A method for spatial discrete mixing of flow fields based on interface characteristics

Abstract

The present invention discloses a flow field space discrete hybrid processing method based on interface characteristics, belonging to the field of aerodynamics and numerical simulation technology. The method identifies the Ruby phenomenon prone area according to the characteristics of the Ruby phenomenon in the high-speed flow field; in the Ruby phenomenon prone area, the geometric micro-element of the aircraft surface closest to the spatial micro-element interface is traversed and searched, and each spatial micro-element interface is divided into a high-resolution interface and a strong stable interface by comparing the geometric characteristics; for the high-resolution interface, a higher resolution format is adopted; for the strong stable interface, a more stable format is adopted. The present invention takes into account the stability and accuracy of the numerical simulation of the high-speed flow field of the aircraft through this spatial discrete format mixing.

Description

Technical Field

[0001] The invention belongs to the technical field of aerodynamics and numerical simulation, and in particular relates to a flow field space discrete mixing processing method based on interface characteristics. Background Art

[0002] The numerical format has a great influence on the stability and accuracy of the numerical simulation of high-speed flow fields. The second-order spatial discretization format currently used in the development of high-speed aircraft can be roughly divided into two categories from the perspective of numerical dissipation: high-resolution format and strong stability format. These two formats have their own advantages and disadvantages.

[0003] High-resolution formats, such as AUSMPW+, AUSMPW, and AUSMDV, have relatively small numerical dissipation, and relatively high resolution of shock waves and flow field fine structures, thus having high computational accuracy. However, due to their small numerical dissipation, non-physical numerical fluctuations may be difficult to effectively suppress, affecting computational stability and causing the "ruby" phenomenon.

[0004] Strong stability formats, such as Stegerwarming and Vanleer, have relatively large numerical dissipation, which can effectively suppress numerical fluctuations during iteration and have good numerical calculation stability. However, due to their excessive dissipation, they may smooth out the subtle flow field structure during the flow process, affecting the calculation accuracy.

[0005] In the Chinese invention patent with application number CN202310576273.X, a shock discontinuity identification and classification processing method is disclosed, which can effectively improve the resolution of shock discontinuity in the AUSM-type space discrete format, and to a certain extent take into account both accuracy and computational stability, and can effectively improve the red jade phenomenon when the grid quality is poor, and obtain better results. However, this method mainly improves shock discontinuity, and cannot completely solve the problem of "difficulty in suppressing non-physical fluctuations in relatively low-computation formats in smooth areas". Summary of the invention

[0006] The purpose of the present invention is to provide a flow field space discrete hybrid processing method based on interface characteristics in order to overcome the defects of the prior art. Starting from the characteristics of the ruby ​​phenomenon in the high-speed flow field, the ruby ​​phenomenon is prone to occur in the area; in the ruby ​​phenomenon is prone to occur, the aircraft surface geometric micro-elements closest to the spatial micro-element interface are traversed and searched, and each spatial micro-element interface is divided into a high-resolution interface and a strong stable interface by comparing the geometric characteristics; for the high-resolution interface, a lower dissipation space discrete format is adopted to ensure the high precision of the numerical simulation; for the strong stable interface, a higher dissipation space discrete format is adopted to ensure the stability of the numerical simulation; through this spatial discrete format mixing, the stability and accuracy of the numerical simulation of the aircraft high-speed flow field are taken into account.

[0007] The object of the present invention is achieved through the following technical solutions:

[0008] A flow field space discrete mixing processing method based on interface characteristics, the method comprising:

[0009] Import numerical calculation grids, build a basic framework for numerical iteration, and simulate the flow field structure during the flow process;

[0010] During the iteration process, based on the characteristics of the Ruby phenomenon, the area where the Ruby phenomenon may occur is predicted;

[0011] In the area where the ruby ​​phenomenon may occur, determine the interface type of each flow field space microelement, which includes a high-resolution interface and a strong stable interface. The high-resolution interface is along the normal direction of the aircraft surface, and the strong stable interface is along other directions of the normal direction of the aircraft surface.

[0012] For high-resolution interfaces, the first numerical format is used, and for strongly stable interfaces, the second numerical format is used. The resolution of the first numerical format is higher than that of the second numerical format, and the stability of the second numerical format is higher than that of the first numerical format.

