CN114295214A - Turbine blade radiation temperature measurement method and device in complex environment based on effective emissivity - Google Patents

Abstract

The invention discloses a turbine blade radiation temperature measurement method and device in a complex environment based on effective emissivity, belongs to the technical field of turbine blade radiation temperature measurement, and solves the problems that in the prior art, 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 cannot be met. The method of the invention comprises the following steps: acquiring radiation data of a turbine blade to be detected under multiple wavelengths; constructing a turbine blade reflected radiation analysis model to obtain the radiation quantity projected to a blade point to be measured in a surrounding complex environment; setting an emissivity model, combining actually received radiation data of a pyrometer and the radiation quantity projected to a point to be measured in a complex environment to construct an optimization target equation; 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; and solving the effective emissivity of the surface of the turbine blade to be measured, and calculating the real temperature of the surface of the turbine blade. The invention is suitable for radiation temperature measurement of the turbine blade.

Description

Turbine blade radiation temperature measurement method and device in complex environment based on effective emissivity

Technical Field

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

Background

The turbine blade is the main hot end component of the engine, the operating environment is extremely severe, the temperature of the turbine blade is accurately measured, and the monitoring and evaluation of the working state of the blade are of great significance for ensuring the safe operation of the engine. Aiming at the special operating environment of the turbine blade, a high-temperature radiometer is generally adopted to measure the radiation temperature of the blade, and the high-temperature radiometer is provided with a plurality of wavelength channels to obtain radiation information of the blade under the condition of a plurality of wavelengths, so that the real temperature of the blade can be indirectly obtained. However, the radiation temperature measurement of the turbine blade in the complex environment has the following difficulties:

1. when the radiation pyrometer is used for measuring 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 by the surrounding complex environment such as a preceding stage guide vane and an adjacent movable vane after being projected to a point to be measured, and the turbine blade temperature measurement result can be seriously influenced when the reflected radiation accounts for a high proportion of the total radiation quantity received by the pyrometer. In the prior art, when the radiation heat transfer shielding condition between blades is judged after a turbine blade reflection radiation analysis model is constructed, the adopted judgment method has overlarge calculated amount and higher misjudgment rate when the curvature change of the surface of the blade is overlarge or the precision of a discrete blade model is lower, and the calculation result is seriously influenced.

2. Because the actual emissivity of the surface of the turbine blade is unknown, the real radiation quantity of the blade to be measured and the reflection radiation quantity of the surrounding complex environment cannot be directly obtained, and if the solved emissivity is different from the real emissivity of the surface of the blade, the temperature measurement result is also affected. In the prior art, a multi-target optimization algorithm is constructed by utilizing a plurality of wave band radiation information to solve the emissivity problem, and the algorithm has the problems of easy trapping of a local optimal solution, overlarge calculated amount, weak convergence and the like. .

3. When the turbine blade is subjected to radiation temperature measurement, radiation data information received by the pyrometer needs to spend a great deal of time and energy to process so as to obtain the real temperature of the blade, and the real-time performance and the high efficiency required by the turbine blade temperature measurement cannot be met.

Disclosure of Invention

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

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

step 1, acquiring radiation data of a turbine blade to be detected under multiple wavelengths by using a multi-wavelength pyrometer;

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

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

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 measured by utilizing the actual received radiation quantity, the complex environment projected radiation quantity and the emissivity value of the pyrometer, and calculating the real temperature of the surface of the turbine blade.

Further, the step 2 specifically includes:

step 2.1, constructing three-dimensional discrete models of the turbine blade to be detected, the preceding stage guide vane and the adjacent movable vane, wherein the discrete models of the blade are represented by discrete triangular surface elements, and the area of each discrete triangular surface element is 2-3mm²；

2.2, performing surface element 'visualization' screening operation, and screening surface elements which leave ambient environments and possibly transfer heat radiation to the point to be measured under the condition that mutual shielding of the surface elements is not considered;

step 2.3, judging whether other blade surface elements are shielded between the visually screened surface element and the surface element to be detected;

and 2.4, the remaining blade surface elements screened according to the steps 2.2 and 2.3 can transmit heat radiation to the surface element to be measured, the angle coefficient between each blade surface element and the surface element to be measured is calculated, and the radiation quantity projected by the surrounding environment of the point to be measured is obtained by combining the known theoretical temperature distribution of the blades and utilizing the Planck's theorem.

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

if thermal radiation is possibly transmitted between the two surface elements, the following formula condition is satisfied between the normal vector and the vector represented by the connecting line of the gravity centers

Wherein the content of the first and second substances,

and

normal vectors for bins 1 and 2 respectively,

is a vector formed by connecting the barycenters of the two surface elements.

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

when judging whether the adjacent movable blade shields the radiation propagation path between the preceding stage guide vane and the surface element to be detected, firstly, obtaining a line segment where the gravity center connecting line of the two surface elements is located, then, judging whether the line segment is intersected with a certain triangle approximately representing the adjacent movable blade, and if the line segment is intersected, shielding exists;

when judging whether the preceding stage guide vane and the adjacent movable vane have other self vane surface elements to shield the radiation propagation path or not, further dispersing the surface element to be detected into a plurality of small triangles and calculating the gravity center of each small triangle;

calculating the line segment where the connecting line of the gravity center of the surface element to be shielded and the gravity center of each discretized small triangle is located so as to obtain a cluster of line segments;

setting a shielding proportion threshold, and calculating the percentage of the intersection number of the line segment cluster 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 step 2.4, the angular coefficient is according to the formula:

is obtained by calculation, wherein A_jIs the area of bin j; a. the_iIs the area of bin i; f_jiThe radiation angle coefficients of the bin j to the bin i; theta_iAnd theta_jIs the corresponding bin normal and connects two infinitesimal regions dA_iAnd dA_jThe 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 represented by the formula:

is obtained by calculation, wherein M_r(lambda, T) is to be measuredThe amount of radiation projected by the environment surrounding the point; m_j,i(λ,T_j) The radiation dose projected to bin i for bin j; m_j(λ,T_j) Is the black body radiation exitance of bin j.

