An automatic unmanned aerial vehicle aerodynamic shape optimization method and system based on SU2
By using an automated UAV aerodynamic shape optimization method based on SU2, efficient and stable optimization of UAV shape is achieved, solving the problems of cumbersome operation and human error in traditional methods, and improving the stability and versatility of optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHUOYI ZHINENG
- Filing Date
- 2026-03-20
- Publication Date
- 2026-06-19
AI Technical Summary
Traditional UAV aerodynamic shape optimization processes are cumbersome, prone to human error, and lack stability and repeatability, resulting in poor versatility.
An automated UAV aerodynamic shape optimization method based on SU2 is adopted. Through parameterized characterization, mesh processing, flow field calculation, gradient solution and unified scheduling of shape deformation, the gradient information of the aerodynamic objective function is obtained by combining the discrete adjoint method, and the elastic body method is used for optimization.
It improves the stability and repeatability of the optimization process, reduces the cost of gradient calculation, and improves the convergence speed and reliability of the results.
Smart Images
Figure CN122241868A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aerodynamic shape technology for unmanned aerial vehicles (UAVs), and in particular to an automated aerodynamic shape optimization method and system for UAVs based on the SU2. Background Technology
[0002] The aerodynamic shape of an unmanned aerial vehicle (UAV) has a decisive impact on its flight performance, endurance, and mission capabilities. Currently, computational fluid dynamics (CFD) methods are widely used in the aerodynamic design process of UAVs, enabling the prediction of aerodynamic characteristics under different flight conditions in a virtual environment. This reduces the number of wind tunnel tests and lowers design costs. With the development of open-source CFD platforms, open-source solvers such as SU2 are increasingly being applied to aerodynamic analysis and optimization design due to their strong scalability, support for adjoint solutions, and ease of secondary development.
[0003] However, traditional aerodynamic shape optimization processes are often characterized by fragmented processes and strong module independence. Each calculation step usually relies on manual configuration and invocation, which is not only cumbersome to operate, but also prone to human error. This makes it difficult to guarantee the stability and repeatability of the optimization process. When facing different types of UAV optimization problems, its optimization framework is difficult to adapt quickly and has poor versatility.
[0004] Therefore, how to provide an automated UAV aerodynamic shape optimization method and system based on SU2 is an urgent problem to be solved. Summary of the Invention
[0005] This invention provides an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method and system based on SU2 to solve the problems mentioned above in the prior art.
[0006] According to a first aspect of the present invention, an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method based on SU2 is provided.
[0007] In one embodiment, the SU2-based automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method includes: The shape of the UAV is parametrically characterized to obtain a control volume model of the UAV shape; The control volume model is meshed using preset mesh parameters, and the processed control volume model is then converted to a new format to obtain a mesh file. Based on preset flow condition parameters, flow field calculations are performed using a mesh file to obtain the aerodynamic objective function; The gradient of the aerodynamic objective function is calculated using preset design variables to obtain the optimized objective gradient information; Based on the optimized target gradient information, and using the elastic body method to rapidly deform the control body model, the optimal UAV shape is obtained; Based on the optimization objective gradient information, the design variables are optimized, and it is determined whether the optimized design variables meet the optimization objective. If they do, the optimal design variables are output and used as the new design variables. If they do not meet the objective, the flow field calculation is performed again.
[0008] In one embodiment, the control volume model is meshed using preset mesh parameters, and the processed control volume model is then converted to a new format to obtain a mesh file, including: Mesh density processing is performed on the parametric model, which includes mesh refinement processing of the leading and trailing edge regions, transition region, wing leading and trailing edge regions, boundary layer region, control surface region, and propeller region of the UAV body surface in the parametric model. The mesh is sparsed in the far-field region far from the UAV body, the flat region on the body surface, and the region without flow gradient changes.
