Local aerodynamic heat optimization method and device based on discrete accompanying

Abstract

The invention provides a local aerodynamic heat optimization method and device based on discrete accompanying. The method comprises the following steps: drawing a structural grid of a local component under an incoming flow working condition according to an initial shape of the local component to be optimized and the incoming flow working condition; a control frame needed by grid deformation of the structural grid is obtained, the control frame internally comprises grid boundary points to be optimized, and the grid boundary points form an object plane of the local component; optimizing and updating the control frame to obtain an optimized coordinate of the control frame; and optimizing the initial appearance according to the optimization coordinates and the incoming flow working condition to obtain a target appearance of the local component. According to the embodiment of the invention, the shape of the local component can be optimized by taking the aerodynamic thermal environment as a target function, and a great heat reduction benefit can be obtained at a lower shape deformation cost on the premise of not influencing the overall aerodynamic performance of the aircraft.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aircraft design, and in particular to a local aerodynamic thermal optimization method and device based on discrete adjoint. Background Art

[0002] In the field of aircraft design, aerodynamic heat is usually a critical constraint. Taking hypersonic aircraft as an example, the heat protection requirements can be met by increasing the thickness of the aircraft's thermal protection layer.

[0003] As hypersonic aircraft continue to develop towards high Mach numbers, long flight times, and reusability, the temperature of the aircraft gradually exceeds the material limits, and aerodynamic thermal shape optimization is imminent.

[0004] The existing aircraft shape design method designs aerodynamic requirements, aero-thermal requirements and other requirements separately, meets each requirement one by one, and finally achieves the design requirements through repeated iterations.

[0005] However, as a highly integrated and complex system, the hypersonic aircraft faces a flight environment with strong nonlinear characteristics, which makes it difficult to effectively use traditional solutions in the design of hypersonic aircraft appearance. Summary of the invention

[0006] The present disclosure provides a local aerodynamic thermal optimization method and device based on discrete adjoint.

[0007] According to a first aspect of the present disclosure, a local aerodynamic thermal optimization method based on discrete adjoint is provided, the method comprising:

[0008] Drawing a structural grid of the local component under the incoming flow condition according to the initial shape of the local component to be optimized and the incoming flow condition;

[0009] Acquire a control frame required for mesh deformation of the structural mesh, wherein the control frame contains mesh boundary points to be optimized, and the mesh boundary points form an object surface of the local component;

[0010] Optimizing and updating the control frame to obtain optimized coordinates of the control frame;

[0011] The initial shape is optimized according to the optimized coordinates and the incoming flow conditions to obtain the target shape of the local component.

[0012] Optionally, optimizing and updating the control frame to obtain optimized coordinates of the control frame includes:

[0013] Obtaining the optimization direction and optimization step required for the control frame;

[0014] The initial coordinates of the control frame are optimized according to the optimization direction and the optimization step size to obtain optimized coordinates of the control frame.

[0015] Optionally, obtaining the optimization direction required by the control frame includes:

[0016] Obtaining an optimization objective function of the local component in the current optimization scenario;

[0017] Solving the flow field according to the incoming flow condition to obtain aerodynamic thermal data under the initial shape;

[0018] Acquire the objective function according to the aerodynamic thermal data;

[0019] Obtain the coefficient matrix based on solving the flow field and automatic differentiation method;

[0020] Obtaining a right-hand vector according to the objective function and the automatic differentiation method;

[0021] Establish an adjoint equation according to the coefficient matrix and the right-hand side vector, and obtain an adjoint vector corresponding to the adjoint equation;

[0022] Obtaining the objective function gradient according to the adjoint vector and a preset gradient calculation model;

[0023] The optimization direction is calculated according to the objective function gradient and the steepest descent method.

[0024] Optionally, obtaining the optimization objective function of the local component in the current optimization scenario includes:

[0025] When the optimization function in the current optimization scenario is a heat flow peak, determining the optimization objective function as a first objective function;

[0026] When the optimization function in the current optimization scenario is resistance, determining the optimization objective function to be a second objective function;

[0027] When the objective function of the current optimization scenario is the resistance under the condition that the maximum heat flow does not increase, determining the optimization objective function to be the third objective function;

[0028] When the objective function of the current optimization scenario is the maximum heat flow without increasing the resistance, the optimization objective function is determined to be the fourth objective function.

[0029] Optionally, obtaining the optimization step required for the control frame includes:

[0030] The flow field of the local component and the golden section method are used to iteratively calculate a step length that meets preset conditions as the optimized step length.

[0031] Optionally, optimizing the initial shape according to the optimized coordinates and the incoming flow conditions to obtain a target shape of the local component includes:

[0032] Based on the optimized coordinates of the control frame, the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid is completed according to the FFD method to obtain the candidate shape of the local component;

[0033] Acquiring aerodynamic thermal data of the local component under a candidate shape according to the optimized coordinates of the control frame;

[0034] Acquire an objective function value of an optimization objective function according to the aerodynamic thermal data;

[0035] When the optimization objective function value of the candidate shape satisfies the optimization termination condition, the candidate shape is determined to be the target shape of the local component.

