Turbocharger impeller strength optimization design method

Abstract

The invention provides a turbocharger impeller strength optimization design method. The turbocharger impeller strength optimization design method comprises the following steps that structural parameters are selected; acquiring sample data; constructing an agent model; improving the model precision; performing multi-objective optimization; secondary improvement of the model precision; and processing an optimization result. The method has the beneficial effects that the targeted design for optimizing the strength of the impeller is realized by accurately selecting the structural parameters related to the strength of the impeller. The proxy model is combined with the multi-objective optimization algorithm, so that the design period and the calculation cost are reduced, and diversified optimization schemes are provided for designers while the machinability of the impeller is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of power machinery, and in particular relates to a method for optimizing the strength design of a turbocharger impeller. Background Art

[0002] In the face of the design requirements of high efficiency and compactness of turbochargers, in order to significantly improve the performance of compressors, the industry often conducts special-shaped designs on impellers. For example, designs such as tilting, sweeping, bending, and twisting are carried out on the blades. These innovative designs have greatly improved the efficiency and pressure ratio of the compressor impeller, resulting in a significant improvement in aspects such as the working efficiency of the turbocharger. However, with the complexity of the impeller design, a series of new problems have emerged. After the actual processing is completed and put into use, the low strength of the impeller has become a prominent problem. Since the special-shaped design changes the original structural stress distribution of the impeller, under high-speed operating conditions, the impeller is extremely prone to cracks or even breakage. This not only seriously affects the normal operation of the turbocharger, but also makes the design achievements after the impeller modification unable to have practical value due to strength problems.

[0003] To solve the above problems, it is imperative to optimize the design of the impeller strength. However, the strength of the impeller is affected by many structural parameters. From the fine dimensions of the leading edge and trailing edge of the blade to the thickness of the back disc of the impeller and the thickness of the back of the shaft hole, the change of each parameter may affect the strength. At the same time, when checking the strength of the impeller, it is necessary to check multiple key positions of the impeller, such as the root of the leading and trailing edges of the blade and the connection area of the shaft hole. Traditional optimization design methods are inefficient and difficult to accurately grasp the complex relationship between each parameter and the impeller strength in the face of such complex multi-parameter and multi-position checking problems, and cannot meet the current urgent need for impeller strength optimization. Therefore, there is an urgent need for an efficient optimization design method that can comprehensively and accurately improve the impeller strength and ensure the reliability and stability of the turbocharger under high-performance operation. Summary of the Invention

[0004] In view of this, the present invention aims to propose a method for optimizing the strength design of a turbocharger impeller to solve at least one of the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the technical solution of the present invention is realized as follows: A method for optimizing the strength design of a turbocharger impeller includes the following steps: S1. Selection of structural parameters: Determine the coordinates and fillet sizes of the leading edge of the blade, the coordinates and fillet sizes of the trailing edge of the blade, the thickness of the back disc of the impeller, and the thickness parameter of the back of the shaft hole as the structural parameters related to the impeller strength; S2. Acquisition of sample data: The Latin hypercube sampling method is adopted to collect the impeller sample data corresponding to different combinations of structural parameters, and the finite element analysis software is used to simulate and calculate each sample to obtain the impeller strength data such as the stress value at the leading edge of the blade, the stress value at the trailing edge of the impeller, and the stress value at the shaft hole; S3. Surrogate model construction: The sample data set is divided into a training data set and a validation data set according to a ratio. Among them, the training data set is used to construct the Kriging model, and the validation data set is used to test the generalization ability of the model; the Kriging model is used to fit the implicit function relationship between the impeller structural parameters and the stress values at different positions of the impeller; S4. Model accuracy improvement: An adaptive sampling point addition algorithm is introduced in the process of constructing the Kriging model. According to the prediction error of the current Kriging model, the Latin hypercube sampling method is used to extract sample points in the sample-deficient area with a large prediction error, and the sample point with the largest variance is selected and added to the training data set to reconstruct the Kriging model and improve the fitting accuracy of the model; S5. Multi-objective optimization: The multi-objective differential evolution algorithm is adopted. With the goal of reducing the stress values at the leading edge of the blade, the trailing edge of the impeller, and the shaft hole, the mathematical optimization calculation is carried out on the impeller structural parameters to achieve the goal of simultaneously reducing the stress values in the three regions and improving the strength of the impeller; S6. Secondary improvement of model accuracy: Some solutions in the Pareto optimal solution set obtained by the optimization calculation are used as new training points of the Kriging model and added to the training sample point set to further improve the fitting accuracy of the surrogate model; after updating the model, the optimization calculation is carried out again to complete the optimal design of the impeller strength; S7. Optimization result processing: From the Pareto optimal solution set obtained by the optimization algorithm, select the appropriate combination of impeller structural parameters according to the actual engineering requirements, use 3D modeling software to construct the 3D model of the optimized impeller, import the model into the finite element analysis software for strength calculation, and compare the stress values at the key parts such as the leading edge, trailing edge, and shaft hole of the blade before and after optimization to evaluate the optimization effect.

