JP6100182B2 - Turbine member temperature estimation method - Google Patents

Description

  The present invention relates to a temperature estimation method for a turbine member.

Turbine members used in gas turbines such as blades and vanes are manufactured from nickel-based alloys. In many nickel-based alloys, a Ni ₃ Al intermetallic compound called a γ ′ (gamma prime) phase is precipitated. The precipitation of the γ ′ phase, which is a precipitation strengthening phase, strengthens the precipitation of the nickel-based alloy, thereby improving the high temperature strength of the turbine member.

  The turbine member is used for a long time while receiving stress in a high temperature environment. When the γ ′ phase is placed in a high temperature environment, the particle size of the γ ′ phase changes (coarse). The coarsening of the γ ′ phase causes deterioration of the turbine member. Therefore, in the gas turbine, the material of the turbine member is controlled so that the deterioration of the turbine member is suppressed based on the temperature (metal temperature) of the turbine member assumed in the operation of the gas turbine, the usage time, the stress analysis result, and the like. The composition and shape are designed, and the life of the turbine member is predicted.

  There is a possibility that the actual metal temperature in the operation of the gas turbine and the metal temperature assumed in the design are different. If the actual metal temperature is higher than the metal temperature in the design, deterioration of the turbine member may progress, and the life of the turbine member may be shorter than the predicted life.

  The rate of coarsening of the γ 'phase is sensitive to temperature. Therefore, the actual metal temperature of the turbine member is obtained based on the amount of change in the particle size of the γ ′ phase. Patent Document 1 discloses that the difference between the cube of the particle diameter of the γ ′ phase after heating the nickel-base alloy part and the cube of the particle diameter of the γ ′ phase of the initial material and the heating time A technique for destructively estimating the temperature of a nickel-based alloy part has been disclosed.

Japanese Patent No. 3935692

  When the metal temperature is estimated based on the change amount of the particle size of the γ ′ phase, if the change amount of the particle size is small, the metal temperature may not be estimated with sufficient accuracy. For example, when the metal temperature is low, there is a high possibility that the amount of change in particle size is small. When the metal temperature is low, for example, if the metal temperature is estimated based on the amount of change in the average particle diameter of the γ ′ phase, the metal temperature may not be estimated with sufficient accuracy.

  An object of the present invention is to provide a turbine member temperature estimation method capable of accurately estimating the temperature even when the metal temperature is low.

  The present invention is a temperature estimation method for a turbine member including a nickel-based alloy, and detects a γ 'phase included in the turbine member before heating, and results of the detection before heating. Based on the first distribution data including the first particle size and having the largest number in the first particle size, and the number in the second particle size including the second particle size larger than the first particle size. Deriving particle size distribution data before heating indicating the relationship between the particle size of the γ ′ phase before heating and the number of the γ ′ phase per unit area of the γ ′ phase having the maximum second distribution data And detecting the γ ′ phase contained in the turbine member after heating, and based on the detection result after heating, the particle size of the γ ′ phase after heating and the particle size thereof Derived particle size distribution data after heating showing the relationship with the number of γ 'phases per unit area Comparing the particle size distribution data before heating and the particle size distribution data after heating, and estimating the temperature of the turbine member after heating based on the result of the comparison. A temperature estimation method for a turbine member is provided.

  In the turbine member temperature estimation method according to the present invention, based on the comparison result, the user selects either the first distribution data or the second distribution data based on the comparison result. And estimating the temperature.

  In the turbine member temperature estimation method according to the present invention, the post-heating particle size distribution data includes third distribution data changed from the first distribution data by the heating, and fourth distribution data changed from the second distribution data. The comparison includes comparing the first particle size of the first distribution data with a third particle size that maximizes the number of the third distribution data; and Comparing the second particle size with a fourth particle size that maximizes the number of the fourth distribution data.

  In the turbine member temperature estimation method according to the present invention, the post-heating particle size distribution data includes third distribution data changed from the first distribution data by the heating, and fourth distribution data changed from the second distribution data. The comparison may include comparing the maximum value of the number of the third distribution data with the maximum value of the number of the fourth distribution data.

  According to the present invention, even when the metal temperature is low, the metal temperature of the turbine member can be accurately estimated.

FIG. 1 is a schematic diagram illustrating an example of a gas turbine according to the present embodiment. FIG. 2 is a cross-sectional view showing an example of the turbine section according to the present embodiment. FIG. 3 is a schematic diagram illustrating an example of a temperature estimation device according to the present embodiment. FIG. 4 is a flowchart for explaining an example of the temperature estimation method according to the present embodiment. FIG. 5 is a diagram illustrating an example of particle size distribution data before heating according to the present embodiment. FIG. 6 is a flowchart for explaining an example of the temperature estimation method according to the present embodiment. FIG. 7 is a diagram illustrating an example of particle size distribution data after heating according to the present embodiment. FIG. 8 is a diagram illustrating an example of particle size distribution data after heating according to the present embodiment. FIG. 9 is a diagram showing an example of the relationship between the heating time of the γ ′ phase and the amount of change in particle diameter according to the present embodiment.

  Hereinafter, embodiments according to the present invention will be described with reference to the drawings, but the present invention is not limited thereto. The requirements of the embodiments described below can be combined as appropriate. Some components may not be used.

  FIG. 1 is a schematic diagram illustrating an example of a gas turbine 1 according to the present embodiment. The gas turbine 1 includes a compressor 2 that compresses combustion air, a combustor 3 that generates fuel gas FG by injecting fuel into the compressed air supplied from the compressor 2, and a gas turbine 1 that is supplied from the combustor 3. The turbine section 4 driven by the generated combustion gas FG, the generator 6, the compressor 2, the turbine section 4, and the rotating shaft 5 disposed in the generator 6 are provided.