[0036] According to a second aspect of the present disclosure, a local aerodynamic thermal optimization device based on discrete adjoint is provided, the device comprising:

[0037] A structural grid drawing module, used for drawing a structural grid of the local component under the incoming flow condition according to the initial shape of the local component to be optimized and the incoming flow condition;

[0038] A control frame acquisition module, used for acquiring a control frame required for mesh deformation of the structural mesh, wherein the control frame contains mesh boundary points to be optimized, and the mesh boundary points form the object surface of the local component;

[0039] An optimized coordinate acquisition module, used to optimize and update the control frame to obtain the optimized coordinates of the control frame;

[0040] The target shape acquisition module is used to optimize the initial shape according to the optimized coordinates and the incoming flow conditions to obtain the target shape of the local component.

[0041] Optionally, the optimized coordinate acquisition module includes:

[0042] A direction step acquisition submodule is used to acquire the optimization direction and optimization step required by the control frame;

[0043] The optimized coordinates acquisition submodule is used to optimize the initial coordinates of the control frame according to the optimization direction and the optimization step size to obtain the optimized coordinates of the control frame.

[0044] Optionally, the direction step length acquisition submodule includes:

[0045] An optimization function acquisition unit, used to acquire the optimization objective function of the local component in the current optimization scenario;

[0046] an aerodynamic thermal data acquisition unit, used for solving the flow field according to the incoming flow condition to acquire the aerodynamic thermal data under the initial shape;

[0047] An objective function acquisition unit, used for acquiring the objective function according to the aerodynamic heat data;

[0048] A coefficient matrix acquisition unit is used to obtain the coefficient matrix according to the flow field solution and the automatic differentiation method;

[0049] A right-hand side vector acquisition unit, used for obtaining the right-hand side vector according to the objective function and the automatic differentiation method;

[0050] An adjoint vector acquisition unit, used for establishing an adjoint equation according to the coefficient matrix and the right-hand side vector, and obtaining an adjoint vector corresponding to the adjoint equation;

[0051] A function gradient acquisition unit, used to acquire the objective function gradient according to the adjoint vector and a preset gradient calculation model;

[0052] The optimization direction acquisition unit is used to calculate the optimization direction according to the objective function gradient and the steepest descent method.

[0053] Optionally, the optimization function acquisition unit includes:

[0054] A first function determination subunit, configured to determine the optimization objective function as a first objective function when the optimization function is a heat flow peak value in the current optimization scenario;

[0055] A second function determination subunit, configured to determine the optimization objective function as a second objective function when the optimization function is resistance in the current optimization scenario;

[0056] A third function determination subunit, configured to determine the optimization objective function as a third objective function when the objective function of the current optimization scenario is the resistance under the condition that the maximum heat flow does not increase;

[0057] The fourth function determination subunit is used to determine the optimization objective function as the fourth objective function when the objective function of the current optimization scenario is the maximum heat flow without increasing the resistance.

[0058] Optionally, the direction step length acquisition submodule includes:

[0059] The optimization step length acquisition unit is used to iteratively calculate a step length that meets preset conditions by using the flow field of the local component and the golden section method as the optimization step length.

[0060] Optionally, the target shape acquisition module includes:

[0061] A candidate shape acquisition submodule is used to complete the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid according to the FFD method based on the optimized coordinates of the control frame to obtain the candidate shape of the local component;

[0062] an aerodynamic thermal data acquisition submodule, for acquiring aerodynamic thermal data of the candidate shape of the local component according to the optimized coordinates of the control frame;

[0063] An objective function value acquisition submodule, used for acquiring an objective function value of an optimization objective function according to the aerodynamic thermal data;

[0064] The target shape determination submodule is used to determine that the candidate shape is the target shape of the local component when the optimization objective function value of the candidate shape meets the optimization termination condition.

[0065] According to a third aspect of the present disclosure, there is provided an electronic device, comprising: a processor and a memory;

[0066] The memory is used to store a computer program executable by the processor;

[0067] The processor is used to execute the computer program in the memory to implement the method as described in any one of the first aspects.

[0068] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, and when an executable computer program in the storage medium is executed by a processor, the method described in any one of the first aspects can be implemented.

[0069] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0070] In the solution provided in this embodiment, the structural grid of the local component under the incoming flow condition can be drawn according to the initial shape of the local component to be optimized and the incoming flow condition; then, the control frame required for the grid deformation of the structural grid is obtained, and the control frame contains the grid boundary points to be optimized, and the grid boundary points form the physical surface of the local component; then, the control frame is optimized and updated to obtain the optimized coordinates of the control frame; finally, the initial shape is optimized according to the optimized coordinates and the incoming flow condition to obtain the target shape of the local component. In this way, this embodiment can optimize the shape of the local component with the aerodynamic thermal environment as the objective function, and can obtain a large heat reduction benefit at the cost of a small shape deformation without affecting the overall aerodynamic performance of the aircraft.

