CN114841032B - Design method for life stability of thermal component of gas turbine - Google Patents
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Abstract
The invention discloses a design method for life robustness of a thermal component of a gas turbine, in particular to the technical field of gas turbines, which comprises the following steps of S1: a flow-heat-solid coupling analysis method is adopted to obtain a temperature field, and a transient finite element analysis method is adopted to obtain a cyclic stress-strain curve from the starting of the gas turbine to the checking point of the heat component in the stopping process; step S2: establishing a low cycle fatigue life agent model of the thermal component by adopting a response surface method; step S3: setting a robustness design variable of a thermal component, and establishing a quantile optimization model and constraint conditions of low cycle fatigue life; step S4: and solving the quantile optimization model by adopting a SPEA-II multi-objective optimization algorithm to obtain an objective function value. In the design stage of the thermal component of the gas turbine, the design variable which ensures the service life of the thermal component to have robustness can be searched in a larger design space, the design service life of the thermal component is prolonged, and the maintenance cost in the whole life cycle is reduced.
Description
Technical Field
The invention relates to the technical field of gas turbines, in particular to a method for designing life robustness of a thermal component of a gas turbine.
Background
Gas turbine thermal components, particularly turbine blades, combustors, shrouds, and the like, are high temperature critical components of ground gas turbines. The reliability of the thermal component of the gas turbine is critical to the stable operation of the gas turbine under the severe environments of high temperature, high pressure, high rotation speed and the like. Since frequent starts result in changing operating conditions, the thermal component will be subjected to a large centrifugal load of low frequency variation and a large thermal load, and therefore low cycle fatigue is an important factor limiting the useful life of the thermal component. Thus, during the design phase, it is ensured that the thermal component has a sufficiently low cycle fatigue life.
The existing life prediction of the thermal component of the ground gas turbine is mostly based on the calculation results of the temperature field and the flow field of the thermal component, finite element analysis of a determined structure is carried out to obtain stress/strain distribution, and then the life of the thermal component is obtained by using a Manson-Coffin (Mansen-Ke Fen _name) formula. However, during the manufacturing process and service process of the thermal component, the material parameters, the geometric parameters and the running load have different degrees of uncertainty, and the above factors cause the fatigue life of the thermal component to have a larger probability interval. The conventional design method of the thermal component does not consider the influence of fluctuation of the factors on the service life of the thermal component, so that the design service life of the thermal component cannot be estimated accurately.
There is a need for reasonable control and optimization of factors that affect thermal component life reliability. The service life robustness design of the thermal component of the gas turbine is developed, the service life of the thermal component can be prolonged, the sensitivity of the fatigue life of the thermal component to load, material parameters and other random variables is reduced, the accuracy and reliability of life prediction are improved, and finally an accurate basis is provided for the overhaul plan formulation of the gas turbine.
Chinese patent CN105608316B discloses a method for calculating the actual service life of the main combustor basket of an engine. The method for calculating the actual service life of the flame tube of the main combustion chamber of the engine comprises the following steps of: acquiring the design point state life, the flying spot state life and the high-temperature flying spot state life of the engine for comparison; step 2: acquiring the design point state life, the flying spot state life and the high-temperature flying spot state life of the engine for comparison; step 3: acquiring the design point state life, the flying spot state life and the high-temperature flying spot state life of the engine to be tested under a second load spectrum; step 4: acquiring the designed point state life, the flying spot state life and the high-temperature flying spot state life of the theoretical first turning period of the engine to be tested under the second load spectrum; step 5: and calculating the actual service life of the flame tube of the engine to be tested. The method does not consider uncertain factors such as temperature load, material parameters and the like, so that the design life of the flame tube cannot be estimated accurately.
Chinese patent CN201910433274.2 discloses a method for predicting and calculating the fatigue-creep damage coupling probability life of turbine blade. The method comprises the following steps: s1, collecting turbine blade attributes; s2, determining an examination part; s3, performing finite element simulation on the turbine blade to obtain stress strain information of a test point of the turbine blade; s4, calculating fatigue damage: calculating to obtain fatigue life and fatigue damage information through a low-cycle fatigue life model; s5, calculating creep damage: calculating creep life and creep damage information through a creep life model; s6, calculating total damage and performing life distribution fitting; and S7, based on the accumulated damage theory, combining various working condition life information to obtain final probability life distribution of the blade. Although this patent considers the impact of materials, loads, geometry on life, it does not disclose how to address the technical problem of improving the robustness of turbine blade life design.