[0013] The numerical iteration of the flow equations is completed according to the adopted numerical format, and the simulation results are given according to the numerical simulation requirements.

[0014] Furthermore, the numerical calculation grid is imported to build the basic framework of numerical iteration, which specifically includes:

[0015] The flow control equations are discretized in arbitrary numerical format and the basic framework for numerical iterative solution is constructed.

[0016] Furthermore, the characteristics of the ruby ​​phenomenon include the spatial distribution characteristics of the ruby ​​phenomenon. Based on the spatial distribution characteristics of the ruby ​​phenomenon, the areas where the ruby ​​phenomenon may occur are predicted to specifically include:

[0017] Identify the possible occurrence areas of the red jade phenomenon based on the spatial distribution of pressure during the iteration process.

[0018] Furthermore, the Ruby phenomenon characteristics include the Ruby phenomenon iteration time variation characteristics. Based on the Ruby phenomenon iteration time variation characteristics, the areas where the Ruby phenomenon may occur are predicted to specifically include:

[0019] When the numerical iteration fails to converge, the area where the ruby ​​phenomenon may occur is identified based on the relative residuals of typical parameters of each flow field space element along the virtual time. Typical parameters include pressure or density.

[0020] Furthermore, the determination of the interface type of each flow field space microelement specifically includes:

[0021] For each spatial element interface in the area where the ruby ​​phenomenon may occur, a traversal search is performed on the aircraft surface element surface closest to the spatial element interface. If the included angle between the spatial element interface and the aircraft surface element surface is less than the angle threshold a or greater than 180°-a, it is a high-resolution interface, otherwise it is a strongly stable fixed interface.

[0022] Furthermore, searching for the aircraft surface microelement surface closest to the spatial microelement interface specifically includes:

[0023] Use vector containers to initialize and store the geometric data of the given spatial microelement interface;

[0024] Generate a balanced binary tree retrieval structure in recursive mode;

[0025] Recursively calculate the distance from the micro-element surface of the aircraft to the micro-element interface of the space and locate the nearest point.

[0026] Furthermore, the flow control equations adopted by the basic framework of numerical iteration include the conservative NS equations or the Euler equations.

[0027] Further, the first numerical format includes AUSMPW, AUSMPW+, AUSMDV or M-AUSMPW+ format;

[0028] The second numerical format includes a Stegerwarming or Vanleer format.

[0029] The beneficial effects of the present invention are:

[0030] (1) The present invention adopts a high-resolution format to construct a basic framework for numerical iterative solution, which can simulate the subtle flow field structure during the flow process with high resolution and ensure the high accuracy of the overall simulation.

[0031] (2) The present invention identifies areas where the ruby ​​phenomenon may occur based on the spatial distribution characteristics of the ruby ​​phenomenon or its iterative time variation characteristics, and applies a hybrid strategy to local key areas to avoid the loss of accuracy caused by excessive use of high dissipative formats.

[0032] (3) The present invention adopts different spatial discretization formats based on the differences in the types of spatial micro-element interfaces in the flow field to form a local mixed discrete spatial format, taking advantage of the advantages and making up for the disadvantages, taking into account both high accuracy and high stability, and has a wide range of applications. The computational grids include but are not limited to common high-speed flow simulation grids such as structured grids, unstructured grids, or structured-unstructured hybrid grids; the flow control equations include but are not limited to the conservation NS equations or Euler equations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1It is a flowchart of a flow field space discrete mixing processing method based on interface characteristics according to an embodiment of the present invention;

[0034] Figure 2 It is a schematic diagram of dimensionless pressure distribution of a flow field in AUSMPW+ format according to an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of surface heat flux distribution using the AUSMPW+ format in an embodiment of the present invention;

[0036] Figure 4 Schematic diagram of dimensionless pressure distribution of flow field using Steger-Warming format in an embodiment of the present invention;

[0037] Figure 5 This is a schematic diagram of surface heat flux distribution using the Steger-Warming format in an embodiment of the present invention;