[0009] In one embodiment, based on preset flow condition parameters and combined with a mesh file, flow field calculations are performed to obtain the aerodynamic objective function, which includes: Flow condition parameters include at least Mach number, angle of attack, and Reynolds number; Write the mesh file and flow condition parameters into the SU2 configuration file, and set the pre-selected turbulence model; The viscous and inviscid terms in the preset flow control equations are discretized to obtain a set of nonlinear equations, and the aerodynamic objective function is obtained by iteratively solving the set of nonlinear equations.
[0010] In one embodiment, the viscous and inviscid terms in the preset flow control equations are discretized to obtain a set of nonlinear equations, and the aerodynamic objective function is obtained by iteratively solving the set of nonlinear equations, including: The implicit Euler method and multigrid were used to iteratively calculate the nonlinear equations and obtain the convergent flow field solution vector. After obtaining the flow field solution vector, the pressure distribution and viscous stress on the surface of the UAV are integrated to obtain the aerodynamic objective function.
[0011] In one embodiment, the gradient calculation of the aerodynamic objective function using preset design variables yields the following optimization objective gradient information: Calculate the derivatives of the aerodynamic objective function and the design variables, and construct the discrete adjoint equations corresponding to the aerodynamic objective function based on the pre-introduced adjoint variables; The discrete adjoint equation is solved iteratively to obtain the numerical values of the adjoint variables. Combined with the relationship between the aerodynamic objective function and the design variables, the optimization objective gradient information is calculated.
[0012] In one embodiment, calculating the derivatives of the aerodynamic objective function and the design variables, and constructing the discrete adjoint equation corresponding to the aerodynamic objective function based on pre-introduced adjoint variables includes: Calculate the partial derivatives of the objective function with respect to the grid, the partial derivatives of the flow field residuals with respect to the computational grid, and the derivatives of the grid with respect to the design variables.
[0013] In one embodiment, the design variables are optimized based on the optimization target gradient information, and it is determined whether the optimized design variables meet the optimization target. If they do, the optimal design variables are output and used as the new design variables; otherwise, the flow field calculation is performed again, including: The optimization objectives include whether the optimized numerical results meet the set targets, whether the optimization process converges, and whether the geometric and aerodynamic constraints meet the constraint conditions.
[0014] In one embodiment, the expression for the aerodynamic objective function is: ; In the formula, v Indicates the variable to be counted. W(v) Represents the flow field solution vector. X(v) Represents a spatial grid.
[0015] In one embodiment, the expression for the flow control equation is: ; In the formula, Q Represents a conserved variable. V Indicates the volume of the control volume. S Indicates the area of the control surface. F Indicates the viscosity-free flux. G Indicates viscous flux. This represents the rate of change of the conserved variable within the control body over time, i.e., the increment of the conserved quantity within the control body per unit time. d S represents the area vector of the control surface.
[0016] According to a second aspect of the present invention, an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization system based on SU2 is provided.
[0017] In one embodiment, the SU2-based automated unmanned aerial vehicle (UAV) aerodynamic shape optimization system includes: The parameter characterization module is used to parameterize the shape of the UAV and obtain the control volume model of the UAV shape. The mesh processing module is used to mesh the control volume model using preset mesh parameters, and then convert the processed control volume model into a mesh file. The flow field calculation module is used to perform flow field calculations based on preset flow condition parameters and mesh files to obtain the aerodynamic objective function; The gradient calculation module is used to calculate the gradient of the aerodynamic objective function using pre-configured design variables to obtain the optimization target gradient information. The mesh deformation module is used to rapidly deform the control volume model based on the optimization target gradient information and the elastic body method to obtain the optimal UAV shape; The optimization module is used to optimize the design variables based on the optimization target gradient information and determine whether the optimized design variables meet the optimization target. If they do, the optimal design variables are output and used as the new design variables. If they do not meet the target, the flow field calculation is performed again.
[0018] According to a third aspect of the present invention, a computer device is provided.
[0019] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.
[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.
[0021] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.