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

wherein epsilon_iIs the 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) Projecting the radiation quantity of the surrounding environment to the surface of the target to be measured; f (λ, T) is the selected emissivity model, where the undetermined coefficients are unknown; function M^-1And { lambda, M } is the temperature of the target to be measured obtained by the inverse operation of the Planck formula.

Further, the step 4 specifically includes:

step 4.1, setting population initialization parameters, 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 and the maximum iteration number D according to the selected emissivity model;

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

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

4.3, calculating individual fitness in the population 1 and the population 2 according to an optimization objective equation, and arranging the individual fitness in the population according to the fitness in a descending order;

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-introspection parameter, and the value of c is usually 0-1; r is a random number from 0 to 1; agbest_jThe j-dimension characteristic value of the current generation optimal individual in the corresponding population;

and

respectively updating j-dimension characteristic values before and after the ith individual, carrying out improved evolution on each individual by taking the optimal individual of the population as a guide, recalculating the fitness of the new individual and arranging the new individual in a descending order according to the fitness;

4.5, selecting a certain number of inferior individuals in the current generation of the population 2 according to the reverse learning ratio RL to execute reverse learning operation, and arranging the updated individuals of the population 2 in a descending order according to the fitness;

step 4.6, adopting immigration migration operation to the optimal individuals in the population 2 to move the optimal individuals into the population 1, and adopting an elite elimination mechanism which is superior and eliminated to replace the individuals with the worst fitness in the population 1;

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

if f(x_i)is better than f(x_k)

else

Wherein r is₁、r₂And r₃Random numbers, X, each of 0 to 1_kFor an individual randomly selected from the corresponding population as a learning object, bgbest_jThe jth dimension characteristic value, agbest, of the optimal individual of the two species_jFor the j-dimension characteristic value of the optimal individual in the corresponding population, each individual is updated and evolved under the guidance of the optimal individual in the population, the random individual in the population and all the optimal individuals in the population, and finally, the fitness of the new individual in each population is recalculated and is arranged in a descending order according to the fitness;

step 4.8, reserving the Gaussian variation individuals with better fitness than the original optimal individuals by adopting Gaussian variation operation on the optimal individuals in the population 2, and then arranging the individuals in the population 2 in a descending order according to the fitness;

step 4.9, repeating the steps 4.3 to 4.8 until the maximum iteration times are finished and the termination condition is reached; at the moment, 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 the moment.

Further, the step 5 specifically includes:

according to the formula, the actual radiation receiving amount, the complex environment projection radiation amount and the emissivity value of the pyrometer are utilized

Wherein epsilon_eff(λ) 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 emittance of a target point to be detected; m (lambda, T)_r) Projecting the radiation amount for the surrounding environment of the target to be measured.

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

and solving the real temperature of the surface of the turbine blade by utilizing the inverse operation of the Planck formula.

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

the radiation data acquisition module is used for acquiring radiation data of the turbine blade to be detected under multiple wavelengths by using the multi-wavelength pyrometer;

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

the optimization target equation construction module is used for setting an emissivity model, combining actual received radiation data of the pyrometer and the radiation quantity projected to a point to be measured in a complex environment, and constructing an optimization target equation;

the emissivity solving module is used for 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;

the effective emissivity and real temperature calculating module is used for solving the effective emissivity of the surface of the turbine blade to be measured by utilizing numerical values of the actually received radiation quantity, the complex environment projection radiation quantity and the emissivity of the pyrometer 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 present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program stored in the memory to execute the steps of the method for measuring radiation temperature of the turbine blade in the complex environment based on the effective emissivity.

The invention has the beneficial effects that:

the turbine blade radiation temperature measurement method based on the effective emissivity in the complex environment obtains the actual radiation quantity received by the high-temperature radiometer,

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

Secondly, the emissivity of the blade is solved by combining the actual receiving radiation quantity of the pyrometer and the projection radiation quantity of the complex environment quantity around the blade to be measured by using a proposed double population society group optimization algorithm (DPSGO).

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

The turbine blade radiation temperature measurement method based on the effective emissivity in the complex environment eliminates the influence of reflected radiation of the turbine blade in the complex environment and meets the requirements of instantaneity and high efficiency of the turbine blade radiation temperature measurement. The invention designs an intelligent optimization algorithm with high precision and low time consumption, which gives consideration to global and local search capabilities, can accurately obtain emissivity information of a turbine blade under multiple 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.

The invention can accurately measure the temperature of the turbine blade under the influence of the reflected radiation in the complex environment, and simultaneously, the radiation quantity actually received by the pyrometer is related to the real temperature of the blade to meet the requirements of real-time performance and high efficiency of the temperature measurement of the turbine blade.