[0038] Figure 6 It is a schematic diagram of dimensionless pressure distribution of a flow field according to a flow field space discrete mixing processing method based on interface characteristics in an embodiment of the present invention;

[0039] Figure 7 It is a schematic diagram of surface heat flux distribution in a flow field spatial discrete mixing processing method based on interface characteristics in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0041] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0042] The prior art discloses a shock discontinuity identification and classification processing method, which can effectively improve the shock discontinuity resolution of the AUSM-type space discrete format, and to a certain extent take into account both accuracy and computational stability, and can effectively improve the red jade phenomenon when the grid quality is poor, and obtain better results. However, this method mainly improves shock discontinuity, and cannot completely solve the problem of "difficulty in suppressing non-physical fluctuations in relatively smooth areas with low computational consumption formats".

[0043] In order to solve the above technical problems, the following embodiments of a flow field spatial discrete mixing processing method based on interface characteristics are proposed in the present invention.

[0044] Example 1

[0045] Reference Figure 1 ,like Figure 1 The figure shows a flowchart of a flow field space discrete mixing processing method based on interface characteristics in this embodiment. The method specifically includes the following steps:

[0046] Step 1: Build a high-speed flow numerical simulation framework.

[0047] Specifically, the numerical calculation grid is imported, and the present embodiment uses a high-resolution format to discretize the flow control equations to construct a basic framework for numerical iterative solution. In the present embodiment, the calculation grids involved include but are not limited to common high-speed flow simulation grids such as structured grids, unstructured grids, or structured-unstructured hybrid grids. The flow control equations include but are not limited to the conservation NS equations or Euler equations, which are the most commonly used control equation forms for high-flow simulation. The convection terms of the flow control equations are discretized using a high-resolution format, where the high-resolution format includes but is not limited to AUSMPW, AUSMPW+, AUSMDV, M-AUSMPW+ and other dissipative formats with relatively low numerical dissipation. The use of a high-resolution format can simulate the subtle flow field structure in the flow process with high resolution, so the accuracy is relatively high.

[0048] Step 2: Identify areas where the ruby ​​phenomenon may occur.

[0049] Specifically, in the iterative process, the area where the ruby ​​phenomenon may occur is identified based on the spatial distribution characteristics of the ruby ​​phenomenon or its iterative time variation characteristics. In this embodiment, the ruby ​​phenomenon generally occurs in the strong pressure area of ​​the aircraft head stagnation point or the strong pressure area on the windward side, so it can identify the area where the ruby ​​phenomenon may occur based on the spatial distribution of pressure in the iterative process.

[0050] When the ruby ​​phenomenon occurs, the corresponding area in the flow field will experience strong oscillations with the change of virtual time (such as the virtual time of steady flow and the virtual time in the dual-time method of unsteady flow), resulting in non-convergence of the flow field calculation. Therefore, when non-convergence occurs in the numerical iteration, the area where the ruby ​​phenomenon may occur can be identified based on the relative residuals of the typical parameters (such as pressure, density, etc.) of each flow field spatial element along the virtual time.

[0051] Step 3: Determine the type of spatial element interface.

[0052] In the area where the ruby ​​phenomenon may occur, each spatial micro-element interface is divided into a high-resolution interface and a strong stable interface.

[0053] The specific judgment method is as follows:

[0054] For each spatial microelement interface in the area where the ruby ​​phenomenon may occur, a traversal search is performed on the nearest aircraft surface microelement surface. If the angle between the two surface element directions is less than the angle threshold a or greater than 180°-a, it is a high-resolution interface; otherwise, it is a strongly stable fixed interface.

[0055] There are many methods for searching for the micro-element surface of the aircraft surface that is closest to the interface, and any method involving the calculation of the distance from a spatial point to a given point set can be applied to this embodiment.

[0056] As an implementation mode, this embodiment selects to adopt a recursive calculation method of the distance from a spatial point to a given point set or a non-recursive calculation method of the distance from a spatial point to a given point set, wherein the specific steps of the recursive calculation method of the distance from a spatial point to a given point set include:

[0057] Step A: Use a vector container to initialize and store the geometric data of a given point set. In the initial stage, assume that The given point set of point elements is , define a A vector container ( ), sequential storage Points The address value is recorded as , where the class describing the point element information contains two basic data: ① The spatial coordinates of the point element, in the form of a double-precision floating-point array The identifier, its length and spatial dimension Same;②Point elements in The initial sequence number in the signed integer variable Identification; at a given point set In the example, any two different point elements have different value.

