A solid-liquid combined power performance optimization method based on a hybrid optimization framework

Abstract

The application relates to the field of combined engines and discloses a solid-liquid combined power performance optimization method based on a hybrid optimization framework, which adopts a heuristic optimization algorithm to quickly iterate in a solid and liquid power sub-optimization model, establishes the connection between a solid power charge scheme and an optimal solid and liquid power scheme, and adopts a surrogate optimization algorithm to optimize the solid power charge parameters. The solid-liquid combined power performance optimization model can strengthen the coupling degree of the solid and liquid power design, improve the design optimization efficiency, and overcome the shortcomings of long time consumption and low optimization precision of the solid and liquid power design by using a traditional power design optimization method.

Description

Technical Field

[0001] This invention relates to the field of combined engines, and more specifically to a method for optimizing the dynamic performance of solid-liquid combined engines based on a hybrid optimization framework. Background Technology

[0002] The use of a combined propulsion system based on solid rocket motors and turbojet engines is crucial for enabling aircraft to achieve short-duration boost and long-duration cruise. The combination of two different engine configurations increases the difficulty of performance optimization design for the propulsion system. Furthermore, the different simulation time consumption of solid rocket motors and turbojet engines places higher demands on optimization methods. Traditional single-architecture optimization methods are insufficient for efficiently solving the performance optimization problem of solid-liquid combined propulsion. To improve the efficiency of solid-liquid combined propulsion performance optimization and shorten the optimization time, the relationship between solid-liquid combined propulsion performance and the overall performance of the aircraft is decoupled, and a hybrid optimization framework is established to achieve efficient and high-precision propulsion scheme optimization design. Summary of the Invention

[0003] The purpose of this invention is to provide a solid-liquid combined dynamic performance optimization method based on a hybrid optimization framework, in order to solve the problems of long time consumption and poor optimization efficiency of traditional engine performance optimization methods when facing solid-liquid combined dynamic performance optimization problems.

[0004] To achieve the above objectives, the present invention employs the following technical solution: A method for optimizing the combined dynamic performance of solid and liquid propellants based on a hybrid optimization framework includes: constructing a two-layer hybrid optimization framework, the inner layer containing a solid dynamic sub-optimization model and a liquid dynamic sub-optimization model; and the outer layer being a proxy optimization sub-model for solid dynamic charge parameters. Step 1: The input to the outer solid dynamic charge parameter proxy optimization sub-model is multiple sets of solid dynamic charge parameters, and an initial solid dynamic charge scheme is established and stored in the charge scheme library to be optimized. Step 2, optimize the solid propellant charge scheme based on the surrogate optimization algorithm, including: taking the solid propellant charge scheme as the input of the solid propellant sub-optimization model, optimizing the solid propellant design parameters by using a heuristic optimization algorithm under a preset first optimization objective, obtaining the optimal solid propellant scheme that matches the solid propellant charge scheme, and taking it as the input of the liquid propellant sub-optimization model; Step 3: The liquid propulsion sub-optimization model is based on the liquid propulsion start-up flight condition. Under the current optimal solid propulsion scheme, the optimal overall flight performance corresponding to the scheme is taken as the second optimization objective. The heuristic optimization algorithm is used to optimize the liquid propulsion fuel weight and liquid propulsion design parameters, and the optimal liquid propulsion scheme and the corresponding optimal overall flight performance are output. The surrogate model is constructed using the solid propulsion charge parameters and the optimal overall flight performance. Step 4: Use the surrogate model to perform a global search and optimization of solid propellant charge parameters. Each time the surrogate optimization algorithm generates a new set of solid propellant charge parameters, it adds the corresponding new solid propellant charge scheme to the charge scheme library and drives the solid propellant sub-optimization model and the liquid propellant sub-optimization model to perform the next round of calculation. When the surrogate optimization algorithm converges, it outputs the optimal solid propellant charge parameters, as well as the optimal solid propellant scheme and the optimal liquid propellant scheme that are precisely matched with them.