  FIG. 2 is a cross-sectional view showing a part of the turbine section 4. The turbine section 4 is disposed outside the turbine blade 8 with respect to the radial direction with respect to the rotation shaft 5, the turbine stationary blade 7 disposed around the rotation shaft 5, the turbine blade 8 disposed around the rotation shaft 5, and the radial direction with respect to the rotation shaft 5. The split ring 9 is provided.

  The split ring 9 is supported by the passenger compartment 10. The split ring 9 is an annular member, and includes a plurality of split bodies arranged in the circumferential direction of the rotating shaft 5. A gap is provided between the split ring 9 and the tip of the turbine rotor blade 8.

  In the following description, a member of the gas turbine 1 is appropriately referred to as a turbine member M. The turbine member M may be a member of the turbine unit 4 or a member of the combustor 3. The turbine member M may be the turbine stationary blade 7, the turbine moving blade 8, or the split ring 9.

In the present embodiment, the turbine member M includes a nickel-based alloy. As an example, the material composition of the nickel-base alloy is as follows: Cr: 20 wt%, Co: 20 wt%, Mo: 6 wt%, Ti: 2.2 wt%, Al: 0.5 wt%, C: 0.05 % By weight, Ni: 51.25% by weight. In the present embodiment, the nickel-based alloy is a precipitation strengthened alloy based on a γ ′ phase (Ni ₃ Al intermetallic compound). A fine γ ′ phase is precipitated in the turbine member M. By precipitation of the γ ′ phase, which is a precipitation strengthening phase, the turbine member M is strengthened by precipitation, and the high temperature strength of the turbine member M is improved.

  The turbine member M is used for a long time while receiving stress in a high temperature environment. When the γ ′ phase is placed in a high temperature environment, the γ ′ phase changes in shape. The change in the shape of the γ ′ phase includes a change in the particle size of the γ ′ phase. When the γ ′ phase is arranged in a high temperature environment, the particle size of the γ ′ phase increases and the γ ′ phase becomes coarse. The coarsening of the γ ′ phase causes the turbine member M to deteriorate. Therefore, the material of the turbine member M is controlled so that the deterioration of the turbine member M is suppressed based on the temperature (metal temperature) of the turbine member M assumed in the operation of the gas turbine 1, the usage time, the stress analysis result, and the like. The composition and shape are designed, and the life of the turbine member M is predicted.

  There is a possibility that the actual metal temperature Tr of the turbine member M in the operation of the gas turbine 1 and the metal temperature assumed in the design are different. If the actual metal temperature Tr is higher than the metal temperature in the design, the turbine member M is further deteriorated, and the life of the turbine member M may be shorter than the predicted life.

  In the present embodiment, the actual metal temperature Tr of the turbine member M is estimated based on the amount of change in the particle size of the γ ′ phase. Based on the estimated metal temperature Tr, the material composition and shape of the turbine member M are optimized so that deterioration of the turbine member M is suppressed, and the cooling condition of the turbine member M during operation of the gas turbine 1 , Optimizing the quality (deterioration state) of the turbine member M, and evaluating (predicting) the life of the turbine member M are performed.

  The rate of γ ′ phase morphology change (coarsening) is temperature sensitive. In the present embodiment, the γ ′ phase before the turbine member M is heated and the γ ′ phase after the turbine member M is heated are compared, and the actual result of the turbine member M is based on the comparison result. The metal temperature Tr is estimated.

  The state before the turbine member M is heated includes a state before the gas turbine 1 is operated, and includes a state before the turbine member M is placed in a high temperature environment. After the turbine member M is heated includes a state after the gas turbine 1 is operated, and includes a state after the turbine member M is disposed in a high temperature environment. That is, before the turbine member M is heated includes a state before the turbine member M is used in the gas turbine 1 (before being placed in a high temperature environment). After the turbine member M is heated includes a state after the turbine member M is used in the gas turbine 1 (after being disposed in a high temperature environment). In the present embodiment, the high temperature environment includes an environment of 700 ° C. or higher and 1000 ° C. or lower. In the present embodiment, the heating includes heating the turbine member M at 700 ° C. or more and 1000 ° C. or less.

  In the present embodiment, a process for detecting the γ ′ phase contained in the turbine member M before heating and a process for detecting the γ ′ phase contained in the turbine member M after heating. And a process of comparing the detection result of the γ ′ phase contained in the turbine member M before heating with the detection result of the γ ′ phase contained in the turbine member M after heating, Based on the comparison result, a process of estimating the metal temperature Tr of the heated turbine member M is performed.

  Next, an example of the temperature estimation device 11 for the turbine member M according to this embodiment will be described. FIG. 3 is a diagram schematically illustrating an example of the temperature estimation device 11 according to the present embodiment. The temperature estimation device 11 estimates the metal temperature Tr of the heated turbine member M. As shown in FIG. 3, the temperature estimation device 11 includes a control device 12, a detection device 13, a storage device 14, and a display device 15.

  The detection device 13 detects the γ ′ phase contained in the turbine member M. The detection device 13 includes, for example, an electron microscope, and can acquire an image of the turbine member M including the γ ′ phase. The detection device 13 is connected to the control device 12. The detection result of the detection device 13 is output to the control device 12.

  The storage device 14 stores various types of information related to the estimation of the metal temperature Tr of the turbine member M. The storage device 14 includes at least one of a random access memory (RAM), a read only memory (ROM), a flash memory, and a hard disk drive. The storage device 14 is connected to the control device 12.