[0058] Step B: Generate a balanced binary tree retrieval structure in a recursive mode. In the current step, you need to All elements in are divided into multiple finite point subsets for storage, and each finite point subset forms a one-to-one mapping relationship with the relevant nodes on the balanced binary tree; among them, the finite point subset contained in each non-terminal node is the union of the finite point subsets contained in its left child and right child nodes, and the finite point subsets contained in the same-level brother nodes do not intersect with each other.

[0059] Furthermore, step B can be divided into the following three sub-steps:

[0060] Step B1: Set a finite point subset length threshold for a balanced binary tree and use a signed integer member variable If the current node contains a finite number of point subset elements Satisfy the conditions , then the current node is identified as a non-terminal node and needs to continue to perform recursive decomposition; otherwise, the current node is identified as a terminal node. In the embodiment of the present invention, based on the comprehensive consideration of the binary tree height control factor and the local nearest point fast search factor, the setting The value of is 24.

[0061] Step B2: Declare a binary tree recursive generation function without a return type and its parameter list. The binary tree recursive generation function is named , its input parameters are: ①Subtree root node (reference pointer transfer);② The node contains the point subset elements in The starting subscript in (Signed integer value transfer); ③ The node contains the point subset elements in The end subscript in (Signed integer value is passed). Therefore, the form of the binary tree recursive generation function is .

[0062] Step B3: Global root node pointer for a balanced binary tree Construct an instantiated object. In the binary tree constructor, the global root node pointer The initial value is the empty address . In the current step, Passed by reference to The first parameter of , at the same time, let and The values ​​are 0 and ( for After executing the related steps B31 to B34, if the condition Established, then Return immediately, otherwise continue to execute step B35 and then return.

[0063] Step B31: Open up dynamic memory to implement the current Object instantiation, and through the node type The assignment operation inside the constructor uses Private member variables of an object , The values ​​of the current binary tree recursive generation function Of , Be consistent.

[0064] Step B32: Follow The current coordinate axis directions are calculated separately The point elements contained in the object (Continuous subscript The range is ) and use signed integer register variables Record the direction number corresponding to the maximum variance.

[0065] Step B33: Along the direction of maximum variance, according to the coordinate components of the point elements The rules from small to large are currently The point element address value contained in the object (Continuous subscript The range is ) to sort. The present invention directly calls the C++ standard template library The generic algorithm completes this sorting process to achieve the optimal combination of quick sort, heap sort and insertion sort, thereby reducing the time cost of recursive construction of a balanced binary tree.

[0066] Step B34: Calculate the root node of the current subtree The upper and lower bounds of the bounding box of the space. coordinate axis directions, respectively calculate the current The point elements contained in the object (Continuous subscript The range is ) The infimum of Supremum , (subscript The range is ). The present invention directly calls the C++ standard template library and The generic algorithm completes this calculation process.

[0067] Step B35: construct subtrees at all levels of the balanced binary tree. In steps B31 to B34, and Identifies the current subtree at a given point sequence The starting and ending range of the point subset involved in . Since in step B33, along the sequence number The maximum variance direction of the subscript The range is The point elements are reordered, so the signed integer register variable is defined For use in sequence Mark the median element on the top. According to step B3, if the condition Established, then call Complete the generation of the next level left subtree and right subtree in sequence, otherwise jump out of the current one directly For the generation of the left subtree, the binary tree recursive generation function The first parameter value is set to the current root node The left child pointer , the second parameter is set to , the third parameter is set to For the generation of the right subtree, the binary tree recursive generation function The first parameter value is set to the current root node The right child pointer , the second parameter is set to , the third parameter is set to .