[0005] Furthermore, in the initialization phase, a space-filling sampling strategy is first used to generate multiple sets of initial solid dynamic charge parameters within the parameter design space. Each set of solid dynamic charge parameters serves as a sample point, containing specific values ​​for the outer diameter of the charge, the inner diameter, the number of fins, the fin height, the fin thickness, the fin length, and the fin tilt angle. Based on each set of solid dynamic charge parameters, corresponding solid dynamic charge schemes are constructed, i.e., the specific three-dimensional geometric configuration of the solid charge determined by the solid dynamic charge parameters. The solid dynamic charge schemes are stored in the charge scheme library to be optimized, serving as the starting point for subsequent iterative evaluation of the inner layer model.

[0006] Furthermore, a surrogate optimization algorithm is employed, with the goal of maximizing overall flight performance, to perform outer-layer iterative optimization of the solid propellant parameters; the specific steps of the surrogate optimization algorithm are as follows: (i) Based on the propellant loading scheme library, drive the inner solid dynamics sub-optimization model and liquid dynamics sub-optimization model to complete the evaluation and obtain the optimal overall flight performance for each sample point; construct the initial surrogate model based on the sample points and the corresponding optimal overall flight performance; (ii) Apply the optimization filling criterion to the current proxy model, actively predict and screen out new sample points in the parameter design space, that is, new solid dynamic charge parameters; (iii) Based on the new sample points, construct the corresponding new solid propellant charge scheme and store it in the charge scheme library to be optimized. At the same time, use the inner solid propellant sub-optimization model and liquid propellant sub-optimization model to evaluate and obtain the optimal overall flight performance corresponding to the new sample points, which is used to update the surrogate model. (iv) Repeat steps (ii) to (iii) above until the algorithm converges.

[0007] Furthermore, the algorithm convergence means that: after multiple iterations, the change in the optimal objective function value found by the system is less than a preset minimum tolerance threshold; or when the total number of iterations of the algorithm reaches the preset resource limit of the engineering task.

[0008] Furthermore, the processing steps for the solid dynamics optimization model include: Retrieve solid dynamic charge schemes from the charge scheme library, perform burning face shift calculations based on the corresponding three-dimensional geometric configuration of the solid charge, and obtain the burning face shift results; The combustion surface displacement results are coupled with solid dynamic design parameters to obtain weighted solid dynamic performance. A heuristic optimization algorithm is adopted, with weighted solid dynamic performance as the primary optimization objective and solid dynamic design parameters as optimization variables for global iterative optimization. When the algorithm converges, the optimal solid dynamic scheme matching the solid dynamic charge scheme is output.

[0009] Furthermore, the solid propellant design parameters include propellant burning rate, nozzle throat diameter, and nozzle expansion ratio.

[0010] Furthermore, the processing steps for the fluid dynamics sub-optimization model include: The optimal solid propulsion scheme output by the solid propulsion sub-optimization model is received, and combined with the pre-given liquid propulsion fuel weight constraint, the liquid propulsion start-up flight condition is obtained through ballistic integral calculation. Based on the liquid propulsion start-up flight conditions and combined with the liquid propulsion design parameters, the liquid propulsion flight performance is obtained by establishing a component-level performance simulation model of the liquid propulsion engine. Based on the performance of liquid propulsion flight and the current optimal solid propulsion scheme, the overall flight performance of the aircraft is obtained using an aircraft dynamics simulation model; A heuristic optimization algorithm is adopted, with the overall flight performance as the second optimization objective, and the weight of liquid propellant fuel and liquid propellant design parameters as optimization variables for iterative optimization. When the algorithm converges, it outputs the optimal liquid propellant scheme that matches the current solid propellant design scheme. The overall flight performance at this time is the corresponding optimal overall flight performance.

[0011] Furthermore, the hydrodynamic design parameters include compressor boost ratio, turbine inlet temperature, and nozzle throat diameter.

[0012] A terminal device includes a processor, a memory, and a computer program stored in the memory; when the processor executes the computer program, it implements the solid-liquid combined dynamic performance optimization method based on a hybrid optimization framework.

