JP7512073B2 - System and method for evaluating and analyzing soundness - Google Patents

Description

Embodiments of the present invention relate to a system and method for evaluating the integrity of equipment or structures in a plant facility.

As nuclear power generation facilities age, improving the accuracy and reliability of integrity assessments is important for the effective use of the entire power generation facility and for improving safety. In conventional integrity assessments, a progression analysis of anticipated age-related deterioration events in equipment and structures is performed, and integrity is evaluated using the occurrence or probability of damage leading to destruction or loss of function during the assessment period as an indicator.

For example, one of the most notable age-related degradation phenomena in nuclear power generation facilities is irradiation-induced stress corrosion cracking (IASCC), which is dealt with in the integrity assessment of the core shroud, an in-reactor structure installed inside the reactor pressure vessel. In this example, the accumulation of neutron irradiation generated by the reaction of nuclear fuel inside the reactor causes and progresses degradation of the material, resulting in an increased likelihood of IASCC occurring and a decrease in fracture toughness.

Conventional IASCC evaluations are performed using evaluation conditions that are conservatively set by analysis based on the relationship between previously acquired material property data and irradiation dose, and on design conditions. In this case, conservative estimation of the variations and uncertainties contained in various conditions may result in overly conservative evaluation results that are far removed from the actual service history of the facility. In addition, quantitatively understanding the degree of conservatism or non-conservatism contained in the final evaluation results and each process will not only lead to the optimization of evaluations, but will also be an effective basis for determining the optimal operation and management of the power generation facility from the perspective of the efficiency and safety of the entire facility, the method and timing of appropriate maintenance measures for this purpose, and the necessary resource allocation, etc.

JP 2014-62780 A JP 2005-345421 A JP 2015-81901 A

In conventional defect assessments of the soundness of structures, the conditions required for the assessment, the size and density of the defects to be assessed, and material properties such as defect (crack) growth rate, strength, and fracture toughness are determined using values or functions that are preset based on test data and analysis. With this method, it is expected that accuracy will improve through the expansion of test data, streamlining of assessment methods, and refinement of analysis. However, such improvements generally require time and money, and because they are premised on mechanistic similarity or equivalence to actual behavior, as well as representativeness and envelopment of conditions or data, there are limitations to understanding and optimizing the conservativeness or non-conservativeness of the assessment.

The embodiment of the present invention has been made in consideration of the above circumstances, and aims to provide a soundness evaluation and analysis system and a soundness evaluation and analysis method that can realize a highly accurate and reliable soundness evaluation that is in line with the actual situation of the plant equipment by using measurement data, performance data, etc. of the plant equipment.

A soundness evaluation and analysis system according to an embodiment of the present invention is a soundness evaluation and analysis system for evaluating the soundness of equipment or structures in a plant facility, comprising: a sampling and measurement device for sampling materials or environmental media of the equipment or structures to measure their characteristics or state quantities, or for directly measuring the characteristics or state quantities to obtain measurement data; an inspection device for detecting and sizing defects, including cracks and corrosion, that occur in the equipment or structures, and obtaining inspection data relating to the defects; an operation history storage device for storing the state quantities of the equipment or structures, operation management information including operation history, performance data of repairs and maintenance measures, the measurement data from the sampling and measurement device, and the inspection data from the inspection device; The system includes a data analysis device that optimizes defect evaluation parameters set by analysis or design for use in evaluating soundness based on actual data from at least one of the inspection device and the operating history storage device, and a soundness evaluation analysis device that evaluates the soundness of the equipment or structure using the defect evaluation parameters optimized by the data analysis device , wherein the data analysis device is configured to obtain neutron flux data from radioactivity data of the material of the equipment or structure actually measured by the sampling/measuring device, and to optimize the neutron irradiation dose or neutron flux value, its distribution, and its time history as the defect evaluation parameters set by analysis using a function that creates an optimization curve based on the neutron flux data .

A soundness evaluation and analysis method according to an embodiment of the present invention is a soundness evaluation and analysis method for evaluating the soundness of equipment or structures in a plant facility, the method comprising the steps of: sampling a material or an environmental medium of the equipment or structure, respectively, to measure its characteristics or state quantities, or directly measuring the characteristics or state quantities, and acquiring measurement data, using a sampling and measurement device; detecting and sizing defects, including cracks and corrosion, that occur in the equipment or structure, using an inspection device , and acquiring inspection data relating to the defects; storing, in an operation history storage device , the state quantities of the equipment or structure, operation management information including an operation history, performance data of repairs and maintenance measures, the measurement data from the sampling and measurement device, and the inspection data from the inspection device; The method includes a step of optimizing defect evaluation parameters set by analysis or design for use in evaluating the soundness based on actual data from at least one of the sampling and measuring device, the inspection device, and the operating history storage device, and a step of evaluating the soundness of the equipment or structure using the defect evaluation parameters optimized by the data analysis device by a soundness evaluation analysis device , wherein the data analysis device further obtains neutron flux data from radioactivity data of the material of the equipment or structure actually measured by the sampling and measuring device, and optimizes the neutron exposure dose or neutron flux value, its distribution, and its time history as the defect evaluation parameters set by analysis using a function that creates an optimization curve based on this neutron flux data .

According to an embodiment of the present invention, by using measurement data and performance data of the plant equipment, it is possible to realize a highly accurate and reliable health evaluation that is in line with the actual situation of the plant equipment.

FIG. 1 is a block diagram showing a soundness evaluation and analysis system according to an embodiment of the present invention. 2 is a flowchart showing a procedure for evaluating soundness in the soundness evaluation analysis system of FIG. 1 . 3A shows a core shroud as the equipment or structure of FIG. 1, and FIG. 3B is a partial cross-sectional view showing a part of FIG. 3A together with a crack that has occurred. 4 is a graph showing an example of optimizing the neutron flux distribution in the thickness direction of the core shroud of FIG. 3 . 3 is a graph showing an example of optimizing the circumferential neutron flux distribution in the core shroud of FIG. 2 .

