Rocket aerodynamic configuration optimization method considering carrying capacity

Abstract

The invention provides a rocket aerodynamic configuration optimization method considering carrying capacity. The method comprises the following steps: step 1, determining an optimization problem taking the carrying capacity as an optimization target, wherein the optimization problem comprises the optimization target and constraint conditions; 2, design variables are determined to describe the aerodynamic configuration of the carrier rocket, and a carrier rocket optimization design space construction sample set is determined; step 3, utilizing computational fluid mechanics to simulate and calculate aerodynamic characteristic parameters of the sample points; 4, calculating the aerodynamic resistance coefficient of the sample point according to a simulation result, and calculating the real effective load mass under the condition of considering the aerodynamic resistance through a speed loss formula; step 5, constructing proxy models according to the sample set by using different methods, and selecting an optimal proxy model; and step 6, optimizing by using a global optimization algorithm, and finding an optimal solution meeting the optimization problem, namely obtaining the maximum carrying quality of the effective load. The method provides a systematic efficient optimization approach for aerodynamic configuration design of the carrier rocket.

Description

Technical Field

[0001] The present invention designs an optimization method for the aerodynamic shape of a rocket considering the carrying capacity, belonging to the field of the shape design of launch vehicles. Background Art

[0002] The carrying capacity refers to the maximum mass that a rocket can send a payload to a specified orbit or a position in the target space, and it is the most important index to measure the performance of a launch vehicle, which determines the diversity and complexity of its missions. An overestimated carrying capacity valuation may lead to mission failure due to overload, while conversely, there will be a redundancy in the carrying capacity, increasing the engineering cost. The carrying capacity of a rocket does not simply depend on the increase in engine thrust and fuel load, and the aerodynamic shape design plays a crucial role in it. A rocket will experience a complex aerodynamic environment during flight, especially when passing through the atmosphere, and the influence of the aerodynamic shape on the flight performance is particularly prominent. An optimized aerodynamic shape can effectively reduce air resistance, improve flight efficiency, and enhance the carrying capacity of the rocket. Traditional methods need to iterate through the "design - simulation - improved design" process repeatedly, which is costly, inefficient, and may not necessarily obtain the optimal solution. Therefore, the carrying capacity of a rocket and its aerodynamic shape are closely related, and the optimization of both needs to complement each other. The present invention designs an optimization method for the aerodynamic shape of a rocket considering the carrying capacity. Summary of the Invention

[0003] Aiming at the problems such as the high iteration cost and difficulty in obtaining the global optimal solution in the optimization design of the aerodynamic shape of a launch vehicle when improving the carrying capacity, the present invention proposes an optimization method for the aerodynamic shape of a rocket considering the carrying capacity.

[0004] To solve the above - mentioned technical problems, the present invention is realized through the following technical solutions: An optimization method for the aerodynamic shape of a rocket considering the carrying capacity, comprising the following steps: Step 1, determine the optimization problem with the carrying capacity as the optimization objective, including the optimization objective and the constraint conditions; Step 2, determine the design variables to fully describe the aerodynamic shape of the launch vehicle, and confirm the design space to construct a sample set; Step 3, use computational fluid dynamics simulation to calculate the aerodynamic characteristic parameters of the sample points; Step 4, calculate the aerodynamic drag coefficient of the sample points according to the simulation results, and calculate the true payload mass considering the aerodynamic drag through the velocity loss formula; Step 5, construct a surrogate model using different methods according to the sample set, and select the best surrogate model; Step 6, use a global optimization algorithm for optimization to find the best solution that meets the optimization problem, that is, obtain the carrying mass of the maximum payload.

[0005] The optimization problem determined in Step 1 with the optimization objective of launch vehicle carrying capacity includes the optimization objective and constraints.

[0006] The optimization objective is to maximize the payload mass of the launch vehicle under special working conditions; the constraints are that the masses of the engines at all levels of the launch vehicle are constant, the maximum surface heat flux does not exceed the maximum heat flux of the initial shape, and the volume of the main body of the launch vehicle is not less than the initial shape, etc.

[0007] In Step 2, the design variables include: the continuous variable of the leading edge radius of the main body, the length-to-diameter ratio of the launch vehicle, the leading edge radius of the booster, and the geometric parameters of the booster airfoil; the discrete variable of the flight angle of attack of the launch vehicle; the optimization design space of the launch vehicle is composed of the value ranges of the continuous variables and the discrete variables. Reasonable sampling is carried out in each dimension of the design variables to ensure the sampling efficiency and the accuracy of the results.

[0008] In Step 3, computational fluid dynamics simulation is used to calculate the aerodynamic drag of the sample points.