  The display device 15 displays various types of information related to the estimation of the metal temperature Tr of the turbine member M. The display device 15 includes a flat panel display such as a liquid crystal display. The display device 15 is connected to the control device 12.

  The control device 12 executes various processes related to the estimation of the metal temperature Tr of the turbine member M. The control device 12 includes a CPU (Central Processing Unit), and executes various processes including a calculation process and a signal process related to the estimation of the metal temperature Tr of the turbine member M. The control device 12 estimates the metal temperature Tr of the turbine member M based on the detection result of the detection device 13 and the storage information of the storage device 14.

  Next, an example of a method for estimating the metal temperature Tr of the turbine member M according to this embodiment will be described. In the present embodiment, the particle size distribution before heating is based on the result of detecting the γ ′ phase contained in the turbine member M before heating and the detection of the γ ′ phase contained in the turbine member M before heating. Based on the results of the process of deriving data BD, the process of detecting the γ ′ phase contained in the heated turbine member M, and the detection of the γ ′ phase contained in the heated turbine member M Comparison between the process of deriving the diameter distribution data AD, the process of comparing the particle size distribution data BD before heating and the particle size distribution data AD after heating, and the particle size distribution data BD before heating and the particle size distribution data AD after heating Based on the result, a process of estimating the metal temperature Tr of the heated turbine member M is performed.

  A process for detecting the γ ′ phase contained in the turbine member M before heating and a process for deriving the particle size distribution data BD before heating will be described. FIG. 4 is a flowchart showing an example of processing related to the turbine member M before heating. The turbine member M before heating includes the turbine member M before use, and includes the turbine member M in an initial state that is not arranged in a high temperature environment.

  The γ ′ phase contained in the turbine member M before heating is detected by the detection device 13 (step SA1). The detection device 13 acquires an image of the turbine member M including the γ ′ phase. The detection device 13 may acquire an image for the entire region of the turbine member M. The detection result of the detection device 13 is output to the control device 12.

  The control device 12 processes the image acquired by the detection device 13 (step SA2). The control device 12 derives the particle diameter of the γ ′ phase before heating and the number of the γ ′ phase per unit area of the particle diameter based on the result of the image processing.

  The control device 12 derives the pre-heating particle size distribution data BD based on the detection result of the detection device 13 related to the turbine member M before heating and the result of image processing (step SA3). The pre-heating particle size distribution data BD is particle size distribution data indicating the relationship between the particle size of the γ 'phase before heating and the number of the γ' phase per unit area. In the present embodiment, the particle size distribution data BD before heating is derived by fitting the detection result of the detection device 13 with a normal distribution. The derived pre-heating particle size distribution data BD is stored in the storage device 14 (step SA4).

  FIG. 5 is a diagram showing an example of the particle size distribution data BD before heating according to the present embodiment. In the graph shown in FIG. 5, the horizontal axis indicates the particle diameter of the γ ′ phase before heating. The vertical axis indicates the number of γ ′ phases per unit area of the particle size. The graph shown in FIG. 5 may be displayed on the display device 15.

  As shown in FIG. 5, the particle size distribution data BD before heating includes first distribution data D1 and second distribution data D2. Each of the first distribution data D1 and the second distribution data D2 is particle size distribution data indicating the relationship between the particle size of the γ ′ phase before heating and the number of the γ ′ phase per unit area of the particle size. .

  The first distribution data D1 is particle size distribution data of a γ ′ phase having a small particle size. The second distribution data D2 is particle size distribution data of the γ ′ phase having a particle size larger than the particle size of the γ ′ phase of the first distribution data D1. In the present embodiment, the first distribution data D1 is data relating to a particle size smaller than a boundary value (boundary particle size) SB related to the particle size of the γ ′ phase. The second distribution data D2 is data relating to a particle size larger than the boundary value SB.

  The boundary value SB may be defined based on the material composition of the turbine member M, for example, or may be arbitrarily defined by the operator.

  The first distribution data D1 includes data related to the first particle size S1 of the γ ′ phase. In the present embodiment, the first particle size S1 is an average value (average particle size) of the particle sizes of the first distribution data D1. In the first distribution data D1, the number of first particle diameters S1 is the maximum. The number K1 of the first particle size S1 is the maximum value of the number in the first distribution data D1. In the first distribution data D1, the particle size having the largest number is the first particle size S1.

  The second distribution data D2 includes data related to the second particle size S2 of the γ ′ phase. The second particle size S2 is larger than the first particle size S1. In the present embodiment, the second particle size S2 is an average value (average particle size) of the particle sizes of the second distribution data D2. In the second distribution data D2, the number of second particle diameters S2 is maximized. The number K2 of the second particle size S2 is the maximum value of the number in the second distribution data D2. The particle size with the largest number in the second distribution data D2 is the second particle size S2.

  In the present embodiment, the number K1 of the first particle size S1 is larger (larger) than the number K2 of the second particle size S2.

  In the following description, the first particle size (average particle size) S1 of the γ ′ phase in the turbine member M before heating (initial state) is appropriately referred to as initial particle size S1, and the turbine member before heating (initial state). The second particle size (average particle size) S2 of the γ ′ phase in M is appropriately referred to as initial particle size S2.

  Next, a process of detecting the γ ′ phase contained in the heated turbine member M, a process of deriving the particle size distribution data AD after heating, the particle size distribution data BD before heating and the particle size distribution data AD after heating are The process to compare and the process to estimate the metal temperature Tr of the turbine member M after heating based on the comparison result will be described. FIG. 6 is a flowchart showing an example of processing related to the turbine member M after heating and processing related to estimation of the metal temperature Tr. The turbine member M after heating includes the turbine member M after use, and includes the turbine member M after being placed in a high temperature environment.

