CN114295214B

CN114295214B - Effective emissivity-based turbine blade radiation temperature measurement method and device under complex environment

Info

Publication number: CN114295214B
Application number: CN202111560825.5A
Authority: CN
Inventors: 高山; 张先岐; 陈立伟; 王桐; 崔颖; 王超; 姜晶; 牛夷
Original assignee: Harbin Engineering University
Current assignee: Harbin Engineering University
Priority date: 2021-12-20
Filing date: 2021-12-20
Publication date: 2023-07-25
Anticipated expiration: 2041-12-20
Also published as: CN114295214A

Abstract

The invention discloses a turbine blade radiation temperature measurement method and device based on effective emissivity in a complex environment, belongs to the technical field of turbine blade radiation temperature measurement, and solves the problems that the calculated amount is too large, the misjudgment rate is high, and the real-time performance and the high efficiency of the turbine blade temperature measurement requirements cannot be met in the prior art. The method of the invention comprises the following steps: acquiring radiation data of a turbine blade to be tested under a plurality of wavelengths; constructing a turbine blade reflected radiation analysis model to obtain the radiation quantity projected to a blade to-be-measured point by a surrounding complex environment; setting an emissivity model, and constructing an optimization target equation by combining radiation data actually received by a pyrometer and radiation quantity projected to a point to be measured by a complex environment; solving an optimization target equation by using a double-population social group optimization algorithm to obtain emissivity values of the turbine blade under each wavelength; and solving the effective emissivity of the surface of the turbine blade to be measured, and calculating the actual temperature of the surface of the turbine blade. The invention is suitable for radiation temperature measurement of the turbine blade.

Description

Effective emissivity-based turbine blade radiation temperature measurement method and device under complex environment

Technical Field

The application relates to the technical field of turbine blade radiation temperature measurement, in particular to a turbine blade radiation temperature measurement method and device under a complex environment based on effective emissivity.

Background

The turbine blade is a main hot end part of the engine, the running environment is extremely severe, the temperature of the turbine blade is accurately measured, and the working state of the blade can be monitored and evaluated, so that the method has important significance for ensuring the safe running of the engine. Aiming at the special running environment of the turbine blade, a high-temperature radiometer is generally adopted to carry out radiation temperature measurement on the blade, and the high-temperature radiometer is provided with a plurality of wavelength channels so as to obtain radiation information of the blade under a plurality of wavelength conditions, thereby indirectly obtaining the real temperature of the blade. However, the radiation temperature measurement of the turbine blade in the complex environment has the following difficulties:

1. when the radiation pyrometer measures the temperature of the turbine blade, the received radiation quantity not only comprises the radiation quantity of the blade to be measured, but also comprises the radiation quantity reflected after the surrounding complex environment such as the front guide vane and the adjacent movable vane are projected to the point to be measured, and the reflected radiation seriously affects the temperature measurement result of the turbine blade when the proportion of the reflected radiation to the total radiation quantity received by the pyrometer is high. In the prior art, when the radiation heat transfer shielding condition among the blades is judged after the turbine blade reflected radiation analysis model is constructed, the adopted judgment method has overlarge calculated amount, and when the curvature change of the blade surface is overlarge or the precision of the discrete blade model is lower, the misjudgment rate is higher, so that the calculation result is seriously influenced.

2. Because the actual emissivity of the surface of the turbine blade is unknown, the actual emissivity of the blade to be measured and the reflected emissivity of the surrounding complex environment cannot be directly obtained, and the temperature measurement result is affected if the solved emissivity deviates from the actual emissivity of the surface of the blade. The prior art mainly utilizes a plurality of wave band radiation information to construct a multi-objective optimization algorithm to solve the emissivity problem, and the algorithm has the problems of easiness in sinking into a local optimal solution, overlarge calculated amount, weak convergence and the like.

3. The radiation data information received by the pyrometer during the radiation temperature measurement of the turbine blade can obtain the real temperature of the blade only by spending a great deal of time and effort, and the real-time performance and the high efficiency of the temperature measurement requirement of the turbine blade can not be met.

Disclosure of Invention

The invention aims to solve the problem that the real-time performance and accuracy of radiation temperature measurement of a turbine blade are poor due to the fact that the existing turbine blade is affected by reflected radiation of a complex environment, and provides a method and a device for radiation temperature measurement of the turbine blade under the complex environment based on effective emissivity.

The invention is realized by the following technical scheme, and in one aspect, the invention provides a turbine blade radiation temperature measurement method under a complex environment based on effective emissivity, which comprises the following steps:

Step 1, acquiring radiation data of a turbine blade to be tested under a plurality of wavelengths by utilizing a multi-wavelength pyrometer;

step 2, constructing a turbine blade reflected radiation analysis model to obtain the radiation quantity projected to a blade to-be-measured point by a surrounding complex environment;

step 3, setting an emissivity model, and constructing an optimization target equation by combining actual received radiation data of a pyrometer and radiation quantity projected to a point to be measured by a complex environment;

step 4, solving an optimization target equation by using a double-population social group optimization algorithm to obtain emissivity values of the turbine blade under each wavelength;

and 5, solving the effective emissivity of the surface of the turbine blade to be tested by utilizing the actual received radiation quantity of the pyrometer, the projected radiation quantity of the complex environment and the emissivity value, and calculating the actual temperature of the surface of the turbine blade.

Further, the step 2 specifically includes:

step 2.1, constructing a three-dimensional discrete model of a turbine blade to be tested, a front stage guide vane and an adjacent moving blade, wherein the discrete model of the blade is represented by discrete triangular surface elements, and the area of each discrete triangular surface element is 2-3mm ² ；

Step 2.2, performing a screening operation of 'visualization' of the surface elements, and screening the surface elements which leave the surrounding environment and possibly transmit heat radiation to a to-be-measured point under the condition of not considering the mutual shielding of the surface elements;

Step 2.3, judging whether other blade surface element shielding exists between the surface element subjected to the 'visualization' screening and the surface element to be detected;

and 2.4, according to the step 2.2 and the step 2.3, the blade surface elements which are screened and left can transmit heat radiation to the surface element to be tested, the angle coefficient between each blade surface element and the surface element to be tested is calculated, and the radiation quantity projected around the point to be tested is obtained by utilizing the Planckian theorem in combination with the known theoretical temperature distribution of the blade.