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

Drawings

In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

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 process (demonstrating that the bin to be measured is at a position with a leaf height of 50% and a relative chord length of 0.5);

FIG. 3 is a schematic view of an improved method 1 for determining inter-element shading of a turbine blade;

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

FIG. 5 is a temperature error graph of three different blade heights of the pressure surface of the moving blade caused by ambient reflected radiation;

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

in the figure, 1-the bucket; 2-guide vanes.

Detailed Description

The first embodiment is a turbine blade radiation temperature measurement method in a complex environment based on effective emissivity, and the method comprises the following steps:

step 1, acquiring radiation data of a turbine blade to be detected under multiple wavelengths by using a multi-wavelength pyrometer;

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

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

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 measured by utilizing the actual received radiation quantity, the complex environment projected radiation quantity and the emissivity value of the pyrometer, and calculating the real temperature of the surface of the turbine blade.

According to the method, 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 from the complex environment. And then solving the emissivity of the blade by using a proposed double population society group optimization algorithm (DPSGO) in combination with the actual receiving radiation quantity of the pyrometer and the projection radiation quantity of the complex environment quantity around the blade to be measured. And finally, calculating the effective emissivity of the surface of the blade by using the solving information to eliminate the influence of reflected radiation to obtain the true temperature of the blade, and directly obtaining the true temperature of the blade to be measured by combining the actual radiation quantity received by the pyrometer with the known effective emissivity of the blade in the subsequent temperature measurement process of the blade by constructing an effective emissivity database of the surface of the blade under various operating conditions. The turbine blade radiation temperature measurement method based on the effective emissivity in the complex environment eliminates the influence of reflected radiation of the turbine blade in the complex environment and meets the requirements of real-time performance and high efficiency of the turbine blade radiation temperature measurement.

It should be noted that, in the following, the method of the present invention may further construct the solved effective emissivity and temperature measurement data under various working conditions and operating states into an effective emissivity database of the turbine blade surface under various operating conditions, and further, the database may be supplemented, perfected and updated after each measurement, and the database may be used to rapidly solve and monitor the temperature of the turbine blade under a complex high temperature environment, so as to further improve the safety detection capability of the engine.

The method specifically comprises the following steps: the effective emissivity database of the turbine blade surface under various operating conditions is constructed in subsequent measurement, when only the actual radiation receiving amount information of the radiation pyrometer is obtained, the real temperature of the blade to be measured is obtained by utilizing the inverse operation of the Planck formula in combination with the effective emissivity related information provided by the database, 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 changed suddenly and unconventionally, the engine can be detected safely in time, so that the safety monitoring capability of the engine is further improved.

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

step 2.1, constructing three-dimensional discrete models of the turbine blade to be detected, the preceding stage guide vane and the adjacent movable vane, wherein the discrete models of the blade are represented by discrete triangular surface elements, and the area of each discrete triangular surface element is 2-3mm²；

2.2, performing surface element 'visualization' screening operation, and screening surface elements which leave ambient environments and possibly transfer heat radiation to the point to be measured under the condition that mutual shielding of the surface elements is not considered;

step 2.3, judging whether other blade surface elements are shielded between the visually screened surface element and the surface element to be detected;

and 2.4, the remaining blade surface elements screened according to the steps 2.2 and 2.3 can transmit heat radiation to the surface element to be measured, the angle coefficient between each blade surface element and the surface element to be measured is calculated, and the radiation quantity projected by the surrounding environment of the point to be measured is obtained by combining the known theoretical temperature distribution of the blades and utilizing the Planck's theorem.

In this embodiment, the general idea of the turbine blade reflected radiation analysis model is as follows: firstly, a three-dimensional discrete model of the turbine blade is established, a surface element to be measured of the movable blade is selected, visual screening is carried out on discrete surface elements of a preceding stage guide blade and adjacent movable blades, and the surface elements of the turbine blade which are possibly transmitted to the surface element 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 among the surface elements of the blade, and deleting the surface elements of the blade which are shielded with the surface element to be detected. And finally, calculating the angular coefficient between the screened surface element of the blade 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 turbine blade reflected radiation analysis model flow is shown in fig. 1. A schematic diagram of a process of a reflected radiation analysis model is shown in fig. 2, wherein a bin to be measured is shown as a position with a blade height of 50% and a relative chord length of 0.5, and a first diagram in fig. 2 is a constructed three-dimensional discrete model of a turbine blade, and a movable blade and a guide vane are dispersed into a triangular bin. In the second diagram of fig. 2, the dark color regions are the blade surface elements of the preceding stage guide vane and the adjacent movable vane after 'visualization' screening after the surface element to be detected is selected, and the blade surface elements can transmit radiation to the surface element to be detected without considering shielding. The region with medium depth color in the third image in fig. 2 is the surrounding blade surface elements after 'visualization' screening and occlusion judgment, and the blade surface elements can transfer heat radiation to the blade surface elements to be measured and participate in the final calculation of the environmental projection radiation amount.

In the embodiment, the three-dimensional discrete models of the turbine blade to be measured, the preceding stage guide vane and the adjacent movable vane are constructed, and the structural characteristics of the blade can be better embodied compared with the simplified model of the blade.

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

if thermal radiation is possibly transmitted between the two surface elements, the following formula condition is satisfied between the normal vector and the vector represented by the connecting line of the gravity centers

Wherein the content of the first and second substances,

and

normal vectors for bins 1 and 2 respectively,

is a vector formed by connecting the barycenters of the two surface elements.

The present embodiment provides a 'visualization' determination method.