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
Fig. 1 is a block diagram showing an integrity evaluation analysis system according to the present embodiment. The integrity evaluation analysis system 100 shown in Fig. 1 evaluates the integrity of a device or structure 10 in a plant facility, for example, a nuclear power plant facility, and is configured with an integrity evaluation analysis device 200, a data analysis device 300, a sampling and measurement device 400, an inspection device 500, and an operation history storage device 600.

The sampling and measurement device 400 performs steps of sampling the materials of the equipment or structure 10 in the plant facility to be evaluated, or the environmental medium 30 such as reactor cooling water, measuring their characteristics and state quantities, and acquiring measurement data that can be used to evaluate the soundness. At this time, for example, if the characteristics of the materials of the equipment or structure 10, or state quantities such as the temperature and conductivity of the reactor cooling water, etc., can be directly measured continuously or periodically, the measurement data may be acquired by the measurement without sampling.

The inspection device 500 detects and sizes defects 20, such as cracks and corrosion, that occur in the equipment or structure 10, and performs steps to acquire inspection data related to the defects, including the distribution, density, and dimensions of the defects 20. In the inspection device 500, if the state values (distribution, density, dimensions, etc.) of defects during equipment use can be directly measured continuously or intermittently, the measurement data may be used instead of periodic inspection data. This inspection device 500 may also acquire inspection data other than the defects 20.

The operation history storage device 600 performs steps to store state quantities such as temperature and pressure of the equipment or structure 10, operation management information including operation history such as start-up and shutdown, performance data on repairs and maintenance measures, various measurement data from the sampling and measuring device 400, and inspection data from the inspection device 500.

The data analysis device 300 performs a step of optimizing defect evaluation parameters set by analysis or design for use in evaluating the soundness of the equipment or structure 10 based on actual data from at least one of the sampling and measurement device 400, the inspection device 500, and the operation history storage device 600. Here, the defect evaluation parameters are, for example, an initial defect model (i.e., the position, shape, size, density, etc. of the initial defect), environmental parameters, neutron irradiation dose (including neutron flux), residual stress, material property parameters (crack growth rate, strength, fracture toughness, etc. of the material), etc.

In this embodiment, the soundness evaluation analysis device 200 performs a step of evaluating the soundness of the equipment or structure 10 using defect evaluation parameters optimized by the data analysis device 300, but first, the original function will be explained using Figure 2. Here, Figure 2 is a flowchart showing the procedure for evaluating the soundness against irradiation assisted stress corrosion cracking (IASCC) as an example of soundness evaluation by the soundness evaluation analysis system 100.

The soundness evaluation analysis device 200 first performs an evaluation target/part setting step 201 in which an evaluation target and evaluation part are selected. The equipment or structure 10 of the nuclear power plant facility to be evaluated is, for example, in a boiling water reactor, a structure that constitutes a pressure boundary such as a recirculation system piping, or an in-reactor structure such as a core shroud. The evaluation target may be a part of equipment or a structure of other nuclear power plant facilities, and is not limited to nuclear power plant facilities, but may also be equipment or a structure of other power plant facilities.

Next, the soundness evaluation analysis device 200 performs an initial defect model setting step 202, which models the distribution, dimensions, density, etc. of the assumed initial defects. In this example, the assumed initial defect is a semi-elliptical crack that occurs on the inner surface of the equipment or structure 10 and extends in the circumferential direction of the equipment or structure 10, and its minimum dimensions are, for example, 1 mm deep x 10 mm long, taking into account the inspection limit dimensions in visual inspection of the reactor internal structure. Figure 3 shows a crack 21 as a defect 20 that occurs on the inner surface 11A of the core shroud 11 as the equipment or structure 10. The depth a of this crack 21 is set in the thickness direction of the core shroud 11, and the length 2c of the crack 21 is set in the circumferential direction of the core shroud 11.

The minimum dimension of the expected initial defect as described above may be a different dimension if there is a clear technical reason for setting it. Alternatively, it may be the dimension of a sized crack when cracks detected in an actual inspection are the subject of the study, or the dimension of a crack modeled as one crack when multiple adjacent cracks are detected. Based on the set minimum initial defect dimension, appropriate initial defect dimensions are set at appropriate dimensional intervals using parameters such as depth (a), length (2c), aspect ratio (a/c), etc.

Then, the soundness evaluation analysis device 200 performs a temperature, pressure, and water quality condition setting step 203 to set environmental parameters such as temperature and water quality conditions. Since the illustrated IASCC evaluation involves irradiation with neutrons, this is followed by an irradiation dose distribution setting step 204. Next, the soundness evaluation analysis device 200 performs a stress distribution evaluation step 205 to evaluate the stress distribution applied to the evaluation target part.

Next, the soundness evaluation analysis device 200 performs a stress intensity factor evaluation step 206 to evaluate the stress intensity factor acting on the crack. The stress intensity factor acting on the crack is calculated from the crack dimensions in the initial defect model setting step 202 and the distribution of external load and residual stress in the stress distribution evaluation step 205. As a method for calculating the stress intensity factor, a simplified evaluation formula published in the Japan Society of Mechanical Engineers Maintenance Standards or ASME Boiler & Pressure Vessel Code Section XI or other standards, or a widely known simplified evaluation formula such as the Raju & Newman formula or the influence function method, may be used, or a direct evaluation may be performed using finite element method (FEM) analysis.

