Data processing method for CFD simulation and computer storage medium

Abstract

The invention provides a data processing method for CFD (computational fluid dynamics) simulation. CFD input parameters are extracted from the feature examples to form an original example data set, and function categories and hierarchical relationships of parameter data in the original example data set are identified; converting the original example data set into a structured parameter set according to the hierarchical relationship by taking a function category as a unit; furthermore, a rule data set is generated based on the dependency relationship between the parameter data and the structured parameter set, so that example configuration auxiliary data corresponding to each function category is determined according to the rule data set. Implicit rules among parameters in the CFD examples are changed from experiences into explicit recordable structures (namely example configuration auxiliary data), and a user can perform CFD simulation example parameter configuration according to the explicit recordable structures, so that the configuration accuracy and efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computational fluid dynamics, and particularly relates to a data processing method for CFD simulation and a computer storage medium. BACKGROUND

[0002] Computational fluid dynamics (CFD) is a science that uses numerical methods and algorithms to simulate fluid flow, studies the behavior of fluids by solving the control equations of fluid dynamics, and analyzes flow, heat transfer, mass transfer, etc. Software for CFD simulation such as OpenFOAM provides solvers, algorithms and modules required to realize CFD theory, and is used to simulate and solve numerical problems of fluid dynamics and related physical phenomena.

[0003] Although the modular system (physical model, mesh tool, solver) of OpenFOAM provides flexibility, users need to spend time to understand how each module works together. There are a large number of example configurations for calling OpenFOAM to solve the calculation, and the combination is complex, and improper user configuration may cause solving errors. OpenFOAM relies on the Linux environment, and users need to use and develop based on C++ code and CFD theory knowledge. Users need to invest a lot of time and learning costs to use OpenFOAM for example parameter configuration as smoothly as conventional software. SUMMARY

[0004] To alleviate, mitigate or eliminate the above technical problems, the present application provides a data processing method for CFD simulation, which improves the CFD example parameter configuration efficiency.

[0005] In a first aspect, the present application provides a data processing method for CFD simulation, comprising: acquiring a plurality of characteristic examples for CFD simulation;

[0006] According to the preset dimension, each characteristic example is identified, and the example category of each characteristic example is determined;

[0007] The characteristic examples contained in each example category are subjected to parameter processing to obtain an original example data set corresponding to each example category; the parameter processing includes CFD input parameter extraction and parameter function division, and the original example data set includes the function category of each parameter;

[0008] According to the function category, the parameter data in the original example data set is divided into a plurality of data subsets, and one data subset corresponds to one function category;

[0009] Identify the hierarchical relationship between parameter data in each data subset, and convert each data subset into a structured parameter set corresponding to each functional category based on the hierarchical relationship, with one structured parameter set corresponding to one functional category;

[0010] Obtain the dependencies between parameter data in each structured parameter set, and convert each structured parameter set into a rule dataset according to the dependencies. One function category corresponds to one rule dataset, and one case category includes multiple rule datasets corresponding to multiple function categories.

[0011] Based on the rule dataset, determine the auxiliary data for the example configuration corresponding to each functional category.

[0012] In one embodiment, the process of extracting CFD input parameters for any feature instance includes:

[0013] Read the dictionary file of the target folder under any of the feature examples in sequence, and extract the basic information and detailed setting parameters of the example from the dictionary file. The target folder includes the 0, constant and system folders.

[0014] In one embodiment, the preset dimensions include multiple categories, and each preset dimension includes multiple dimension categories. Identifying any feature instance based on the preset dimensions to determine the instance category of that feature instance includes:

[0015] Based on the key fields in any feature instance, identify the target dimension category to which any feature instance belongs under each preset dimension;

[0016] Based on the target dimension category to which any feature instance belongs in each preset dimension, the instance category of any feature instance is determined.

[0017] In one embodiment, any instance category contains N feature instances. Parameter extraction is performed on the N feature instances to obtain the original instance dataset corresponding to the any instance category, including:

[0018] Parameters are extracted sequentially for N feature cases under any given case category;

[0019] By comparing the parameter extraction results of each of the N feature examples, the differences in parameter extraction results among the N feature examples are obtained;

[0020] Output the difference.