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

when judging whether the adjacent movable blade shields the radiation propagation path between the preceding stage guide vane and the surface element to be detected, firstly, obtaining a line segment where the gravity center connecting line of the two surface elements is located, then, judging whether the line segment is intersected with a certain triangle approximately representing the adjacent movable blade, and if the line segment is intersected, shielding exists;

when judging whether the preceding stage guide vane and the adjacent movable vane have other self vane surface elements to shield the radiation propagation path or not, further dispersing the surface element to be detected into a plurality of small triangles and calculating the gravity center of each small triangle;

calculating the line segment where the connecting line of the gravity center of the surface element to be shielded and the gravity center of each discretized small triangle is located so as to obtain a cluster of line segments;

setting a shielding proportion threshold, and calculating the percentage of the intersection number of the line segment cluster 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.

In the embodiment, the shielding judgment is divided into two types, wherein the first type is that the adjacent movable blade shields a radiation propagation path between the preceding stage guide vane and the surface element to be detected; the second type is that the preceding stage stator blade and other blade surface elements close to the movable blade self block the radiation propagation path. The first type of occlusion can be judged by adopting an improved method 1, and the second type of occlusion can be judged by adopting an improved method 2. And screening and leaving a surface element without radiation propagation path shielding after shielding judgment, namely the surface element which finally participates in the projection radiation calculation of the surrounding environment.

Compared with the existing turbine blade reflected radiation analysis model, the method makes certain improvements, and specifically comprises the following steps:

improvement of the method for judging shading between discrete turbine blade surface elements:

the improved method 1: aiming at the problem that when the surface element to be detected is located on the pressure surface of the movable blade, most of shielding conditions come from the adjacent suction surface of the movable blade to shield a radiation propagation path between the preceding stage guide blade and the surface element to be detected, and a small part of shielding conditions come from the shielding of the preceding stage guide blade and the adjacent movable blade. In order to reduce the calculation amount, a large number of surface elements which represent the suction surfaces of adjacent movable blades can be approximately replaced by a small number of large triangular planes for shielding judgment, when judging whether the surface element to be detected and the preceding stage guide blade surface element have shielding of the suction surfaces of the adjacent movable blades, a line segment where the gravity centers of the two surface elements are connected is firstly solved, then whether the line segment is intersected with a certain triangle which approximately represents the suction surfaces of the adjacent movable blades is judged, and if the line segment is intersected, shielding exists. The method can greatly reduce the calculation amount for judging the shielding condition of the adjacent movable blades between the surface element to be detected and the preceding stage guide vane surface element, and has small influence on the calculation result, and the schematic diagram is shown in fig. 3.

The improved method 2: when judging preceding stage stator and close on the movable vane whether have other blade surface elements of self to shelter from radiation propagation path, if the change of surface curvature of current stage stator or close on the movable vane is big or turbine blade discrete model precision is not high, only adopt the mode of judging whether the line segment of two face yuan focus connecting line intersects in other blade surface elements to shelter from the judgement and can cause very big erroneous judgement to produce the very big influence to the result. The method includes the steps of further dispersing a surface element to be detected into a plurality of small triangles, calculating the gravity center of each small triangle, calculating the gravity center of a surface element to be shielded and judged and the line segment where the gravity center of each discretized small triangle is connected to obtain a cluster of line segments, setting a shielding proportion threshold value, calculating the percentage of the number of the line segment clusters intersected with other blade surface elements, and judging that shielding exists between the two surface elements if the percentage of the number of the intersected line segments reaches the shielding proportion threshold value.

The existing method for judging the shielding of the discrete surface element of the blade usually utilizes a mode of judging whether a line segment where the gravity center connecting lines of two surface elements are intersected with other surface elements of the blade, the method is simple to realize, but the calculated amount is very large, the misjudgment rate is high when the curvature change of the surface of the blade is too large or the accuracy of a discrete blade model is low, and the calculation result is seriously influenced. Therefore, the two new occlusion judgment methods are provided aiming at reducing the calculation amount and improving the judgment precision.

Fifth, in this 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 this embodiment, the angle coefficient in the step 2.4 and the radiation amount projected by the environment around the point to be measured are further defined, specifically including:

the angle coefficient calculation formula is as follows:

wherein A is_jIs the area of bin j; a. the_iIs the area of bin i; f_jiThe radiation angle coefficients of the bin j to the bin i; theta_iAnd theta_jIs the corresponding bin normal and connects two infinitesimal regions dA_iAnd dA_jThe angle between the straight lines of (a); r is the distance between two bins;

wherein M (λ, T) is the radiant emittance at a target temperature T and a wavelength λ, c₁＝3.7418×10^-16W.m is a first Planck coefficient; c. C₂＝1.4388×10^-2m.K is a second Planck coefficient;

the formula for calculating the radiation quantity projected by the surrounding environment of the point to be measured is as follows:

wherein M is_r(lambda, T) is the radiation quantity projected by the surrounding environment of the point to be measured; m_j,i(λ,T_j) The radiation dose projected to bin i for bin j; m_j(λ,T_j) Is the black body radiation exitance of bin j.

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

In a sixth embodiment, the present embodiment is a further limitation on the method for measuring a temperature of radiation of a turbine blade in a complex environment based on an effective emissivity in the first embodiment, and in the present embodiment, the optimization objective equation in the step 3 is further limited, and specifically includes:

the optimization objective equation is as follows:

wherein epsilon_iIs the 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) Projecting the radiation quantity of the surrounding environment to the surface of the target to be measured; f (λ, T) is the selected emissivity model, where the undetermined coefficients are unknown; function M^-1{ lambda, M } is obtained by inverse operation of Planck's formulaThe temperature of the target to be measured.