[0013] A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the solid-liquid combined dynamic performance optimization method based on a hybrid optimization framework.

[0014] Compared with the prior art, the present invention has the following technical features: This invention employs a heuristic optimization algorithm for rapid iteration in the solid and liquid dynamic sub-optimization model, establishing a connection between the solid propellant charge scheme and the optimal solid and liquid dynamic schemes. A surrogate optimization algorithm is then used to optimize the solid propellant charge parameters. Applying this method strengthens the coupling between solid and liquid dynamic design, improves design optimization efficiency, and overcomes the shortcomings of traditional dynamic design optimization methods, such as long design time, low optimization accuracy, and low efficiency. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the proxy optimization sub-model for solid propellant charge parameters according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the solid dynamic sub-model optimization model according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the liquid dynamics sub-optimization model according to an embodiment of the present invention. Detailed Implementation

[0016] See Figure 1 This invention discloses a method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework, constructing a two-layer hybrid optimization framework: the inner layer contains a solid dynamic sub-optimization model (…). Figure 2 ) and fluid dynamics optimization model ( Figure 3 The outer layer is a proxy optimization sub-model for solid propellant charge parameters (corresponding to...). Figure 1 (Closed-loop process within the dashed box). Through iterative coupling of inner and outer layers, efficient optimization of solid-liquid combined dynamics is achieved.

[0017] The technical solution of the present invention is as follows: Step 1: The input to the outer solid dynamic charge parameter proxy optimization sub-model is multiple sets of solid dynamic charge parameters, and an initial solid dynamic charge scheme is established and stored in the charge scheme library to be optimized.

[0018] Step 2, optimize the solid propellant charge scheme based on the surrogate optimization algorithm, including: taking the solid propellant charge scheme as the input of the solid propellant sub-optimization model, optimizing the solid propellant design parameters by using a heuristic optimization algorithm under a preset first optimization objective, obtaining the optimal solid propellant scheme that matches the solid propellant charge scheme, and taking it as the input of the liquid propellant sub-optimization model.

[0019] Step 3: The liquid propulsion sub-optimization model, based on the liquid propulsion start-up flight condition, takes the optimal overall flight performance (calculated based on the aircraft dynamics simulation model) of the current optimal solid propulsion scheme as the second optimization objective. It then uses a heuristic optimization algorithm to optimize the liquid propulsion fuel weight and liquid propulsion design parameters, outputting an optimal liquid propulsion scheme that coordinates with the current optimal solid propulsion scheme and its corresponding optimal overall flight performance. Finally, a proxy model is constructed using the solid propulsion charge parameters and the optimal overall flight performance. Step 4: Use a surrogate model to perform a global search and optimization of solid propellant charge parameters (search for new sample points, i.e., new solid propellant charge parameters, within the parameter design space based on methods such as the expected improvement criterion); for each new set of solid propellant charge parameters generated by the surrogate optimization algorithm, it adds the corresponding new solid propellant charge scheme to the charge scheme library and drives the solid propellant sub-optimization model and the liquid propellant sub-optimization model to perform the next round of calculation; when the surrogate optimization algorithm converges, it outputs the optimal solid propellant charge parameters, as well as the optimal solid propellant scheme and the optimal liquid propellant scheme that are precisely matched with them.

[0020] The specific processing steps for each sub-model in the above scheme are explained in detail below: 1. Sub-model for proxy optimization of solid propellant charge parameters.

[0021] This sub-model is used to drive the global agent optimization loop, and its specific processing includes: (1.1) In the initialization phase, a space-filling sampling strategy (such as Latin hypercube sampling) is first used to generate multiple sets of initial solid dynamic charge parameters in the parameter design space. Each set of solid dynamic charge parameters is a sample point, which includes a set of specific values ​​for the outer diameter of the charge, the inner diameter of the hole, the number of wings, the wing height, the wing thickness, the wing length, and the wing tilt angle. Based on each set of solid dynamic charge parameters, their corresponding solid dynamic charge schemes (i.e., the specific three-dimensional geometric configuration of the solid charge determined by the solid dynamic charge parameters) are constructed respectively, and they are stored together in the charge scheme library to be optimized as the starting point for subsequent inner layer model iterative evaluation.