[0071] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 The present invention is a flowchart of a local aerodynamic thermal optimization method based on discrete adjoint according to an embodiment of the present invention.

[0073] Figure 2 A schematic diagram of a structural grid of an initial shape of a local component according to an embodiment of the present disclosure.

[0074] Figure 3 The present invention is a schematic diagram of drawing a control frame on a structural grid according to an embodiment of the present invention.

[0075] Figure 4 The present invention is a flowchart of obtaining optimized coordinates of a control frame according to an embodiment of the present invention.

[0076] Figure 5 The present invention is a flowchart of a local aerodynamic thermal optimization method based on discrete adjoint according to an embodiment of the present invention.

[0077] Figure 6 The present invention is a flowchart of another local aerodynamic thermal optimization method based on discrete adjoint according to an embodiment of the present invention.

[0078] Figure 7 This is a flow field density cloud diagram of the initial shape of a local component according to an embodiment of the present disclosure.

[0079] Figure 8 The figure is a schematic diagram of projecting the objective function gradient in the X direction according to an embodiment of the present disclosure.

[0080] Fig. 9 The figure is a schematic diagram of projecting the objective function gradient in the Y direction according to an embodiment of the present disclosure.

[0081] Fig.10 Schematic diagram of an aerodynamic thermal environment calculation grid obtained through six rounds of optimization deformation according to an embodiment of the present disclosure.

[0082] Fig.11 This is a flow field density cloud map corresponding to the final optimized shape obtained by six rounds of optimization according to an embodiment of the present disclosure.

[0083] Fig.12 The block diagram of a local aerodynamic thermal optimization device based on discrete adjoint according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0084] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices consistent with some aspects of the present disclosure as detailed in the appended claims.

[0085] The embodiments of the present disclosure provide a method, device, electronic device and storage medium for local aerodynamic thermal optimization based on discrete adjoint. Figure 1 , including steps 11 to 14.

[0086] In step 11, a structural grid of the local component under the incoming flow condition is drawn according to the initial shape of the local component to be optimized and the incoming flow condition.

[0087] In this step, the initial shape of the local component to be optimized in the aircraft can be obtained first. It is understandable that since the specific structure of the aircraft is known, the initial shape of its local component is also known.

[0088] In this step, the performance parameters of the aircraft can be obtained, and the incoming flow conditions of the aircraft can be determined based on the above performance parameters. When the incoming flow conditions of the aircraft are known, the incoming flow conditions of the local components are also known.

[0089] In this step, the structural grid required for the calculation of the aerodynamic thermal environment can be drawn according to the initial shape of the local component and the incoming flow of the local component. The structural grid needs to have good wall grid orthogonality, uniform and smooth transition, the wall normal growth ratio is controlled within 1.1, and the first layer distance of the wall meets the requirement that the grid Reynolds number is less than 8 (the grid Reynolds number is equal to the product of the wall distance and the incoming flow Reynolds number per meter). Among them, the incoming flow Reynolds number per meter is determined by the incoming flow conditions.

[0090] In one example, Pointwise software can be used to automatically mesh the surface and volume of local components; a structural mesh is established around the aircraft, and the generation of the mesh can be controlled by setting parameters such as the maximum aspect ratio, mapping options, and subdivision. In another example, ANSYS ICEM CFD software generates a structural mesh. Those skilled in the art can select a structural mesh drawing method according to a specific scenario, and the corresponding solution falls within the protection scope of the present disclosure.

[0091] See also Figure 2 The outer surface 22 of the partial component 21 is sequentially drawn with structural grids 23 outward, and the density of the structural grids 23 increases as the distance from the outer surface 22 increases.

[0092] In step 12, a control frame required for mesh deformation of the structural mesh is obtained, wherein the control frame contains mesh boundary points to be optimized, and the mesh boundary points form the object surface of the local component.

[0093] In this step, a control frame required for mesh deformation of the structured mesh is obtained. It is understandable that the control frame can be drawn in the same way as the structured mesh, except that the control frame contains the mesh boundary points to be optimized. In addition, the size of the control frame is larger than the size of the structured mesh, thereby reducing the amount of calculation while ensuring the smoothness of the optimized shape object surface.

[0094] See also Figure 3 , the control frame 31 includes the grid boundary points 32 to be optimized. It is understandable that the structural grid 23 surrounds the local component, so the outer surface of the local component 21 is in contact with the innermost grid edge boundary points of the structural grid, that is, the grid boundary points 32 form the object surface of the local component 21. Then, when adjusting the position of the control frame 31, the position of the grid boundary points 32 inside the control frame can be adjusted synchronously; in other words, the adjustment of the position of the grid boundary points 32 is equivalent to adjusting the shape of the outer surface of the local component, that is, adjusting the shape of the local component 21.