Chinese patent CN107895088B relates to a method for predicting the life of an aeroengine combustion chamber, comprising: CFD analysis of an aero-engine combustion chamber; elastoplastic statics analysis of an aero-engine combustion chamber; load spectrum compiling of a combustion chamber of the aero-engine; design of an aeroengine combustion chamber matrix alloy fatigue test piece: designing a hastelloy creep fatigue test standard part; designing a fatigue test load of an alloy matrix of a combustion chamber of an aero-engine; aero-engine combustion chamber matrix alloy test; establishing an aeroengine combustion chamber matrix alloy damage prediction model by adopting a method of combining a Support Vector Machine (SVM) with a Genetic Algorithm (GA); prediction of aero-engine combustion chamber life. Although this patent discloses algorithms for CFD and support vector machines, the invention solves the problem of how to predict combustor life, and does not disclose how to solve the technical problems of improving combustor robustness and combustor life.
Disclosure of Invention
In order to solve the problem of low service life robustness of the thermal component of the gas turbine, the invention provides a design method of the service life robustness of the thermal component of the gas turbine.
In order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
a design method for life robustness of a thermal component of a gas turbine comprises
Step S1: a flow-heat-solid coupling analysis method is adopted to obtain a temperature field, and a transient finite element analysis method is adopted to obtain a cyclic stress-strain curve from the starting of the gas turbine to the checking point of the heat component in the stopping process;
step S2: obtaining the strain amplitude of the core point of the thermal component through the cyclic stress-strain curve, and calculating the low cyclic fatigue life of the thermal component according to the Manson-Coffin formula of the thermal component material and the obtained strain amplitude; and a response surface method is adopted to establish a low cycle fatigue life agent model of the thermal component;
step S3: setting a robustness design variable of the thermal component, wherein the robustness design variable comprises a controllable variable and a noise variable, setting a maximum average value and a minimum standard deviation of the low cycle fatigue life of the thermal component as design targets, determining constraint conditions, and establishing a parameter design optimization model based on quantiles;
further, the constraints are different for different thermal components;
step S4: and solving a parameter design optimization model based on quantiles by adopting a SPEA-II multi-objective optimization algorithm to obtain objective function values and corresponding design variables.
Further, the step of obtaining the stress-strain curve in the step S1 includes:
step 1: calculating boundary condition parameters of a thermal component temperature field through thermodynamic formula calculation;
step 2: solving a flow field and a solid temperature field of the thermal component by adopting CFD software to obtain the distribution of the temperature field of the thermal component in the process from start-up to shutdown;
step 3: directly mapping the temperature on the solid domain node obtained in the CFD calculation to a thermal component solid domain node calculated by ANSYS, and carrying out thermal component stress-strain calculation;
step 4: and determining the examination point, and obtaining a cyclic stress-strain curve of the examination point of the solid domain of the thermal component.
Further, the assessment points are areas where structural strength failure of the thermal component occurs.
Further, the step of obtaining the low cycle fatigue life agent model in the step S2 includes:
step 11: selecting a design variable X, setting the number n of original samples, adopting an orthogonal test to obtain the original samples, carrying out finite element analysis on a thermal component to obtain the average stress and strain amplitude of a test point, and calculating through a Mason-Coffin formula with average stress correction:obtaining low cycle fatigue life N f The calculated low cycle fatigue life N f Called original sample points;
step 12: transforming the design variables:wherein x is i Mu, as the ith sample of the design variable X i Sum sigma i To design the mean and standard deviation of the variables, x' i The initial sample point after the space size is changed;
step 13: randomly selecting n1 initial sample points, and performing machine learning by a response surface method to obtain a learning model;
step 14: taking the rest n-n1 initial sample points as detection sample points, monitoring the accuracy of a learning model, repeating the step 13 and the step 14 if the error between the low cycle fatigue life calculated by the learning model and the initial sample points is more than a%, and performing the step 15 if the accuracy of the monitored learning model meets the condition that the error between the low cycle fatigue life calculated by the learning model and the initial sample points is less than or equal to a%;
further, the error a is less than or equal to 5;
step 15: and sampling the agent model by adopting a Monte Carlo sampling method to obtain a low cycle fatigue life distribution curve of the examination points.