  The turbine member M after heating described with reference to FIG. 6 is the turbine member M after being heated for a predetermined time ta from the initial state. The predetermined time ta is a heating time for heating the turbine member M in an initial state, and is a predetermined time (known time).

  The γ ′ phase contained in the heated turbine member M is detected by the detection device 13 (step SB1). The detection device 13 acquires an image of the turbine member M including the γ ′ phase. The detection device 13 acquires an image for the same region as the region of the turbine member M that acquired the image in step SA1. The detection result of the detection device 13 is output to the control device 12.

  The control device 12 processes the image acquired by the detection device 13 (step SB2). The control device 12 derives the particle diameter of the γ ′ phase after heating and the number per unit area of the γ ′ phase after the heating based on the result of the image processing.

  The control device 12 derives the post-heating particle size distribution data AD based on the detection result of the detection device 13 regarding the heated turbine member M and the result of image processing (step SB3). The particle size distribution data AD after heating is particle size distribution data indicating the relationship between the particle size of the γ ′ phase after heating and the number of the particle size per unit area of the γ ′ phase. In the present embodiment, the particle size distribution data AD after heating is derived by fitting the detection result of the detection device 13 with a normal distribution. The derived post-heating particle size distribution data AD is stored in the storage device 14 (step SB4).

  After the post-heating particle size distribution data AD is derived, the control device 12 compares the pre-heating particle size distribution data BD with the post-heating particle size distribution data AD (step SB5).

  Each of FIG. 7 and FIG. 8 is a diagram illustrating an example of post-heating particle size distribution data AD according to the present embodiment. FIG. 7 shows an example of post-heating particle size distribution data AD after the turbine member M is heated at the first temperature TL for a predetermined time ta. FIG. 8 shows an example of the particle size distribution data AD after heating when the turbine member M is heated at the second temperature TH for a predetermined time ta. The first temperature TL is lower than the second temperature TH. The first temperature TL is a temperature lower than 800 ° C., for example. The second temperature TH is, for example, a temperature of 800 ° C. or higher.

  In the graphs shown in FIGS. 7 and 8, the horizontal axis represents the particle diameter of the γ ′ phase after heating. The vertical axis indicates the number of γ ′ phases per unit area of the particle size. The graphs shown in FIGS. 7 and 8 may be displayed on the display device 15.

  As shown in FIGS. 7 and 8, the post-heating particle size distribution data AD includes third distribution data D3 and fourth distribution data D4. Each of the third distribution data D3 and the fourth distribution data D4 is particle size distribution data indicating the relationship between the particle size of the γ ′ phase after heating and the number of the γ ′ phase per unit area of the particle size. .

  The third distribution data D3 is particle size distribution data changed from the first distribution data D1 by heating. The fourth distribution data D4 is particle size distribution data changed from the second distribution data D2 by heating.

  The third distribution data D3 includes data related to the third particle size S3 of the γ ′ phase. In the present embodiment, the third particle size S3 is an average value (average particle size) of the particle sizes of the third distribution data D3. In the third distribution data D3, the number of the third particle size S3 is the maximum. The number K3 of the third particle size S3 is the maximum value of the number in the third distribution data D3. The particle size with the largest number in the third distribution data D3 is the third particle size S3.

  The fourth distribution data D4 includes data related to the fourth particle size S4 of the γ ′ phase. The fourth particle size S4 is larger than the third particle size S3. In the present embodiment, the fourth particle size S4 is an average value (average particle size) of the particle sizes of the fourth distribution data D4. In the fourth distribution data D4, the number of the fourth particle size S4 is the maximum. The number K4 of the fourth particle size S4 is the maximum value of the number in the fourth distribution data D4. The particle size with the largest number in the fourth distribution data D4 is the fourth particle size S4.

  By heating, the γ 'phase becomes coarse. The amount of change from the first particle size S1 to the third particle size S3 (difference between the first particle size S1 and the third particle size S3) is Δa. The amount of change from the second particle size S2 to the fourth particle size S4 (difference between the second particle size S2 and the fourth particle size S4) is Δb.

  As shown in FIGS. 7 and 8, the particle size distribution data AD after heating differs depending on whether the heating temperature of the turbine member M is high or low. That is, the post-heating particle size distribution data AD after heating at the first temperature TL is different from the post-heating particle size distribution data AD after heating at the second temperature TH.

  In the following description, as shown in FIG. 7, after heating at the first temperature TL, the post-heating particle size distribution data AD is appropriately referred to as post-heating particle size distribution data ADL, and after heating at the first temperature TL. The third distribution data D3 is appropriately referred to as third distribution data D3L, and the fourth distribution data D4 after being heated at the first temperature TL is appropriately referred to as fourth distribution data D4L.

  Further, as shown in FIG. 7, the third particle size S3 of the third distribution data D3 after heating at the first temperature TL is appropriately referred to as a third particle size S3L, and the number K3 of the third particle size S3L is expressed as The fourth particle diameter S4 of the fourth distribution data D4 after heating at the first temperature TL is appropriately referred to as the number K3L, and is appropriately referred to as the fourth particle diameter S4L, and the number K4 of the fourth particle diameter S4L is appropriately determined. , Number K4L.