Next, the soundness evaluation analysis device 200 performs a crack growth rate evaluation step 207 to calculate the crack growth rate. In this crack growth rate evaluation step 207, the crack growth rate at the crack tip is calculated using the stress intensity factor calculated in the stress intensity factor evaluation step 206 and an evaluation equation for the crack growth rate described by other material property parameters. Next, the soundness evaluation analysis device 200 performs a crack growth amount evaluation step 208 to calculate the crack growth amount per certain time interval using the crack growth rate. The time interval at this time may be any value.

Next, the soundness assessment analysis device 200 performs a post-progression defect model setting step 209 in which a crack (defect) is modeled after it has progressed by the amount of crack progress calculated in the crack progress amount evaluation step 208. The soundness assessment analysis device 200 then performs a destruction evaluation parameter evaluation step 210 in which the crack dimensions, the load applied to the cross-sectional area excluding the crack, or the stress intensity factor are evaluated for the crack modeled in the post-progression defect model setting step 209, and destruction evaluation parameters such as material strength and fracture toughness value that serve as judgment criteria are evaluated.

If the defect (crack) does not exceed the tolerance criteria in the next defect tolerance judgment step 211, the integrity evaluation analysis device 200 returns to the temperature, pressure, and water quality condition setting step 203, and performs a crack growth calculation by repeating the above-mentioned steps 203 to 210. Finally, in the integrity evaluation step 212, the integrity evaluation analysis device 200 evaluates the remaining life at the time when the crack (defect) exceeds the tolerance criteria in the defect tolerance judgment step 211 or at the end of the evaluation, and the tolerance for the above-mentioned tolerance criteria.

The soundness assessment method using the above-mentioned soundness assessment analysis device 200 is not limited to deterministic methods using simplified solutions or FEM analysis, but can also use probabilistic fracture mechanics (PFM) methods that take into account the variation in defect assessment parameters such as defect (crack) dimensions, neutron irradiation dose, residual stress, crack growth rate, and fracture toughness value.

In this PFM method, the defect evaluation parameters having these variations are set by a probability density function such as a normal distribution or a Weibull distribution, and are sampled by a sampling method such as the Monte Carlo method. In the defect tolerance judgment step 211, the target equipment or structure 10 is evaluated based on the tolerance judgment criteria leading to destruction, leakage, and loss of function. Then, the calculations in steps 202 to 211 are repeated, and the number of samples judged to exceed the above tolerance judgment criteria such as destruction or leakage is divided by the total number of samples to obtain the probability that the defect will lead to destruction, leakage, etc., which is a general method. Therefore, even when the PFM method is used in this embodiment, the essential procedure is the same as the flow shown in FIG. 2. The incorporation of various data from at least one of the sampling and measurement device 400, the inspection device 500, and the operation history storage device 600, which is a feature of this embodiment described below, into the soundness evaluation, may be more effectively utilized in the PFM method in some cases.

In the above-mentioned soundness evaluation analysis device 200, as described above, the data analysis device 300 uses data acquired by at least one of the sampling and measuring device 400, the inspection device 500, and the operation history storage device 600 to generate optimized defect evaluation parameters for use in the soundness evaluation (i.e., defect evaluation) of the equipment or structure 10, and the optimized defect evaluation parameters are incorporated into the soundness evaluation by the soundness evaluation analysis device 200. The specific configuration and procedure for this are shown below.

As described above, the sampling and measurement device 400 samples the material or environmental medium (such as reactor cooling water) 30 of the equipment or structure 10 to be evaluated, respectively, to measure its characteristics and state quantities, or directly measures the characteristics and state quantities, and obtains measurement data. This sampling and measurement device 400 has an irradiation dose data acquisition unit 401, a residual stress data acquisition unit 402, a material data acquisition unit 403, and an environmental data acquisition unit 404.

The dose data acquisition unit 401 acquires data used for evaluating the neutron dose. In the IASCC evaluation, the neutron dose is used to calculate physical parameters such as crack growth rate, strength, fracture toughness, and stress relaxation required for crack growth analysis or fracture evaluation. Since the neutron dose strongly affects these physical parameters, appropriate evaluation and setting of the neutron dose is extremely important for the accuracy and reliability of the soundness evaluation of the equipment or structure 10. Conventionally, the neutron flux (neutron dose per unit time (n/m ² /s) required for evaluating the neutron dose has been conservatively set based on an analysis using a neutron transport code. The dose data acquisition unit 401 obtains measurement data of the neutron dose by directly measuring the amount of activation of the equipment or structure 10 to be evaluated or by collecting the amount of activation using a minute sample.

Using the measurement data obtained by the dose data acquisition unit 401, the dose evaluation unit 301 of the data analysis device 300 optimizes the neutron flux used to set the dose distribution in the dose distribution setting step 204. In other words, the dose evaluation unit 301 obtains neutron flux data from the radioactivity data of the equipment or material of the structure 10 actually measured by the sampling and measurement device 400, and optimizes the neutron dose or neutron flux value, its distribution, and its time history as defect evaluation parameters set by analysis using a function that creates optimization curves P1 and P2 (described below) based on this neutron flux data.

When equipment or structure 10 is irradiated with neutrons from the inside, the neutron flux is measured on both the inside and outside surfaces of the equipment or structure 10, and the irradiation dose evaluation unit 301 uses this measurement data to optimize the neutron flux distribution, making it possible to set the neutron flux distribution more accurately.