Further, in the step 2.2, in the 'visualization' screening operation, the screening conditions are as follows:

if heat radiation is likely to be transmitted between two surface elements, the normal vector and the vector represented by the connecting line of the gravity center meet the following formula condition

Wherein,,and->Normal vector of bin 1 and 2, respectively, < >>Is a vector formed by connecting the centers of gravity of two surface elements.

Further, in the step 2.3, the method for judging shielding specifically includes:

when judging whether the adjacent moving blades block the radiation propagation path between the front-stage guide blade and the surface element to be detected, firstly solving a line segment where the gravity center connecting line of the two surface elements is located, and then judging whether the line segment intersects with a certain triangle approximately representing the adjacent moving blades, if so, blocking exists;

when judging whether other blade surface elements of the front stage guide vane and the adjacent movable vanes shield the radiation propagation path, further dispersing the surface elements to be detected into a plurality of small triangles and calculating the gravity center of each small triangle;

Calculating a line segment where the gravity center of the judging face element to be shielded and the gravity center connecting line of each discretized small triangle are located so as to obtain a cluster of line segments;

setting a shielding proportion threshold value, and calculating the percentage of the intersection number of the line segment clusters and other blade surface elements;

if the percentage of the number of intersections reaches the occlusion ratio threshold, then it is determined that occlusion exists between the two bins.

Further, in the step 2.4, the angle coefficient is according to the formula:

calculated, wherein A _j Is the area of bin j; a is that _i Is the area of bin i; f (F) _ji The radiation angle coefficients for bin j through bin i; θ _i And theta _j Is the normal line of the corresponding surface element and connects two infinitesimal areas dA _i And dA _j Is the angle between the straight lines of (a); r is the distance between two bins;

the radiation quantity projected by the surrounding environment of the point to be measured is calculated by the formula:

calculated, where M _r (lambda, T) is the amount of radiation projected by the environment surrounding the point to be measured; m is M _j,i (λ,T _j ) The amount of radiation projected to bin i for bin j; m is M _j (λ,T _j ) The blackbody radiation emittance of bin j.

Further, in the step 3, the optimization objective equation is:

wherein ε _i Emissivity under the ith channel of the multi-wavelength pyrometer; m (lambda) _i ,T _m ) The amount of radiation received for the pyrometer; m (lambda) _i ,T _r ) The radiation quantity projected to the surface of the object to be measured for the surrounding environment; f (λ, T) is a selected emissivity model, where the coefficients to be determined are unknown; function M ^-1 { lambda, M } is the inverse of the Planck formula to obtain the temperature of the target to be measured.

Further, the step 4 specifically includes:

step 4.1, setting population initialization parameters, namely setting parameters such as a feasible solution range of undetermined parameters of an emissivity model, the number N of population individuals, a reverse learning proportion RL, a maximum iteration number D and the like according to the selected emissivity model;

step 4.2, generating an initial population in a feasible solution parameter range of the emissivity model;

performing double-population grouping operation, and randomly dividing an initialized population into two populations 1 and 2 with the same scale size;

step 4.3, calculating individual fitness in the population 1 and the population 2 according to an optimization target equation, and arranging the fitness in the respective populations according to the descending order of fitness;

step 4.4, individuals in the population 1 and the population 2 enter an 'improvement stage', and the updating modes of the individuals in the various populations adopt an improved improvement stage evolution algorithm such as a formula

Wherein c is a self-contrast parameter, and the value of c is usually 0-1; r is a random number of 0-1; agbest _j The j-th dimension characteristic value of the current generation optimal individual in the corresponding population is obtained;and->The j-th dimension characteristic values before and after the updating of the i-th individuals are respectively obtained, each individual is subjected to improvement and evolution by taking the optimal individual of the population as a guide, the fitness of the new individual is recalculated, and the new individuals are arranged according to the descending order of fitness;

Step 4.5, selecting a certain number of individuals with poor quality in the current generation of the population 2 according to the reverse learning proportion RL, executing reverse learning operation, and arranging the updated individuals of the population 2 according to the descending order of fitness;

step 4.6, transferring the optimal individuals in the population 2 into the population 1 by adopting a immigration operation, and replacing the individuals with the worst fitness in the population 1 by adopting an elite elimination mechanism for wining elimination;

step 4.7, individuals in the population 1 and the population 2 enter an acquisition stage', and the updating modes of the individuals in the various populations adopt an improved acquisition stage evolution algorithm such as a formula

if f(x _i )is better than f(x _k )

else

Wherein r is ₁ 、r ₂ And r ₃ Random numbers, X, of 0-1 _k To randomly select an individual from a corresponding population as a learning object, bgbest _j The j-th dimension characteristic value, agbest, of the optimal individuals of the double species _j The j-th dimension characteristic value of the optimal individuals in the corresponding population is updated and evolved by taking the optimal individuals in the population, the random individuals in the population and the optimal individuals in all the population as guidance, and finally, the fitness of new individuals in various populations is recalculated and arranged according to the descending order of the fitness;

step 4.8, adopting Gaussian variation operation to the optimal individuals in the population 2 to keep Gaussian variation individuals with fitness better than that of the original optimal individuals, and then arranging the individuals in the population 2 according to the descending order of fitness;

Step 4.9, repeating the steps 4.3 to 4.8 until the maximum iteration number reaches the termination condition; at this time, the individual with the optimal fitness in the population 1 is the optimal solution, and the value of the emissivity of the surface of the turbine blade can be obtained at this time.