[0068] Step C: Recursively calculate the distance from the spatial point to the given point set and locate the nearest point. Any point in dimensional space The coordinates of , To a given set of points The square value of the distance uses a double-precision floating-point register variable Storage, whose initial value is a sufficiently large floating-point constant (In the embodiment of the present invention, ). Declare a recursive search function with no return type and its parameter list as , where the double-precision floating-point parameter for The first address of the point coordinate array (constant pointer transfer), The root node address of the current subtree (pointer transfer), double-precision floating-point parameter That is To a given set of points The square value of the distance (reference transfer). In particular, at the initial moment, the global root node pointer of the balanced binary tree is used initialization The second parameter of the function When the parameter transfer is completed, the distance from the spatial point to the given point set can be calculated according to steps B1 to B3 and the nearest point can be located.

[0069] Step C1: If the root node of the current subtree is a non-terminal node, that is The left child pointer of the object and the right child pointer Both exist, then define and initialize two local node pointers as follows:

[0070] , .

[0071] Calculate separately Point to the nearest node pointer (To a known point The node pointer variable to which the nearer subtree bounding box belongs) and the far node pointer (To a known point The node pointer variable to which the bounding box of the farther subtree belongs) is the square value of the distance between the bounding boxes of the two subtrees of the root node and . Set the near node pointer The corresponding bounding box infra and supremum parameters are and ,at the same time Point at dimension( ) Defined as: If ,So ;if ,So ;if ,So . Further, .Pick The corresponding bounding box infra and supremum parameters of the node, by analogy The calculation process can be obtained If , then exchange and Values ​​and swap and If , then call the recursive function , its three parameters are Point coordinates first address , near node pointer With reference variables .if , then call the recursive function , its three parameters are Point coordinates first address , far node pointer With reference variables .

[0072] Step C2: If the root node of the current subtree is a terminal node, that is The left child pointer of the object and the right child pointer All point to empty address values , then extract the current The object contains the starting and ending range parameters of the given point subset, and stores their values ​​in signed integer register variables and Next, traverse A fragment of a sequence, where the subscript The range is , calculate successively Point to point elements The square of the distance ,if , then The value of is updated to Simultaneously balance the nearest point pointer of a binary tree Point to the current point element The address of.

[0073] Step C3: Return The square root of As a point To a given point set sequence The distance, at this time the nearest point pointer of the balanced binary tree Pointing Distance point in sequence The first address of the most recent element, which can be accessed through the interface function It is noted that, in the implementation of step C1 to step C2 of this embodiment, only the square value of the relevant distance parameter is stored to avoid the influence of frequent square root operations on the computing performance.

[0074] Step D: When the query process is completed, release the dynamic memory opened by the internal members of the balanced binary tree, which are: ① Given point set sequence ② The heap space occupied by each element of the tree is released according to the sequential traversal process of the vector. ② The heap space occupied by each layer of the nodes in the balanced binary tree is released according to the pre-order traversal process of the fork tree structure.

[0075] A non-recursive method for calculating the distance from a spatial point to a given point set comprises the following specific steps:

[0076] For the flow field grid element S, take three point coordinates (x01, x02, x03), (y01, y02, y03), (z01, z02, z03), and calculate the intersection of the plane equation and the straight line as follows:

[0077] m=(y02-y01)*(z03-z01)-(z02-z01)*(y03-y01);

[0078] n=(z02-z01)*(x03-x01)-(x02-x01)*(z03-z01);

[0079] e=(x02-x01)*(y03-y01)-(y02-y01)*(x03-x01);

[0080] f=-a*x01-b*y01-c*z01;

[0081] y2=-(m*a+e*c+f) / n.

[0082] The computational efficiency of the above two methods is much higher than that of the traditional cutting box algorithm or alternating direction digital tree (ADT) algorithm.

[0083] The selection of the angle threshold must be based on the specific grid type, grid characteristics, etc. Here, a=30°~50° is recommended.

[0084] The ruby ​​phenomenon mainly occurs in the area near the shock wave and close to the surface of the aircraft. The flow field gradient usually changes greatly along the normal direction of the aircraft surface. When numerically discretizing, the flow field gradient characteristics must be extracted with high resolution to ensure the simulation accuracy of the boundary layer, heat flow and friction. Therefore, this type of flow field space micro-element interface is defined as a high-resolution interface; and for non-surface normals, when numerically discretizing, it is necessary to ensure the stable exchange and transmission of mass, momentum and energy in the boundary layer along the flow direction to improve the simulation stability. Therefore, this type of flow field space micro-element interface is defined as a high-stability interface.