In optimizing the neutron flux using measurement data, for example, a neutron flux function that takes into account the attenuation behavior of the neutron flux in the depth direction of the crack inside the structure, such as the linear function of equation (1) or the exponential function of equation (2), is used.
Where, T: Dimension (thickness) of the equipment or structure in the crack depth direction
t: Distance from the inner surface of the component or structure in the direction of the crack depth φ ₀ : Neutron flux at the inner surface of the component or structure (t = 0) φ _T : Neutron flux at the outer surface of the component or structure (t = T) φt: Neutron flux at a distance t from the inner surface of the component or structure

When considering the attenuation behavior of the neutron flux in the crack depth direction (thickness direction of the equipment or structure), the method of optimizing the neutron flux distribution is to adjust the coefficients and constants of the functions in formula (1) and formula (2), and as shown in Figure 4, the measurement data of the inner and outer surfaces of the equipment or structure 10 is continuously connected with the above function that creates the optimization curve P1 to optimize the neutron flux distribution A1 obtained by the original analysis, and obtain the optimized neutron flux distribution B1. As another method for optimizing the neutron flux distribution in this case, the parameters of the original neutron flux analysis code may be adjusted, or the neutron flux may be conservatively set to a constant value between the inner and outer surfaces of the structure and the neutron flux may be optimized by adjusting this value.

In addition, when neutron flux variation is taken into consideration, this neutron flux variation may be reflected in the parameter setting of the neutron flux probability density function and used in the health assessment by the PFM method in the health assessment analysis device 200, or the neutron flux variation may be set as a conservative envelope function or a constant value and used in the health assessment by the health assessment analysis device 200.

Furthermore, when considering the distribution of neutron flux in the crack length direction (circumferential direction of the equipment or structure) as shown in Figure 5, the irradiation dose assessment unit 301 measures the neutron flux in the crack length direction in the same manner as when considering the attenuation behavior of neutron flux in the crack depth direction (Figure 4), and optimizes the neutron flux distribution A2 from the original analysis using the measurement data in succession with a predetermined function that creates an optimization curve P2. By using this optimized neutron flux distribution B2, the integrity assessment analysis device 200 can perform a more accurate integrity assessment.

Patent Document 2 describes a method of using sampling material obtained from an actual structure, measuring the amount of radioactivity to obtain the neutron irradiation dose, and evaluating defect evaluation parameters such as crack growth rate, material strength, and fracture toughness. However, the method of Patent Document 2 determines defect evaluation parameters such as crack growth rate by substituting the neutron irradiation dose value obtained from the sampling into a relational equation between a preset irradiation dose and the defect evaluation parameter, and therefore has an essential problem of being unable to handle evaluations that take into account the distribution and variation of the neutron flux itself in this embodiment, or evaluations under conditions where the neutron flux is different even with the same neutron irradiation dose.

By using the operation and maintenance data stored in the operation and maintenance data storage unit 603 of the operation history storage device 600, the above-mentioned irradiation dose evaluation unit 301 can also handle detailed evaluations that take into account the effects of changes in neutron flux or its distribution during the service period due to increases in fuel burnup or shuffling, repairs and maintenance measures for equipment or structures 10, and power adjustment operations, etc.

In such an evaluation by the irradiation dose evaluation unit 301, by combining it with data analysis by the repair/replacement timing setting unit 307 of the data analysis device 300, it becomes possible to evaluate the neutron irradiation dose when the neutron flux or its distribution changes during the service life by switching the neutron flux at a timing corresponding to the actual operating history, or by setting the neutron flux as a continuous function of time and integrating it over time.

The residual stress data acquisition unit 402 acquires data used for evaluating residual stress. When a simplified solution is used for evaluating the stress intensity factor, the residual stress distribution is generally input as a third or fourth order polynomial. One method for acquiring data used for evaluating residual stress is, for example, a method of non-destructively measuring the residual stress on the surface of the actual equipment or structure 10 using a portable measuring device such as an X-ray residual stress measuring device. In this way, the method of directly measuring the residual stress of the equipment or structure 10 is not limited to non-destructive, but may also be a method of taking and measuring a sample using a partially destructive measuring method such as the ring core method or the DHD (Deep Hole Drilling) method, or a completely destructive measuring method such as the cutting method.

In addition, if it is difficult to measure the equipment or structure 10 to be evaluated, a method may be used in which a sample is taken from a structure made of similar materials or manufactured by a similar method, and the residual stress of the structure or the like is indirectly measured. In addition, if the correlation between the physical property value such as the microhardness of the surface of the equipment or structure 10 and the residual stress is known, the residual stress of the equipment or structure 10 may be evaluated using indirect measurement data (microhardness measurement data). Note that, as a loading condition used in such a soundness evaluation, although residual stress has an important effect particularly in the evaluation of welded structures, in this embodiment, a method of measuring or measuring loading conditions other than residual stress, such as internal pressure and repeated load, may be included in the same manner as described above.

Using the measurement data obtained by the residual stress data acquisition unit 402, the stress distribution evaluation unit 302 of the data analysis device 300 optimizes the stress distribution used to set the stress distribution in the stress distribution evaluation step 205.

In other words, the stress distribution evaluation unit 302 optimizes the value of the residual stress as a defect evaluation parameter set by analysis, its distribution, and the time history of its relaxation behavior, using a function that creates an optimization curve based on the residual stress data actually measured directly on the equipment or structure 10 by the sampling and measuring device 400 in a destructive or non-destructive manner. In addition, the stress distribution evaluation unit 302 obtains residual stress data from the physical property data actually measured directly or using a collected sample by the sampling and measuring device 400 for the target part of the equipment or structure 10 or a similar structure, using the correlation between the physical property data including the microhardness prepared in advance and the residual stress data, and optimizes the value of the residual stress as a defect evaluation parameter set by analysis, its distribution, and the time history of its relaxation behavior, using a function that creates an optimization curve based on this residual stress data.

The stress distribution evaluation unit 302 also performs an evaluation when considering the relaxation of residual stresses due to heat or neutron irradiation during the service life of the plant equipment being evaluated. As a method for optimizing the stress distribution, similar to the method for the neutron flux distribution shown in Figure 4, the measurement data of the residual stress is connected with an optimization curve based on a specified function, the residual stress distribution set by the analysis is optimized, and an optimized residual stress distribution is obtained.