Further, the step 5 specifically includes:

the actual received radiation quantity, the projected radiation quantity of complex environment and the emissivity value of the pyrometer are utilized according to the formula

Wherein ε _eff (lambda) is the effective emissivity of the turbine blade surface; epsilon is the surface of the turbine bladeThe inter-emissivity; m (lambda, T) _m ) The amount of radiation received for the pyrometer; m (lambda, T) _b ) The blackbody radiation emergent degree of the target point to be measured; m (lambda, T) _r ) And projecting radiation quantity for the surrounding environment of the object to be measured.

The effective emissivity of the surface of the turbine blade to be measured can be solved;

and solving the actual temperature of the surface of the turbine blade by using the inverse operation of the Planck formula.

In another aspect, the present invention provides a device for measuring the radiation temperature of a turbine blade in a complex environment based on effective emissivity, the device comprising:

the radiation data acquisition module is used for acquiring radiation data of the turbine blade to be tested under a plurality of wavelengths by utilizing the multi-wavelength pyrometer;

the model construction module is used for constructing a turbine blade reflected radiation analysis model and obtaining the radiation quantity projected to a blade to-be-measured point by the surrounding complex environment;

The optimized target equation construction module is used for setting an emissivity model, combining radiation data actually received by the pyrometer and radiation quantity projected to a point to be measured by the complex environment to construct an optimized target equation;

the emissivity solving module is used for solving an optimization target equation by utilizing a double-population social group optimization algorithm to obtain emissivity values of the turbine blade under each wavelength;

the effective emissivity and real temperature calculation module is used for calculating the effective emissivity of the surface of the turbine blade to be measured by utilizing the actual received radiation quantity of the pyrometer, the projected radiation quantity of the complex environment and the emissivity value, and calculating the real temperature of the surface of the turbine blade;

and the effective emissivity database construction module is used for constructing an effective emissivity database of the surface of the turbine blade under various operating conditions.

In a third aspect, the invention provides a computer device comprising a memory and a processor, the memory having stored therein a computer program which when executed by the processor performs the steps of a method for turbine blade radiation thermometry in a complex environment based on effective emissivity as described above.

The invention has the beneficial effects that:

the invention relates to a turbine blade radiation temperature measurement method based on effective emissivity under complex environment, which is based on obtaining the actual radiation quantity received by a high-temperature radiometer,

Firstly, constructing a reflected radiation analysis model under a complex environment of a turbine blade to obtain the radiation quantity projected to a point to be measured by the complex environment.

Secondly, the emissivity of the blade is solved by utilizing the proposed double-population social group optimization algorithm (DPSGO) and combining the actual received radiation quantity of the pyrometer and the projected radiation quantity of the complex environment quantity around the blade to be tested.

And finally, calculating the effective emissivity of the blade surface by utilizing the solving information to eliminate the influence of reflected radiation to obtain the real temperature of the blade, and directly obtaining the real temperature of the blade to be measured by utilizing the actual radiation quantity received by a pyrometer in the subsequent turbine blade temperature measurement process and combining the known effective emissivity of the blade by constructing an effective emissivity database of the turbine blade surface under various running conditions.

The method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity eliminates the influence of the reflected radiation of the complex environment of the turbine blade and meets the requirements of instantaneity and high efficiency of the radiation temperature measurement of the turbine blade. The invention designs an intelligent optimization algorithm with high precision and low time consumption, which has global and local searching capability, can accurately obtain emissivity information of a turbine blade under a plurality of wavelengths by means of known information, and is a real-time and efficient temperature measurement method which directly correlates radiation information actually received by a pyrometer with real temperature.

According to the invention, the temperature of the turbine blade under the influence of reflected radiation of a complex environment can be accurately measured, and meanwhile, the radiation quantity actually received by the pyrometer is related to the actual temperature of the blade, so that the real-time and high-efficiency requirements of the temperature measurement of the turbine blade are met.

The invention is suitable for radiation temperature measurement of the turbine blade.

Drawings

In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a flow chart of a turbine blade reflected radiation analysis model;

FIG. 2 is a schematic diagram of a reflected radiation analysis model (demonstrating that the bin to be measured is at 50% leaf height and 0.5 relative chord length);

FIG. 3 is a schematic diagram of a method 1 for improving the judgment of the shielding among the turbine blade surface elements;

FIG. 4 is a flow chart for solving the emissivity of the surface of a turbine blade based on a Dual Population Social Group Optimization (DPSGO) algorithm;

FIG. 5 is a graph of temperature error caused by reflected radiation from the surrounding environment for three different sets of bucket pressure surfaces;

FIG. 6 is a graph of turbine blade temperature calculations and errors based on effective emissivity;

In the figure, 1-moving blades; 2-guide vanes.

Detailed Description

In a first embodiment, a method for measuring radiation temperature of a turbine blade in a complex environment based on effective emissivity, the method includes:

According to the method for measuring the radiation temperature of the turbine blade under the complex environment based on the effective emissivity, on the basis of obtaining the actual radiation quantity received by the high-temperature radiometer, firstly, a reflected radiation analysis model under the complex environment of the turbine blade is constructed to obtain the radiation quantity projected to a point to be measured by the complex environment. And then solving the emissivity of the blade by utilizing the proposed double-population social group optimization algorithm (DPSGO) and combining the actual received radiation quantity of the pyrometer and the projected radiation quantity of the complex environment quantity around the blade to be tested. And finally, calculating the effective emissivity of the blade surface by utilizing the solving information to eliminate the influence of reflected radiation to obtain the real temperature of the blade, and directly obtaining the real temperature of the blade to be measured by utilizing the actual radiation quantity received by a pyrometer in the subsequent turbine blade temperature measurement process and combining the known effective emissivity of the blade by constructing an effective emissivity database of the turbine blade surface under various running conditions. The method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity eliminates the influence of the reflected radiation of the complex environment of the turbine blade and meets the requirements of real-time performance and high efficiency of the radiation temperature measurement of the turbine blade.