[0095] In step 13, the control frame is optimized and updated to obtain the optimized coordinates of the control frame.

[0096] In this step, the control frame is optimized and updated to obtain the optimized coordinates of the control frame. Figure 4 , including step 41 and step 42.

[0097] In step 41, the optimization direction and optimization step length required for the control frame are obtained.

[0098] In one example, an optimization direction required by the control frame may be obtained.

[0099] First, obtain the optimization objective function of the aircraft in the current optimization scenario, including:

[0100] (1) When the current optimization scenario is the heat flow peak, the optimization objective function is determined to be the first objective function. The first objective function is shown in formula (1).

[0101] F=F max =q max (1)

[0102] In formula (1), F max represents the first objective function for optimizing the peak heat flux, q max Indicates the maximum heat flux value on the surface of the local component.

[0103] (2) When the current optimization scenario is resistance, the optimization objective function is determined to be the second objective function. The second objective function is shown in equations (2) to (4).

[0104] F=F drag =F p +Fμ (2)

[0105]

[0106] In formula (2) to formula (4), F p represents piezoresistance, Ψ wall It is represented as the grid cell containing the object surface, P represents the surface pressure, P 0 Indicates the incoming flow pressure, S m represents the mesh surface area, α represents the incoming flow angle, represents the unit normal vector of the wall, Re represents the Reynolds number, Δh represents the distance from the center point of the first layer of grid on the wall to the wall, (N x , N y ) represents the normal vector of the unit center point of the wall, and (uv) is the velocity vector of the center of the first grid on the wall.

[0107] (3) When the current optimization scenario is that the objective function is the resistance without increasing the maximum heat flow, the optimization objective function is determined to be the third objective function. The third objective function is shown in formula (5).

[0108]

[0109] In formula (5), The resistance of the initial shape of the local component, F max Indicates the maximum heat flux value of the current shape, Indicates the maximum heat flux value of the initial shape, Large indicates a larger coefficient (such as 1000, adjustable), and Tanh indicates the hyperbolic tangent function.

[0110] (4) When the current optimization scenario is that the objective function is the maximum heat flow without increasing the resistance, the optimization objective function is determined to be the fourth objective function. The fourth objective function is shown in formula (6).

[0111]

[0112] Then, the optimization termination condition can be obtained. The optimization termination condition can be used to determine whether the optimization process is terminated. In one example, the difference between the current objective function value and the previous round objective function value satisfies: the ratio of the difference to the objective function value of the initial original shape is less than or equal to the preset ratio. The preset ratio ranges from [0.5%, 5%]. In one example, the above preset ratio is 1%.

[0113] Afterwards, the aerodynamic thermal data of the local components under the initial shape can be obtained. For example, the flow field of the above-mentioned local components under the above-mentioned incoming flow conditions can be solved by using hypersonic numerical simulation methods, and aerodynamic thermal data such as resistance, heat flux distribution, and viscous resistance can be obtained. The corresponding parameters can be selected as aerodynamic thermal data according to the specific scenario.

[0114] Afterwards, the objective function value of the local component under the initial shape can be calculated based on the aerodynamic thermal data and the above objective function.

[0115] Again, the adjoint equation can be established according to the objective function value, the coefficient matrix and the right-hand side vector, as shown in equation (7).

[0116]

[0117] In formula (7), R represents the residual of the flow field solution control equation (i.e., the Navier-Stokes equations), W represents the flow field variable, represents the coefficient matrix, F represents the gradient of the objective function selected in the above step, Λ represents the adjoint variable to be solved, represents the right-hand side vector (i.e., the constant term vector of the linear equation system).

[0118] It is understandable that the coefficient matrix in the above adjoint equation is and the right hand vector It can be calculated by automatic differentiation method, and then the adjoint equation can be solved by Block-LUSGS iterative method to obtain the adjoint vector Λ in equation (7). In this example, the efficiency of solving the adjoint equation can be improved.

[0119] Then, the objective function gradient can be obtained according to the adjoint vector and the preset gradient calculation model, and the calculation formula is shown in formulas (8) to (10).

[0120]

[0121] In formulas (8) to (10), β k represents the coordinates of the kth control box, Δβ k represents a small change in the kth design variable, in one example Δβ k Take 10 -5 .

[0122] When the objective function gradient is obtained, the optimization direction can be calculated. For example, the optimization direction can be calculated by using the steepest descent method with the objective function gradient as the input condition. The steepest descent method selects the direction that makes the objective function drop the fastest in each iteration, and uses this direction as the optimization direction of this example solution, gradually approaching the minimum value of the objective function.