The material data acquisition unit 403 acquires material data used to evaluate material property parameters related to material properties such as crack growth rate, strength, and fracture toughness. The most direct method of acquiring this material data is to sample material from the actual device or structure 10 and measure it. However, such measurements involve a variety of large-scale difficulties, so by establishing in advance a correlation between the material data and physical property values such as microhardness, which can be measured relatively easily, it is possible to indirectly acquire material data from, for example, measured values of microhardness.

The material data may be measured by directly measuring the surface of the equipment or structure 10, or by taking a sample of a size that can be taken and measuring it. If it is difficult to measure the target equipment or structure 10, a sample may be taken from an equipment or structure made of a similar material or manufactured by a similar method, and the material data may be measured indirectly. Patent Document 3 describes a method of loading a sample in advance to evaluate local corrosion of a material exposed to an actual environment when collecting material data. This embodiment is not limited to the above-mentioned method in obtaining material data, and does not exclude the method described in Patent Document 3 of loading a sample in advance in an actual environment or a similar environment and measuring it.

Using the measurement data obtained by the material data acquisition unit 403, the crack growth rate evaluation unit 303 of the data analysis device 300 performs data optimization processing to evaluate the crack growth rate in the crack growth rate evaluation step 207, and the strength/fracture toughness evaluation unit 304 of the data analysis device 300 performs data optimization processing to evaluate the fracture evaluation parameters (strength and fracture toughness values) in the fracture evaluation parameter evaluation step 210.

That is, the crack growth rate evaluation unit 303 optimizes the values of the material property parameters including the crack growth rate as the defect evaluation parameters set by design and the functions related to the material property parameters based on the material data actually measured directly by the sampling and measuring device 400 for the samples taken from the equipment or structure 10, in the same way as the method for the neutron flux distribution shown in FIG. 4. Furthermore, when the distribution of the material property parameters including the crack growth rate and the time history of their changes are taken into consideration, they are optimized. In addition, the crack growth rate evaluation unit 303 uses the correlation between the physical property value data including the microhardness prepared in advance and the material data to obtain material data from the physical property value data actually measured directly or using the taken samples by the sampling and measuring device 400 for the target part of the equipment or structure 10 or a similar structure, and optimizes the values of the material property parameters including the crack growth rate set by design and the functions related to the material property parameters based on this material data. Furthermore, when the distribution of the material property parameters including the crack growth rate and the time history of their changes are taken into consideration, they are optimized.

The strength/fracture toughness evaluation unit 304 optimizes the values of the material property parameters including strength and fracture toughness as defect evaluation parameters set by design, and the functions related to the material property parameters, based on the material data actually measured directly by the sampling/measuring device 400 for the samples taken from the equipment or structure 10. Furthermore, when the distribution of the material property parameters including the strength and fracture toughness and the time history of their changes are taken into consideration, they are optimized. Furthermore, the strength/fracture toughness evaluation unit 304 uses the correlation between the physical property value data including the microhardness prepared in advance and the material data to obtain material data from the physical property value data actually measured directly or using the taken samples by the sampling/measuring device 400 for the target part of the equipment or structure 10 or a similar structure, and optimizes the values of the material property parameters including strength and fracture toughness set by design, and the functions related to the material property parameters, based on this material data. Furthermore, when the distribution of the material property parameters including the strength and fracture toughness and the time history of their changes are taken into consideration, they are optimized.

It is generally known that the mechanical properties of a material, such as strength and fracture toughness as material property parameters, are distributed in the thickness direction of the material near the surface of the material of the equipment or structure 10 and inside the material due to differences in the mechanical and thermal history that the material undergoes during the manufacturing process. When considering such changes in material properties inside the material, the strength/fracture toughness evaluation unit 304 sets a distribution function that represents the changes in material properties with respect to positions inside the material, and optimizes the distribution of the material properties set by the design using the actual measured material data acquired by the material data acquisition unit 403, thereby obtaining an optimized material property distribution.

In particular, in the case of material properties that depend on the neutron irradiation dose, by taking into account the irradiation dose distribution set in the irradiation dose distribution setting step 204, the material property distribution inside the material that has been optimized in this way can be accurately incorporated into the evaluation of soundness.

Similarly, in the case of material properties such as a crack growth rate that depends on residual stress, the distribution of material properties within the material is optimized taking into account the stress distribution set in the stress distribution evaluation step 205, and this optimized distribution of material properties is used to evaluate the stress intensity factor in the stress intensity factor evaluation step 206 and the crack growth rate in the crack growth rate evaluation step 207, thereby making it possible to more accurately evaluate the soundness.

In addition, when taking into consideration the inevitable variation in such material properties, this variation can be reflected in the parameter settings of the probability density function of the material property values and used in the soundness assessment using the PFM method, or the variation can be used in the soundness assessment by setting a function that conservatively envelopes the variation or a constant value.

In the sampling and measurement device 400, the environmental data acquisition unit 404 performs sampling and measurement of, for example, the reactor cooling water, and acquires environmental data such as the actual temperature, pressure, electrical conductivity, dissolved oxygen concentration, dissolved hydrogen concentration, and injection agent concentration for water quality improvement of the reactor cooling water. These environmental data are stored as environmental data in the environmental data storage unit 601 in the operation history storage device 600. This environmental data is used to set the time history of temperature, pressure, and water quality in the temperature, pressure, and water quality condition setting step 203 by optimizing the environmental parameters (temperature, pressure, water quality, etc.) as defect evaluation parameters in the environmental parameter evaluation unit 306.