[0021] In one embodiment, each structured parameter set includes one or more of the following information: module fields representing the functional category to which it belongs, hierarchical parameters, example values ​​corresponding to the hierarchical parameters, and field types corresponding to the hierarchical parameters.

[0022] In one embodiment, the dependency relationship includes any one or more of the following: a linkage relationship, a conditional activation, and a mutual exclusion relationship.

[0023] In one embodiment, the preset dimensions include a calculation scenario, a series-parallel relationship, a turbulent flow model, and a solution algorithm.

[0024] In one embodiment, the functional categories include a geometry module, a mesh module, a physics module, and a solution module.

[0025] In one embodiment, the preset dimensions include a plurality of dimensions, each preset dimension including a plurality of dimension categories, and the method further includes:

[0026] obtaining a target dimension category to which the calculation example configuration auxiliary data belongs under each preset dimension;

[0027] storing the calculation example configuration auxiliary data and a dimension label matched with the target dimension category in association to a parameter template library;

[0028] determining a simulation scenario to be simulated;

[0029] obtaining and displaying target calculation example configuration auxiliary data matched with the simulation scenario from the parameter template library.

[0030] In a second aspect, the present application provides an electronic device, comprising:

[0031] at least one processor; and

[0032] at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor alone or in combination, causing the electronic device to perform the method of the first aspect.

[0033] Compared with the prior art, the present application has the following advantages:

[0034] The hierarchical relationship and the dependency relationship between parameters in a characteristic example can be changed from experience to explicit recordable calculation example configuration auxiliary data, and a user can configure CFD simulation example parameters based on the calculation example configuration auxiliary data, thereby improving configuration accuracy and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings are included to provide a further understanding of the present application, and they are collected and constitute a part of the present application, and the drawings show embodiments of the present application, and together with the present specification, they play a role in explaining the principles of the present application. In the drawings:

[0036] Figure 1 is a flowchart of a data processing method for CFD simulation provided by an embodiment of the present application.

[0037] Figure 2is a structural schematic diagram of a feature algorithm example provided by an embodiment of the present application.

[0038] Figure 3 is a schematic diagram of a constructor provided by an embodiment of the present application.

[0039] Figure 4 is a schematic diagram of algorithm example configuration auxiliary data provided by an embodiment of the present application.

[0040] Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can also be applied to other similar scenarios without creative labor. Unless it is obvious from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0042] Flowcharts are used in the present application to illustrate the operations performed by the devices or equipment according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in sequence. On the contrary, these steps can be processed in reverse order or simultaneously. Meanwhile, or other operations can be added to these processes, or a step or several steps of operation can be removed from these processes.

[0043] The data processing method for CFD simulation of the present application can change the implicit rules between fields in the CFD algorithm example from experience to explicit recordable structure. By establishing the explicit dependency rules between parameters, not only the configuration accuracy is improved, but also the parameter template is reused, and multiple simulation scenarios are adapted.

[0044] Figure 1 is a data processing method for CFD simulation provided by an embodiment of the present application. The method can be executed by an electronic device, and the method comprises:

[0045] S100: Obtain a plurality of feature algorithm examples for CFD simulation. Wherein, the feature algorithm example refers to a representative algorithm example in a typical CFD simulation scenario, which can cover main working condition types and reflect multiple parameter configuration characteristics, and can be used to summarize and extract general input parameter dependency relationships.

[0046] Exemplarily, a plurality of feature algorithm examples can be selected from the tutorial tutorial algorithm example library of OpenFOAM. Or a local algorithm example library, which is not specifically limited.

[0047] S101: Identify each feature instance according to a preset dimension to determine the instance category of each feature instance.

[0048] In one embodiment, when performing step S101, key fields in any feature instance can be identified based on preset dimensions to determine the instance category of any feature instance. That is, it is not necessary to identify all fields in the feature instance; only key fields need to be identified to determine the corresponding instance category.

[0049] The aforementioned preset dimensions include multiple sub-dimensions, and each preset dimension includes multiple dimension categories. The process for identifying the category of any given feature instance can be as follows: based on the key field in any given feature instance, identify the target dimension category to which the given feature instance belongs under each preset dimension; and based on the target dimension category to which the given feature instance belongs under each preset dimension, determine the category of the given feature instance. The key field can be pre-set.