[0085] Step 4: Classify and process the interfaces of each flow field space element.

[0086] Specifically, for high-resolution interfaces, a lower dissipation spatial discretization format is used to ensure high accuracy of numerical simulation; for strongly stable interfaces, a higher dissipation spatial discretization format is used to ensure stability of numerical simulation.

[0087] In this embodiment, high-resolution formats, including but not limited to AUSMPW+, AUSMPW, AUSMDV, etc., have relatively small numerical dissipation, relatively high resolution of shock waves and flow field fine structures, and thus have high calculation accuracy. However, due to its small numerical dissipation, it may be difficult to effectively suppress non-physical numerical fluctuations, affecting the calculation stability and causing the "ruby" phenomenon.

[0088] Strong stability formats, including but not limited to Stegerwarming and Vanleer, have relatively large numerical dissipation, can effectively suppress numerical fluctuations during iteration, and have good numerical calculation stability. However, due to its excessive dissipation, it may smooth out the subtle flow field structure during the flow process, affecting the calculation accuracy.

[0089] Here, different spatial discretization formats are adopted according to the differences in the types of spatial micro-element interfaces in the flow field, absorbing the advantages of both and avoiding their disadvantages, so as to achieve both high precision and high stability.

[0090] Step 5: Complete the numerical iteration of the flow equations and give the simulation results according to the numerical simulation requirements.

[0091] The specific methods of numerical iteration of the flow control equations and processing of numerical simulation results are discussed or introduced in detail in many public documents or materials, which will not be repeated here. The present embodiment can be well applied to these general methods.

[0092] The numerical simulation results provided by this embodiment, including but not limited to the temporal or spatial distribution of high-speed flow parameters, the influence laws of flow phenomena, aircraft aerodynamic characteristics, aerodynamic thermal environment, flight trajectory, control strategy, system design scheme, etc., can provide key technical support for the design and evaluation of high-speed aircraft.

[0093] Example 2

[0094] This embodiment takes the numerical simulation of the flow field of the head of a high-speed aircraft in Canberra's T-ADFA shock wind tunnel as an example to illustrate the application effect of the flow field spatial discrete mixing processing method based on interface characteristics provided by the above embodiments.

[0095] When an aircraft flies at high speed, it faces strong aerodynamic heating, and its surface heat flux is an important part of the aircraft thermal protection design. The high-speed aircraft head test model is a hemispherical head with a radius of 30mm. The test flow conditions are: angle of attack 0 degrees, static temperature 54.143K, static pressure 115.438Pa, Mach number 9.7, wall temperature 294.44K. The test measured the head stagnation heat flux (maximum heat flux) to be about 190kW / m 2 .

[0096] A common low-dissipation higher-resolution format (AUSMPW+ format), a common higher-dissipation low-resolution format (Steger-Warming format) and the aforementioned embodiment method are respectively adopted.

[0097] Reference Figure 2 and Figure 3 ,like Figure 2 FIG. 1 is a schematic diagram of dimensionless pressure distribution of the flow field in the AUSMPW+ format in this embodiment. Figure 3 The figure shows the surface heat flux distribution diagram of this embodiment using the AUSMPW+ format. It can be seen that a relatively obvious ruby ​​phenomenon occurs; the pressure near the surface oscillates irregularly, the heat flux result is not smooth, does not meet theoretical expectations, and the calculation stability is poor; but the maximum heat flux is 184kW / m 2 , which is consistent with the test value.

[0098] Reference Figure 4 and Figure 5 ,like Figure 4 FIG. 1 is a schematic diagram of dimensionless pressure distribution of the flow field using the Steger-Warming format in this embodiment. Figure 5 The figure shows the schematic diagram of the surface heat flux distribution using the Steger-Warming format in this embodiment. It can be seen that the calculation results are stable and no ruby ​​phenomenon occurs; the flow field pressure and surface heat flux distribution are relatively smooth, which is in line with theoretical expectations; but the stagnation point heat flux (maximum value) is 287kW / m 2 , which is much higher than the experimental value, and the calculation accuracy is low.