[0022] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention achieves unified scheduling of geometric modeling, mesh generation, flow field calculation, gradient solving, shape deformation, and optimization by parametrically representing the shape of a UAV. It constructs a complete automated aerodynamic shape optimization process, effectively reducing manual intervention, avoiding uncertainties caused by manual operation, and improving the stability and repeatability of the optimization process. Flow field calculation is performed based on the SU2 platform, and the discrete adjoint method is introduced to obtain the gradient information of the aerodynamic objective function relative to the design variables, reducing the gradient calculation cost. By combining the gradient information with a sequential quadratic programming optimization algorithm, the design variables are iteratively updated under the premise of satisfying geometric and aerodynamic constraints. This allows the UAV shape to evolve along the direction most favorable to aerodynamic performance during the optimization process, improving the optimization convergence speed and the reliability of the optimization results.
[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description
[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0025] Figure 1 This is a flowchart illustrating an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method based on SU2, according to an exemplary embodiment. Figure 2 This is a schematic diagram illustrating the principle of an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization system based on SU2, according to an exemplary embodiment. Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment; Figure 4 This is a control body model of the UAV shape in an automated UAV aerodynamic shape optimization method based on SU2, as illustrated in an exemplary embodiment. Figure 5 This is a pressure cloud map of the upper wing surface of an SU2-based automated unmanned aerial vehicle (UAV) before optimization, according to an exemplary embodiment. Figure 6 This is a pressure cloud map of the outer upper wing surface of an automated unmanned aerial vehicle (UAV) optimized according to an exemplary embodiment of an SU2-based aerodynamic shape optimization method. Figure 7 This is a flowchart illustrating a specific application of an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method based on SU2, according to an exemplary embodiment. Detailed Implementation
[0026] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.
[0027] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0028] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0029] Figure 1 and Figure 7 An embodiment of an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method based on SU2 according to the present invention is shown.
[0030] In this optional embodiment, the SU2-based automated unmanned aerial vehicle (UAV) aerodynamic shape optimization method includes: S101. Parametrically characterize the shape of the UAV to obtain the control body model of the UAV shape; S102. The control volume model is meshed using preset mesh parameters, and the processed control volume model is converted to a new format to obtain a mesh file. In this optional embodiment, the control volume model is meshed using preset mesh parameters, and the processed control volume model is converted to a new format to obtain a mesh file. This includes: performing mesh density processing on the parametric model, wherein the density processing includes refining the mesh in the leading and trailing edge regions, transition regions, wing leading and trailing edge regions, boundary layer regions, control surface regions, and propeller regions of the UAV body surface in the parametric model; and sparsening the mesh in the far-field regions far from the UAV body, the flat regions of the body surface, and the regions without flow gradient changes.
[0031] S103. Based on the preset flow condition parameters, the flow field is calculated using the mesh file to obtain the aerodynamic objective function; In this optional embodiment, based on preset flow condition parameters and combined with a mesh file, flow field calculations are performed to obtain the aerodynamic objective function, which includes: The flow condition parameters should include at least Mach number, angle of attack, and Reynolds number; write the mesh file and flow condition parameters into the SU2 configuration file and set the pre-selected turbulence model; The viscous and inviscid terms in the preset flow control equations are discretized to obtain a set of nonlinear equations, and the aerodynamic objective function is obtained by iteratively solving the set of nonlinear equations.
[0032] In this optional embodiment, the viscous and inviscid terms in the preset flow control equations are discretized to obtain a set of nonlinear equations, and the aerodynamic objective function is obtained by iteratively solving the set of nonlinear equations, including: The implicit Euler method and multigrid were used to iteratively calculate the nonlinear equations to obtain the convergent flow field solution vector. After obtaining the flow field solution vector, the pressure distribution and viscous stress on the surface of the UAV were integrated to obtain the aerodynamic objective function.