[0022] (1.2) A surrogate optimization algorithm (such as a surrogate model based on Gaussian process regression, radial basis function, or response surface method) is adopted to perform outer-layer iterative optimization of solid propellant parameters with the goal of maximizing overall flight performance; the specific steps of the surrogate optimization algorithm are as follows: (i) Based on the charge scheme library, drive the inner solid dynamics sub-optimization model and liquid dynamics sub-optimization model to complete the evaluation and obtain the optimal overall flight performance of each sample point; based on the sample points and the corresponding optimal overall flight performance (i.e., as the real performance label), construct the initial surrogate model (such as the Kriging model or the radial basis function model).

[0023] (ii) Apply optimization filling criteria (such as expected improvement criteria or confidence lower bound criteria) to the current surrogate model to actively predict and screen new sample points in the parameter design space, i.e. new solid dynamic charge parameters.

[0024] (iii) Based on the new sample points, construct the corresponding new solid propellant charge scheme and store it in the charge scheme library to be optimized. At the same time, use the inner solid propellant sub-optimization model and liquid propellant sub-optimization model to evaluate and obtain the optimal overall flight performance corresponding to the new sample points, which is used to update the surrogate model. (iv) Repeat steps (ii) to (iii) above until the algorithm converges.

[0025] The convergence of the proxy optimization algorithm refers to the following: after multiple iterations, the change in the optimal objective function value (i.e., the overall flight performance value) found by the system is less than the preset minimum tolerance threshold; or when the total number of evaluations or total number of iterations of the algorithm reaches the preset resource limit of the engineering task.

[0026] 2. Solid dynamics optimization model.

[0027] like Figure 2 As shown, this sub-model is used to find the optimal solid dynamic design parameters under a given solid dynamic charge scheme. Its specific processing steps include: (2.1) Take the solid dynamic charge scheme to be evaluated from the charge scheme library, perform the burning surface shift calculation according to the corresponding three-dimensional geometric configuration of the solid charge, and obtain the burning surface shift result; specifically, this process simulates the change process of the burning surface of the charge continuously moving outward with the combustion process, and after discretization calculation, obtains the relationship curve of the solid charge burning surface area changing with the combustion time.

[0028] (2.2) The combustion surface shift results are coupled with solid propulsion design parameters (propellant burning rate, nozzle throat diameter, and nozzle expansion ratio) to obtain weighted solid propulsion performance. In one embodiment, the internal ballistic calculation model of the solid rocket engine is established to calculate the curves of the engine combustion chamber pressure and thrust over time. Based on this, several key performance indicators such as total impulse, average thrust, working time, and specific impulse are further extracted, and corresponding evaluation weight coefficients are assigned to each indicator in combination with the actual requirements of the flight mission. The weighted solid propulsion performance under this solid propellant charge scheme is obtained through weighted comprehensive calculation.

[0029] (2.3) Heuristic optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) are adopted, with weighted solid propulsion performance as the first optimization objective and solid propulsion design parameters as optimization variables for global iterative optimization. When the algorithm converges, the optimal solid propulsion scheme matching the solid propulsion charge scheme is output, including the optimal propellant burning rate, the optimal nozzle throat diameter and the optimal nozzle expansion ratio.

[0030] 3. Liquid dynamics sub-optimization model.

[0031] like Figure 3 As shown, this sub-model is used to find the optimal hydrodynamic scheme given the optimal solid dynamic scheme. Its specific processing steps include: (3.1) Receive the optimal solid propulsion scheme output by the solid propulsion sub-optimization model, and combine it with the pre-given liquid propulsion fuel weight constraint. Calculate the flight altitude, speed, and available overload of the aircraft through ballistic integration. This is the liquid propulsion start-up flight condition.