[0009] In Step 4, the aerodynamic drag coefficients of each sample point are calculated using the aerodynamic drag obtained in Step 3, and the surface reference area of the aerodynamic shape is obtained through modeling software. The dynamic pressure of the oncoming atmosphere corresponding to the working conditions is obtained by querying the atmospheric parameter table. After obtaining the aerodynamic drag coefficient, the aerodynamic drag loss coefficient of the launch vehicle is obtained by looking up the table according to the altitude and Mach number. Under the condition of constant thrust, the speed loss caused by aerodynamic drag is considered in the design process. According to the calculation formulas of speed loss and the effective payload of the launch vehicle, the true effective payload of the launch vehicle is calculated under the condition that the masses of other components remain unchanged and the speed losses in other links are constant, forming a training set composed of design variables and effective payload. The calculation formulas for the aerodynamic drag coefficient, speed loss, and their relationships with the rocket's effective payload are as follows: ; where \(R\) represents the aerodynamic drag, decomposed into drag \(X\), lift \(Y\), and side force \(Z\), \(C y α is the derivative of the lift coefficient with respect to the angle of attack α , S ref is the rocket reference area, \(q\) is the dynamic pressure, α is the angle of attack, β is the sideslip angle, ΔV D represents the speed loss caused by aerodynamic drag during the flight segment, K D is the aerodynamic drag loss coefficient, which can be obtained by looking up the table according to the altitude and Mach number corresponding to the aerodynamic drag coefficient C D , Δ V ideal is the ideal speed increment, ΔVreal is the actual speed increment, ΔV need is the speed increment required to complete the mission, ΔV g is the gravitational speed loss, ΔV T is the nozzle pressure speed loss, ΔV α is the angle of attack speed loss, m 0 represents the total mass excluding the payload, m payload represents the payload mass, t represents the engine shutdown time, I SPV represents the vacuum specific impulse.

[0010] The surrogate models that can be used in Step 5 are one of the following models: Kriging model, Response Surface Methodology (RSM), Radial Basis Functions (RBF), etc. Select 3 error analysis indicators to compare the prediction accuracy of the surrogate models: coefficient of determination R 2 , root mean square error RMSE, maximum error ME, and select the best surrogate model.

[0011] The global optimization algorithm used is the genetic algorithm, which searches for the global optimum through iterative genetics.

[0012] The advantages of the present invention compared with the prior art are as follows: The present invention proposes an aerodynamic shape optimization method considering the carrying capacity, including the following steps: determining the optimization problem with the carrying capacity as the optimization goal, including the optimization goal and constraints; determining the design variables to fully describe the aerodynamic shape of the launch vehicle; determining the design space and sampling using a suitable method to obtain sample points; calculating the aerodynamic characteristic parameters of the sample points using computational fluid dynamics simulation; calculating the aerodynamic drag coefficient of the sample points according to the simulation results, and calculating the true payload mass considering aerodynamic drag through the speed loss formula; constructing surrogate models using different methods according to the sample set, and selecting the best surrogate model; using the global optimization algorithm for optimization to find the best solution that satisfies the optimization problem, that is, obtaining the carrying mass of the maximum payload. Through this method, the aerodynamic shape design is directly introduced into the calculation of the rocket payload, simplifying the complex iterative process and improving the design efficiency.

[0013] Any technical solution of the present invention may not necessarily achieve all the above beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1Flow chart for obtaining a training set for the invention.

[0015] Figure 2 Schematic diagram of the aerodynamic shape parameters of the launch vehicle. Detailed implementation manners

[0016] The following further elaborates on the present invention in conjunction with the accompanying drawings and embodiments.

[0017] In the current embodiment, an optimization problem with the launch capacity as the optimization objective is determined, including the optimization objective and constraints; design variables are determined to fully describe the aerodynamic shape of the launch vehicle; a design space is determined, and a suitable method is used to sample and obtain sample points; a sample set is constructed, and the launch capacity parameters of the sample points are calculated by combining simulation calculation methods and theoretical formulas; different methods are used to construct a surrogate model based on the sample set, and the best surrogate model is selected; a global optimization algorithm is used for optimization to find the best solution that satisfies the optimization problem, that is, to obtain the launch mass of the maximum effective payload. The flow block diagram is as Figure 1 shown, specifically including: Determine an optimization problem with the launch capacity as the optimization objective, including: the optimization objective and constraints. The optimization objective is that the effective payload mass of the launch vehicle is the largest under special working conditions; the constraints are that the masses of the engines of each stage of the launch vehicle are constant, the maximum surface heat flux does not exceed the maximum heat flux of the initial shape, and the volume of the main body of the launch vehicle is not less than the initial shape. The specific functions are as follows: ； Among them, m i is the mass of the engines of each stage of the launch vehicle, Q max is the peak surface heat flux of the launch vehicle, Volume is the volume of the launch vehicle, and the subscript initial represents the initial shape.