[0123] In one example, the optimization step length required for the control frame can be obtained. For example, the process of calculating the optimization step length using the golden section method can include: obtaining the initial search interval [a0, b0], and calculating the relationship between the interval length and the golden ratio, where the golden ratio φ can be taken as 0.618. Then, two trial points p and q are selected in the above initial search area, where p = a0 + (1-φ) * (b0-a0); q = a0 + φ * (b0-a0); the above two trial points p and q divide the initial search interval into three parts, and the length ratio of the two parts is the golden ratio. Afterwards, the objective function values, i.e., F(p) and F(q), are calculated at the above two trial points p and q. When F(p) is less than F(q), the minimum value is in the interval [a0, q], and the search interval is updated to [a0, q]; when F(p) is greater than or equal to F(q), the minimum value is in the interval [p, b0], and the search interval is updated to [p, b0]; then reselect two trial points p and q in the updated search interval, and reacquire the search interval until the preset stop condition is met (such as the search area is less than 10 -5 ), get the endpoints [a, b] of the minimum interval containing the minimum point, or output the optimization step size.

[0124] In step 42, the initial coordinates of the control frame are optimized according to the optimization direction and the optimization step size to obtain the optimized coordinates of the control frame.

[0125] In this step, when the basic optimization direction and the optimization step are known, the initial coordinates of each control frame can be moved according to the above optimization direction and the above optimization step to obtain the optimized coordinates of the control frame.

[0126] In step 14, the initial shape is optimized according to the optimized coordinates and the incoming flow conditions to obtain the target shape of the local component.

[0127] In this step, according to the latest coordinates of the control frame, the FFD free-format deformation method is used to complete the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structural grid. That is, after the local component shape changes, the structural grid of its flow field changes accordingly, and the updated structural grid still needs to meet the requirements of aerodynamic thermal calculation (such as the wall normal grid is orthogonal to the wall, the first layer distance meets the requirements, the transition is uniform and the wall normal growth ratio is not greater than 1.1, etc.), to obtain the candidate shape of the local component.

[0128] It should be noted that FFD free-format deformation is used as a mapping function between the component structural grid and the control frame. The coordinate position of the structural grid is updated by updating the coordinate position of the control frame. When the coordinate position of the structural grid changes, the shape of the local component is changed.

[0129] Then, the aerodynamic thermal data of the candidate shape of the local component can be obtained according to the optimized coordinates of the control frame, that is, the flow field of the updated local component is solved by using the hypersonic numerical simulation method, and the aerodynamic thermal data of the (updated) candidate shape such as resistance and heat flow distribution are obtained, and the objective function value of the objective function is calculated. When the optimization objective function value of the candidate shape meets the optimization termination condition, the candidate shape can be determined as the target shape of the local component.

[0130] At this point, the solution provided in this embodiment can draw the structural grid of the local component under the incoming flow condition according to the initial shape of the local component to be optimized and the incoming flow condition; then, obtain the control frame required for the grid deformation of the structural grid, the control frame contains the grid boundary points to be optimized, and the grid boundary points form the physical surface of the local component; then, optimize and update the control frame to obtain the optimized coordinates of the control frame; finally, optimize the initial shape according to the optimized coordinates and the incoming flow condition to obtain the target shape of the local component. In this way, this embodiment can optimize the shape of the local component with the aerodynamic thermal environment as the objective function, and can obtain a large heat reduction benefit at the cost of a small shape deformation without affecting the overall aerodynamic performance of the aircraft.

[0131] The following example describes the local aerodynamic thermal optimization method based on discrete adjoints. Figure 5 , including steps 501 to 514.

[0132] In step 501, the initial shape and incoming flow conditions of the local configuration to be optimized are obtained, and the structural grid required for the aerodynamic thermal environment calculation is drawn.

[0133] In step 502, a control frame required for mesh deformation is drawn. The boundary points of the object surface inside the control frame are the boundary points that need to be deformed, and the coordinates of the control frame are the optimized design variables. By adjusting the coordinates of the control frame to adjust the boundary points of the object surface inside the control frame, the shape of the local component is adjusted.

[0134] In step 503, an optimization objective function is selected according to the current optimization scenario, such as the first objective function, the second objective function, the third objective function and the fourth objective function respectively exemplified in equations (1) to (6).

[0135] In step 504, an optimization termination condition is set, which can be used to determine whether the optimization process is terminated. In one example, the difference between the current objective function value and the previous round objective function value satisfies: the ratio of the difference to the objective function value of the initial original shape is less than or equal to a preset ratio. The preset ratio ranges from [0.5%, 5%]. In one example, the preset ratio is 1%.

[0136] In step 505, the flow field of the above-mentioned local component under the above-mentioned incoming flow condition is solved by using a hypersonic numerical simulation method, and aerodynamic thermal data such as resistance, heat flux distribution, and viscous resistance can be obtained; and the objective function value of the objective function in step 503 is further calculated.