It should be noted that, in the following embodiments, the effective emissivity and the temperature measurement data under various working conditions and running states can be constructed into an effective emissivity database of the surface of the turbine blade under various running conditions according to the method of the present invention, and further, the database can be updated after each measurement, and the database can be used for quickly solving and monitoring the temperature of the turbine blade under a complex high-temperature environment, so that the safety detection capability of the engine is further improved.

The method comprises the following steps: when the effective emissivity database of the surface of the turbine blade under various running conditions is built and the radiation pyrometer only actually receives radiation quantity information in the follow-up measurement, the real temperature of the blade to be measured is obtained by combining the effective emissivity related information provided by the database and performing inverse operation by using the Planckian formula, the requirements of eliminating reflection radiation errors and instantaneity in the radiation temperature measurement of the turbine blade are met, and when the measured temperature is subjected to irregular abrupt change, the safety detection of the engine can be timely carried out, so that the safety monitoring capability of the engine is further improved.

In a second embodiment, the method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity according to the first embodiment is further defined, where the step 2 is further defined, and specifically includes:

In this embodiment, the overall thought of the proposed turbine blade reflected radiation analysis model is as follows: firstly, a three-dimensional discrete model of the turbine blade is established, a rotor blade to be measured is selected, visual screening is carried out on a front-stage guide vane and discrete surface elements of adjacent rotor blades, and the surface elements of the blade which can transmit heat radiation to the surface elements to be measured are screened and left under the condition that mutual shielding among the surface elements is not considered. And then judging the shielding problem between the blade surface elements, and deleting the blade surface elements which are shielded from the surface elements to be detected. And finally, calculating the angle coefficient between the screened blade surface element and the surface element to be measured, and combining the theoretical temperature distribution of the blade to obtain the radiation quantity projected to the point to be measured by the surrounding environment. The flow of the turbine blade reflected radiation analysis model is shown in fig. 1. The process schematic diagram of the reflected radiation analysis model is shown in fig. 2, wherein the surface element to be detected is 50% of the blade height and the relative chord length is 0.5, the first diagram in fig. 2 is a constructed three-dimensional discrete model of the turbine blade, and the moving blade and the guide vane are discrete into triangular surface elements. After the dark area in the second graph of fig. 2 is the bin to be measured, the front stage guide vane and the blade bin adjacent to the moving blade after 'visualization' screening can transmit radiation to the bin to be measured without considering shielding. The middle-depth color area in the third graph of fig. 2 is the surrounding blade surface elements after 'visualization' screening and shielding judgment, and the blade surface elements can transmit heat radiation to the blade surface elements to be tested to participate in the final calculation of the environment projection radiation quantity.

In the embodiment, a three-dimensional discrete model of the turbine blade to be tested, the front stage guide vane and the adjacent moving blades is constructed, and the structural characteristics of the blade can be more embodied than a simplified model of the blade.

In a third embodiment, the method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity in the second embodiment is further defined, and in the present embodiment, the method for determining 'visualization' in the step 2.2 is further defined, and specifically includes:

The embodiment gives a judgment method of 'visualization'.

In a fourth embodiment, the method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity in the second embodiment is further defined, and in the present embodiment, the method for determining the shielding in the step 2.3 is further defined, and specifically includes:

In the embodiment, shielding judgment is divided into two types, wherein the first type is that adjacent moving blades shield a radiation propagation path between a front stage guide vane and a surface element to be detected; the second type is that the front stage guide vanes and other blade surface elements adjacent to the movable blades shield the radiation propagation path. The first type of shielding judgment can be judged by adopting the improvement method 1, and the second type of shielding judgment can be judged by adopting the improvement method 2. And screening after the shielding judgment, and leaving the surface element without shielding of the radiation propagation path, namely, finally participating in the calculation of the projection radiation of the surrounding environment.

Compared with the existing turbine blade reflected radiation analysis model, the embodiment makes certain improvement, specifically:

Improvement of a shielding judgment method between discrete turbine blade surface elements:

the improvement method 1: aiming at the radiation propagation path between the front stage guide vane and the surface element to be tested, when the surface element to be tested is positioned on the pressure surface of the movable vane, most of shielding conditions come from the suction surface of the adjacent movable vane, and the small shielding conditions come from the shielding of the front stage guide vane and the adjacent movable vane. In order to reduce the calculation amount, a small number of large triangle planes are used for approximately replacing a large number of surface elements representing the suction surface of the adjacent movable blade to carry out shielding judgment, when judging whether shielding exists between the surface element to be detected and the surface element of the front-stage guide blade, a line segment where the gravity center connecting line of the two surface elements is located is firstly solved, then whether the line segment intersects with a triangle approximately representing the suction surface of the adjacent movable blade is judged, and if the line segment intersects with the triangle, shielding exists. The method can greatly reduce the calculated amount for judging the shielding condition of the adjacent movable blades between the surface element to be detected and the surface element of the front-stage guide vane, and has small influence on the calculated result, and the schematic diagram is shown in figure 3.

The improvement method 2 comprises the following steps: aiming at judging whether other blade surface elements of the current stage guide vane and the adjacent movable vane shield the radiation propagation path, if the curvature change of the surface of the current stage guide vane or the adjacent movable vane is large or the accuracy of a turbine blade discrete model is not high, the shielding judgment is carried out in a mode of only judging whether the line segment where the connecting line of the gravity centers of the two surface elements is intersected with the other blade surface elements, so that the great misjudgment result is greatly influenced. Therefore, the method comprises the steps of further dispersing the to-be-detected surface element into a plurality of small triangles, calculating the gravity center of each small triangle, calculating the line segment where the gravity center of the to-be-shielded judging surface element and the gravity center of each small triangle after discretization are connected to obtain a cluster of line segments, setting a shielding proportion threshold value, calculating the percentage of the intersection number of the line segment cluster and other blade surface elements, and judging that shielding exists between the two surface elements if the percentage of the intersection number reaches the shielding proportion threshold value.