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

ε(λ,T)＝a+bλ (5)

ε(λ,T)＝e^a+bλ (6)

ε(λ,T)＝aλ²+bλ+c (8)

wherein, lambda is the wavelength, and a, b and c are the waiting coefficients of the emissivity model.

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

wherein λ is_nIs the wavelength of the nth channel,. epsilon_nEmissivity at wavelength for the nth channel, M (λ)_n,T_b) Is λ_nBlack body temperature at wavelength of T_bTime ideal blackbody radiation emittance, M (lambda)_n,T_r) The temperature of the high-temperature environment is T_rDegree of time of ambient radiation exitance, M (lambda)_n,T_m) Is the degree of radiation exitance received by the pyrometer.

It is worth noting that the real temperatures of the blades to be measured, which are inverted through Planck's inverse operation at each wavelength of the multi-wavelength radiation pyrometer, are all the same, so that the difference between the temperatures obtained through inversion at each wavelength is the smallest, and the emissivity model parameters at the moment most accord with the actual emissivity situation of the turbine blade surface, and an optimization target equation is constructed accordingly:

seventh, in the present embodiment, the method for measuring temperature of radiation of a turbine blade in a complex environment based on effective emissivity in the first embodiment is further defined, in the present embodiment, the step 4 is further defined, and an overall process for solving emissivity of a surface of a turbine blade by using a dual population group optimization (DPSGO) algorithm is as follows, and a corresponding flowchart is shown in fig. 4, and specifically includes:

step 4.1, setting population initialization parameters, 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 and the maximum iteration number D according to the selected emissivity model;

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

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

4.3, calculating individual fitness in the population 1 and the population 2 according to an optimization objective equation, and arranging the individual fitness in the population according to the fitness in a descending order;

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 as a formula:

wherein c is a self-introspection parameter, and the value of c is usually 0-1; r is a random number from 0 to 1; agbest_jThe j-dimension characteristic value of the current generation optimal individual in the corresponding population;

and

and respectively updating the characteristic values of the j-th dimension before and after the ith individual. Each individual is improved and evolved under the guidance of the optimal individual of the population, the fitness of the new individual is recalculated, and the new individual is arranged in a descending order according to the fitness;

4.5, selecting a certain number of inferior individuals in the current generation of the population 2 according to the reverse learning ratio RL to execute reverse learning operation, and arranging the updated individuals of the population 2 in a descending order according to the fitness;

step 4.6, adopting immigration migration operation to the optimal individuals in the population 2 to move the optimal individuals into the population 1, and adopting an elite elimination mechanism which is superior and eliminated to replace the individuals with the worst fitness in the population 1;

4.7, enabling the individuals in the population 1 and the population 2 to enter an 'obtaining stage', and adopting an improved obtaining stage evolution algorithm in various population internal individual updating modes as a formula:

if f(x_i)is better than f(x_k)

else

wherein r is₁、r₂And r₃Random numbers, X, each of 0 to 1_kFor an individual randomly selected from the corresponding population as a learning object, bgbest_jThe jth dimension characteristic value, agbest, of the optimal individual of the two species_jAnd obtaining the j-th dimension characteristic value of the optimal individual in the corresponding population. Each individual is updated and evolved under the guidance of the optimal individual of the population, the random individual of the population and all the optimal individuals of the population, and finally, the fitness of the new individual in each population is recalculated and arranged in a descending order according to the fitness;

step 4.8, reserving the Gaussian variation individuals with better fitness than the original optimal individuals by adopting Gaussian variation operation on the optimal individuals in the population 2, and then arranging the individuals in the population 2 in a descending order according to the fitness;

and 4.9, repeating the steps 4.3 to 4.8 until the maximum iteration number is finished and the termination condition is reached. At the moment, 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 the moment.

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

In the embodiment, compared with a double population group optimization (SGO) algorithm, the double population group social group optimization (DPSGO) algorithm is improved as follows:

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

The SGO algorithm is divided into two evolution stages, namely an improvement stage and an acquisition stage, and the individuals are updated by taking the optimal individuals in the current generation population as reference. Practice shows that the convergence rate of the algorithm can be accelerated by guiding evolution by using the optimal individuals in the population, but the diversity of the population is not maintained, and the algorithm is single in updating method, so that the algorithm is easy to fall into the local optimal solution and fail. In the aspect of comprehensively considering limitations of individual learning ability, diversity of learning methods and the like, compared with the SGO algorithm, the DPSGO algorithm firstly realizes double-population grouping operation on an original population, secondly improves updating methods of individuals in various groups, and fully utilizes optimal individual information in various groups and 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 strong local search capability, the population 2 has strong global search capability, the evolution modes and the convergence rates of the two populations are different, and different complementary evolution strategies are adopted to effectively improve the algorithm performance.

2. Method improvements for individual updates of both evolution phases.

In the 'improvement stage' of the DPSGO algorithm, the individuals in the two populations improve the self-ability by simulating the behavior of the optimal individual in each population, and the updating formula of the individual in each subgroup is as follows:

wherein c is a self-introspection parameter, and the value of c is usually 0-1; r is a random number from 0 to 1; agbest_jThe j-dimension characteristic value of the current generation optimal individual in the corresponding population;

and

respectively, j-th characteristic values before and after the ith individual update.