[0033] S104. Calculate the gradient of the aerodynamic objective function using preset design variables to obtain the optimized objective gradient information; In this optional embodiment, the gradient calculation of the aerodynamic objective function is performed using preset design variables to obtain the optimized objective gradient information, including: Calculate the derivatives of the aerodynamic objective function and design variables, and construct the discrete adjoint equations corresponding to the aerodynamic objective function based on the pre-introduced adjoint variables; calculate the partial derivatives of the objective function with respect to the grid, the partial derivatives of the flow field residuals with respect to the computational grid, and the derivatives of the grid with respect to the design variables.
[0034] The discrete adjoint equation is solved iteratively to obtain the numerical values of the adjoint variables. Combined with the relationship between the aerodynamic objective function and the design variables, the optimization objective gradient information is calculated.
[0035] S105. Based on the optimized target gradient information, and using the elastic body method to rapidly deform the control body model, the optimal UAV shape is obtained. S106. Based on the optimization target gradient information, optimize the design variables and determine whether the optimized design variables meet the optimization target. If they do, output the optimal design variables and use them as new design variables. If they do not meet the target, recalculate the flow field.
[0036] In this optional embodiment, the design variables are optimized based on the optimization target gradient information, and it is determined whether the optimized design variables meet the optimization target. If they do, the optimal design variables are output and used as new design variables; otherwise, the flow field calculation is performed again, including: The optimization objectives include whether the optimized numerical results meet the set targets, whether the optimization process converges, and whether the geometric and aerodynamic constraints meet the constraint conditions.
[0037] Specifically, such as Figure 4 As shown, the shape of the UAV is parametrically represented, and a control volume model is obtained by modeling the UAV shape using the free deformation method (FFD). The dimensions of the control volume are 10×8×1, and the corresponding number of control points is: 11 × 9 × 2 = 198; Then, the mesh size parameters and the control volume model are input into mesh generation software, such as Pointwise, Gmsh, and Salome. The mesh density settings must be appropriate. Density adjustments include refining the mesh in the leading and trailing edges, transition zones, wing leading and trailing edges, boundary layer, control surfaces, and propeller regions of the UAV body surface in the parametric model; and sparsening the mesh in far-field regions far from the UAV body, flat regions on the body surface, and regions without flow gradient changes. The control volume model, after density adjustments, is then converted into a mesh file adapted for the SU2 format.
[0038] Next, the mesh file and flow condition parameters are written into the SU2 configuration file, and the turbulence model is set to perform flow field calculations. The turbulence model is a mathematical model of the additional momentum transport caused by turbulence. The additional momentum transport caused by turbulence can be mathematically modeled as either the SA model or the SST model.
[0039] The viscous term is discretized using a second-order central difference scheme. The Gaussian divergence theorem is applied to transform the gradient calculation on the cell surface into a linear combination of the centroids of adjacent cells. The inviscid term is discretized using a second-order upwind JST scheme. In smooth regions, a low-dissipation central scheme is used to maintain accuracy, while in discontinuous regions, sufficient dissipation is automatically introduced to maintain stability.
[0040] The SU2 smooth solver (SU2_CFD) is called to perform flow field calculations on the control volume model and obtain the starting objective function values, such as lift coefficient, drag coefficient, and lift-to-drag ratio.
[0041] exist( x 1 , x 2 , x 3 In a three-dimensional Cartesian coordinate system, the three-dimensional conserved flow control equations are: ; In the formula, Q Represents a conserved variable. V Indicates the volume of the control volume. S Indicates the area of the control surface. F Indicates the viscosity-free flux. G Indicates viscous flux. This represents the rate of change of the conserved variable within the control group over time. dS The vector representing the area of the control surface.
[0042] in, Q The expression is: ; In the formula, ρ Indicates gas density, as well as This represents the momentum components in three directions. This represents the total energy term.
[0043] F The expression is: ; In the formula, Let represent the momentum component in the i-th coordinate direction. The convection flux term represents the momentum in one coordinate direction. This represents the pressure term in the i-th, 1-th component. Let represent the inviscid energy flux in the i-th direction.
[0044] The expression for G is: ; In the formula, This represents the component extraction term of the viscous stress tensor in the first direction. This represents the mechanical work flux term corresponding to viscous stress. This represents the heat conduction term along the i-th direction.