[0018] In the embodiment, the geometric shape model of the launch vehicle is as Figure 2 shown, and the design parameters are shown in Table 1 below: Table 1 Design variables of the optimization design problem of the launch vehicle

[0019] In the process of optimizing the design of the launch vehicle, in order to effectively improve the performance and efficiency of the design, the optimal Latin hypercube sampling method is used to select sample points from the design space. This method can evenly cover the entire design space, ensure that the selected sample points can comprehensively represent the characteristics of the design space, and then be used to construct a high-precision surrogate model.

[0020] Specifically, these sample points are first used to construct a three-dimensional geometric model through modeling software such as SolidWorks to ensure that the designed shape and dimensions can be accurately expressed. Then, the model is meshed using mesh generation software such as Pointwise to generate high-quality computational meshes required for calculations. Next, based on these meshes, numerical calculations of aerodynamic performance are carried out using computational fluid dynamics (CFD) software, and the payload mass corresponding to each aerodynamic shape is calculated using the theoretical formula between the payload and the aerodynamic relationship. The calculation formulas for the aerodynamic drag coefficient, velocity loss, and their relationships with the rocket payload are as follows: ; where R represents the aerodynamic drag, decomposed into the drag X, lift Y, and side force Z, C y α is the derivative of the lift coefficient with respect to the angle of attack α , S ref is the reference area of the rocket, q is the dynamic pressure, α is the angle of attack, β is the sideslip angle, ΔV D represents the velocity loss caused by aerodynamic drag during the flight segment, K D is the aerodynamic drag loss coefficient, which can be obtained by looking up the table according to the altitude and Mach number corresponding to the aerodynamic drag coefficient C D , Δ V ideal is the ideal velocity increment, ΔV real is the actual velocity increment, ΔV need is the velocity increment required to complete the mission, ΔV g is the gravitational velocity loss, ΔV T is the nozzle pressure velocity loss, ΔV α is the angle-of-attack velocity loss, m 0 represents the total mass excluding the payload, m payload represents the payload mass, t represents the engine shutdown time, I SPV represents the vacuum specific impulse.

[0021] After obtaining the aerodynamic drag parameters through computational fluid dynamics simulation, the aerodynamic drag coefficient is calculated using the drag calculation formula in the oncoming flow direction. After obtaining the aerodynamic drag coefficient, the aerodynamic drag loss coefficient of the launch vehicle is obtained by looking up the table according to the set altitude and Mach number. Under the condition of constant thrust, the velocity loss caused by aerodynamic drag is considered in the design process. ΔV D , according to the calculation formulas of velocity loss and the effective payload of the launch vehicle, with the total mass of other components m 0 remaining unchanged and the velocity losses in other links ΔV g , ΔV T , ΔV α being constant, the true effective payload of the launch vehicle is calculated, and finally a training set is formed with 12 shape design variables in Table 1 as the input and the true effective payload considering the velocity loss caused by aerodynamic drag as the output.

[0022] The training set composed of the effective payload data obtained through the above process and the design parameters of the sample points is used for the radial basis network model method surrogate model. The radial basis network surrogate model has a simple structure and high training efficiency, especially through parameter update algorithms such as the least squares method. The local response characteristics of the radial basis network make it perform well in classification and regression tasks and also have good generalization ability. To evaluate the accuracy of the surrogate model, a randomly sampled test set is used for verification, and the goodness-of-fit indexes of the model are calculated, including the coefficient of determination R², the root mean square error (RMSE), and the mean error (ME).

[0023] After the construction of the radial basis network model, combined with this surrogate model, the multi-island genetic algorithm (IGA) is used to optimize the aerodynamic shape of the launch vehicle. The multi-island genetic algorithm is a parallel evolutionary strategy. By dividing the population into multiple sub-populations, namely "islands", each island evolves independently in a local area and exchanges and migrates information between islands regularly. This method can accelerate the convergence process, avoid falling into local optimal solutions, and thus improve the global search ability. The population of each island adopts standard genetic algorithm operations such as selection, crossover, and mutation, while the migration mechanism between islands is achieved by regularly exchanging a certain proportion of individuals. The specific parameter settings of the algorithm are as follows: the number of islands is 4, the population size of each island is 10, the maximum number of generations is 1500 generations, and the stagnation judgment threshold is 1e-10 to ensure timely stopping when the algorithm converges. A mixed coding method of real numbers and integers is adopted within each island to flexibly represent the design variables. The migration period between islands is set to 50 generations, and the migration ratio is set to 10%. Through this multi-island parallel strategy, optimization can be carried out simultaneously in multiple search spaces, avoiding the early convergence problem that may occur in traditional genetic algorithms. Finally, through the optimization process of the multi-island genetic algorithm, the optimal launch vehicle shape parameters under the constraint conditions are obtained.