The existing method for judging the shielding of the discrete surface element of the blade often utilizes a mode of judging whether a line segment where the gravity centers of two surface elements are connected with each other is intersected with other blade surface elements, is simple to realize, has very large calculated amount, has higher misjudgment rate when the curvature change of the blade surface is overlarge or the precision of a discrete blade model is lower, and seriously affects the calculation result. For this reason, we propose the above two new methods of occlusion judgment for reducing the calculation amount and improving the judgment accuracy.

In a fifth embodiment, the method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity in the second embodiment is further defined, where the angle coefficient in step 2.4 and the radiation amount projected by the environment around the point to be measured are further defined, and specifically includes:

the angle coefficient calculation formula is as follows:

wherein A is _j Is the area of bin j; a is that _i Is the area of bin i; f (F) _ji The radiation angle coefficients for bin j through bin i; θ _i And theta _j Is the normal line of the corresponding surface element and connects two infinitesimal areas dA _i And dA _j Is the angle between the straight lines of (a); r is the distance between two bins;

wherein M (lambda, T) is the target temperature and the radiation emittance at the T wavelength is lambda, c ₁ ＝3.7418×10 ^-16 W.m is a first Planck coefficient; c ₂ ＝1.4388×10 ^-2 m.K is a second Planck coefficient;

the calculation formula of the radiation amount projected by the surrounding environment of the point to be measured is as follows:

wherein M is _r (lambda, T) is the amount of radiation projected by the environment surrounding the point to be measured; m is M _j,i (λ,T _j ) The amount of radiation projected to bin i for bin j; m is M _j (λ,T _j ) The blackbody radiation emittance of bin j.

The embodiment provides a calculation method of the angle coefficient and the radiation quantity projected by the environment around the point to be measured.

In a sixth embodiment, the method for measuring the radiation temperature of a turbine blade in a complex environment based on effective emissivity according to the first embodiment is further defined, where the optimizing objective equation in the step 3 is further defined, and specifically includes:

the optimization objective equation is:

In this embodiment, after the radiation amount projected to the point to be measured by the surrounding environment of the blade to be measured is obtained by using the turbine blade reflected radiation analysis model in step 2, emissivity models of the surface of the blade to be measured are set, and four kinds of emissivity models are commonly used as shown in the following formulas (5) to (8):

ε(λ,T)＝a+bλ (5)

ε(λ,T)＝e ^a+bλ (6)

ε(λ,T)＝aλ ² +bλ+c (8)

Where λ is the wavelength and a, b and c are the undetermined coefficients of the emissivity model.

When the temperature of the surface of the turbine blade is measured by using the multi-wavelength radiation temperature measurement method, the reflected radiation from the surrounding complex environment is not negligible, and for a multi-wavelength radiation pyrometer with n wavelength channels, a mathematical model shown in the following formula can be obtained:

wherein lambda is _n Epsilon for the wavelength of the nth channel _n For the emissivity of the nth channel at wavelength, M (lambda _n ,T _b ) Lambda is lambda _n The black body temperature at the wavelength is T _b Ideal blackbody radiation emittance at the time, M (lambda) _n ,T _r ) Is a high-temperature environment temperature T _r Ambient radiation emittance at time, M (lambda) _n ,T _m ) For the received radiation emittance of the pyrometer.

It is worth noting that the real temperatures of the blades to be measured, which are inverted and calculated by planck's inverse calculation, are the same for each wavelength of the multi-wavelength radiation pyrometer, so that the difference between temperatures obtained by inversion at each wavelength is the smallest, and the emissivity model parameters at the moment are the most suitable for the actual emissivity condition of the surfaces of the turbine blades, so that an optimization target equation is constructed:

in a seventh embodiment, the method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity according to the first embodiment is further defined, in the embodiment, the step 4 is further defined, the overall flow of solving the surface emissivity of the turbine blade by using a Dual Population Social Group Optimization (DPSGO) algorithm is shown as follows, and a corresponding flow chart is shown in fig. 4, and specifically includes:

step 4.4, the individuals in the population 1 and the population 2 enter an 'improvement stage', and the updating modes of the individuals in various populations adopt an improved improvement stage evolution algorithm such as a formula:

wherein c is a self-contrast parameter, and the value of c is usually 0-1; r is a random number of 0-1; agbest _j The j-th dimension characteristic value of the current generation optimal individual in the corresponding population is obtained;and->The j-th dimensional feature values before and after the i-th individual update, respectively. Each individual takes the optimal individual of the population as a guide to carry out improved evolution, recalculates the fitness of the new individual and arranges according to the descending order of the fitness;

step 4.7, the individuals in the population 1 and the population 2 enter an acquisition stage', and the updating modes of the individuals in various populations adopt an improved acquisition stage evolution algorithm such as a formula:

if f(x _i )is better than f(x _k )

else

wherein r is ₁ 、r ₂ And r ₃ Are allRandom number of 0-1, X _k To randomly select an individual from a corresponding population as a learning object, bgbest _j The j-th dimension characteristic value, agbest, of the optimal individuals of the double species _j And (5) the j-th dimension characteristic value of the optimal individual in the corresponding population. Each individual updates and evolves under the guidance of the optimal individual of the group, the random individuals in the group and the optimal individuals of all groups, and finally, the fitness of the new individuals in each group is recalculated and arranged according to the descending order of fitness;

And step 4.9, repeating the steps 4.3 to 4.8 until the maximum iteration number reaches the termination condition. At this time, the individual with the optimal fitness in the population 1 is the optimal solution, and the value of the emissivity of the surface of the turbine blade can be obtained at this time.