[0050] For example, the preset dimensions include computational scenario, serial / parallel flow, turbulence model, and solution algorithm. Each preset dimension includes multiple dimension categories as shown in Table 1. The combination of case classifications includes, but is not limited to, the following four categories: Category 1: Steady-state + Compressible + Heat Transfer + Serial + Laminar + SIMPLE; Category 2: Steady-state + Incompressible + Heat Transfer + Parallel + RAS + SIMPLE; Category 3: Transient + Incompressible + No Heat Transfer + Serial + LES + PIMPLE; Category 4: Transient + Incompressible + No Heat Transfer + Serial + LES + PIMPLE.

[0051] Table 1

[0052] Computational scenario Serial / parallel Turbulence model Solution algorithm Compressible / incompressible Serial Laminar SIMPLE Heat transfer / no heat transfer Parallel RAS SIMPLEC Steady state / transient LES PIMPLE Combustion DES PISO Lagrangian Multiphase flow

[0053] Taking example CASE1, its structure is as follows: Figure 2 As shown in Table 2, each dictionary file includes basic information about the computational example and detailed setting parameters, such as parameter name, configuration value, file path, nesting hierarchy, and location information. Key fields in the dictionary files under the 0, constant, and system folders of CASE1 can be read to identify the target dimension category of CASE1 in each preset dimension (computation scenario, serial / parallel flow, turbulence model, and solution algorithm). Assuming these categories are: steady-state, compressible, heat transfer, serial, laminar, and SIMPLE, then CASE1 is determined to belong to the first category of computational examples. This method can be used to classify all other feature computational examples in the same way.

[0054] Table 2

[0055]

[0056] S102: Parameter processing is performed on the feature cases included in each case category to obtain an original case data set corresponding to each case category. The parameter processing includes CFD input parameter extraction and parameter function division, and the original case data set includes the function category of each parameter.

[0057] In one embodiment, the process of CFD input parameter extraction for any feature case includes: sequentially reading the dictionary file of the target folder under any feature case, extracting case basic information and detailed setting parameters such as parameter name, configuration value, belonging file path, nested hierarchical structure, position information, etc. from the dictionary file. The target folder includes 0, constant and system folders.

[0058] Among them, as a feasible way, in the process of extracting case basic information and detailed setting parameters, semantic recognition can also be performed, and parameter function division is performed on the parameters based on the semantic recognition result to determine the function category corresponding to each parameter, and the parameter function division is completed. The function category includes the following categories:

[0059] 1. Geometry module: related to geometric size, shape description, etc., and the data is mainly based on system / blockMeshDict.

[0060] 2. Grid module: related to grid generation method, local encryption method, etc., and the data is mainly based on system / blockMeshDict, system / snappyHexMeshDict.

[0061] 3. Physical module: related to initial conditions, boundary conditions, thermal physical model, transport model, turbulence model, gravity, etc., and the data is mainly based on 0 / all, constant / transportProperties, constant / turbulenceProperties, constant / RASProperties, constant / thermophysicalProperties, constant / g, etc.

[0062] 4. Solution module: related to numerical algorithm, time control method, etc., and the data is mainly based on system / controlDict, system / fvSchemes, system / fvSolution, system / decomposeParDict, etc.

[0063] Exemplarily, the original case data set is shown in Table 3.

[0064] Table 3

[0065]

[0066]

[0067]

[0068]

[0069] In one embodiment, after obtaining the original case data set corresponding to any case category through step S102, the parameter data in the original case data set can also be standardized to obtain a case data set. The standardization process includes type specification and unit conversion. The type specification includes: unifying all parameter types to a basic type, including scalar, vector, bool, enum, string, list, etc., and specifying field name, type and example value. For example, density and viscosity are scalar types, initial velocity is a vector type, turbulence model selection is an enum type, whether to enable implicit format is a bool type, turbulence sub-model is a string type, and domain partition is a list type.

[0070] The unit conversion includes using the international standard unit system as a standard, i.e., meters, kilograms, seconds, etc., and converting fields with inconsistent units according to physical conversion formulas.