[0045] The equation of state for an ideal gas is: ; In the formula, ρ Let γ represent the gas density, and γ represent the specific heat ratio of the gas. In an ideal gas, γ = 1.4. E Represents total energy per unit mass. This represents the sum of squares of the velocity components.
[0046] The expression for the viscous stress tensor is: ; In the formula, p Indicates pressure. T Indicates temperature. μ Indicates the first viscosity coefficient. λ This represents the second viscosity coefficient. k Indicates the heat transfer coefficient. δij The symbol represents the Kronnico symbol, whose value range is (i=1,2,3; j=1,2,3). i=j hour, δij= 1; i ≠j hour, δij= 0, and This represents the components of velocity in different coordinate directions. and Represents spatial coordinate components, as well as Represents the velocity gradient term. This represents the velocity divergence term.
[0047] The relationship between the first viscosity coefficient and the second viscosity coefficient is as follows: : For viscosity calculations, the expressions for the viscosity coefficient and thermal conductivity coefficient are as follows: ; ; In the formula, μ l The viscosity is the laminar viscosity coefficient. μ t The turbulent viscosity coefficient; k l The laminar thermal conductivity coefficient; k t is the turbulent thermal conductivity coefficient.
[0048] The laminar viscosity coefficient is calculated using the temperature-viscosity correlation law formula: ; Simultaneously, the thermal conductivity coefficient is calculated using a turbulence model: ; In the formula, K Indicates the thermal conductivity coefficient. Pr l The Planck number for laminar flow; Pr t is the Planck number for turbulence; both have a value of 0.72.
[0049] Specifically, define the optimization objective, design variables, and constraints.
[0050] For example: the objective is to minimize C. D The design variable is Z. wing The constraint is C L =C L0 T1≥T 10 T2≥T 20 T3≥T 30 T4≥T 40 T5≥T 50 .
[0051] Where: C D Z is the drag coefficient; wing For the Z-axis displacement of the FFD control points, there are a total of 198; C L The lift coefficient is used to ensure that the lift and gravity are in equilibrium during flight by constraining the lift to remain constant; T1 and T 10 T2 and T 20 T3 and T 30 T4 and T 40 T5 and T 50 These are the optimized and original airfoil profile thicknesses at five cross-sections along the wing span (η=y / b=0, 0.2, 0.4, 0.6, 0.8), meaning the airfoil thickness at these five cross-sections should not be reduced after optimization.
[0052] As shown in Table 1, the drag coefficient decreased from 0.0189 in the basic configuration to 0.0158, a reduction of approximately 16.4%, while the section thickness met the constraint conditions. Table 1: Comparison of Aerodynamic Coefficients Before and After Optimization
[0053] Next, gradient calculation is performed to calculate the first derivative of the design variables with respect to the objective function in order to obtain the gradient information of the optimization objective.
[0054] Define the aerodynamic objective function as I The expression for the aerodynamic objective function is: ; In the formula, v Indicates design variables; W(v) Represents the flow field solution vector; X(v) Represents a spatial grid.
[0055] After obtaining the flow field solution vector, the above formula can be transformed into a set of discretized flow control equations. R Its expression is: ; Then, apply the above two formulas to... v Finding the total derivative, we obtain the formula for the first total derivative: ; And the formula for the second total derivative: ; Substituting the second total derivative formula into the first total derivative formula, we obtain the discrete adjoint equation: ; Due to the large computational cost of discrete adjoint equations, adjoint variables are introduced. : ; The expression for the accompanying variable is: ; Since the adjoint variables involve matrix inversion, the adjoint variables are transformed into a system of linear equations for calculation: ; In the formula, This represents the partial derivative of the aerodynamic objective function with respect to the computational grid. This represents the partial derivative of the flow field residuals with respect to the computational grid. This represents the derivative of the grid with respect to the design variables.