[0099] Reference Figure 6 and Figure 7 ,like Figure 6 FIG. 1 is a schematic diagram of dimensionless pressure distribution of the flow field according to the flow field space discrete mixing processing method based on the interface characteristics of this embodiment. Figure 7 The figure shows the surface heat flux distribution diagram of the flow field space discrete hybrid processing method based on the interface characteristics of this embodiment. It can be seen that the calculation results are stable and no ruby ​​phenomenon occurs; the flow field pressure and surface heat flux distribution are relatively smooth, which is in line with theoretical expectations; the stagnation point heat flux is about 186kW / m 2 , which is highly consistent with the test value and has high calculation accuracy.

[0100] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A flow field space discrete mixing processing method based on interface characteristics, characterized in that: The method comprises: Import numerical calculation grids, build a basic framework for numerical iteration, and simulate the flow field structure during the flow process; During the iteration process, based on the characteristics of the Ruby phenomenon, the area where the Ruby phenomenon may occur is predicted; In the area where the ruby ​​phenomenon may occur, determine the interface type of each flow field space microelement, which includes a high-resolution interface and a strong stable interface. The high-resolution interface is along the normal direction of the aircraft surface, and the strong stable interface is along other directions of the normal direction of the aircraft surface. Determining the interface type of each flow field space microelement specifically includes: For each spatial micro-element interface in the area where the ruby ​​phenomenon may occur, the closest aircraft surface micro-element surface to the spatial micro-element interface is searched traversally. If the included angle between the spatial micro-element interface and the aircraft surface micro-element surface is less than the angle threshold a or greater than 180°-a, it is a high-resolution interface, otherwise it is a strongly stable interface. For high-resolution interfaces, the first numerical format is used, and for strongly stable interfaces, the second numerical format is used. The resolution of the first numerical format is higher than that of the second numerical format, and the stability of the second numerical format is higher than that of the first numerical format. The numerical iteration of the flow equations is completed according to the adopted numerical format, and the simulation results are given according to the numerical simulation requirements.

2. The flow field space discrete mixing processing method based on interface characteristics according to claim 1 is characterized in that: Import the numerical calculation grid and build the basic framework of numerical iteration, including: The flow control equations are discretized in arbitrary numerical format and the basic framework for numerical iterative solution is constructed.

3. The flow field space discrete mixing processing method based on interface characteristics according to claim 1, characterized in that: The characteristics of the ruby ​​phenomenon include the spatial distribution characteristics of the ruby ​​phenomenon. Based on the spatial distribution characteristics of the ruby ​​phenomenon, the areas where the ruby ​​phenomenon may be predicted include: Identify the possible occurrence areas of the red jade phenomenon based on the spatial distribution of pressure during the iteration process.

4. The flow field space discrete mixing processing method based on interface characteristics according to claim 1, characterized in that: The characteristics of the Ruby phenomenon include the characteristics of the iteration time change of the Ruby phenomenon. Based on the characteristics of the iteration time change of the Ruby phenomenon, the areas where the Ruby phenomenon may be predicted include: When the numerical iteration fails to converge, the area where the ruby ​​phenomenon may occur is identified based on the relative residuals of typical parameters of each flow field space element along the virtual time. Typical parameters include pressure or density.

5. The flow field space discrete mixing processing method based on interface characteristics according to claim 1, characterized in that: Searching for the closest surface microelement of the aircraft to the spatial microelement interface specifically includes: Use vector containers to initialize and store the geometric data of the given spatial microelement interface; Generate a balanced binary tree retrieval structure in recursive mode; Recursively calculate the distance from the micro-element surface of the aircraft to the micro-element interface of the space and locate the nearest point.

6. The flow field space discrete mixing processing method based on interface characteristics according to claim 1, characterized in that: The flow control equations used in the basic framework of numerical iteration include the conservative NS equations or the Euler equations.

7. The flow field space discrete mixing processing method based on interface characteristics according to claim 1, characterized in that: The first numerical format includes AUSMPW, AUSMPW+, AUSMDV or M-AUSMPW+ format; The second numerical format includes a Stegerwarming or Vanleer format.

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