[0071] It can be understood that the above-mentioned any feature case can be used to obtain the original case data set, and in this way, the original case data set of each feature case can be obtained by traversing the feature cases included in each case category, and the original case data set corresponding to each case category can be obtained by integrating all original case data sets under a case category.

[0072] In one embodiment, any example category includes N characteristic examples, and parameter extraction is performed on the N characteristic examples to obtain an original example data set corresponding to any example category, including: sequentially performing parameter extraction on the N characteristic examples under any example category, comparing the parameter extraction results corresponding to the N characteristic examples respectively, obtaining the difference part of the parameter extraction results between the N characteristic examples, and outputting the difference part. The user can view the difference part to adjust the original example data set. Since the characteristic examples obtained in step S100 are not necessarily standard and normative, for example, OpenFOAM in principle will not affect the calculation if it is written more, which will lead to redundant items in the characteristic examples. When performing feature extraction on all characteristic examples under any example category, the application can also output the difference part of all examples to the user, for example, any example category includes 10 characteristic examples CASE1-CASE10, the output difference part shows that CASE1-CASE9 are the same for the same parameter setting, and CASE10 is different from the other 9 characteristic examples for the same parameter. Therefore, the user can determine the specific reason based on the difference part, and adjust the original example data set if necessary.

[0073] S103: Dividing the parameter data in the original example data set into a plurality of data subsets according to the functional categories, one data subset corresponding to one functional category.

[0074] S104: Identifying the hierarchical relationship between the parameter data in each data subset, and converting each data subset into a structured parameter set corresponding to each functional category according to the hierarchical relationship, one functional category corresponding to one structured parameter set.

[0075] In one embodiment, when the original example data set is obtained in step S102, if the parameter data in the original example data set is further standardized to obtain an example data set, then when step S103 is executed, the processing object is adjusted from the original example data set to the example data set.

[0076] In one embodiment, each structured parameter set includes any one or more of the following information: a module field representing the functional category to which it belongs, a hierarchical parameter, an example value corresponding to the hierarchical parameter, and a field type corresponding to the hierarchical parameter. For example, assuming that any functional category is a physical module, the structured parameter set corresponding to the functional module can be as shown in Table 4, where the first-level parameter, the second-level parameter, the third-level parameter, and the fourth-level parameter correspond to the hierarchical path of the parameter field, and are suitable for the multi-level nested structure of CFD simulation software such as OpenFOAM.

[0077] Table 4

[0078]

[0079]

[0080] It can be understood that the above structured parameter set is classified by function categories, that is, one function category corresponds to one structured parameter set.

[0081] S105: Obtain the dependency relationship between the parameter data in each structured parameter set, and convert each structured parameter set into a rule data set according to the dependency relationship, one function category corresponding to one rule data set, and multiple function categories corresponding to multiple rule data sets under one example category.

[0082] The dependency relationship includes any one or more of the following: linkage relationship, conditional activation, and mutual exclusion relationship. The linkage relationship represents that the configurations must be linked together, the conditional activation represents that another parameter is effective only when a certain parameter is a specific value, and the mutual exclusion relationship represents that the parameters cannot be configured at the same time. In order to better understand the meanings of the linkage relationship, the conditional activation, and the mutual exclusion relationship, refer to the examples shown in Table 5.

[0083] Table 5

[0084]

[0085]

[0086] In one embodiment, after the execution of S104 to obtain the structured parameter set is completed, the structured parameter set can be output for the user to view, and the user can identify and judge the dependency relationship between the parameters at each level based on his own experience, obtain and import the dependency relationship result (including the dependency relationship between the parameters at each level in the structured parameter set) for the structured parameter set. After the electronic device obtains the dependency relationship result imported by the user, each structured parameter set is converted into a rule data set according to the dependency relationship result. Exemplarily, still taking any function category as a physical module, the rule data set corresponding to the physical module under one example category can be as shown in Table 6, in which the field "turbulenceProperties" is the configuration switch of the turbulence model, and the specific turbulence model needs to be set in this file, and the field "simulationType" is the total configuration of the turbulence model, and the values are: laminar-no turbulence model is enabled, RAS-RAS turbulence model is enabled, LES-LES turbulence model is enabled, DES-DES turbulence model is enabled, and the like.