After an optimization target equation is determined, solving the optimization target equation by using a double-population social group optimization algorithm (DPSGO) algorithm to find an optimal solution of the coefficient to be determined of the emissivity model, and when the DPSGO algorithm reaches the maximum iteration times, the output optimal solution is the coefficient to be determined of the emissivity model of the surface of the turbine blade to be detected, so that the emissivity value of the surface of the turbine blade under each wavelength can be obtained.

In this embodiment, the following improvements are made to the Dual Population Social Group Optimization (DPSGO) algorithm over the Social Group Optimization (SGO) algorithm, specifically:

1. the initial population performs a double population grouping operation.

The SGO algorithm is divided into an 'improvement stage' and an 'acquisition stage', and the individuals are updated by taking the optimal individuals in the current generation population as references. Practice shows that the convergence rate of the algorithm can be accelerated by guiding evolution of the optimal individuals in the population, but the diversity of the population is not favorably maintained, and the algorithm updating method is single, so that the algorithm is easy to fall into a local optimal solution to fail. Compared with the SGO algorithm, the DPSGO algorithm firstly realizes double-population grouping operation of the original population, and secondly improves the updating method of individuals in each population, thereby fully utilizing the optimal individual information in each population and the optimal information of the whole population. The initial population is divided into a population 1 and a population 2 at random, the population 1 has stronger local searching capability, the population 2 has stronger global searching capability, the evolution modes and the convergence rates of the two populations are different, and the algorithm performance can be effectively improved by adopting different complementary evolution strategies.

2. The method of updating individuals in two evolutionary stages improves.

In the 'improving stage' of the DPSGO algorithm, the individuals in the two populations improve the capacity of the individuals by simulating the behaviors of the optimal individuals in the respective populations, and the updating formulas of the individuals in each subgroup are as follows:

wherein c is a self-contrast parameter, and the value of c is usually 0-1; r is a random number of 0-1; agbest _j The j-th dimension characteristic value of the current generation optimal individual in the corresponding population is obtained;and->The j-th feature values before and after the i-th individual update, respectively.

In the 'acquisition stage' of the DPSGO algorithm, the interaction of information among subgroups can provide more information for individual learning, and an information exchange mechanism between two subgroups can lead the individual to have a larger chance to jump out of a local extremum in the learning to avoid premature algorithm caused by too high convergence rate, and an individual update formula inside each subgroup is as follows:

wherein r is ₁ 、r ₂ And r ₃ Random numbers, X, of 0-1 _k To be from corresponding toRandomly selected one individual in the population as a learning object, bgbest _j The j-th dimension characteristic value, agbest, of the optimal individuals of the double species _j And (5) the j-th dimension characteristic value of the optimal individual in the corresponding population.

3. Reverse learning techniques are introduced to individuals of poor quality.

Aiming at the defects that the global searching capability of an SGO algorithm is weaker, a better solution cannot be found and is easy to fall into local optimum, a reverse learning technology is introduced into individuals with poor population 2 adaptability to enhance the global searching capability of the individuals, the basic idea is to compare the current solution with a reverse solution, and if the reverse solution is superior to the current solution, the reverse solution replaces the current solution. The reverse learning technology can quickly expand the search space, enrich population diversity, and has better capability of exploring unknown solutions, and the possibility of finding global optimal solutions and jumping out of local optimal solutions is increased. Let x= [ X ] ₁ ,x ₂ ,...,x _D ]Is a solution to the D-dimensional space, where x _i ∈[a _i ,b _i ]I=1, 2..n, inverse solution of X is X ^* ＝[x ^* ₁ ,x ^* ₂ ,...,x ^* _D ]And respectively comparing the adaptation values of the current solution and the inverse solution, and reserving individuals with better adaptation values. Wherein the inverse solution is calculated according to the inverse learning update formula,

x _i ^* ＝a _i +b _i -x _i (13)

wherein x is _i ^* An inverse solution to the i-th dimension of the current individual, a _i And b _i Respectively, the lower limit value and the upper limit value of the ith dimension of all individuals of the population, x _i Is the i-th dimension value of the current individual.

4. A one-way immigration technique is performed between the two populations and a elite elimination mechanism is utilized to retain the more optimal individuals.

Population 2 has stronger global searching capability, population 1 has stronger local searching capability, optimal individuals in population 2 are transferred into population 1 by introducing immigration operation between two populations, and the worst adaptability individuals in population 1 are replaced by adopting elite mechanism of superior and inferior elimination, so that population 1 is prevented from being trapped into local optimal solution and being unable to escape.

5. And introducing Gaussian variation operation to the optimal individuals of the population 2.

Compared with other mutation operators, the probability of mutation near the mean value of the Gaussian mutation operator is maximum, and the mutation is generated by taking the position of the previous generation as the center, so that the algorithm is more effective in local development, a large number of Gaussian mutation individuals are generated near the current optimal individuals of the population 2, replacement is performed according to the fitness of the individuals, the population 2 not only ensures global searching capability, but also increases the local searching capability of the optimal solution by introducing Gaussian mutation, and the Gaussian mutation rule is as follows:

X _G,j ＝X _i,j +c _j ×N(0,1) (14)

Wherein X is _G,j The j-th dimension value, X, of the Gaussian variant _i,j N (0, 1) is a normal Gaussian distribution random variable with mean value of 0 and variance of 1, c is the current optimal individual _j The variation step in dimension j is indicated, j=1, 2.

In an eighth embodiment, the method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity according to the first embodiment is further defined, where the step 5 is further defined, and specifically includes:

actual received radiation quantity, complex environment projected radiation quantity and emissivity values are brought into the formula by using a pyrometer:

wherein ε _eff (lambda) is the effective emissivity of the turbine blade surface; epsilon is the actual emissivity of the surface of the turbine blade; m (lambda, T) _m ) The amount of radiation received for the pyrometer; m (lambda, T) _b ) The blackbody radiation emergent degree of the target point to be measured; m (lambda, T) _r ) And projecting radiation quantity for the surrounding environment of the object to be measured.