  Further, as shown in FIG. 7, the amount of change from the first particle size S1 to the third particle size S3L (difference between the first particle size S1 and the third particle size S3L) Δa is appropriately referred to as ΔaL, The amount of change (the difference between the second particle size S2 and the fourth particle size S4L) Δb from the second particle size S2 to the fourth particle size S4L is appropriately referred to as ΔbL.

  In the following description, as shown in FIG. 8, after heating at the second temperature TH, the post-heating particle size distribution data AD is appropriately referred to as post-heating particle size distribution data ADH, and after heating at the second temperature TH. The third distribution data D3 is appropriately referred to as third distribution data D3H, and the fourth distribution data D4 after being heated at the second temperature TH is appropriately referred to as fourth distribution data D4H.

  Further, as shown in FIG. 8, the third particle size S3 of the third distribution data D3 after heating at the second temperature TH is appropriately referred to as a third particle size S3H, and the number K3 of the third particle size S3H is expressed as The number K4 is appropriately referred to as the number K3H, the fourth particle size S4 of the fourth distribution data D4 after heating at the second temperature TH is referred to as the fourth particle size S4H, and the number K4 of the fourth particle size S4H is determined as appropriate. , Number K4H.

  In addition, as shown in FIG. 8, the change amount (difference between the first particle size S1 and the third particle size S3H) Δa from the first particle size S1 to the third particle size S3H is appropriately referred to as ΔaH, The amount of change (the difference between the second particle size S2 and the fourth particle size S4H) Δb from the second particle size S2 to the fourth particle size S4H is appropriately referred to as ΔbH.

  As shown in FIG. 7, the number K3L is smaller (less) than the number K1. The number K4L is substantially equal to the number K2. The number K3L is larger (larger) than the number K4L. The number K1 is larger than the number K3L, the number K2, and the number K4L. The number K2 and the number K4L are smaller than the number K1 and the number K3L.

  The third particle size S3L is larger than the first particle size S1. That is, the initial particle diameter S1 of the first distribution data D1 is coarsened by heating and changes to the third particle diameter S3L. The fourth particle size S4L is slightly larger than the second particle size S2. The third particle size S3L is larger than the first particle size S1 and smaller than the second particle size S2. Of the first particle size S1, the third particle size S3L, the second particle size S2, and the fourth particle size S4L, the fourth particle size S4L is the largest, and the second particle size S2 is the second largest after the fourth particle size S4L. Next to the second particle size S2, the third particle size S3L is the largest and the first particle size S1 is the smallest.

  The amount of change from the first particle size S1 to the third particle size S3L (difference between the first particle size S1 and the third particle size S3L) ΔaL is the amount of change from the second particle size S2 to the fourth particle size S4L ( The difference between the second particle size S2 and the fourth particle size S4L) is greater than ΔbL.

  As shown in FIG. 8, in the present embodiment, the number K3H is smaller than the number K1. The number K4H is larger than the number K2. The number K4H is larger than the number K3H. The number K3H is smaller than the number K1, the number K4H, and the number K2. The number K1 is larger than the number K4H, the number K2, and the number K3H.

  The third particle size S3H is larger than the first particle size S1. That is, the initial particle size S1 of the first distribution data D1 is coarsened by heating and changes to the third particle size S3H. The fourth particle size S4H is larger than the second particle size S2. That is, the initial particle size S2 of the second distribution data D2 becomes coarse by heating and changes to the fourth particle size S4H. The third particle size S3H is larger than the second particle size S2 and smaller than the fourth particle size S4H. Of the first particle size S1, the second particle size S2, the third particle size S3H, and the fourth particle size S4H, the fourth particle size S4H is the largest, and the third particle size S3H is the second largest after the fourth particle size S4H. Next to the third particle size S3H, the second particle size S2 is the largest, and the first particle size S1 is the smallest. The amount of change from the first particle size S1 to the third particle size S3H (difference between the first particle size S1 and the third particle size S3H) ΔaH is the amount of change from the second particle size S2 to the fourth particle size S4H ( The difference between the second particle size S2 and the fourth particle size S4H) is greater than ΔbH.

  As shown in FIGS. 7 and 8, the γ ′ phase becomes coarse by heating. When the heating temperature is both the first temperature TL and the second temperature TH, the first particle size S1 of the first distribution data D1 increases and changes to the third particle size S3. Similarly, when the heating temperature is both the first temperature TL and the second temperature TH, the second particle size S2 of the second distribution data D2 increases and changes to the fourth particle size S4.

  When the metal temperature Tr is the first temperature TL lower than the second temperature TH, the change amount ΔaL from the first particle size S1 to the third particle size S3L is changed from the second particle size S2 to the fourth particle size S4L. It is larger than the change amount ΔbL. According to the knowledge of the present inventor, the smaller the initial particle size, the larger the amount of change in the particle size of the γ ′ phase from before heating to after heating. In other words, the smaller the initial particle size, the more sensitive the change in the particle size to temperature changes. Therefore, when the metal temperature Tr is the first temperature TL, the change amount ΔaL of the particle size of the first distribution data D1 is larger than the change amount ΔbL of the particle size of the second distribution data D2.

  Further, as shown in FIG. 7, when the metal temperature Tr is the first temperature TL lower than the second temperature TH, the maximum value K3L of the number of the third particle size S3L of the third distribution data D3L after heating is The number of the fourth particle diameter S4L of the fourth distribution data D4L after heating is larger than the maximum value K4L. On the other hand, as shown in FIG. 8, when the metal temperature Tr is the second temperature TH higher than the first temperature TL, the maximum value K3H of the number of the third particle size S3H of the third distribution data D3H after heating is The number of the fourth particle size S4H of the fourth distribution data D4H after heating is smaller than the maximum value K4H.