[0137] In step 506, the coefficient matrix and the right-hand side vector are solved by using the automatic differentiation method; and the adjoint equation as shown in equation (7) is established.

[0138] In step 507, the adjoint equation is solved using the Block-LUSGS iterative method to obtain the adjoint vector Λ in equation (7).

[0139] In step 508, the objective function gradient is calculated using the objective function gradient calculation formula as shown in equations (8) to (10).

[0140] In step 509, the objective function gradient obtained in step 508 is used as an input condition, and the optimization direction is calculated using the steepest descent method.

[0141] In step 510, the optimal step length is calculated using the golden section method.

[0142] In step 511, the coordinates of the control frame are optimized and updated using the optimization direction obtained in step 509 and the optimization step size obtained in step 510 to obtain optimized coordinates.

[0143] In step 512, according to the optimized coordinates of the control frame, the FFD method is used to complete the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid, and the candidate shape of the local component is obtained, and the grid deformation that can meet the aerodynamic thermal environment calculation requirements is achieved, thereby improving the effectiveness of the optimization result.

[0144] In step 513, the same hypersonic numerical simulation method as step 505 is used to solve the flow field of the candidate shape of the local component, obtain the aerodynamic thermal data of the candidate shape such as resistance and heat flux distribution, and further calculate the objective function value of the objective function in step 503.

[0145] In step 514, it is determined whether the objective function value of the candidate shape of the local component meets the optimization termination condition. If not, steps 506 to 513 are repeated; if satisfied, the optimization is terminated.

[0146] So far, by utilizing the characteristic that the amount of gradient calculation of the adjoint method is independent of the number of design variables, the objective function gradient based on the thermal environment is efficiently solved, and the local optimization of the aerodynamic thermal environment in the hypersonic field is realized by combining the steepest descent optimization direction calculation method and the golden section optimization step length calculation method. The solution of this embodiment can solve the problem of the amount of calculation caused by the large number of design variables, can be applied to the field of hypersonic aerodynamic thermal environment optimization, and expand the application scope of the adjoint optimization method.

[0147] Based on the above embodiment, the process of adjusting the shape of a ball head flat plate is as follows: Figure 6 As shown, the spherical head radius of the spherical head flat plate shape is 20 mm, the flat plate length is 500 mm, the flight conditions are 60 km, Mach number 10, angle of attack 0 and laminar flow.

[0148] First, the aerodynamic thermal environment is calculated based on the dimensions of the ball head plate and the flight conditions. The structural grid is as follows: Figure 2 shown.

[0149] Second, the optimization objective is set to the resistance without increasing the maximum heat flux, that is, the third objective function is selected.

[0150] Third, the optimization termination condition is set to that the difference between the objective function value of the current optimized shape and the objective function value of the previous round of optimized shape is less than or equal to 1% of the objective function value of the initial shape.

[0151] Fourth, draw the control frame required for ball head flat plate shape optimization, where the control frame is as follows Figure 3 As shown in the figure, the number of each control frame is 2*10. The boundary points of the object surface inside the control frame are the boundary points that need to be deformed, and the coordinates of the control frame are the optimized design variables. Figure 3 shown.

[0152] Fifth, the hypersonic numerical simulation method is used to solve the flow field of the initial shape of the local component to obtain the maximum heat flux value and resistance value of the whole field. After being brought into the third objective function calculation formula, the objective function value of the initial shape can be calculated. Among them, the flow field density cloud map of the initial shape obtained by the flow field solution is as follows: Figure 7 shown.

[0153] Sixth, use the automatic differentiation method to find the coefficient matrix and the right hand vector And establish the adjoint equation as shown in formula (7).

[0154] Seventh, use the Block-LUSGS iterative method to solve the adjoint equation and obtain the adjoint variable.

[0155] Eighth, the objective function gradient is calculated using the objective function gradient calculation formula. The distribution cloud diagram of the objective function gradient projected in the X direction is as follows: Figure 8 As shown, the distribution cloud diagram projected in the Y direction is as follows Fig. 9 shown.

[0156] Ninth, the optimization direction is calculated using the steepest descent method, and the optimization direction is the opposite direction of the objective function gradient direction.

[0157] Tenth, use the golden section method to calculate the optimal step size.

[0158] Eleventh, update the coordinates of the control frame according to the optimization direction and optimization step.

[0159] Twelfth, according to the latest coordinates of the control frame, the FFD method is used to complete the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid.

[0160] Thirteenth, the flow field of the new shape is solved using a hypersonic numerical simulation method to obtain the flow field of the new shape and the corresponding objective function value.

[0161] Fourteenth, the optimization termination is judged according to the objective function value of the new shape. If the optimization termination condition is not met, the fifth to thirteenth steps are repeated until the sixth round of optimization of the shape obtains the objective function value that meets the optimization termination condition. Among them, the aerodynamic thermal environment calculation grid obtained through six rounds of optimization deformation is as follows: Fig.10 As shown in the figure, the flow field density cloud corresponding to the final optimized shape obtained through six rounds of optimization is shown in Fig.11 shown.