Further, σ 'in the step 11' f For fatigue strength coefficient, sigma m For average stress, ε' f For the fatigue ductility factor, Δε is the strain amplitude, b is the fatigue strength index, c is the fatigue ductility index。
Further, setting the robustness design variable in the step S3, and establishing the quantile optimization model and the constraint condition of the low cycle fatigue includes:
thermal component lifetime n=f (y, z), where y, z is a robustness design variable;
the design targets areConstraint is y L ≤y≤y U ；
g j (y,z)≤0；
Further, N 0.5 For a lower quantile with a probability of 0.5,for the difference between the probability P2 and the lower quantile of P1, μ is the life average optimization target, y is the controllable variable, y U Is the upper limit of the controllable variable, y L Is the lower bound of the controllable variable; j is more than or equal to 1 and less than or equal to m, g j (y, z) is the j-th constraint of the m constraints, z being the noise variable.
Further, the steps of the SPEA-II multi-objective optimization algorithm include:
step a: setting a robustness design variable as an evolution individual, simultaneously setting a maximum evolution algebra T, a group size N and an archive set size M, initializing to generate a primary evolution group P0 and an empty archive set Q0, wherein the evolution algebra T=0, and setting the maximum evolution algebra T;
step b: calculating the fitness value F (i) of all individuals i in the current evolutionary population Pt and the archive set Qt by adopting a SPEA-II multi-objective optimization algorithm;
step c: the next generation archive set Qt+1 is used for storing the current evolutionary population Pt and all non-dominant individuals in the current archive set Qt, the number of the non-dominant individuals in the archive set Qt+1 is compared with the size of the set archive set scale M, and the principle of multiple-reduction complement is followed, so that the number of individuals in the archive set Qt+1 is equal to M;
step d: if the evolution algebra T is more than or equal to T or meets other termination conditions, stopping evolution, storing the non-dominant solution in Qt+1 into a non-dominant solution set NDset, and outputting the NDset and the objective function value;
step e: selecting a tournament for the next generation archive set Qt+1 by adopting a binary tournament selection method, and selecting a proper individual to enter a pairing library;
step f: and (3) executing crossover and mutation operations in the evolution algorithm in the pairing library, storing the result in a next generation evolution population set Pt+1, enabling the evolution algebra t=t+1, and repeating the steps b to e until the termination condition step d is met.
Further, the thermal components include components of the gas turbine that operate in high temperature and high pressure environments, including specifically turbine blades, combustor cans, shroud rings, disks, and the like.
Compared with the prior art, the invention has the following beneficial effects:
(1) In the design stage of the thermal component of the gas turbine, as noise factors are introduced as design variables in the initial stage of design, the design space is further expanded, namely the design variables which enable the service life of the thermal component to be stable can be searched in a larger design space;
(2) According to the invention, one of the optimization targets is to minimize the standard deviation, namely the fluctuation range is minimized, the service life dispersion interval of the thermal component of the gas turbine is reduced, and the low cycle fatigue life of the thermal component can be estimated more accurately, so that an accurate basis is provided for the establishment of the overhaul period of the gas turbine;
(3) The invention prolongs the service life of the thermal component, reduces the maintenance times, reduces the maintenance cost in the whole life cycle and improves the market competitiveness of the gas turbine.
Drawings
FIG. 1 is a flow chart of a method of calculating the life robustness of a thermal component of a gas turbine of the present invention;
FIG. 2 is a schematic illustration of example 1 gas turbine blade feature sizes;
FIG. 3 is a graph of the results of a gas turbine blade robustness analysis of example 1;
FIG. 4 is a schematic illustration of example 2 gas turbine combustor basket feature sizes;
FIG. 5 is a comparison of example 2 gas turbine combustor basket life robustness design results;
Detailed Description
In order to make the objects and technical solutions of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to examples.
Example 1
According to the method for designing the life robustness of the thermal components of the gas turbine shown in fig. 1-3, the method for designing the robustness of the low cycle fatigue life of the turbine blade of the gas turbine of a certain ground comprises the following specific steps:
step S1: performing CFD calculation on a flow field and a solid temperature field of the turbine blade to obtain turbine blade temperature field distribution, simultaneously analyzing a solid domain of the turbine blade by adopting a transient finite element analysis method, determining the edge of the blade body air film hole as an assessment point according to a calculation result, and further obtaining a cyclic stress-strain curve of the edge of the blade body air film hole;
in particular, the stress level is high in this region due to the large temperature gradient.