In the embodiment, after the treatment of the steps, the actual received radiation quantity, the projected radiation quantity in the complex environment and the emissivity value are brought into a formula by using a pyrometer

The effective emissivity of the surface of the turbine blade to be measured can be solved, and the real temperature of the surface of the turbine blade can be further solved by utilizing the inverse operation of the Planck formula.

Embodiment nine the present invention will be described more specifically by specific theoretical simulation verification.

The method according to the invention calculates the effective emissivity and temperature of the turbine blade in a complex environment.

The procedure for calculating the effective emissivity and temperature of a turbine blade in a complex environment according to the method of the present invention is shown below.

Step one: obtaining radiation data of a turbine blade to be tested at a plurality of wavelengths using a multi-wavelength pyrometer

The theoretical simulation verifies that radiation data of the turbine blade to be detected is generated by adopting theory under a plurality of wavelengths, the wavelengths are selected to be 1.3 mu m, 1.4 mu m, 1.5 mu m, 1.6 mu m, 1.7 mu m and 1.8 mu m, and the emissivity under each wavelength is respectively set to be 0.6, 0.62, 0.67, 0.7, 0.75 and 0.78 according to the actual emissivity value of the turbine blade measured at an early stage. The temperature of the guide vane is set to 450-980 ℃, and the temperature of the movable vane is set to 530-630 ℃. The formula can be utilized after the radiation quantity projected to the point to be measured in the surrounding complex environment is obtained in the second step

And obtaining the radiation data of the surface of the turbine blade at each wavelength obtained by a pyrometer theory.

Step two: constructing a turbine blade reflected radiation analysis model to obtain the radiation quantity projected to a point to be detected by surrounding complex environment

Step 2.1: constructing a three-dimensional discrete model of a turbine blade to be tested, a front stage guide vane and an adjacent moving blade, wherein the discrete model of the blade is represented by discrete triangular surface elements, and the area of each discrete triangular surface element is 2-3mm ² 。

Step 2.2: and (3) performing a bin 'visualization' screening operation, and screening the bin which leaves the surrounding environment to possibly transmit heat radiation to the point to be tested without considering the mutual shielding of the bin.

Step 2.3: and judging the shielding among the cells, and further judging whether other blade cells are shielded between the cells after 'visualization' screening and the cells to be tested. The method divides shielding judgment into two types, wherein the first type is that adjacent movable vanes shield the radiation propagation path between a front stage guide vane and a surface element to be detected; the second type is that the front stage guide vanes and other blade surface elements adjacent to the movable blades shield the radiation propagation path. The first type of shielding judgment can be judged by adopting the improvement method 1, and the second type of shielding judgment can be judged by adopting the improvement method 2. And screening to leave a surface element which is not blocked by the radiation propagation path after the blocking judgment, namely, a surface element which finally participates in radiation calculation.

Step 2.4: the blade surface elements which are left after the screening in the previous two steps can transmit heat radiation to the surface elements to be measured, the angle coefficient between each blade surface element and the surface elements to be measured is calculated, the temperature of the guide vane is set to be 450-980 ℃, the temperature of the movable vane is set to be 530-630 ℃, and the radiation quantity projected by the surrounding environment of the measuring point can be obtained by combining the set theoretical temperature distribution of the blade.

When the selected wavelength is 1.5 mu m, the emissivity is 0.67, the surface element of the blade to be tested is positioned at three groups of different blade heights of the pressure surface of the movable blade, the temperature error caused by the reflected radiation of the surrounding environment of the blade to be tested is obtained according to the reflected radiation analysis model provided in the second step, which is shown in the figure 5, wherein the relative chord length of the front edge of the pressure surface of the movable blade is 0, the relative chord length of the tail edge is 1, three graphs in the figure respectively show the calculated temperature, the temperature error and the real temperature at 25% of the blade height, 50% of the blade height and 75% of the blade height, the maximum temperature error caused by the reflected radiation interference at the three groups of different blade heights is 46.3 ℃, the average temperature error reaches 26.9 ℃, and the influence of the reflected radiation caused by the surrounding high-temperature background is further proved to be not negligible.

Step three: setting emissivity model, actually receiving radiation data by using pyrometer, and constructing and optimizing target equation by using radiation quantity projected to point to be measured by complex environment

The surface emissivity of the blade set by the theoretical simulation accords with a sine emissivity model which is shown in a formula,

and obtaining actual received radiation data of the pyrometer and the radiation quantity projected to the point to be measured by the complex environment under the theoretical condition from the first step and the second step. Carry-over formula

And obtaining an optimized target equation, wherein the emissivity model coefficient is an unknown parameter.

Step four: solving an optimization objective equation by using a double-population social group optimization algorithm to obtain emissivity values of the turbine blade under each wavelength

The wavelengths were chosen to be 1.3 μm, 1.4 μm, 1.5 μm, 1.6 μm, 1.7 μm and 1.8 μm, while the optimization objective equation was already obtained. Setting a sinusoidal emissivity model parameter value range, setting the number of initial population individuals N as 100, setting the reverse learning proportion RL as 10% and setting the maximum iteration number D as 80. And solving an optimization target equation by using a double-population social group optimization algorithm to obtain emissivity values of the turbine blade to be tested under each wavelength.