  Thus, the amount of change from the initial particle size (difference between the initial particle size and the particle size after heating) Δ differs depending on whether the metal temperature Tr is high or low. Further, the relationship between the maximum values of the numbers in the third distribution data D3 and the fourth distribution data D4 after heating differs depending on whether the metal temperature Tr is high or low.

  Returning to FIG. 6, the control device 12 selects one of the first distribution data D1 and the second distribution data D2 in order to estimate the metal temperature Tr (step SB6). The control device 12 selects either the first distribution data D1 or the second distribution data D2 based on the result of comparing the particle size distribution data BD before heating and the particle size distribution data AD after heating.

  A comparison between the pre-heating particle size distribution data BD and the post-heating particle size distribution data AD shows that the first particle size S1 in which the number of the first distribution data D1 is the maximum and the number of the third distribution data D3 are the maximum. Comparing with 3 particle size S3. Further, the comparison between the pre-heating particle size distribution data BD and the post-heating particle size distribution data AD shows that the second particle size S2 in which the number of the second distribution data D2 is the maximum and the number of the fourth distribution data D4 are the maximum. And comparing with a fourth particle size S4.

  The difference Δa between the average particle size S1 of the first distribution data D1 and the average particle size S3 of the third distribution data D3 that is the distribution data after heating of the first distribution data D1, and the average particle size of the second distribution data D2 The difference Δb between S2 and the average particle size S4 of the fourth distribution data D4 that is the distribution data after heating of the second distribution data D2 is different when the metal temperature Tr is high and low. When the metal temperature Tr is the first temperature TL lower than the second temperature TH, the difference ΔaL between the average particle size S1 before heating and the average particle size S3L before heating is large, and the average particle size S2 before heating and the heating The difference ΔbL from the subsequent average particle size S4L is small. When the metal temperature Tr is the second temperature TH higher than the first temperature TL, the difference ΔaH between the average particle size S1 before heating and the average particle size S3H before heating is large, and the average particle size S2 before heating and the heating The difference ΔbH from the subsequent average particle size S4H is also large.

  Therefore, the difference Δa between the average particle size S1 before heating and the average particle size S3 after heating is larger than the difference Δb between the average particle size S2 before heating and the average particle size S4 after heating, and the metal temperature Tr is low. Is estimated, it is higher to estimate the metal temperature using the first distribution data D1 and the third distribution data D3L than to estimate the metal temperature using the second distribution data D2 and the fourth distribution data D4L. The metal temperature Tr can be estimated with accuracy.

  Therefore, when comparing the particle size distribution data BD before heating and the particle size distribution data AD after heating and determining that the difference Δa is larger than the difference Δb, the control device 12 estimates the metal temperature Tr. The first distribution data D1 is selected.

  As shown in FIG. 8, the difference ΔaH between the average particle size S1 before heating and the average particle size S3H after heating, and the difference ΔbH between the average particle size S2 before heating and the average particle size S4H after heating. If both are large and the metal temperature Tr is expected to be high, either the first distribution data D1 or the second distribution data D2 may be selected in order to estimate the metal temperature Tr. For example, the first distribution data D1 may be selected when the difference Δa is smaller than a predetermined threshold, and the second distribution data D2 may be selected when the difference Δa is larger than a predetermined threshold. .

  In the present embodiment, the comparison between the pre-heating particle size distribution data BD and the post-heating particle size distribution data AD is performed by comparing the maximum value K3 of the number of third distribution data D3 and the maximum value of the number of fourth distribution data D4. Comparing with K4.

  The relationship (magnitude relationship) between the maximum number K3 of the number of third distribution data D3 and the maximum value K4 of the number of fourth distribution data D4 differs depending on whether the metal temperature Tr is high or low. As shown in FIG. 7, when the metal temperature Tr is the first temperature TL lower than the second temperature TH, the maximum value K3L of the number of the third distribution data D3L is the maximum value of the number of the fourth distribution data D4L. It is larger than K4L. On the other hand, as shown in FIG. 8, when the metal temperature Tr is the second temperature TH higher than the first temperature TL, the maximum value K3H of the number of the third distribution data D3H is equal to the number of the fourth distribution data D4H. It is smaller than the maximum value K4H.

  Therefore, when the maximum value K3 of the number of the third distribution data D3 is larger than the maximum value K4 of the number of the fourth distribution data D4 and the metal temperature Tr is expected to be low, the second distribution data D2 and the fourth distribution It is more accurate to estimate the metal temperature Tr using the first distribution data D1 and the third distribution data D3 (D3L) than to estimate the metal temperature Tr using the data D4 (D4L). can do.

  Therefore, when the maximum value K3 of the number of the third distribution data D3 and the maximum value K4 of the number of the fourth distribution data D4 are compared, and it is determined that the maximum value K3 is larger than the maximum value K4, the control device 12 selects the first distribution data D1 in order to estimate the metal temperature Tr.

  On the other hand, when the maximum value K3 of the number of the third distribution data D3 is smaller than the maximum value K4 of the number of the fourth distribution data D4 and the metal temperature Tr is expected to be high, the first distribution data D1 and the third distribution It is more accurate to estimate the metal temperature Tr using the second distribution data D2 and the fourth distribution data D4 (D4H) than to estimate the metal temperature Tr using the data D3 (D3H). can do. As shown in FIG. 8, when the metal temperature Tr is high, the maximum value K3 of the number of the third distribution data D3 becomes small or disappears, and the third distribution data D3 is difficult to distinguish from the fourth distribution data D4. There is a possibility. In that case, if the metal temperature Tr is to be estimated using the first distribution data D1 and the third distribution data D3, it is difficult to estimate the metal temperature Tr with high accuracy.