In other words, the environmental parameter evaluation unit 306 optimizes the values of the environmental parameters as defect evaluation parameters set by design and functions related to the environmental parameters based on the environmental data including the temperature, pressure, conductivity, and dissolved oxygen concentration actually measured by the sampling and measuring device 400 for the environmental media, such as reactor cooling water, to which the equipment or structure 10 is exposed. Furthermore, when the distribution of the above environmental parameters and the time history of their changes are taken into consideration, they are optimized.

When sampling and measuring using the sampling and measuring device 400 as described above, it is expected that there may be time and cost constraints, or physical constraints on the measurement. To avoid this and perform measurement and optimization more effectively, the data analysis device 300 sets the measurement location, measurement range, and number of measurement points in advance using an experimental design method. In addition, to optimize the neutron irradiation dose, residual stress, etc., in the data analysis device 300, for example, the Bayesian updating function described below is used to optimize the parameters of the probability density function using limited data.

As described above, the inspection device 500 detects and sizes defects 20, such as cracks 21 and corrosion, that occur in the equipment or structure 10 of the plant facility to be evaluated, and acquires inspection data such as the distribution, density, and dimensions of the defects 20. This inspection device 500 has a defect data acquisition unit 501. Note that the inspection device 500 may acquire inspection data other than defects 20.

The defect data acquisition unit 501 acquires defect distribution data and defect dimension data used to evaluate the dimensions, distribution, density, etc. of defects 20 such as cracks 21. The dimensions and distribution of defects 20 such as cracks 21 are the most important parameters as initial values for the evaluation, and appropriate settings of these are extremely important for the accuracy and reliability of the overall soundness evaluation of the equipment or structure 10.

For example, if there is a single crack 21, the defect model evaluation unit 305 can directly set the conditions for the position and dimensions of the crack 21 using the position and dimension data identified by the inspection. On the other hand, if multiple cracks 21 are detected and the cracks 21 are close to each other, the defect model evaluation unit 305 appropriately replaces the multiple cracks 21 with a defect (crack) model in which the multiple cracks 21 are combined, based on the crack merging conditions and the like listed in standards such as the Japan Society of Mechanical Engineers Maintenance Standards or ASME Boiler & Pressure Vessel Code Section XI.

In addition, when the crack surface 22 (Figure 3(B)) is inclined relative to the evaluation cross section of the evaluation model, the defect model evaluation unit 305 appropriately replaces the crack 21 with the inclined crack surface 22 with a crack projected onto the evaluation cross section. Furthermore, there have been reported cases of stress corrosion cracking in which the crack 21 branches in a complex manner and develops radially. In such cases, the defect model evaluation unit 305 models the crack 21 so that it has an appropriate shape and dimensions by merging the cracks 21 as described above or projecting them onto the evaluation cross section.

The defect model evaluation unit 305 not only sets the distribution, dimensions, density, etc. of the initial cracks (defects) directly from the inspection data by the inspection device 500 as described above, but also optimizes the initial crack (defect) dimensions set by design using the inspection data. In other words, the defect model evaluation unit 305 optimizes the values and their distribution of the initial defect model (i.e., the position, shape, dimensions, density, etc. of the initial defects) as defect evaluation parameters set by design, based on the defect inspection data including the distribution, density, and dimensions of the defects 20 (cracks 21) actually inspected by the inspection device 500 for the equipment or structure 10, in the same way as in the case of FIG. 3 or FIG. 4.

In addition, an example of optimizing the initial crack (defect) dimensions in a deterministic evaluation is to determine the initial crack (defect) dimensions at the start of the evaluation by reverse analysis from the crack (defect) dimensions detected in the inspection. On the other hand, an example of optimizing the probability distribution (probability density function) of the initial crack (defect) dimensions performed by the defect model evaluation unit 305 using an evaluation based on a probabilistic fracture mechanics (PFM) method is shown below.

First, the defect model evaluation unit 305 sets a probability distribution (probability density function) of the initial crack (defect) size of the structure to be evaluated at the start of the evaluation. Next, the defect model evaluation unit 305 obtains an inspection result X (described later) using the crack (defect) size measured by non-destructive testing during the service life of the plant equipment to be evaluated, and updates the probability distribution (probability density function) of the initial crack (defect) size initially set in the defect size evaluation. This update of the probability distribution (probability density function) of the initial crack (defect) size is a Bayesian update using Bayesian estimation. If the depth a of the initial crack (defect) is a random variable to be subjected to Bayesian estimation and the result of the non-destructive testing is X, the formula for Bayesian estimation is given by Equation (3).

Here, a is the depth of the initial crack (defect), X is the inspection result (crack (defect) not detected/crack (defect) detected), P(a) is the probability distribution (prior distribution) which is the probability density function of the initially set initial crack (defect) dimension, P(X|a) is the likelihood function (the probability that inspection result X will be obtained when the initial crack (defect) depth is a), and P(a|X) is the probability distribution (posterior distribution) which is the probability density function of the updated initial crack (defect) dimension.

The likelihood function is evaluated by a crack (defect) detection probability function using the inspection result X, which reflects the crack (defect) depth at the time of inspection obtained by, for example, a crack growth calculation. The likelihood function reflecting this inspection result X is used to optimize the prior distribution (prior probability density function), and the posterior distribution (posterior probability density function) is obtained.

The defect model evaluation unit 305 uses the past inspection data stored in the inspection data storage unit 604 of the operation history storage device 600 and the pre-service inspection data including repaired or replaced equipment or structures, and combines it with the data analysis performed by the inspection data evaluation unit 308 of the data analysis device 300, making it possible to perform more detailed optimization of the initial defect dimensions. Note that the inspection data storage unit 604 stores the inspection data by the inspection device 500 and the pre-service inspection data including repaired or replaced equipment or structures.