[0162] It is understandable that in the scheme of this embodiment, the objective function gradient under large-scale design variables can be obtained through the adjoint method, and the objective function gradient can be used to achieve local shape optimization with the aerodynamic thermal environment as the objective function. This can achieve great heat reduction benefits at a very small cost of shape deformation without affecting the overall aerodynamic performance of the aircraft.

[0163] Based on a local aerodynamic thermal optimization method based on discrete adjoint provided in an embodiment of the present disclosure, an embodiment of the present disclosure further provides a local aerodynamic thermal optimization device based on discrete adjoint, see Fig.12 , the device comprises:

[0164] A structural grid drawing module 121 is used to draw a structural grid of the local component under the incoming flow condition according to the initial shape of the local component to be optimized and the incoming flow condition;

[0165] A control frame acquisition module 122 is used to acquire a control frame required for mesh deformation of the structural mesh, wherein the control frame contains mesh boundary points to be optimized, and the mesh boundary points form the object surface of the local component;

[0166] An optimized coordinate acquisition module 123 is used to optimize and update the control frame to obtain the optimized coordinates of the control frame;

[0167] The target shape acquisition module 124 is used to optimize the initial shape according to the optimized coordinates and the incoming flow conditions to obtain the target shape of the local component.

[0168] In one embodiment, the optimized coordinate acquisition module includes:

[0169] A direction step acquisition submodule is used to acquire the optimization direction and optimization step required by the control frame;

[0170] The optimized coordinates acquisition submodule is used to optimize the initial coordinates of the control frame according to the optimization direction and the optimization step size to obtain the optimized coordinates of the control frame.

[0171] In one embodiment, the direction step length acquisition submodule includes:

[0172] An optimization function acquisition unit, used to acquire the optimization objective function of the local component in the current optimization scenario;

[0173] an aerodynamic thermal data acquisition unit, used for solving the flow field according to the incoming flow condition to acquire the aerodynamic thermal data under the initial shape;

[0174] An objective function acquisition unit, used for acquiring the objective function according to the aerodynamic heat data;

[0175] A coefficient matrix acquisition unit is used to obtain the coefficient matrix according to the flow field solution and the automatic differentiation method;

[0176] A right-hand side vector acquisition unit, used for obtaining the right-hand side vector according to the objective function and the automatic differentiation method;

[0177] An adjoint vector acquisition unit, used for establishing an adjoint equation according to the coefficient matrix and the right-hand side vector, and obtaining an adjoint vector corresponding to the adjoint equation;

[0178] A function gradient acquisition unit, used to acquire the objective function gradient according to the adjoint vector and a preset gradient calculation model;

[0179] The optimization direction acquisition unit is used to calculate the optimization direction according to the objective function gradient and the steepest descent method.

[0180] In one embodiment, the optimization function acquisition unit includes:

[0181] A first function determination subunit, configured to determine the optimization objective function as a first objective function when the optimization function is a heat flow peak value in the current optimization scenario;

[0182] A second function determination subunit, configured to determine the optimization objective function as a second objective function when the optimization function is resistance in the current optimization scenario;

[0183] A third function determination subunit, configured to determine the optimization objective function as a third objective function when the objective function of the current optimization scenario is the resistance under the condition that the maximum heat flow does not increase;

[0184] The fourth function determination subunit is used to determine the optimization objective function as the fourth objective function when the objective function of the current optimization scenario is the maximum heat flow without increasing the resistance.

[0185] In one embodiment, the direction step length acquisition submodule includes:

[0186] The optimization step length acquisition unit is used to iteratively calculate a step length that meets preset conditions by using the flow field of the local component and the golden section method as the optimization step length.

[0187] In one embodiment, the target shape acquisition module includes:

[0188] A candidate shape acquisition submodule is used to complete the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid according to the FFD method based on the optimized coordinates of the control frame to obtain the candidate shape of the local component;

[0189] an aerodynamic thermal data acquisition submodule, for acquiring aerodynamic thermal data of the candidate shape of the local component according to the optimized coordinates of the control frame;

[0190] An objective function value acquisition submodule, used for acquiring an objective function value of an optimization objective function according to the aerodynamic thermal data;

[0191] The target shape determination submodule is used to determine that the candidate shape is the target shape of the local component when the optimization objective function value of the candidate shape meets the optimization termination condition.

[0192] It should be noted that the device shown in this embodiment matches the content of the method embodiment, and the content of the above method embodiment can be referred to, which will not be repeated here.

[0193] In some possible embodiments, an electronic device is provided, including: a processor and a memory;

[0194] The memory is used to store a computer program executable by the processor;

[0195] The processor is used to execute the computer program in the memory to implement the above-mentioned local aerodynamic thermal optimization method based on discrete adjoint.