  Therefore, when the maximum value K3 of the number of the third distribution data D3 and the maximum value K4 of the number of the fourth distribution data D4 are compared, and it is determined that the maximum value K3 is smaller than the maximum value K4, the control device 12 selects the second distribution data D2 in order to estimate the metal temperature Tr.

  As shown in FIG. 7, the third particle size S3L of the third distribution data D3L is smaller than the second particle size S2 of the second distribution data D2. As shown in FIG. 8, the third particle size S3H of the third distribution data D3H is larger than the second particle size S2 of the second distribution data D2. Further, the maximum value K3L of the number of the third particle size S3L is larger than the maximum value K2 of the number of the second particle size S2, and the maximum value K3H of the number of the third particle size S3H is the number of the second particle size S2. Is smaller than the maximum value K2. Therefore, the control device 12 can distinguish the third distribution data D3L and the third distribution data D3H.

  The control device 12 estimates the metal temperature of the heated turbine member M based on one of the distribution data selected from the first distribution data D1 and the second distribution data D2 (step SB7).

Hereinafter, an example of a method for estimating the metal temperature of the turbine member M based on the amount of change in the particle size of the γ ′ phase from before heating to after heating will be described. In the following description, the particle diameter (initial particle diameter) of the γ ′ phase before heating of the turbine member M, which is a nickel-based alloy part, is r ₀ , the particle diameter of the γ ′ phase after heating is r, and the gas turbine 1 Let t be the operation time (heating time). The particle diameter (initial particle diameter) r ₀ of the γ ′ phase before heating corresponds to the above-mentioned initial particle diameter S1 or initial particle diameter S2. When the first distribution data D1 is selected, the initial particle size r ₀ is the initial particle size S1. If the second distribution data D2 is selected, the initial particle size r ₀ is the initial particle size S2. Further, the particle size r of the γ ′ phase after heating corresponds to the above-described third particle size S3 or fourth particle size S4. When the first distribution data D1 is selected, the particle size r after heating is the third particle size S3. When the second distribution data D2 is selected, the particle size r after heating is the fourth particle size S4.

For example, as disclosed in Japanese Patent No. 4440124, when the nickel-based alloy on which the γ ′ phase is precipitated is heated,
r ³ −r ₀ ³ = kt (1)
k = C · exp (−Q / RT) / T (2)
The conditions are satisfied. k is a proportionality constant obtained experimentally and corresponds to the coarsening rate of the γ ′ phase. k is a value determined for each material of the turbine member M.

As shown in the equation (2), the proportionality constant k (the coarsening rate of the γ ′ phase) has temperature dependence. The particle size (r ³ −r ₀ ³ ) of the γ ′ phase is proportional to time for each temperature. The constant C and the constant Q can be obtained based on the relationship between the metal temperature T, the heating time t, and the amount of change in particle size by conducting a long-time heating test at, for example, 700 ° C. to 1000 ° C. By determining the constant C and the constant Q, the equation (1) that is a temperature estimation equation is derived.

FIG. 9 is a graph showing an example of the formula (1), and shows the relationship between the heating time t for heating the turbine member M and the particle size (r ³ −r ₀ ³ ) of the γ ′ phase. The horizontal axis is the heating time t. The vertical axis represents the difference between the cube of the particle diameter r ₀ of the γ ′ phase before heating and the cube of the particle diameter r of the γ ′ phase after heating. In FIG. 9, line La shows the relationship between r ³ -r ₀ ³ and heating time t when the metal temperature T is the temperature Ta. The temperature Ta is 750 ° C., for example. Line Lb shows the relationship between r ³ -r ₀ ³ and heating time t when metal temperature T is temperature Tb. The temperature Tb is, for example, 800 ° C. Line Lc shows the relationship between r ³ -r ₀ ³ and heating time t when metal temperature T is temperature Tc. The temperature Tc is, for example, 850 ° C.

If the equations (1) and (2) are used, the metal temperature of the turbine member M heated for a certain time can be obtained based on the particle size (r ³ -r ₀ ³ ) of the γ ′ phase and the heating time t. it can.

  As described above, according to the present embodiment, the particle size distribution data BD before heating and the particle size distribution data AD after heating are derived, and the particle size distribution data BD before heating and the particle size distribution data AD after heating are compared. Since the metal temperature Tr of the turbine member M after heating is estimated based on the result, the metal temperature of the turbine member M can be accurately estimated even when the metal temperature Tr is low.

That is, when estimating the metal temperature Tr based on the change amount of the particle diameter of the γ ′ phase, when trying to estimate the metal temperature Tr based on the change amount of the average particle diameter of the γ ′ phase contained in the nickel-based alloy, When the amount of change from the initial particle size (r ₀ ) to the particle size (r) after heating is small, the metal temperature Tr may not be estimated with sufficient accuracy. For example, when the metal temperature is lower than 800 ° C., there is a high possibility that the difference between the initial particle size (r ₀ ) and the particle size (r) after heating is small. Therefore, if the metal temperature Tr is to be estimated based on the amount of change in the average particle size of the γ ′ phase, the metal temperature Tr may not be estimated with sufficient accuracy.

  For example, when the turbine member M is a moving blade or stationary blade at the rear stage of the gas turbine 1, the metal temperature Tr during operation is often lower than 800 ° C, and it is confirmed whether the metal temperature during design is satisfied. In order to achieve this, it is necessary to estimate the temperature even at a lower metal temperature Tr.