Step S2: obtaining the strain amplitude of a test point IN the process from starting to stopping of the turbine blade through a cyclic stress-strain curve, and calculating the low cyclic fatigue life of the blade according to the Manson-Coffin curve of the material IN738LC and the obtained strain amplitude; and a response surface method is adopted to establish a low cycle fatigue life agent model of the blade;
step S3: setting a life stability design variable of the blade, setting a maximum mean value and a minimum standard deviation of the low cycle fatigue life of the blade as design targets, determining constraint conditions, and establishing a parameter design optimization model based on quantiles;
specifically, the robustness design variables are controllable random variables and noise variables. Establishing a parameter design optimization model based on quantiles to calculate the maximum mean value and ensure that the fluctuation range of the maximum mean value is minimum;
step S4: and optimizing the parameter design optimization model based on the quantiles by adopting a SPEA-II multi-objective optimization algorithm to obtain an output design variable and a target function value.
Specifically, the step of obtaining the stress-strain curve in the step S1 includes:
step 1: calculating boundary condition parameters calculated by a three-dimensional temperature field of the blade through the overall characteristics of the gas turbine;
step 2: solving a flow field and a solid temperature field of the blade by adopting CFD software to obtain a distribution cloud picture of the flow field and the temperature field in a combustion chamber in the process of starting-rated working condition-stopping of the blade;
step 3: using the same solid domain grid as in CFD calculation, directly mapping the temperature on the blade solid domain grid node obtained in CFD calculation to the flame tube solid domain grid calculated by ANSYS; carrying out transient stress-strain analysis on the blade, and circularly loading for 3-4 times;
step 4: and determining the edge of the air film hole of the blade as an assessment point according to the calculation result, and obtaining a stress-strain curve of the assessment point.
Specifically, the checking point in the step 4 is a region where the structural strength of the thermal component fails.
Specifically, the step of obtaining the low cycle fatigue life agent model in the step S2 includes:
step 11: selecting design variables, setting 32 original samples, acquiring the original samples by using a Latin hypercube sampling method, performing finite element analysis on the turbine blade to obtain average stress and strain amplitude of a test point, and calculating by using a Mason-Coffin formula with average stress correction:obtaining low cycle fatigue life N f The calculated low cycle fatigue life N f Called original sample points; wherein σ' f For fatigue strength coefficient, sigma m For average stress, ε' f Delta epsilon is the strain amplitude, b is the fatigue strength index, and c is the fatigue ductility index, which are the fatigue ductility coefficients.
Step 12: converting the design variable:wherein x is i Mu, as the ith sample of the design variable X i Sum sigma i To design the mean and standard deviation of the variables, x' i The initial sample point after the space size is changed; the purpose of performing space size transformation on input data is to reduce rounding errors of a computer, improve stability and generalization of a training support vector regression machine, and the basic design variables are used as the input data, have larger difference of respective value ranges due to different physical meanings and dimensions, and are easy to generate unstable phenomenon in training of the support vector regression machine, so that the basic design variables need to perform size transformation, and have the same importance in training;
step 13: randomly selecting 70% of initial sample points, and performing machine learning by a response surface method to obtain a learning model;
step 14: taking the rest 30% of initial sample points as detection sample points, monitoring the accuracy of a learning model, repeating the step 13 and the step 14 if the error between the low cycle fatigue life calculated by the learning model and the initial sample points is more than 5%, and performing the step 15 if the accuracy of the monitored learning model meets the condition that the error between the low cycle fatigue life calculated by the learning model and the initial sample points is less than or equal to 5%;
specifically, the error represents the error between the actual response value and the predicted value of the original sample point;
step 15: sampling the agent model by adopting a Monte Carlo sampling method to obtain a low cycle fatigue life distribution curve of the examination points;
specifically, setting the robustness design variable in the step S3, and establishing the quantile optimization model and the constraint condition of the low cycle fatigue includes:
thermal component lifetime n=f (y, z), where y, z is a robustness design variable;
the design targets areSpecific N 0.5 Lower quantile with probability of 0.5, +.>For the difference in lower quantiles of probability P2 and P1, μ is the life mean optimization target, where P2 is 0.9999, P1 is 0.0001,
the constraint conditions are as follows:
the maximum temperature does not exceed the design value: ti < T0 is chosen from the group consisting of,
the average temperature of the cross section is not more than the allowable temperature, tavg is less than T1;
y is a controllable variable, and comprises a gas film hole diameter d, a gas film hole inclination angle a, a gas film hole compound angle beta, a gas film hole circumferential distance L1, a gas film hole axial distance L2 and an elastic modulus E of In738 LC;
z is a noise variable, Z includes blade inlet gas temperature T, blade inlet gas pressure P
The SPEA-II multi-objective optimization algorithm specifically comprises the following steps:
step (1) initializing: setting the scale N of the evolutionary population, the scale M of the filing set and the maximum evolutionary algebra T, randomly initializing an evolutionary population, namely a point set P0 and an empty filing set Q0 in an 8-dimensional variable space consisting of variables d, a, beta, L1, L2, E, T and P on the premise that the highest temperature Ti < T0 and the average temperature Tavg of a cross section are met, enabling the evolutionary algebra T to be=0, and setting the maximum evolutionary algebra T.