[0056] Then, based on the optimization gradient objective information, update the geometry of the control volume model.
[0057] Specifically, firstly, a computational domain is defined around the UAV's shape, and the mesh within this domain is treated as a continuous medium with elastic properties. Corresponding material property parameters are set for the computational domain. Then, displacement boundary conditions are applied to the boundary of the computational domain based on the updated UAV shape, causing the mesh nodes attached to the UAV's surface to displace. Simultaneously, constraint boundary conditions are applied to the far-field boundary. Next, the mesh deformation control equations based on linear elasticity theory are solved, allowing the mesh nodes within the computational domain to undergo continuous and smooth displacement changes while satisfying the boundary conditions. Finally, the original flow field computational mesh is updated based on the solved mesh node displacement field, resulting in a deformable mesh that matches the updated UAV shape.
[0058] After obtaining the gradient derivative of the aerodynamic objective function with respect to the design variables, a gradient optimization algorithm is used to implement the optimization process. Specifically, SLSQP (Sequential Quadratic Programming) can be selected as the optimization exploration algorithm in high-dimensional space. The optimization algorithm uses the gradient derivative as the new optimization direction and judges whether the optimization result satisfies the optimization objective. If it does, the loop terminates and the optimal design variables are output. If it does not, the calculation is repeated.
[0059] like Figure 5 and Figure 6 As shown, a comparison diagram of the pressure cloud map on the upper surface of the UAV wing is presented. Figure 6 It can be seen that the shock wave is significantly weakened after optimization, where Pressure_Coefficient represents the pressure coefficient.
[0060] Figure 2 An embodiment of an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization system based on SU2 is shown.
[0061] In this optional embodiment, an automated unmanned aerial vehicle (UAV) aerodynamic shape optimization system based on the SU2 includes: The parameter characterization module 201 is used to perform parameterized characterization of the UAV's shape to obtain a control volume model of the UAV's shape. The mesh processing module 202 is used to perform meshing processing on the control volume model using preset mesh parameters, and to convert the format of the processed control volume model to obtain a mesh file; The flow field calculation module 203 is used to perform flow field calculations based on preset flow condition parameters and in combination with a mesh file to obtain the aerodynamic objective function; Gradient calculation module 204 is used to calculate the gradient of the aerodynamic objective function using pre-configured design variables to obtain the optimized objective gradient information; The mesh deformation module 205 is used to rapidly deform the control volume model based on the optimization target gradient information and the elastic body method to obtain the optimal UAV shape; The optimization module 206 is used to optimize the design variables based on the optimization target gradient information and determine whether the optimized design variables meet the optimization target. If they do, the optimal design variables are output and used as new design variables. If they do not meet the target, the flow field calculation is performed again.
[0062] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.
[0063] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0064] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.
[0065] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.
[0066] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0067] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.
Claims
1. A method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2, characterized in that, include: The shape of the UAV is parametrically characterized to obtain a control volume model of the UAV shape; The control volume model is meshed using preset mesh parameters, and the processed control volume model is then converted to a new format to obtain a mesh file. Based on preset flow condition parameters, flow field calculations are performed using a mesh file to obtain the aerodynamic objective function; The gradient of the aerodynamic objective function is calculated using pre-configured design variables to obtain the optimization target gradient information; Based on the optimized target gradient information, and using the elastic body method to rapidly deform the control body model, the optimal UAV shape is obtained; Based on the optimization objective gradient information, the design variables are optimized, and it is determined whether the optimized design variables meet the optimization objective. If they do, the optimal design variables are output and used as the new design variables. If they do not meet the objective, the flow field calculation is performed again.
2. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 1, characterized in that, The process of meshing the control volume model using preset mesh parameters and then converting the processed control volume model to obtain a mesh file includes: Mesh density processing is performed on the parametric model, which includes mesh refinement processing of the leading and trailing edge regions, transition region, wing leading and trailing edge regions, boundary layer region, control surface region, and propeller region of the UAV body surface in the parametric model. The mesh is sparsed in the far-field region far from the UAV body, the flat region on the body surface, and the region without flow gradient changes.
3. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 1, characterized in that, The flow field calculation based on preset flow condition parameters and a mesh file yields the following aerodynamic objective function: Flow condition parameters include at least Mach number, angle of attack, and Reynolds number; Write the mesh file and flow condition parameters into the SU2 configuration file, and set the pre-selected turbulence model; The viscous and inviscid terms in the preset flow control equations are discretized to obtain a set of nonlinear equations, and the aerodynamic objective function is obtained by iteratively solving the set of nonlinear equations.
4. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 3, characterized in that, The discretization of the viscous and inviscid terms in the preset flow control equations yields a set of nonlinear equations, and the iterative solution of these equations yields the aerodynamic objective function, including: The implicit Euler method and multigrid were used to iteratively calculate the nonlinear equations and obtain the convergent flow field solution vector. After obtaining the flow field solution vector, the pressure distribution and viscous stress on the surface of the UAV are integrated to obtain the aerodynamic objective function.
5. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 1, characterized in that, The step of calculating the gradient of the aerodynamic objective function using preset design variables to obtain the optimized objective gradient information includes: Calculate the derivatives of the aerodynamic objective function and the design variables, and construct the discrete adjoint equations corresponding to the aerodynamic objective function based on the pre-introduced adjoint variables; The discrete adjoint equation is solved iteratively to obtain the numerical values of the adjoint variables. Combined with the relationship between the aerodynamic objective function and the design variables, the optimization objective gradient information is calculated.
6. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 5, characterized in that, The calculation of the derivatives of the aerodynamic objective function and the design variables, and the construction of the discrete adjoint equations corresponding to the aerodynamic objective function based on the pre-introduced adjoint variables, includes: Calculate the partial derivatives of the objective function with respect to the grid, the partial derivatives of the flow field residuals with respect to the computational grid, and the derivatives of the grid with respect to the design variables.
7. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 1, characterized in that, The process of optimizing the design variables based on the optimization target gradient information and determining whether the optimized design variables meet the optimization target, if so, outputting the optimal design variables as the new design variables; if not, recalculating the flow field, includes: The optimization objectives include whether the optimized numerical results meet the set targets, whether the optimization process converges, and whether the geometric and aerodynamic constraints meet the constraint conditions.
8. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 3, characterized in that, The expression for the aerodynamic objective function is: ； In the formula, v represents the variable to be counted. W(v) This represents the flow field solution vector. X(v) Represents a spatial grid.
9. The method for optimizing the aerodynamic shape of an automated unmanned aerial vehicle based on SU2 according to claim 3, characterized in that, The expression for the flow control equation is: ； In the formula, Q Represents a conserved variable. V Indicates the volume of the control volume. S Indicates the area of the control surface. F Indicates the viscosity-free flux. G Indicates viscous flux. This represents the rate of change of the conserved variable within the control body over time, i.e., the increment of the conserved quantity within the control body per unit time. d S represents the area vector of the control surface.
10. An automated unmanned aerial vehicle (UAV) aerodynamic shape optimization system based on SU2, characterized in that, include: The parameter characterization module is used to parameterize the shape of the UAV and obtain the control volume model of the UAV shape. The mesh processing module is used to mesh the control volume model using preset mesh parameters, and then convert the processed control volume model into a mesh file. The flow field calculation module is used to perform flow field calculations based on preset flow condition parameters and mesh files to obtain the aerodynamic objective function; The gradient calculation module is used to calculate the gradient of the aerodynamic objective function using pre-configured design variables to obtain the optimization target gradient information. The mesh deformation module is used to rapidly deform the control volume model based on the optimization target gradient information and the elastic body method to obtain the optimal UAV shape; The optimization module is used to optimize the design variables based on the optimization target gradient information and determine whether the optimized design variables meet the optimization target. If they do, the optimal design variables are output and used as the new design variables. If they do not meet the target, the flow field calculation is performed again.