[0006] Furthermore, in step S1, for each structural parameter, determine the data type and change range of each structural parameter. The size of the fillet at the root of the leading edge of the blade is discrete data, and the value range is 1mm, 1.5mm, 2mm, 2.5mm; the thickness of the back disk of the impeller is continuous data, and the value range is 1.3mm to 2.5mm; the thickness of the back of the wheel at the shaft hole is continuous data, and the value range is 3mm to 8mm.

[0007] Further, in step S3, the Kriging model assumes that there is a certain spatial correlation between sample points, and constructs a covariance function to describe this correlation; the covariance function is a Gaussian kernel function.

[0008] Gaussian kernel function ; In the formula is the variance, is the parameter related to the k th structural parameter, and are the i and j th structural parameter values of the sample points k respectively. For the point to be predicted, by solving the system of equations , the weight coefficients are obtained, and then the predicted value In the formula is the Lagrange multiplier.

[0009] Further, in step S4, after initially constructing the Kriging model, the adaptive sampling point addition algorithm is used to calculate the sample points with large prediction errors in the sample-deficient area, and they are added to the training sample point set of the model as new training sample points, so as to improve the fitting accuracy of the Kriging model.

[0010] Further, in step S5, in the multi-objective differential evolution algorithm, the population size is determined to be 100, the mutation factor F ranges from 0.4 to 0.9, and the crossover probability CR ranges from 0.6 to 0.95. The initial population is randomly initialized. In each generation of evolution, a differential mutation operation is performed on each individual to generate mutant individuals, and then trial individuals are generated through crossover operations. The Kriging surrogate model is used to evaluate the multi-objective values corresponding to the impeller structure parameters of the trial individuals. After comparison with the original individuals, the better individuals are selected to enter the next generation population, and continuous iteration is performed until the termination condition is met to obtain a series of Pareto optimal solutions.

[0011] Further, in step S6, after initially performing the optimization solution, some solutions in the obtained Pareto optimal solution set are added to the training sample point set of the Kriging model to further improve the fitting accuracy of the surrogate model, and the multi-objective differential evolution algorithm is used again for optimization calculation, determining that the population size is 500, the mutation factor F ranges from 0.6 to 0.9, and the crossover probability CR ranges from 0.6 to 0.95.

[0012] Further, in step S7, the three-dimensional modeling software used maps the three-dimensional model of the impeller to the optimized structural parameters, and the finite element analysis software is used to check the structural strength of the impeller. If the design requirements are still not met, new solutions should be selected on the Pareto front for verification.

[0013] Compared with the prior art, the method for optimizing the strength of a turbocharger impeller according to the present invention has the following advantages: The method for optimizing the strength of a turbocharger impeller according to the present invention realizes a targeted design for optimizing the impeller strength by accurately selecting the structural parameters strongly related to the impeller strength. By combining the surrogate model with the multi-objective optimization algorithm, the design cycle and calculation cost are reduced. At the same time, while ensuring the machinability of the impeller, a variety of optimization solutions are provided for designers. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 is the flow chart of the strength optimization design of the turbocharger impeller according to the embodiment of the present invention; Figure 2 is the schematic diagram of the comparative analysis of the predicted values of the Kriging model before and after adding points and the finite element calculation results according to the embodiment of the present invention; Figure 3 is the schematic diagram of the Pareto front obtained by solving the multi-objective optimization algorithm according to the embodiment of the present invention; Figure 4 is the stress distribution nephogram of the impeller before optimization according to the embodiment of the present invention; Figure 5 is the stress distribution nephogram of the impeller after optimization according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0016] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0017] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood through specific circumstances.

[0018] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0019] As Figures 1 to 5 shown, a method for optimizing the strength of a turbocharger impeller includes: Determine the structural parameters that affect the strength of the impeller. For the impeller after special-shaped design, pay attention to the von Mises stress distribution at the blade leading-edge root, the von Mises stress distribution at the blade trailing-edge root, and the von Mises stress distribution at the position of the back disk shaft hole. Therefore, select the control point coordinates at the blade leading-edge root, the control point coordinates at the blade trailing-edge root, the fillet radius at the root, the impeller back thickness, the impeller back thickness in the shaft hole area, and other structural parameters as the optimization variables for strength optimization design.