  According to the present embodiment, the smaller the particle size of the γ ′ phase, the more sensitive the change amount of the particle size is to the temperature change, so that the number of the first particle size S1 becomes the maximum value K1. The pre-heating particle size distribution data BD including the distribution data D1 and the second distribution data D2 having the maximum number K2 in the second particle size S2 is derived, and the pre-heating particle size distribution data BD and the post-heating particle size distribution are derived. Compare with data AD. Thereby, even when the metal temperature Tr is low, the metal temperature Tr of the turbine member M can be accurately estimated. That is, when the metal temperature Tr is low and there is a high possibility that the change amount of the particle size is small, the metal temperature Tr is calculated by using the first distribution data D1 and the third distribution data D3 that increase the change amount of the particle size. Even when the temperature is low, the metal temperature Tr of the turbine member M can be accurately estimated.

  When the metal temperature Tr is low and the change amount of the particle size is small, the first distribution data D1 is selected. When the metal temperature Tr is high and the change amount of the particle size is large, the second distribution data D2 is selected. Thus, the metal temperature Tr of the turbine member M can be accurately estimated not only when the metal temperature Tr is low but also when the metal temperature Tr is high.

  As described with reference to FIGS. 7 and 8, the post-heating particle size distribution data AD changes according to the metal temperature Tr. In the comparison between the pre-heating particle size distribution data BD and the post-heating particle size distribution data AD, the comparison between the first particle size S1 of the first distribution data D1 and the third particle size S3 of the third distribution data D3, and the second By selecting the first distribution data D1 and the second distribution data D2 by making a comparison between the second particle size S2 of the distribution data D2 and the fourth particle size S4 of the fourth distribution data D4. Can do.

  Further, in the comparison of the particle size distribution data BD before heating and the particle size distribution data AD after heating, the maximum value K3 of the number of the third distribution data D3 and the maximum value K4 of the number of the fourth distribution data D4 are compared. This also makes it possible to make an appropriate selection in selecting the first distribution data D1 and the second distribution data D2.

  In the above-described embodiment, the turbine member M detected using the detection device 13 may be a turbine member actually used for the gas turbine 1 (so-called actual machine), or a turbine member imitating an actual machine (so-called so-called machine). , Replica). Further, the turbine member may be heated by being used in the gas turbine 1 which is an actual machine, or may be heated in a heating test apparatus simulating the gas turbine 1 which is an actual machine.

  In the above-described embodiment, the pre-heating particle size distribution data BD has the first distribution data D1 and the second distribution data D2, and has two peak values. The pre-heating particle size distribution data BD may have any number of peak values of three or more. Similarly, the post-heating particle size distribution data AD may have an arbitrary number of peak values of three or more.

DESCRIPTION OF SYMBOLS 1 Gas turbine 2 Compressor 3 Combustor 4 Turbine part 5 Rotating shaft 6 Generator 7 Turbine stationary blade 8 Turbine moving blade 9 Split ring 10 Car interior 11 Temperature estimation device 12 Control device 13 Detection device 14 Storage device 15 Display device AD Heating Post-particle size distribution data ADH Particle size distribution data after heating ADL Particle size distribution data after heating BD Particle size distribution data before heating D1 First distribution data D2 Second distribution data D3 Third distribution data D3H Third distribution data D3L Third distribution Data D4 Fourth distribution data D4H Fourth distribution data D4L Fourth distribution data K1 Number K2 Number K3 Number K3H Number K3L Number K4 Number K4H Number K4L Number M Turbine member S1 First particle size S2 Second particle size S3 Third particle size S3H 3rd particle size S3L 3rd particle size S4 4th particle size S4H 4th particle size S4L 4th particle size Δa difference ΔaH difference ΔaL difference Δb difference ΔbH difference ΔbL difference

Claims (4)

A temperature estimation method for a turbine member including a nickel-based alloy,
Detecting a γ ′ phase contained in the turbine member before heating;
Based on the result of the detection before heating, the first distribution data including the first particle size and the number of the first particle size being the largest, and the second particle size larger than the first particle size Before the heating, which has the second distribution data in which the number is the maximum in the second particle size, and shows the relationship between the particle size of the γ ′ phase before heating and the number of the γ ′ phase per unit area of the particle size Deriving particle size distribution data;
Detecting a γ ′ phase contained in the turbine member after heating;
Deriving particle size distribution data after heating indicating the relationship between the particle size of the γ ′ phase after heating and the number of the γ ′ phase per unit area of the particle size based on the detection result after heating. When,
Comparing the pre-heating particle size distribution data with the post-heating particle size distribution data;
Based on the result of the comparison, estimating the temperature of the turbine member after heating;
The temperature estimation method of the turbine member containing this. Selecting one of the first distribution data and the second distribution data based on the result of the comparison;
Estimating the temperature based on the selected one distribution data;
The temperature estimation method of the turbine member according to claim 1, comprising: The post-heating particle size distribution data has third distribution data changed from the first distribution data by the heating, and fourth distribution data changed from the second distribution data,
The comparison is
Comparing the first particle size of the first distribution data with a third particle size that maximizes the number of the third distribution data;
Comparing the second particle size of the second distribution data with the fourth particle size that maximizes the number of the fourth distribution data;
The temperature estimation method of the turbine member of Claim 2 containing these. The post-heating particle size distribution data has third distribution data changed from the first distribution data by the heating, and fourth distribution data changed from the second distribution data,
The comparison is
Comparing the maximum value of the number of the third distribution data with the maximum value of the number of the fourth distribution data;
The temperature estimation method of the turbine member of Claim 2 containing these.

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