Step five: solving the effective emissivity of the surface of the turbine blade to be measured by utilizing the actual received radiation quantity of the pyrometer, the projected radiation quantity of the complex environment and the emissivity numerical value, and calculating the actual temperature of the surface of the turbine blade

The method comprises the steps of obtaining the actual radiation receiving quantity of the pyrometer under the theoretical condition, obtaining the projection radiation quantity of the complex environment, and obtaining the surface emissivity value of the turbine blade to be detected. Substitution formula

And solving to obtain the effective emissivity of the surface of the turbine blade and the actual temperature of the surface of the turbine blade. The calculated results and errors of the turbine blade temperature based on the effective emissivity, which are theoretically verified when the surface element to be measured is positioned at three groups of different blade heights of the movable blade pressure surface, are shown in fig. 6, wherein the three graphs respectively represent calculated temperatures and temperature errors at 25% of the blade height, 50% of the blade height and 75% of the blade height, the two-population social group optimization algorithm is utilized to combine the reflected radiation analysis model to correct the radiation temperature measurement data of the turbine blade according to the graph shown in fig. 6, the maximum temperature error in the three groups of different blade height calculated results is 3.5 ℃, the average temperature error is 0.98 ℃, and the maximum temperature error caused by reflected radiation is 46.3 ℃ and the average temperature error is 26.9 ℃ compared with those shown in the second step, which are shown in fig. 5. The method can well realize correction of reflected radiation interference caused by surrounding environment, and improves the accuracy of radiation temperature measurement of the turbine blade.

Step six: constructing an effective emissivity database of the surface of the turbine blade under various running conditions, and meeting the real-time temperature measurement requirement of the subsequent turbine blade

Constructing an effective emissivity database of the surface of the turbine blade under various operating conditions, and utilizing a formula according to the actual radiation quantity obtained by the pyrometer

And directly obtaining the real temperature of the surface of the turbine blade to be measured.

The invention provides a turbine blade radiation temperature measurement method based on effective emissivity in a complex environment, and the principle and the implementation mode of the invention are explained by applying specific examples, and the explanation of the above implementation mode is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. The method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity is characterized by comprising the following steps:

the step 4 specifically comprises the following steps:

Wherein c is a self-contrast parameter, and the value of c is 0-1; r is a random number of 0-1; agbest _j The j-th dimension characteristic value of the current generation optimal individual in the corresponding population is obtained;and->The j-th dimension characteristic values before and after the updating of the i-th individuals are respectively obtained, each individual is subjected to improvement and evolution by taking the optimal individual of the population as a guide, the fitness of the new individual is recalculated, and the new individuals are arranged according to the descending order of fitness;

if f(x _i )is better than f(x _k )

else

Wherein r is ₁ 、r ₂ And r ₃ Random numbers, X, of 0-1 _k To randomly select an individual from a corresponding population as a learning object, bgbest _j The j-th dimension characteristic value, agbest, of the optimal individuals of the double species _j The j-th dimension characteristic value of the optimal individuals in the corresponding population is updated and evolved by taking the optimal individuals in the population, the random individuals in the population and the optimal individuals in all the population as guidance, and finally, the fitness of new individuals in various populations is recalculated and arranged according to the descending order of the fitness; x is X _i,j 、X _k，j The j-th dimension characteristic values of the individuals i and k before the current generation population is updated respectively;

Step 4.9, repeating the steps 4.3 to 4.8 until the maximum iteration number reaches the termination condition; at this time, the individual with the optimal fitness in the population 1 is the optimal solution, and the surface emissivity value of the turbine blade can be obtained at this time;

2. The method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity according to claim 1, wherein the step 2 specifically comprises the following steps:

3. The method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity according to claim 2, wherein in the step 2.2, in the 'visualization' screening operation, the screening conditions are as follows:

4. The method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity according to claim 2, wherein in the step 2.3, the method for judging the shielding specifically comprises the following steps:

5. The method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity according to claim 2, wherein in the step 2.4, the angle coefficient is calculated according to the formula:

calculated, where M _r (lambda, T) is the amount of radiation projected by the environment surrounding the point to be measured; m is M _j,i (λ,T _j ) The amount of radiation projected to bin i for bin j; m is M _j (λ,T _j ) Is a doughBlack body radiation exitance of element j.

6. The method for measuring the temperature of the radiation of the turbine blade in the complex environment based on the effective emissivity according to claim 1, wherein in the step 3, the optimization objective equation is as follows:

7. The method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity according to claim 1, wherein the step 5 specifically comprises the following steps:

Wherein ε _eff (lambda) is the effective emissivity of the turbine blade surface; epsilon is the actual emissivity of the surface of the turbine blade; m (lambda, T) _m ) The amount of radiation received for the pyrometer; m (lambda, T) _b ) The blackbody radiation emergent degree of the target point to be measured; m (lambda, T) _r ) Projecting radiation quantity for the surrounding environment of the target to be measured;

8. A turbine blade radiation temperature measurement device in a complex environment based on effective emissivity, the device comprising:

the emissivity solving module is used for solving an optimization target equation by utilizing a double-population social group optimization algorithm to obtain the emissivity value of the turbine blade under each wavelength, and specifically comprises the following steps:

Wherein c is a self-contrast parameter, and the value of c is 0-1; r isA random number of 0 to 1; agbest _j The j-th dimension characteristic value of the current generation optimal individual in the corresponding population is obtained;and->The j-th dimension characteristic values before and after the updating of the i-th individuals are respectively obtained, each individual is subjected to improvement and evolution by taking the optimal individual of the population as a guide, the fitness of the new individual is recalculated, and the new individuals are arranged according to the descending order of fitness;

if f(x _i )is better than f(x _k )

else

Wherein r is ₁ 、r ₂ And r ₃ Random numbers, X, of 0-1 _k To randomly select an individual from a corresponding population as a learning object, bgbest _j The j-th dimension characteristic value, agbest, of the optimal individuals of the double species _j Is the most inside the corresponding populationThe j-th dimension characteristic value of the optimal individuals, each individual updates and evolves under the guidance of the optimal individuals of the population, the random individuals in the population and the optimal individuals of all the populations, and finally, the fitness of the new individuals in each population is recalculated and arranged according to the descending order of the fitness; x is X _i,j 、X _k，j The j-th dimension characteristic values of the individuals i and k before the current generation population is updated respectively;

9. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the processor, when running the computer program stored in the memory, performs the steps of the method of any one of claims 1 to 7.