[0020] In a preferred embodiment of the present invention, determine the variation range of the optimization variables and select the type of the optimization variables. For example, the fillet radius at the root should be set as a discrete variable according to actual processing requirements. Optimization variables such as the impeller back thickness can be set as continuous variables.

[0021] In a preferred embodiment of the present invention, collect impeller sample data covering different combinations of structural parameters and construct a three-dimensional model of the impeller. Use a finite element analysis tool to calculate the strength of the impeller and obtain the stress distribution at different positions of the impeller corresponding to different combinations of structural parameters.

[0022] In a preferred embodiment of the present invention, construct a data set from the combination of impeller structural parameters and the stress values at different positions of the impeller for training a surrogate model, such as a Kriging model, to fit the implicit mapping relationship between the impeller structural parameters and the stress values at different positions of the impeller.

[0023] In a preferred embodiment of the present invention, to improve the fitting accuracy of the surrogate model, an adaptive sampling algorithm is used to improve the fitting accuracy while reducing the training cost. Randomly generate several sample points in the design space using the Latin hypercube sampling method, calculate the prediction variance at each sample point, and select the sample points with large prediction variance as the new training points added to the surrogate model to improve the fitting accuracy of the surrogate model.

[0024] In a preferred embodiment of the present invention, the optimization objectives are clearly defined as reducing the maximum von Mises stress in the blade leading-edge root region, the maximum von Mises stress in the blade trailing-edge root region, and the maximum von Mises stress in the back disk shaft hole region. By combining the surrogate model with the multi-objective optimization algorithm, the impeller structure parameters are optimized and calculated.

[0025] In a preferred embodiment of the present invention, some solutions in the Pareto optimal solution set obtained from the optimization calculation are used as new training points for the surrogate model to further improve the fitting accuracy of the surrogate model.

[0026] In a preferred embodiment of the present invention, the multi-objective optimization algorithm is used to simultaneously optimize the stress values at different positions of the impeller. The surrogate model with improved accuracy is used to calculate the corresponding objective values under different combinations of structure parameters, realizing fast optimization iteration and obtaining a series of Pareto optimal solutions.

[0027] In a preferred embodiment of the present invention, appropriate combinations of impeller structure parameters are selected from the optimization solution set according to actual engineering requirements, and a three-dimensional model of the optimized impeller is constructed. The finite element analysis software is used to perform strength analysis on the optimized impeller, and the stress values at the key parts of the impeller are carefully checked to ensure that the strength of the optimized impeller meets the design requirements.

[0028] The present invention realizes the targeted design of impeller strength optimization by accurately selecting the structure parameters strongly related to impeller strength. By combining the surrogate model with the multi-objective optimization algorithm, the design cycle and calculation cost are reduced. At the same time, while ensuring the machinability of the impeller, it provides designers with diversified optimization schemes.

[0029] Example 1 A method for optimizing the strength design of a turbocharger impeller includes: According to the impeller structure model after special-shaped design, the coordinates of the control points at the blade leading-edge root and the fillet size, the coordinates of the control points at the blade trailing-edge root and the fillet size, the thickness of the impeller back disk, the thickness of the wheel back at the shaft hole, and the size of the connecting arc at the wheel back connection are determined as the structure parameters affecting the impeller strength.

[0030] For each structure parameter, according to the manufacturing process limitations, the data type and variation range of each structure parameter are determined. For example, the fillet size at the blade leading-edge root is discrete data, and the value range is 1mm, 1.5mm, 2mm, 2.5mm. The thickness of the impeller back disk is continuous data, and the value range is 1.3mm to 2.5mm. The thickness of the wheel back at the shaft hole is continuous data, and the value range is 3mm to 8mm. All structure parameters and their variation ranges together constitute the design space.

[0031] In the design space, the Latin hypercube sampling method is used for random sampling, and the number of sampling points is set to 5 times the number of structural parameters. The finite element analysis software is used to calculate the stress of the impeller model corresponding to each sample point, and key indexes such as the von Mises stress values at the leading edge root, trailing edge root of the blade and the back disk shaft hole area are obtained to generate a sample data set.

[0032] The sample data set is divided into a training data set and a validation data set according to a ratio of 4:1. The training data set is used to construct the Kriging model, and the validation data set is used to test the generalization ability of the model. The Kriging model is an interpolation model based on spatial autocorrelation. Its core idea is to use the information of known sample points to predict the values of unknown points. The Kriging model assumes that there is a certain spatial correlation between sample points and describes this correlation by constructing a covariance function. The commonly used covariance function is the Gaussian kernel function.