[0196] In some possible embodiments, a non-transitory computer-readable storage medium is provided. When an executable computer program in the storage medium is executed by a processor, the above-mentioned local aerodynamic thermal optimization method based on discrete adjoint can be implemented.

[0197] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0198] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A local aerodynamic thermal optimization method based on discrete adjoint, characterized in that: The method comprises: Drawing a structural grid of the local component under the incoming flow condition according to the initial shape of the local component to be optimized and the incoming flow condition; Acquire a control frame required for mesh deformation of the structural mesh, wherein the control frame contains mesh boundary points to be optimized, and the mesh boundary points form an object surface of the local component; Optimizing and updating the control frame to obtain optimized coordinates of the control frame; The initial shape is optimized according to the optimized coordinates and the incoming flow conditions to obtain the target shape of the local component.

2. The method according to claim 1, characterized in that: Optimizing and updating the control frame to obtain optimized coordinates of the control frame includes: Obtaining the optimization direction and optimization step required for the control frame; The initial coordinates of the control frame are optimized according to the optimization direction and the optimization step size to obtain optimized coordinates of the control frame.

3. The method according to claim 2, characterized in that Obtain the optimization direction required by the control frame, including: Obtaining an optimization objective function of the local component in the current optimization scenario; Solving the flow field according to the incoming flow condition to obtain aerodynamic thermal data under the initial shape; Acquire the objective function according to the aerodynamic thermal data; Obtain the coefficient matrix based on solving the flow field and automatic differentiation method; Obtaining a right-hand vector according to the objective function and the automatic differentiation method; Establish an adjoint equation according to the coefficient matrix and the right-hand side vector, and obtain an adjoint vector corresponding to the adjoint equation; Obtaining the objective function gradient according to the adjoint vector and a preset gradient calculation model; The optimization direction is calculated according to the objective function gradient and the steepest descent method.

4. The method according to claim 3, characterized in that Obtaining the optimization objective function of the local component in the current optimization scenario includes: When the optimization function in the current optimization scenario is a heat flow peak, determining the optimization objective function as a first objective function; When the optimization function in the current optimization scenario is resistance, determining the optimization objective function to be a second objective function; When the objective function of the current optimization scenario is the resistance under the condition that the maximum heat flow does not increase, determining the optimization objective function to be the third objective function; When the objective function of the current optimization scenario is the maximum heat flow without increasing the resistance, the optimization objective function is determined to be the fourth objective function.

5. The method according to claim 2, characterized in that: Obtaining the optimization step required for the control frame includes: The flow field of the local component and the golden section method are used to iteratively calculate a step length that meets preset conditions as the optimization step length.

6. The method according to claim 1, characterized in that Optimizing the initial shape according to the optimized coordinates and the incoming flow conditions to obtain a target shape of the local component includes: Based on the optimized coordinates of the control frame, the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid is completed according to the FFD method to obtain the candidate shape of the local component; Acquiring aerodynamic thermal data of the local component under a candidate shape according to the optimized coordinates of the control frame; Acquire an objective function value of an optimization objective function according to the aerodynamic thermal data; When the optimization objective function value of the candidate shape satisfies the optimization termination condition, the candidate shape is determined to be the target shape of the local component.

7. A local aerodynamic thermal optimization device based on discrete adjoint, characterized in that: The device comprises: A structural grid drawing module, used for drawing a structural grid of the local component under the incoming flow condition according to the initial shape of the local component to be optimized and the incoming flow condition; A control frame acquisition module, used for acquiring a control frame required for mesh deformation of the structural mesh, wherein the control frame contains mesh boundary points to be optimized, and the mesh boundary points form the object surface of the local component; An optimized coordinate acquisition module, used to optimize and update the control frame to obtain the optimized coordinates of the control frame; The target shape acquisition module is used to optimize the initial shape according to the optimized coordinates and the incoming flow conditions to obtain the target shape of the local component.

8. The device according to claim 7, characterized in that The target shape acquisition module comprises: A candidate shape acquisition submodule is used to complete the deformation of the local component shape and the corresponding aerodynamic thermal environment calculation structure grid according to the FFD-method based on the optimized coordinates of the control frame to obtain the candidate shape of the local component; an aerodynamic thermal data acquisition submodule, for acquiring aerodynamic thermal data of the candidate shape of the local component according to the optimized coordinates of the control frame; An objective function value acquisition submodule, used for acquiring an objective function value of an optimization objective function according to the aerodynamic thermal data; The target shape determination submodule is used to determine that the candidate shape is the target shape of the local component when the optimization objective function value of the candidate shape meets the optimization termination condition.

9. An electronic device, characterized in that: include: Processor and memory; The memory is used to store a computer program executable by the processor; The processor is used to execute the computer program in the memory to implement the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium, characterized in that: When the executable computer program in the storage medium is executed by a processor, the method according to any one of claims 1 to 6 can be implemented.

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