As described above, the operation history storage device 600 stores operation management information including the temperature and pressure of the equipment or structure 10, operation history such as start-up and shutdown, data on the results of repair and maintenance measures, measurement data from the sampling and measurement device 400, and inspection data from the inspection device 500. This operation history storage device 600 has an environmental data storage unit 601, a repair and replacement data storage unit 602, an operation and maintenance data storage unit 603, and an inspection data storage unit 604.

In conventional defect assessments, conditions such as temperature, pressure, and water quality are generally set at conservative envelope conditions over the entire assessment period. However, by reflecting in the defect assessment, for example, the reduction in the risk of defect occurrence and the rate of crack growth through conservation measures (such as water quality improvement and stress improvement) implemented as measures against aging or life extension, as well as the refreshing of aging deterioration or characteristic improvement through repair or replacement, it becomes possible to perform a more accurate soundness assessment that is in line with actual conditions.

In other words, in the data analysis device 300, for example, the environmental parameter evaluation unit 306 and the stress improvement evaluation unit 309 each reset the values and distributions of the above-mentioned defect evaluation parameters (environmental parameters such as water quality, residual stress) that are updated by the construction of water quality improvement and stress improvement of the equipment or structure 10 and are set by analysis or design, using actual operation history data related to the performance of the construction, at the time of the construction, in accordance with the content and conditions of the construction.

The environmental data storage unit 601 stores environmental data such as the temperature, pressure, electrical conductivity, dissolved oxygen concentration, dissolved hydrogen concentration, and injection agent concentration for water quality improvement of the reactor cooling water, etc., acquired by the environmental data acquisition unit 404. Using this environmental data, the environmental parameter evaluation unit 306 optimizes the environmental parameters as described above, and the time history of the temperature, pressure, and water quality is set in the temperature, pressure, and water quality condition setting step 203.

The repair/replacement data storage unit 602 stores, as repair/replacement data, record information on equipment or structure 10 that has undergone maintenance measures such as repair, replacement, or stress improvement. The repair/replacement timing setting unit 307 uses the repair/replacement data stored in the repair/replacement data storage unit 602 to reset defect evaluation parameters of part or the entire equipment or structure 10 to be evaluated to initial conditions at the time when the maintenance measures were carried out. For example, FIG. 2 shows an example of resetting the dose distribution setting in the dose distribution setting step 204.

Operation and maintenance data storage unit 603 stores operation and maintenance data on the evaluation parts that have been repaired or replaced. This operation and maintenance data is used to set the initial defect model in initial defect model setting step 202 after data processing by repair and replacement timing setting unit 307, and to evaluate the stress distribution in stress distribution evaluation step 205 after data processing by stress improvement evaluation unit 309. Furthermore, when the material characteristics are improved, the operation and maintenance data stored in operation and maintenance data storage unit 603 is used to reset the evaluation of the crack growth rate in crack growth rate evaluation step 207, and to reset the evaluation of the destruction evaluation parameters in destruction evaluation parameter evaluation step 210.

The inspection data storage unit 604 stores the inspection data from the inspection device 500, and pre-service inspection data including repaired or replaced equipment or structures. The inspection data evaluation unit 308 imports this inspection data, and as described above, in combination with the data analysis by the defect model evaluation unit 305, enables more detailed optimization of the initial defect dimensions.

As configured as above, this embodiment provides the following advantages (1) to (3).
(1) The data analysis device 300 optimizes defect evaluation parameters (initial defect model, neutron exposure dose (including neutron flux), environmental parameters, residual stress, material property parameters (crack growth rate, strength, fracture toughness, etc.)) set by analysis or design for use in evaluating the soundness, based on actual data from at least one of the sampling and measurement device 400, the inspection device 500, and the operation history storage device 600, using a function that creates, for example, optimization curves P1, P2. As a result, it is possible to reduce various uncertainties that are contained in the defect evaluation parameters set by analysis or design and that differ from the actual situation of the plant equipment, and evaluate the soundness of the equipment or structure 10 in the plant equipment. As a result, it is possible to realize a highly accurate and reliable soundness evaluation that is in line with the actual situation of the plant equipment.

(2) When sampling or measurement is performed by the sampling and measurement device 400, the data analysis device 300 sets in advance the measurement location, measurement range, and number of measurement points using an experimental design method or the like. Therefore, when sampling or measurement is performed by the sampling and measurement device 400, time and cost constraints, or physical constraints on the measurement can be avoided, and the measurement can be performed more effectively.

(3) In the environmental parameter evaluation unit 306 and the stress improvement evaluation unit 309 of the data analysis device 300, when the defect evaluation parameters (environmental parameters such as water quality, residual stress) set by analysis or design are updated by construction of water quality improvement or stress improvement of the equipment or structure 10 in the plant equipment being evaluated, the values and distributions of the defect evaluation parameters are reset according to the content and conditions of the construction at the time of construction, using actual operating history data related to the performance of the construction. As a result, a more accurate soundness evaluation can be performed that is in line with the actual situation of the plant equipment being evaluated.

Although an embodiment of the present invention has been described above, this embodiment is presented as an example and is not intended to limit the scope of the invention. This embodiment can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. Furthermore, such substitutions and modifications are included in the scope and gist of the invention, and are included in the scope of the invention and its equivalents described in the claims.