And (2) fitness allocation: substituting the points in the combined set of point sets P0 and Q0 into two objective functionsIn the above, the dominant solution and the non-dominant solution can be distinguished from each other according to the relative positions of the points in the target space composed of the objective function values. According to the relation between the allocation and the allocation, calculating the intensity value S (i) of the individual i, and according to the intensity value S (i), initially calculating an original fitness value R (i), then calculating the Euclidean distance between the point i and other points in the target space to obtain the density value D (i) of the point, and calculating the fitness values F (i) =R (i) +D (i) of all the individuals in the current evolutionary population Pt and the current archive set Qt.
And (3) selecting an environment: all non-dominant individuals in Pt and Qt are saved with the next generation archive set qt+1. If the number of individuals in Qt+1 exceeds M, removing the individuals with too close Euclidean distance with other individuals in Qt+1 one by utilizing the relative positions of the individuals in the target space, namely removing part of the individuals in the dense area in the target space; if the number of individuals in Qt+1 is smaller than M, selecting from dominant individual cis-positions in Pt and Qt according to the individual fitness value, and filling Qt+1;
end condition of step (4): if T is more than or equal to T or other end conditions are met, non-dominant individuals in Qt+1 are stored in the NDSet, the NDSet is ended in the evolution process as a final archive set as a final result, and the NDSet and the corresponding objective function value are output;
and (5) pairing selection: and (3) carrying out binary tournament selection on Qt+1, namely selecting two individuals in Qt+1 randomly, enabling the individuals with good adaptability to enter a pairing library, and repeatedly executing selection operation until the pairing library is filled.
Step (6) evolution operation: for individuals in Qt+1, the individuals are randomly paired in pairs by two, binary coding is adopted, through executing cross operation, the individuals can also execute mutation operation through random mutation (0 changes to 1,1 changes to 0) on binary digits, and the evolution result is saved in Qt+1, so that t=t+1 is converted into step (2).
Example 2
According to the method for designing the life robustness of the thermal components of the gas turbine shown in fig. 1, 4 and 5, the method for designing the robustness of the low cycle fatigue life of the flame tube of the gas turbine on the certain ground comprises the following specific steps:
step S1: performing CFD calculation on a flow field and a solid temperature field of the flame tube by adopting a flow-heat-solid coupling analysis method to obtain temperature field distribution of the flame tube, and simultaneously analyzing a solid field of the flame tube of the combustion chamber by adopting a transient finite element analysis method to obtain a cyclic stress-strain curve of the air film hole edge of the flame tube in the process of starting up and stopping the gas turbine through calculation results;
in particular, the stress level is high in this region due to the large temperature gradient.
Step S2: obtaining the strain amplitude of a checking point in the process from starting to stopping of the flame tube through a cyclic stress-strain curve, and calculating the low cyclic fatigue life of the flame tube according to a Manson-Coffin formula of a flame tube material Hastelloy-X and the obtained strain amplitude; and a response surface method is adopted to establish a flame tube low cycle fatigue life agent model;
step S3: setting life robustness design variables of the flame tube, wherein the life robustness design variables comprise controllable variables and noise variables, setting the maximum average value and the minimum standard deviation of the low cycle fatigue life of the flame tube as design targets, determining constraint conditions, and establishing a parameter design optimization model based on quantiles;
specifically, the robustness design variables are controllable random variables and noise variables. Establishing a parameter design optimization model based on quantiles to calculate the maximum mean value and ensure that the fluctuation range of the maximum mean value is minimum;
step S4: and solving a parameter design optimization model based on quantiles by adopting a SPEA-II multi-objective optimization algorithm to obtain an output design variable and a target function value.