[0024] This optimization process not only ensures the efficient exploration of the design space, but also significantly improves the launch performance of the rocket and effectively reduces the computational cost in the design and analysis process by combining the radial basis network surrogate model with the multi-island genetic algorithm.

[0025] This example uses an optimization method to enhance the payload of the launch vehicle based on the aerodynamic shape. Using the surrogate model, an accurate prediction model between the aerodynamic shape and the payload mass is constructed to replace complex numerical calculations, effectively improving the global optimality of the shape design and reducing the high cost of traditional iterative design.

[0026] Although the present invention has been disclosed above with preferred embodiments, it is not used to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solution of the present invention using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes, and decorations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention all belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity, characterized in that, It includes the following steps: Step 1: Determine the optimization problem with the carrying capacity as the optimization goal, including the optimization goal and constraints; Step 2: Determine the design variables to describe the aerodynamic shape of the launch vehicle, and confirm the optimization design space of the launch vehicle to construct a sample set; Step 3: Use computational fluid dynamics simulation to calculate the aerodynamic characteristic parameters of the sample points; Step 4: Calculate the aerodynamic drag coefficient of the sample points according to the simulation results, and calculate the true effective payload mass considering aerodynamic drag through the velocity loss formula; Step 5: Use different methods to construct a surrogate model based on the sample set, and select the best surrogate model; Step 6: Use the global optimization algorithm to perform optimization to find the best solution that satisfies the optimization problem, that is, obtain the carrying mass of the maximum effective payload.

2. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 1, wherein: In step 1, the optimization goal is to maximize the effective payload mass of the launch vehicle under the working conditions; the constraints are that the masses of the engines of each stage of the launch vehicle are constant, the maximum surface heat flux does not exceed the maximum heat flux of the initial shape, and the main body volume of the launch vehicle is not less than the initial shape.

3. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 1, wherein: In step 2, the design variables include continuous variables and discrete variables. The continuous variables include the leading edge radius of the main body, the length-to-diameter ratio of the launch vehicle, the leading edge radius of the booster, and the geometric parameters of the booster airfoil; the discrete variable includes the flight angle of attack of the launch vehicle; the optimization design space of the launch vehicle is composed of the value ranges of the continuous variables and the discrete variables together; sampling is performed on each dimension of the design variables, and the shapes are generated batch by batch according to the sampling data, and the obtained shape parameters are used to generate sample points, and a sample set for constructing a surrogate model is constructed from these sample points.

4. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 3, characterized in that: The optimal Latin hypercube sampling method is used to select sample points from the design space.

5. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 1, wherein: In step 3, computational fluid dynamics simulation is used to calculate the aerodynamic drag of the sample points.

6. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 5, wherein: In step 4, the aerodynamic drag coefficient of each sample point is calculated using the aerodynamic drag obtained in step 3, and the true effective payload mass that the launch vehicle can carry considering the consumption of aerodynamic drag is calculated through the velocity loss formula. The calculation formula is as follows: ； where \(R\) represents the aerodynamic drag, which is decomposed into the drag \(X\), the lift \(Y\), and the side force \(Z\), \(C\) y α is the derivative of the lift coefficient with respect to the angle of attack α , S ref is the reference area of the rocket, \(q\) is the dynamic pressure, α is the angle of attack, β is the sideslip angle, ΔV D represents the velocity loss caused by the aerodynamic drag during the flight segment, K D is the aerodynamic drag loss coefficient, which can be obtained by looking up the table according to the altitude and Mach number corresponding to the aerodynamic drag coefficient C D , Δ V ideal is the ideal velocity increment, ΔV real is the actual velocity increment, ΔV need is the velocity increment required to complete the mission, ΔV g is the gravity velocity loss, ΔV T is the nozzle pressure velocity loss, ΔV α is the angle-of-attack velocity loss, m 0 represents the total mass excluding the payload, m payload represents the payload mass, \(t\) represents the engine cutoff time, I SPV represents the vacuum specific impulse.

7. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 1, wherein: In the said step 5, continuous surrogate models within the design space are constructed by using different surrogate model construction methods, and three error analysis indicators are selected to compare the prediction accuracy of the surrogate models: coefficient of determination R 2 , root mean square error RMSE, and maximum error ME. The best surrogate model is selected by comprehensively considering these three indicators.

8. A method for optimizing the aerodynamic shape of a rocket considering the carrying capacity according to claim 1, wherein: The global optimization algorithm used is the Island Genetic Algorithm (IGA), and the global optimal point is selected through iterative genetic screening.

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