[0033] Gaussian kernel function ; In the formula is the variance, is the parameter related to the k th structural parameter, and are the i and j th structural parameter values of sample points k respectively. For the point to be predicted, by solving the system of equations , the weight coefficients are obtained, and then the predicted value In the formula is the Lagrange multiplier.

[0034] In the construction process of the Kriging model, an adaptive point addition method is introduced to improve the fitting accuracy of the Kriging model at a relatively small computational cost. 10,000 sample points are randomly generated by the Latin hypercube sampling method in the design space, and the prediction variance at each sample point is calculated. Sort according to the magnitude of the prediction variance, and select the sample point with the largest prediction variance as the new training point added to the Kriging model. The calculation formula of the variance is: ; ; ; In the formula, is the weighted average of the original predicted value and the cross-validation predicted value.

[0035] According to the strength requirements of the impeller design, the optimization objective is clearly to reduce the maximum von Mises stress values in the blade leading-edge root region, the blade trailing-edge root region, and the back disk shaft hole region. The multi-objective differential evolution algorithm is used to perform multi-objective optimization design on the impeller strength. The population size is determined to be 100, the mutation factor F is in the range of 0.4 - 0.9, and the crossover probability CR is in the range of 0.6 - 0.95. The initial population is randomly initialized. In each generation of evolution, differential mutation operations are performed on each individual to generate mutant individuals, and then trial individuals are generated through crossover operations. The Kriging surrogate model is used to evaluate the multi-objective values corresponding to the impeller structure parameters of the trial individuals, compare them with the original individuals, and select the better individuals to enter the next generation population. The iteration continues until the termination condition is met, and a series of Pareto optimal solutions are obtained.

[0036] Select some solutions from the Pareto optimal solution set and construct the corresponding impeller models. Use simulation software to calculate the stress distribution of the impellers, and input the structural parameter combinations and the corresponding stress values into the training sample set of the Kriging model again to further improve the fitting accuracy of the Kriging model.

[0037] Use the multi-objective differential evolution algorithm again to perform multi-objective optimization design on the impeller strength. The population size is determined to be 500, the mutation factor F is in the range of 0.6 - 0.9, and the crossover probability CR is in the range of 0.6 - 0.95. The Kriging surrogate model is used to evaluate the multi-objective values corresponding to the impeller structure parameters of the trial individuals, compare them with the original individuals, and select the better individuals to enter the next generation population. The iteration continues until the termination condition is met, and a series of Pareto optimal solutions are obtained.

[0038] From the Pareto optimal solution set, according to the actual engineering requirements, select the appropriate impeller structural parameter combination as the final design scheme. Construct the optimized impeller three-dimensional model based on the selected parameters, import the constructed impeller model into the finite element analysis software, and perform a comprehensive strength calculation again to ensure that the strength of the optimized impeller meets the design requirements.

[0039] Among them, Figure 2 is the comparison chart of the Kriging model prediction and the finite element calculation results before and after adding points by the adaptive point addition algorithm, indicating that the fitting accuracy of the Kriging model is improved after using the adaptive point addition algorithm. Figures 4 to 5 are the stress distribution nephograms of the impeller before and after optimization, where Figure 4 is the stress distribution nephogram of the impeller before optimization, Figure 5 is the stress distribution nephogram of the impeller after optimization, indicating the reduction of the stress values at each key part of the impeller after optimization.

[0040] The present invention significantly improves the structural strength of the impeller by extracting the structural parameters of the impeller, constructing a surrogate model to fit the implicit relationship between the strength and the parameters, and using a multi-objective optimization algorithm to optimize the stress values at multiple positions of the impeller. At the same time, it ensures that the optimized design scheme has good manufacturability.