Specifically, the step of obtaining the stress-strain curve in the step S1 includes:
step 1: calculating boundary condition parameters calculated by a three-dimensional temperature field of the flame tube through a thermodynamic formula;
step 2: solving a flow field and a solid temperature field of a flame tube of a combustion chamber by adopting CFD software to obtain a distribution cloud picture of the flow field and the temperature field in the combustion chamber in the process of starting-rated working condition-stopping of the flame tube;
step 3: using the same solid domain grid as in CFD calculation, directly mapping the temperature on the flame tube solid domain grid nodes obtained in CFD calculation to the flame tube solid domain grid calculated by ANSYS; carrying out transient stress-strain analysis on the flame tube, and circularly loading for 3-4 times;
step 4: and determining the edge of the air film hole as an assessment point according to the calculation result, and obtaining a stress-strain curve of the assessment point.
Specifically, the checking point in the step 4 is a region where the structural strength of the thermal component fails.
Further, the step of obtaining the low cycle fatigue life agent model in the step S2 includes:
step 11: selecting design variables, settingThe number of the original samples is 32, the Latin hypercube sampling method is adopted to obtain the original samples, finite element analysis is carried out on the flame tube, the average stress and strain amplitude of the examination point are obtained, and the calculation is carried out through a Mason-Coffin formula with average stress correction:obtaining low cycle fatigue life N f The calculated low cycle fatigue life N f Called original sample points; wherein σ' f For fatigue strength coefficient, sigma m For average stress, ε' f Delta epsilon is the strain amplitude, b is the fatigue strength index, and c is the fatigue ductility index, which are the fatigue ductility coefficients.
Specifically, the design variables comprise flame tube structural parameters, material parameters and load boundary conditions, and specifically comprise a diameter d of a gas film hole, a circumferential distance L1 of the gas film hole, an axial distance L2 of the gas film hole, an arrangement angle beta of the gas film hole, elastic modulus E of flame tube wall thicknesses T and H-X, a temperature T of flame tube inlet gas, a pressure P of flame tube inlet gas and a fuel flow m.
Step 12: converting the design variable:wherein x is i Mu, as the ith sample of the design variable X i Sum sigma i To design the mean and standard deviation of the variables, x' i The initial sample point after the space size is changed; the purpose of performing space size transformation on input data is to reduce rounding errors of a computer, improve stability and generalization of a training support vector regression machine, and the basic design variables are used as the input data, have larger difference of respective value ranges due to different physical meanings and dimensions, and are easy to generate unstable phenomenon in training of the support vector regression machine, so that the basic design variables need to perform size transformation, and have the same importance in training;
step 13: randomly selecting 70% of initial sample points, and performing machine learning by a response surface method to obtain a learning model;
step 14: taking the rest 30% of initial sample points as detection sample points, monitoring the accuracy of a learning model, repeating the step 13 and the step 14 if the error between the low cycle fatigue life calculated by the learning model and the initial sample points is more than 5%, and performing the step 15 if the accuracy of the monitored learning model meets the condition that the error between the low cycle fatigue life calculated by the learning model and the initial sample points is less than or equal to 5%;
specifically, the error represents the error between the actual response value and the predicted value of the original sample point;
step 15: sampling the agent model by adopting a Monte Carlo sampling method to obtain a low cycle fatigue life distribution curve of the examination points;
specifically, setting the robustness design variable in the step S3, and establishing the quantile optimization model and the constraint condition of the low cycle fatigue includes:
thermal component lifetime n=f (y, z), where y, z is a robustness design variable;
the design targets areSpecific N 0.5 Lower quantile with probability of 0.5, +.>For the difference in lower quantiles of probability P2 and P1, μ is the life mean optimization target, where P2 is 0.9999, P1 is 0.0001,
the constraint conditions are as follows:
the maximum allowable temperature of the wall surface does not exceed the design value: ti < T0 is chosen from the group consisting of,
the wall thickness is not more than a set value t < t0;
y is a controllable variable, and comprises a diameter d1 of the air film hole, a circumferential distance L1 of the air film hole, an axial distance L2 of the air film hole, an arrangement angle beta of the air film hole, a wall thickness d2 of the flame tube and an elastic modulus E of H-X;
the noise design variable z is: the flame tube inlet gas temperature T, the flame tube inlet gas pressure P and the fuel flow m;
the SPEA-II multi-objective optimization algorithm specifically comprises the following steps:
step (1) initializing: setting a scale N of an evolutionary population, a scale M of an archive set and a maximum evolutionary algebra T, randomly initializing an evolutionary population, namely a point set P0 and an empty archive set Q0 in a 9-dimensional variable space consisting of variables d, L1, L2, beta, T, E, T, P and M on the premise that constraint conditions Ti < T0 and wall thickness T < T0 are met, and setting the maximum evolutionary algebra T.
