressure-sensitive paint measurement data from the ratio of the pressure sensor data converted to comparison data and the pressure-sensitive paint measurement data at a predetermined position is compared with the in-situ correction coefficient obtained once. recalculated in terms of the correction applied to the pressure sensitive paint measurement data at a predetermined position in obtaining use temperature data T _TSP, the value and the convergence within the threshold when the obtained in-situ correction coefficient and the previously calculated 5. The pressure-sensitive paint measurement data processing method according to claim 3 or 4 , wherein the calculation is repeatedly performed until the operation is performed. Operation to obtain an in-situ correction coefficient C _PSP pressure sensitive paint measurement data according to any one of claims 3 to 5 is intended to obtain from the arithmetic mean performed for each of a plurality of pressure sensors measurement data arranged in a predetermined position Data processing techniques. Temperature sensor data at a predetermined position is added as basic data, and pressure data P for comparison is calculated from the temperature-sensitive paint measurement data, pressure-sensitive paint measurement data around the temperature sensor, and the pressure-sensitive paint characteristic data and temperature-sensitive paint characteristic data. _PSP And pressure data P for comparison _PSP Temperature-sensitive paint measurement based on the ratio between the temperature sensor data converted for comparison and the temperature-sensitive paint measurement data at the predetermined position constructed from the temperature sensor data using the temperature-sensitive paint characteristic data by the a-priori calibration Data in-situ correction factor C _TSP In-situ correction coefficient C of the temperature-sensitive paint measurement data obtained _TSP And a step of calculating a temperature using the a-priori calibration data after correcting a systematic error in temperature-sensitive paint measurement due to various factors by multiplying the temperature-sensitive paint measurement data by Processing method of pressure-sensitive paint measurement data described in 1. Step ahead once obtained in-situ correction coefficient C _TSP to obtain the in-situ correction coefficient C _TSP temperature sensitive paint measurement data from the ratio of the temperature sensitive paint measurement data of the temperature sensor measurement data and a predetermined position pressure compensation recalculated in terms of the correction to be applied to temperature-sensitive paint measurement data of the predetermined position in the in-situ correction factor C to obtain the _PSP of pressure sensitive paint measurement data, obtained in-situ correction coefficient C _PSP and previously calculated The calculation is repeated until the corresponding value at the time of convergence converges within the threshold value, and when it converges, the in-situ correction coefficient C _TSP at that time is sensed at a predetermined position in calculating the in-situ correction coefficient C _TSP . When the thermal paint measurement data is corrected and recalculated, repeated calculations are performed until the obtained in-situ correction coefficient C _TSP and the corresponding value at the previous calculation converge within the threshold. in-situ correction coefficient C _TSP and in-situ correction coefficient in the final calculation Processing Method of pressure sensitive paint measurement data according to claim 7 is to adopt and _PSP. Pressure sensitive according to claim 7 or 8 operation to obtain the in-situ correction coefficient C _TSP temperature sensitive paint measurement data are those obtained from the arithmetic average is performed for each of a plurality of temperature sensors measured data arranged in a predetermined position Processing method for paint measurement data . As temperature distribution measurement data reflecting the temperature distribution on the model, temperature-sensitive paint is used, pressure-sensitive paint is applied to one half of the model, and temperature-sensitive paint is applied to the other half. 10. The pressure-sensitive paint measurement data processing method according to claim 1, wherein temperature correction of the pressure-sensitive paint is performed assuming symmetry.