In the 'obtaining stage' of the DPSGO algorithm, the interaction of information among subgroups can provide more information for individual learning, an information exchange mechanism between two groups can enable individuals to have a larger chance to jump out of a local extreme value in the learning process, the algorithm is prevented from being premature due to the fact that the convergence speed is too high, and an individual updating formula in each subgroup is as follows:

wherein r is₁、r₂And r₃Random numbers, X, each of 0 to 1_kFor an individual randomly selected from the corresponding population as a learning object, bgbest_jThe jth dimension characteristic value, agbest, of the optimal individual of the two species_jAnd obtaining the j-th dimension characteristic value of the optimal individual in the corresponding population.

3. And introducing a reverse learning technology to the poor individuals.

Aiming at the defects that the SGO algorithm is weak in global search capability, cannot find a better solution and is easy to fall into local optimum, a reverse learning technology is introduced into individuals with poor population 2 fitness to enhance the global search capabilityThe basic idea of the search capability is to compare the current solution with the reverse solution, and if the reverse solution is better than the current solution, the reverse solution replaces the current solution. The reverse learning technology can quickly expand a search space, enrich population diversity, and have better capability of exploring unknown solutions, so that the possibility of finding a globally optimal solution and jumping out of a locally optimal solution is increased. Let X be ═ X₁,x₂,...,x_D]Is a solution of a D-dimensional space, where x_i∈[a_i,b_i]1,2, the inverse solution of N, X is X^*＝[x^* ₁,x^* ₂,...,x^* _D]And respectively comparing the adaptive values of the current solution and the reverse solution, and reserving individuals with better adaptive values. Wherein the reverse solution is calculated according to a reverse learning update formula,

x_i ^*＝a_i+b_i-x_i (13)

wherein x is_i ^*Is an inverse solution of the i-th dimension of the current individual, a_iAnd b_iRespectively, the lower limit value and the upper limit value, x, of the ith dimension of all individuals in the population_iIs the ith dimension value of the current individual.

4. One-way immigration migration techniques are performed between the two populations and better individuals are retained using elite elimination mechanisms.

The population 2 has stronger global search capability, the population 1 has stronger local search capability, optimal individuals in the population 2 are migrated into the population 1 by introducing migration operation between the two populations, and the individuals with the worst fitness in the population 1 are replaced by adopting a superior-inferior elite mechanism, so that the population 1 is prevented from being trapped into the local optimal solution and cannot escape.

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

Compared with other mutation operators, the Gaussian mutation operator has the largest probability of mutation near the mean value, and the generated mutation is the mutation with the above generation position as the center, so that the algorithm is more effectively developed locally, a large number of Gaussian variant individuals are generated near the current optimal individual of the population 2, the Gaussian variant individuals are replaced according to the fitness of the individuals, the population 2 not only ensures the global search capability but also increases the local search capability of the optimal solution by introducing the Gaussian variation, and the Gaussian variation rule is as follows:

X_G,j＝X_i,j+c_j×N(0,1) (14)

wherein, X_G,jIs the j-dimensional value, X, of the Gaussian variant individual_i,jFor the current generation of best individuals, N (0,1) is a normal Gaussian distribution random variable with a mean of 0 and a variance of 1, c_jDenotes the variation step length of the j-th dimension, j 1,2.

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

the actual received radiation quantity, the complex environment projection radiation quantity and the emissivity value of the pyrometer are used to be substituted into a formula:

wherein epsilon_eff(λ) 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 emittance of a target point to be detected; m (lambda, T)_r) Projecting the radiation amount for the surrounding environment of the target to be measured.

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

and solving the real temperature of the surface of the turbine blade by utilizing the inverse operation of the Planck formula.

In the embodiment, after the steps are processed, the actual radiation receiving amount, the complex environment projection radiation amount and the emissivity value of the pyrometer are substituted into a formula

Wherein epsilon_eff(λ) is the effective emissivity of the turbine blade surface; epsilon is vortexActual emissivity of the surface of the wheel blade; m (lambda, T)_m) The amount of radiation received for the pyrometer; m (lambda, T)_b) The blackbody radiation emittance of a target point to be detected; m (lambda, T)_r) Projecting the radiation amount for the surrounding environment of the target to be measured.

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.

In the following, the present invention is described in more detail by specific theoretical simulation verification.

According to the method, the effective emissivity and the temperature of the turbine blade in the complex environment are calculated.

The flow of calculating the effective emissivity and temperature of the turbine blade in a complex environment according to the method of the invention is as follows.

The method comprises the following steps: obtaining radiation data of turbine blade under test at multiple wavelengths using a multi-wavelength pyrometer

The theoretical simulation verifies that the radiation data of the turbine blade to be measured under multiple wavelengths are generated theoretically, 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 in the early stage. The guide vane temperature is set to 450-. After the radiation quantity projected to the point to be measured in the surrounding complex environment is obtained in the second step, the formula can be utilized

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

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

Step 2.1: constructing three-dimensional discrete models of turbine blades to be tested, preceding-stage guide vanes and adjacent movable vanes, wherein the discrete models of the blades are represented by discrete triangular surface elements, and each discrete triangular surfaceThe unit area is 2-3mm²。

Step 2.2: and performing surface element 'visualization' screening operation, and screening surface elements which leave ambient environment and possibly transfer heat radiation to a point to be measured without considering the mutual shielding of the surface elements.