And (2) fitness allocation: substituting the points in the combined set of point sets P0 and Q0 into two objective functionsIn the above, the dominant solution and the non-dominant solution can be distinguished from each other according to the relative positions of the points in the target space composed of the objective function values. According to the relation between the allocation and the allocation, calculating the intensity value S (i) of the individual i, and according to the intensity value S (i), initially calculating an original fitness value R (i), then calculating the Euclidean distance between the point i and other points in the target space to obtain the density value D (i) of the point, and calculating the fitness values F (i) =R (i) +D (i) of all the individuals in the current evolutionary population Pt and the current archive set Qt.
And (3) selecting an environment: all non-dominant individuals in Pt and Qt are saved with the next generation archive set qt+1. If the number of individuals in Qt+1 exceeds M, removing the individuals with too close Euclidean distance with other individuals in Qt+1 one by utilizing the relative positions of the individuals in the target space, namely removing part of the individuals in the dense area in the target space; if the number of individuals in Qt+1 is smaller than M, selecting from dominant individual cis-positions in Pt and Qt according to the individual fitness value, and filling Qt+1;
end condition of step (4): if T is more than or equal to T or other end conditions are met, non-dominant individuals in Qt+1 are stored in the NDSet, the NDSet is ended in the evolution process as a final archive set as a final result, and the NDSet and the corresponding objective function value are output;
and (5) pairing selection: and (3) carrying out binary tournament selection on Qt+1, namely selecting two individuals in Qt+1 randomly, enabling the individuals with good adaptability to enter a pairing library, and repeatedly executing selection operation until the pairing library is filled.
Step (6) evolution operation: for individuals in Qt+1, the individuals are randomly paired in pairs by two, binary coding is adopted, through executing cross operation, the individuals can also execute mutation operation through random mutation (0 changes to 1,1 changes to 0) on binary digits, and the evolution result is saved in Qt+1, so that t=t+1 is converted into step (2).
The foregoing is a description of embodiments of the invention, which are specific and detailed, but are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (3)
1. A method for designing life robustness of a thermal component of a gas turbine is characterized by comprising the following steps of
Step S1: a flow-heat-solid coupling analysis method is adopted to obtain a temperature field, and a transient finite element analysis method is adopted to obtain a cyclic stress-strain curve from the starting of the gas turbine to the checking point of the heat component in the stopping process;
step S2: obtaining the strain amplitude of the core point of the thermal component through the cyclic stress-strain curve, and calculating the low cyclic fatigue life of the thermal component according to the Manson-Coffin formula of the thermal component material and the obtained strain amplitude; and a response surface method is adopted to establish a low cycle fatigue life agent model of the thermal component;
step S3: setting a robustness design variable of the thermal component, wherein the robustness design variable comprises a controllable variable and a noise variable, setting a maximum average value and a minimum standard deviation of the low cycle fatigue life of the thermal component as design targets, determining constraint conditions, and establishing a parameter design optimization model based on quantiles;
step S4: solving a parameter design optimization model based on quantiles by adopting a SPEA-II multi-objective optimization algorithm to obtain objective function values and corresponding design variables;
the stress-strain curve obtaining step in the step S1 is as follows:
step 1: calculating boundary condition parameters of a thermal component temperature field through thermodynamic formula calculation;
step 2: solving a flow field and a solid temperature field of the thermal component by adopting CFD software to obtain the distribution of the temperature field of the thermal component in the process from start-up to shutdown;
step 3: directly mapping the temperature on the solid domain node obtained in the CFD calculation to a thermal component solid domain node calculated by ANSYS, and carrying out thermal component stress-strain calculation;
step 4: determining a core examination point and obtaining a cyclic stress-strain curve of the core examination point of the solid domain of the thermal component;
step S2, obtaining a low cycle fatigue life agent model:
step 11: selecting a design variable X, setting the number n of original samples, adopting an orthogonal test to obtain the original samples, carrying out finite element analysis on a thermal component to obtain the average stress and strain amplitude of a test point, and calculating through a Mason-Coffin formula with average stress correction:obtaining low cycle fatigue life N f The calculated low cycle fatigue life N f Called original sample points; in (1) the->For fatigue strength coefficient, < >>In order for the stress to be an average stress,for fatigue ductility factor, < >>B is the fatigue strength index, c is the fatigue ductility index;