10...equipment or structure, 20...defect, 30...environmental medium, 100...health evaluation analysis system, 200...health evaluation analysis device, 300...data analysis device, 400...sampling and measurement device, 500...inspection device, 600...operation history storage device, P1, P2...optimization curve

Claims (11)

In a soundness evaluation and analysis system for evaluating the soundness of equipment or structures in a plant facility,
a sampling and measuring device for sampling the material of the equipment or structure or the environmental medium, respectively, to measure the characteristics or state quantities thereof, or for directly measuring the characteristics or state quantities thereof to obtain measurement data;
an inspection device that detects and sizes defects, including cracks and corrosion, that occur in the equipment or structure, and obtains inspection data relating to the defects;
an operation history storage device that stores the state quantity of the equipment or structure, operation management information including an operation history, performance data of repairs and maintenance measures, the measurement data from the sampling and measuring device, and the inspection data from the inspection device;
a data analysis device that optimizes defect evaluation parameters set by analysis or design for use in evaluating soundness based on actual data from at least one of the sampling and measurement device, the inspection device, and the operation history storage device;
and a soundness evaluation analysis device that evaluates the soundness of the equipment or structure by using the defect evaluation parameters optimized by the data analysis device ,
The data analysis device is configured to obtain neutron flux data from the radioactivity data of the material of the equipment or structure actually measured by the sampling/measuring device, and based on this neutron flux data, use a function to create an optimization curve to optimize the neutron irradiation dose or neutron flux value, its distribution, and its time history as the defect evaluation parameters set by analysis . The soundness evaluation analysis system described in claim 1 is characterized in that the data analysis device is configured to optimize the residual stress values, their distribution, and the time history of their relaxation behavior as defect evaluation parameters set by analysis using a function that creates an optimization curve based on residual stress data that is actually measured directly on equipment or structures using a sampling and measuring device in a destructive or non-destructive manner. The data analysis device is configured to use the correlation between previously prepared physical property data including microhardness and residual stress data to obtain the residual stress data from the physical property data actually measured directly or using collected samples by a sampling and measuring device for the target portion of the equipment or structure or a similar structure, and to optimize the value of the residual stress as a defect evaluation parameter set by analysis, its distribution, and the time history of its relaxation behavior, using a function that creates an optimization curve based on this residual stress data. The soundness evaluation analysis system described in claim 1 is characterized in that it is configured to obtain the residual stress data from the physical property data actually measured directly or using collected samples by a sampling and measuring device using the correlation between previously prepared physical property data including microhardness and residual stress data, and to optimize the value of the residual stress as a defect evaluation parameter set by analysis, its distribution, and the time history of its relaxation behavior, using a function that creates an optimization curve based on this residual stress data. The soundness evaluation and analysis system according to claim 1, characterized in that the data analysis device is configured to optimize values of material property parameters including crack growth rate, strength, and fracture toughness as defect evaluation parameters set by design, functions related to the material property parameters, distribution of the material property parameters, and time history of their changes, based on material data actually measured directly by a sampling and measuring device for samples taken from equipment or structures. The data analysis device is configured to use the correlation between previously prepared physical property data including microhardness and material data to obtain the material data from the physical property data actually measured directly or using collected samples by a sampling and measuring device for the target portion of the equipment or structure or a similar structure, and to optimize the values of material property parameters including crack growth rate, strength, and fracture toughness as defect evaluation parameters set by design, functions related to the material property parameters, the distribution of the material property parameters, and the time history of their changes based on this material data. The soundness evaluation and analysis system according to claim 1, characterized in that the data analysis device is configured to optimize the values of environmental parameters as defect evaluation parameters set by design, functions related to the environmental parameters, the distribution of the environmental parameters, and the time history of their changes, based on environmental data including temperature, pressure, conductivity, and dissolved oxygen concentration actually measured by a sampling and measurement device for the environmental medium to which the equipment or structure is exposed. The soundness evaluation and analysis system according to claim 1, characterized in that the data analysis device is configured to optimize the values and distribution of the initial defect model as defect evaluation parameters set by design, based on defect inspection data including the distribution, density, and dimensions of defects actually inspected by an inspection device for equipment or structures. The soundness evaluation analysis system according to claim 1, characterized in that the data analysis device is configured to optimize the probability density function of the defect evaluation parameters set by analysis or design using a Bayesian updating function based on data measured or inspected by a sampling/measuring device or an inspection device. The soundness evaluation and analysis system according to any one of claims 1 to 6, characterized in that the data analysis device is configured to set in advance the measurement locations, measurement ranges, and number of measurement points by an experimental design method when various data are measured by a sampling and measurement device. The data analysis device is a defect evaluation parameter that is updated by the construction of conservation measures including repair, replacement, stress improvement, and water quality improvement of equipment or structures, and is configured to reset the value and distribution of the defect evaluation parameter set by analysis or design, using actual operating history data related to the performance of the construction, at the time of carrying out the construction, in accordance with the content and conditions of the construction. A soundness evaluation analysis method for evaluating the soundness of equipment or structures in a plant facility, comprising:
A step of sampling the material of the equipment or structure or the environmental medium by a sampling/measuring device to measure the characteristics or state quantities thereof, or directly measuring the characteristics or state quantities to obtain measurement data;
detecting and sizing defects , including cracks and corrosion, occurring in the equipment or structure by an inspection device , and acquiring inspection data relating to the defects;
storing, by an operation history storage device, the state quantities of the equipment or structure, operation management information including operation history, performance data of repair and maintenance measures, the measurement data from the sampling and measuring device, and the inspection data from the inspection device;
A step of optimizing defect evaluation parameters set by analysis or design for use in evaluating the soundness based on actual data from at least one of the sampling and measuring device, the inspection device, and the operation history storage device by a data analysis device ;
and evaluating , by a soundness evaluation analysis device, the soundness of the equipment or structure using the defect evaluation parameters optimized by the data analysis device;
The data analysis device further obtains neutron flux data from the radioactivity data of the material of the equipment or structure actually measured by the sampling and measuring device, and based on this neutron flux data, uses a function to create an optimization curve to optimize the neutron irradiation dose or neutron flux value, its distribution, and its time history as the defect evaluation parameters set by analysis .