Step 2.3: and (4) judging the shielding between the surface elements, and further judging whether other blade surface elements are shielded between the 'visualized' screened surface element and the surface element to be detected. The method divides the shielding judgment into two types, wherein the first type is that a near movable blade shields a radiation propagation path between a preceding stage guide vane and a surface element to be detected; the second type is that the preceding stage stator blade and other blade surface elements close to the movable blade self block the radiation propagation path. The first type of occlusion can be judged by adopting an improved method 1, and the second type of occlusion can be judged by adopting an improved method 2. And screening and leaving a surface element without radiation propagation path shielding after shielding judgment, namely the surface element which finally participates in radiation calculation.

Step 2.4: the blade surface elements left after the previous two-step screening can transfer heat radiation to surface elements to be measured, the angle coefficient between each blade surface element and the surface element to be measured is calculated, the guide vane temperature is set to be 450-.

When the selected wavelength is 1.5 μm, the emissivity is 0.67, and the surface element of the blade to be measured is located on 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 measured is obtained according to the reflected radiation analysis model provided in the second step, as shown in fig. 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, the three graphs in the graph 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 interference of the reflected radiation at 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 verified to be non-negligible.

Step three: an optimal target equation is constructed by combining the set emissivity model with actual received radiation data of a pyrometer and the radiation quantity projected to a point to be measured in a complex environment

The emissivity of the surface of the blade set by the theoretical simulation conforms to a sine emissivity model as shown in a formula,

and obtaining actual radiation data received by the pyrometer under theoretical conditions and the radiation quantity projected to the point to be measured in the complex environment by the first step and the second step. Substituting into formula

An optimized objective equation can be obtained, and the emissivity model coefficient is an unknown parameter.

Step four: an optimization objective equation is solved by utilizing a double-population social group optimization algorithm to obtain the emissivity value of the turbine blade under each wavelength

The wavelengths were selected 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 had been obtained. Setting the value range of parameters of the sine emissivity model, setting the number N of initial population individuals as 100, setting the reverse learning proportion RL as 10% and the maximum iteration number D as 80. And solving an optimization objective equation by using a double-population social group optimization algorithm to obtain emissivity values of the turbine blade to be detected under each wavelength.

Step five: the effective emissivity of the surface of the turbine blade to be measured is solved by using numerical values of actually received radiation quantity, complex environment projection radiation quantity and emissivity of a pyrometer, and the real temperature of the surface of the turbine blade is calculated

The actual radiation receiving amount of the pyrometer under the theoretical condition is obtained in the step one, the projection radiation amount of the complex environment is obtained in the step two, and the surface emissivity value of the turbine blade to be measured is obtained in the step four. Substitution formula

And solving to obtain the effective emissivity of the surface of the turbine blade and the real temperature of the surface of the turbine blade. The calculation results and errors of the turbine blade temperature obtained based on the effective emissivity of the surface element to be detected when the surface element is located on the movable blade pressure surface and three groups of different blade heights are shown in fig. 6, wherein the three graphs respectively show the calculation temperature and the temperature error of 25% of the blade height, 50% of the blade height and 75% of the blade height, according to the graph shown in fig. 6, the radiation temperature measurement data of the turbine blade is corrected by using a double-group social group optimization algorithm and combining a reflection radiation analysis model, the maximum temperature error in the calculation results of the three groups of different blade heights is 3.5 ℃, the average temperature error is 0.98 ℃, and the maximum temperature error is compared with the maximum temperature error of 46.3 ℃ and the average temperature error of 26.9 ℃ caused by reflection radiation shown in fig. 5 in the step two. The method can well correct the reflected radiation interference caused by the surrounding environment and improve the radiation temperature measurement accuracy of the turbine blade.

Step six: effective emissivity database of the surface of the turbine blade under various operating conditions is constructed, and the real-time temperature measurement requirement of the subsequent turbine blade is met

Constructing an effective emissivity database of the surface of the turbine blade under various operating conditions, and obtaining the actual radiation amount according to the pyrometer to use a formula

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

The turbine blade radiation temperature measurement method based on the effective emissivity in the complex environment is described in detail, the principle and the implementation mode of the invention are explained by applying specific examples, and the description of the implementation mode is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A turbine blade radiation temperature measurement method in a complex environment based on effective emissivity is characterized by comprising the following steps:

step 1, acquiring radiation data of a turbine blade to be detected under multiple wavelengths by using a multi-wavelength pyrometer;

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

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

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 measured by utilizing the actual received radiation quantity, the complex environment projected radiation quantity and the emissivity value of the pyrometer, and calculating the real temperature of the surface of the turbine blade.

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

step 2.1, constructing three-dimensional discrete models of the turbine blade to be detected, the preceding stage guide vane and the adjacent movable vane, wherein the discrete models of the blade are represented by discrete triangular surface elements, and the area of each discrete triangular surface element is 2-3mm²；

2.2, performing surface element 'visualization' screening operation, and screening surface elements which leave ambient environments and possibly transfer heat radiation to the point to be measured under the condition that mutual shielding of the surface elements is not considered;

step 2.3, judging whether other blade surface elements are shielded between the visually screened surface element and the surface element to be detected;

and 2.4, the remaining blade surface elements screened according to the steps 2.2 and 2.3 can transmit heat radiation to the surface element to be measured, the angle coefficient between each blade surface element and the surface element to be measured is calculated, and the radiation quantity projected by the surrounding environment of the point to be measured is obtained by combining the known theoretical temperature distribution of the blades and utilizing the Planck's theorem.