step 12: transforming the design variables:wherein x is i For the ith sample of the design variable X,/>and->For the mean and standard deviation of the design variables +.>The initial sample point after the space size is changed;
step 13: randomly selecting n1 initial sample points, and performing machine learning by a response surface method to obtain a learning model;
step 14: taking the rest n-n1 initial sample points as detection sample points, monitoring the accuracy of a learning model, repeating the step 13 and the step 14 if the error between the low cycle fatigue life calculated by the learning model and the initial sample points is more than a%, and performing the step 15 if the accuracy of the monitored learning model meets the condition that the error between the low cycle fatigue life calculated by the learning model and the initial sample points is less than or equal to a%;
step 15: sampling the agent model by adopting a Monte Carlo sampling method to obtain a low cycle fatigue life distribution curve of the examination points;
setting the robustness design variable in the step S3, and establishing the quantile optimization model and the constraint condition of the low cycle fatigue comprises the following steps:
thermal component lifetime n=f (y, z), where y, z is a robustness design variable;
the design targets are；
The constraint condition is that；
g j (y,z)≤0；
Lower quantile with probability of 0.5, +.>Is the difference between the lower quantiles of probability P2 and P1 +.>For life average optimization target, y is a controllable variable, y U Is the upper limit of the controllable variable, y L Is the lower bound of the controllable variable; j is more than or equal to 1 and less than or equal to m, gj (y, z) is the j constraint in m constraints, and z is a noise variable;
the SPEA-II multi-objective optimization algorithm in step S4 includes the steps of:
step (1) initializing: setting a scale N of an evolutionary population, a scale M of an archiving set and a maximum evolutionary algebra T, randomly initializing an evolutionary population, namely a point set P0 and an empty archiving set Q0 in an 8-dimensional variable space consisting of variables d, a, beta, L1, L2, E, T and P on the premise that the highest temperature Ti < T0 and the average temperature Tavg of a section are met, enabling the evolutionary algebra T to be=0, and setting the maximum evolutionary algebra T;
and (2) fitness allocation: substituting the points in the combined set of point sets P0 and Q0 into two objective functionsAccording to the relative positions of each point in a target space formed by target function values, distinguishing a dominant solution and a non-dominant solution; calculating the intensity value S (i) of an individual i according to the relation between the support and the dominance, primarily calculating an original fitness value R (i) according to the intensity value S (i), obtaining the density value D (i) of the point i by calculating the Euclidean distance between the point i and other points in a target space, and calculating the fitness values F (i) =R (i) +D (i) of all the individuals in the current evolutionary population Pt and the current archive set Qt;
and (3) selecting an environment: saving all non-dominant individuals in Pt and Qt with the next generation archive set qt+1; if the number of individuals in Qt+1 exceeds M, removing the individuals with too close Euclidean distance with other individuals in Qt+1 one by utilizing the relative positions of the individuals in the target space, namely removing part of the individuals in the dense area in the target space; if the number of individuals in Qt+1 is smaller than M, selecting from dominant individual cis-positions in Pt and Qt according to the individual fitness value, and filling Qt+1;
end condition of step (4): if T is more than or equal to T or other end conditions are met, non-dominant individuals in Qt+1 are stored in the NDSet, the NDSet is ended in the evolution process as a final archive set as a final result, and the NDSet and the corresponding objective function value are output;
and (5) pairing selection: selecting binary tournament for Qt+1, namely selecting two individuals in Qt+1 at random, enabling the individuals with good adaptability to enter a pairing library, and repeatedly executing selection operation until the pairing library is filled;
step (6) evolution operation: and (2) performing binary coding on individuals in Qt+1 in a pairwise random pairing mode, performing mutation operation on the individuals through random mutation on binary digits by performing cross operation, storing an evolution result in Qt+1, enabling t=t+1, and performing a conversion step (2).
2. The method of designing a life robustness of a thermal component of a gas turbine of claim 1, wherein the assessment point is a region where structural strength failure of the thermal component occurs.
3. The method for designing life robustness of a thermal component of a gas turbine of claim 1, wherein the thermal component comprises at least a turbine blade, a combustor basket, a shroud, a disk.
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