[0041] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the strength design of a turbocharger impeller, characterized in that: It includes the following steps: S1. Selection of structural parameters: Determine the coordinates of the blade leading edge and the fillet size, the coordinates of the blade trailing edge and the fillet size, the thickness of the impeller back disk, and the thickness parameter of the back of the wheel at the shaft hole as the structural parameters related to the impeller strength; S2. Acquisition of sample data: Use the Latin hypercube sampling method to collect the impeller sample data corresponding to different combinations of structural parameters, and use finite element analysis software to simulate and calculate each sample to obtain the stress values at the blade leading edge, the stress values at the impeller trailing edge, and the stress values at the shaft hole, i.e., the impeller strength data; S3. Construction of surrogate model: Divide the sample data set into a training data set and a validation data set according to a certain proportion. Among them, the training data set is used to construct the Kriging model, and the validation data set is used to test the generalization ability of the model; use the Kriging model to fit the implicit function relationship between the impeller structural parameters and the stress values at different positions of the impeller; S4. Improvement of model accuracy: Introduce an adaptive sampling point addition algorithm during the construction of the Kriging model. According to the prediction error of the current Kriging model, use the Latin hypercube sampling method to extract sample points in the sample-deficient area with a large prediction error, and select the sample point with the largest variance, add it to the training data set, and reconstruct the Kriging model to improve the fitting accuracy of the model; S5. Multi-objective optimization: Adopt the multi-objective differential evolution algorithm, with the goal of reducing the stress values at the blade leading edge, the stress values at the impeller trailing edge, and the stress values at the shaft hole, perform mathematical optimization calculations on the impeller structural parameters, achieve the goal of simultaneously reducing the stress values in the three regions, and improve the strength of the impeller; S6. Secondary improvement of model accuracy: Take some solutions in the Pareto optimal solution set obtained by the optimization calculation as the new training points of the Kriging model, add them to the training sample point set, and further improve the fitting accuracy of the surrogate model; after updating the model, perform the optimization calculation again to complete the optimal design of the impeller strength; S7. Processing of optimization results: From the Pareto optimal solution set obtained by the optimization algorithm, select a suitable combination of impeller structural parameters according to the actual engineering requirements, use 3D modeling software to construct the 3D model of the optimized impeller, import the model into the finite element analysis software for strength calculation, and compare the stress values at the key parts such as the blade leading edge, trailing edge, and shaft hole before and after optimization to evaluate the optimization effect.

2. A method for optimizing the strength design of a turbocharger impeller according to claim 1, characterized in that: In step S1, for each structural parameter, determine the data type and the change range of each structural parameter. The fillet size at the root of the blade leading edge is discrete data, and the value range is 1mm, 1.5mm, 2mm, 2.5mm; the thickness of the impeller back disk is continuous data, and the value range is 1.3mm to 2.5mm; the thickness of the back of the wheel at the shaft hole is continuous data, and the value range is 3mm to 8mm.

3. A method for optimizing the strength design of a turbocharger impeller according to claim 1, characterized in that: In step S3, the Kriging model assumes that there is a certain spatial correlation between sample points, and constructs a covariance function to describe this correlation; the covariance function is a Gaussian kernel function; Gaussian kernel function ; where is the variance, is the parameter related to the k-th structural parameter, and are the values of the k-th structural parameter of sample points i and j respectively. For the point to be predicted , by solving the system of equations , the weight coefficients are obtained, and then the predicted value is calculated where is the Lagrange multiplier.

4. A method for optimizing the strength design of a turbocharger impeller according to claim 1, characterized in that: In step S4, after initially constructing the Kriging model, use the adaptive sampling point addition algorithm to calculate the sample points with a large prediction error in the sample-deficient area, and use them as new training sample points to be added to the training sample point set of the model, so as to improve the fitting accuracy of the Kriging model.

5. A method for optimizing the strength design of a turbocharger impeller according to claim 1, characterized in that: In step S5, in the multi-objective differential evolution algorithm, the population size is determined to be 100, the mutation factor F ranges from 0.4 to 0.9, and the crossover probability CR ranges from 0.6 to 0.

95. The initial population is randomly initialized. In each generation of evolution, differential mutation operation is performed on each individual to generate mutant individuals, and then trial individuals are generated through crossover operation. The Kriging surrogate model is used to evaluate the multi-objective values corresponding to the impeller structure parameters of the trial individuals. After comparison with the original individuals, the better individuals are selected to enter the next generation population. The iteration continues until the termination condition is met, and a series of Pareto optimal solutions are obtained.

6. A method for optimizing the strength design of a turbocharger impeller according to claim 1, characterized in that: In step S6, after the initial optimization solution, some solutions in the obtained Pareto optimal solution set are added to the training sample point set of the Kriging model to further improve the fitting accuracy of the surrogate model, and the multi-objective differential evolution algorithm is used again for optimization calculation. The population size is determined to be 500, the mutation factor F ranges from 0.6 to 0.9, and the crossover probability CR ranges from 0.6 to 0.

95.

7. A method for optimizing the strength design of a turbocharger impeller according to claim 1, characterized in that: In step S7, the three-dimensional modeling software used maps the three-dimensional model of the impeller to the optimized structural parameters, and the finite element analysis software is used to check the structural strength of the impeller. If the design requirements are still not met, new solutions should be selected on the Pareto front for verification.

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