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

if thermal radiation is possibly transmitted between the two surface elements, the following formula condition is satisfied between the normal vector and the vector represented by the connecting line of the gravity centers

Wherein the content of the first and second substances,

and

normal vectors for bins 1 and 2 respectively,

is a vector formed by connecting the barycenters of the two surface elements.

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

when judging whether the adjacent movable blade shields the radiation propagation path between the preceding stage guide vane and the surface element to be detected, firstly, obtaining a line segment where the gravity center connecting line of the two surface elements is located, then, judging whether the line segment is intersected with a certain triangle approximately representing the adjacent movable blade, and if the line segment is intersected, shielding exists;

when judging whether the preceding stage guide vane and the adjacent movable vane have other self vane surface elements to shield the radiation propagation path or not, further dispersing the surface element to be detected into a plurality of small triangles and calculating the gravity center of each small triangle;

calculating the line segment where the connecting line of the gravity center of the surface element to be shielded and the gravity center of each discretized small triangle is located so as to obtain a cluster of line segments;

setting a shielding proportion threshold, and calculating the percentage of the intersection number of the line segment cluster 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.

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

is obtained by calculation, wherein A_jIs the area of bin j; a. the_iIs the area of bin i; f_jiThe radiation angle coefficients of the bin j to the bin i; theta_iAnd theta_jIs the corresponding bin normal and connects two infinitesimal regions dA_iAnd dA_jThe 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 represented by the formula:

is obtained by calculation, wherein M_r(lambda, T) is the radiation quantity projected by the surrounding environment of the point to be measured; m_j,i(λ,T_j) The radiation dose projected to bin i for bin j; m_j(λ,T_j) Is the black body radiation exitance of bin j.

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

wherein epsilon_iIs the 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) Projecting the radiation quantity of the surrounding environment to the surface of the target to be measured; f (λ, T) is the selected emissivity model, where the undetermined coefficients are unknown; function M^-1And { lambda, M } is the temperature of the target to be measured obtained by the inverse operation of the Planck formula.

7. The method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity as claimed in claim 1, wherein the step 4 specifically comprises:

step 4.1, setting population initialization parameters, 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 and the maximum iteration number D according to the selected emissivity model;

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

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

4.3, calculating individual fitness in the population 1 and the population 2 according to an optimization objective equation, and arranging the individual fitness in the population according to the fitness in a descending order;

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-introspection parameter, and the value of c is usually 0-1; r is a random number from 0 to 1; agbest_jFor the best individual of the current generation within the corresponding populationA j-th dimension feature value;

and

respectively updating j-dimension characteristic values before and after the ith individual, carrying out improved evolution on each individual by taking the optimal individual of the population as a guide, recalculating the fitness of the new individual and arranging the new individual in a descending order according to the fitness;

4.5, selecting a certain number of inferior individuals in the current generation of the population 2 according to the reverse learning ratio RL to execute reverse learning operation, and arranging the updated individuals of the population 2 in a descending order according to the fitness;

step 4.6, adopting immigration migration operation to the optimal individuals in the population 2 to move the optimal individuals into the population 1, and adopting an elite elimination mechanism which is superior and eliminated to replace the individuals with the worst fitness in the population 1;

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

if f(x_i)is better than f(x_k)

else

Wherein r is₁、r₂And r₃Random numbers, X, each of 0 to 1_kFor an individual randomly selected from the corresponding population as a learning object, bgbest_jThe jth dimension characteristic value, agbest, of the optimal individual of the two species_jFor the j-dimension characteristic value of the optimal individual in the corresponding population, each individual is the optimal individual in the population and the populationUpdating and evolving by taking the internal random individuals and all population optimal individuals as guidance, and finally recalculating the fitness of the new individuals in each population and arranging in a descending order according to the fitness;

step 4.8, reserving the Gaussian variation individuals with better fitness than the original optimal individuals by adopting Gaussian variation operation on the optimal individuals in the population 2, and then arranging the individuals in the population 2 in a descending order according to the fitness;

step 4.9, repeating the steps 4.3 to 4.8 until the maximum iteration times are finished and the termination condition is reached; at the moment, 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 the moment.

8. The method for measuring the radiation temperature of the turbine blade in the complex environment based on the effective emissivity as claimed in claim 1, wherein the step 5 specifically comprises:

according to the formula, the actual radiation receiving amount, the complex environment projection radiation amount and the emissivity value of the pyrometer are utilized

Wherein epsilon_eff(λ) 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 emittance of a target point to be detected; m (lambda, T)_r) Projecting the radiation quantity for the surrounding environment of the target to be measured;

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

and solving the real temperature of the surface of the turbine blade by utilizing the inverse operation of the Planck formula.

9. A turbine blade radiation temperature measuring device under 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 detected under multiple wavelengths by using the multi-wavelength pyrometer;

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

the optimization target equation construction module is used for setting an emissivity model, combining actual received radiation data of the pyrometer and the radiation quantity projected to a point to be measured in a complex environment, and constructing an optimization target equation;

the emissivity solving module is used for 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;

the effective emissivity and real temperature calculating module is used for solving the effective emissivity of the surface of the turbine blade to be measured by utilizing numerical values of the actually received radiation quantity, the complex environment projection radiation quantity and the emissivity of the pyrometer 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.

10. A computer device comprising a memory and a processor, the memory having stored therein a computer program, characterized in that the steps of the method of any one of claims 1 to 8 are performed when the processor runs the computer program stored by the memory.

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