[0032] (3.2) Based on the liquid propulsion start-up flight conditions and combined with the liquid propulsion design parameters (compressor pressure ratio, turbine inlet temperature, nozzle throat diameter), the liquid propulsion flight performance is obtained; in one embodiment, by establishing a liquid propulsion engine component-level performance simulation model, the instantaneous thrust, unit fuel consumption rate and other performance parameters of the engine under the liquid propulsion start-up flight conditions (i.e., liquid propulsion flight performance) are calculated.

[0033] (3.3) Based on the performance of liquid propulsion flight and the current optimal solid propulsion scheme, the overall flight performance of the aircraft is obtained by using the aircraft dynamics simulation model (e.g., characterized by range or terminal speed).

[0034] (3.4) Heuristic optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) are adopted, with the overall flight performance optimization (maximization) as the second optimization objective, and the liquid fuel weight and liquid power design parameters as optimization variables for iterative optimization. When the algorithm converges, the optimal liquid power scheme matching the current solid propulsion design scheme is output, which includes the optimal liquid fuel weight, the optimal compressor pressure ratio, the optimal turbine inlet temperature and the optimal nozzle throat diameter. The overall flight performance at this time is the corresponding optimal overall flight performance.

[0035] Once the surrogate optimization algorithm converges, the optimal output of the system includes: The optimal solid propellant charge parameters are: (outer diameter of the charge, inner diameter of the bore, number of fins, fin height, fin thickness, fin length, and fin tilt angle), and the final solid propellant charge scheme is constructed based on these parameters.

[0036] Optimal solid propulsion scheme: The optimal propellant burning rate, optimal nozzle throat diameter, and optimal nozzle expansion ratio are obtained by the solid propulsion sub-optimization model during the final evaluation; Optimal hydrodynamic scheme: The optimal liquid fuel weight, optimal compressor pressure ratio, optimal turbine inlet temperature, and optimal nozzle throat diameter are obtained by the hydrodynamic sub-optimization model during the final evaluation.

[0037] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework, characterized in that, include: A two-layer hybrid optimization framework is constructed, with the inner layer containing a solid dynamics sub-optimization model and a liquid dynamics sub-optimization model; the outer layer is a proxy optimization sub-model for solid dynamics charge parameters. Step 1: The input to the outer solid dynamic charge parameter proxy optimization sub-model is multiple sets of solid dynamic charge parameters, and an initial solid dynamic charge scheme is established and stored in the charge scheme library to be optimized. Step 2, optimize the solid propellant charge scheme based on the surrogate optimization algorithm, including: taking the solid propellant charge scheme as the input of the solid propellant sub-optimization model, optimizing the solid propellant design parameters by using a heuristic optimization algorithm under a preset first optimization objective, obtaining the optimal solid propellant scheme that matches the solid propellant charge scheme, and taking it as the input of the liquid propellant sub-optimization model; Step 3: The liquid propulsion sub-optimization model is based on the liquid propulsion start-up flight condition. Under the current optimal solid propulsion scheme, the optimal overall flight performance corresponding to the scheme is taken as the second optimization objective. The heuristic optimization algorithm is used to optimize the liquid propulsion fuel weight and liquid propulsion design parameters, and the optimal liquid propulsion scheme and the corresponding optimal overall flight performance are output. The surrogate model is constructed using the solid propulsion charge parameters and the optimal overall flight performance. Step 4: Use the surrogate model to perform a global search and optimization of solid propellant charge parameters. Each time the surrogate optimization algorithm generates a new set of solid propellant charge parameters, it adds the corresponding new solid propellant charge scheme to the charge scheme library and drives the solid propellant sub-optimization model and the liquid propellant sub-optimization model to perform the next round of calculation. When the surrogate optimization algorithm converges, it outputs the optimal solid propellant charge parameters, as well as the optimal solid propellant scheme and the optimal liquid propellant scheme that are precisely matched with them.

2. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 1, characterized in that, In the initialization phase, a space-filling sampling strategy is first used to generate multiple sets of initial solid dynamic charge parameters in the parameter design space. Each set of solid dynamic charge parameters serves as a sample point, containing specific values ​​for the outer diameter of the charge, the inner diameter, the number of fins, the fin height, the fin thickness, the fin length, and the fin tilt angle. Based on each set of solid dynamic charge parameters, corresponding solid dynamic charge schemes are constructed, which are the specific three-dimensional geometric configurations of the solid charge determined by the solid dynamic charge parameters. The solid dynamic charge schemes are stored in the charge scheme library to be optimized, serving as the starting point for subsequent iterative evaluation of the inner layer model.

3. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 1, characterized in that, A surrogate optimization algorithm is employed, with the goal of maximizing overall flight performance, to perform outer-layer iterative optimization of the solid propellant parameters. The specific steps of the surrogate optimization algorithm are as follows: (i) Based on the propellant loading scheme library, drive the inner solid dynamics sub-optimization model and liquid dynamics sub-optimization model to complete the evaluation and obtain the optimal overall flight performance for each sample point; construct the initial surrogate model based on the sample points and the corresponding optimal overall flight performance; (ii) Apply the optimization filling criterion to the current proxy model, actively predict and screen out new sample points in the parameter design space, that is, new solid dynamic charge parameters; (iii) Based on the new sample points, construct the corresponding new solid propellant charge scheme and store it in the charge scheme library to be optimized. At the same time, use the inner solid propellant sub-optimization model and liquid propellant sub-optimization model to evaluate and obtain the optimal overall flight performance corresponding to the new sample points, which is used to update the surrogate model. (iv) Repeat steps (ii) to (iii) above until the algorithm converges.

4. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 3, characterized in that, The algorithm convergence refers to the following: after multiple iterations, the change in the optimal objective function value found by the system is less than the preset minimum tolerance threshold; or the total number of iterations of the algorithm reaches the preset resource limit of the engineering task.

5. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 1, characterized in that, The processing steps for the solid dynamics optimization model include: Retrieve solid dynamic charge schemes from the charge scheme library, perform burning face shift calculations based on the corresponding three-dimensional geometric configuration of the solid charge, and obtain the burning face shift results; The combustion surface displacement results are coupled with solid dynamic design parameters to obtain weighted solid dynamic performance. A heuristic optimization algorithm is adopted, with weighted solid dynamic performance as the primary optimization objective and solid dynamic design parameters as optimization variables for global iterative optimization. When the algorithm converges, the optimal solid dynamic scheme matching the solid dynamic charge scheme is output.

6. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 5, characterized in that, The solid propellant design parameters include propellant burning rate, nozzle throat diameter, and nozzle expansion ratio.

7. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 1, characterized in that, The processing steps for the fluid dynamics sub-optimization model include: The optimal solid propulsion scheme output by the solid propulsion sub-optimization model is received, and combined with the pre-given liquid propulsion fuel weight constraint, the liquid propulsion start-up flight condition is obtained through ballistic integral calculation. Based on the liquid propulsion start-up flight conditions and combined with the liquid propulsion design parameters, the liquid propulsion flight performance is obtained by establishing a component-level performance simulation model of the liquid propulsion engine. Based on the performance of liquid propulsion flight and the current optimal solid propulsion scheme, the overall flight performance of the aircraft is obtained using an aircraft dynamics simulation model; A heuristic optimization algorithm is adopted, with the overall flight performance as the second optimization objective, and the weight of liquid propellant fuel and liquid propellant design parameters as optimization variables for iterative optimization. When the algorithm converges, it outputs the optimal liquid propellant scheme that matches the current solid propellant design scheme. The overall flight performance at this time is the corresponding optimal overall flight performance.

8. The method for optimizing the dynamic performance of solid-liquid composite systems based on a hybrid optimization framework according to claim 7, characterized in that, The hydrodynamic design parameters include compressor boost ratio, turbine inlet temperature, and nozzle throat diameter.

9. A terminal device, comprising a processor, a memory, and a computer program stored in the memory; characterized in that, When the processor executes the computer program, it implements the solid-liquid combined dynamic performance optimization method based on the hybrid optimization framework as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program; characterized in that, When the computer program is executed by the processor, it implements the solid-liquid combined dynamic performance optimization method based on the hybrid optimization framework as described in any one of claims 1-8.

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