[0087] Table 6

[0088]

[0089] Or, in another embodiment, the OpenFOAM source code can also be identified, by parsing the constructor of the source code, identifying the field reading statement to determine the field existence and call relationship, so as to realize the acquisition of the dependency relationship between the parameter data in the structured parameter set. The OpenFOAM source code is mainly used to determine the dependency relationship between the turbulence model type and the parameter field. Taking kEpsilon as an example, its implementation is located in kEpsilon.C, and in its constructor, it is pointed out that Figure 3 As shown in the figure, it is shown that in the RASModel folder, if the kEpsilon model is set, the sub-dictionary kEpsilonCoeffs is a sub-dictionary that must be set, and the parameters Cmu and the like are also parameters that must be set.

[0090] It can be seen that the construction method of the rule data set proposed in the present application is suitable for parameter dependency relationships of different modules (such as geometry module, grid module, physical module, solving module, etc.) and different levels, and has good scalability and maintainability.

[0091] S106: Determine the case configuration auxiliary data corresponding to each function category according to the rule data set.

[0092] In one embodiment, after step S105 is performed, the rule data set can be output for user to view. The user can quickly understand the dependency relationship between the parameter fields, the dependency condition, the example value of the parameter setting, etc. in the rule data set in the unit of the function category, so as to quickly input the case configuration auxiliary data corresponding to each function category in the visual interface provided by the electronic device, and determine the target preset dimension (such as calculation scene, string parallel, turbulence model, solving algorithm) associated with the case configuration auxiliary data, and the target dimension category (such as compressible / incompressible, steady state / transient state, RAS, etc.) under the target preset dimension. The data format of the case configuration auxiliary data may, for example, be JSON data convenient for CFD simulation software to recognize, which is easy to integrate with the software, suitable for secondary development and cross-platform deployment, and has strong engineering portability. As shown in the figure, the case configuration auxiliary data of the turbulence model-RAS-physical module is taken as an example. Subsequently, each case configuration auxiliary data can be tested in the unit of the function category and in the module (such as geometry module, grid module, physical module, solving module, etc.), so as to reduce the number of codes involved in each test, facilitate code checking or adjustment according to the test results, and be beneficial to improving the test efficiency. Figure 4

[0093] ​Further, in an embodiment, the target dimension category to which the above-mentioned example configuration assistance data belongs in each preset dimension can also be obtained, and the example configuration assistance data and the dimension label associated with the target dimension category are stored in the parameter template library. Subsequently, when the user performs CFD simulation, the electronic device determines the simulation scenario to be simulated, and obtains and displays the target example configuration assistance data matching the simulation scenario from the parameter template library.

[0094] Still taking the example configuration assistance data shown in the table as an example, assuming that the corresponding target dimension category thereof is RAS, the electronic device can add a dimension label corresponding to “RAS” to the example configuration assistance data, and store the example configuration assistance data and the corresponding dimension label in the parameter template library. By analogy, the electronic device can use the same method to store other example configuration assistance data and corresponding dimension labels in the parameter template library. The parameter template library includes example configuration assistance data of different function categories, different calculation scenarios, series / parallel, different turbulence models, and different solving algorithms, and can adapt to various simulation scenarios required by multiple CFD simulations, and has universality. Figure 4 Subsequently, when the user performs CFD simulation, the electronic device displays a scene type configuration control on the user interface, the user selects the simulation scenario to be simulated, such as the RAS turbulence model, through the scene type configuration control of the user interface, and the electronic device determines the simulation scenario, and can obtain and display the target example configuration assistance data matching the RAS turbulence model from the parameter template library. The user can complete the CFD simulation input parameter configuration according to the target example configuration assistance data, reduce the CFD technology application threshold, improve the simulation calculation efficiency and reliability, and improve the configuration accuracy and efficiency.

[0095]

[0096] ​In one embodiment, since the case category includes multiple categories, assuming M (M is an integer greater than 0) case categories, multiple rule data sets corresponding to multiple function categories under M case categories can be obtained by performing the above steps S100-S105. Still taking any function category as a physical module, taking the physical module as a unit, M case categories, corresponding rule data sets corresponding to M physical modules can be obtained. Taking Table 6 as an example, M Table 6s can be obtained. It is found through research that the rule data sets of the physical modules under M case categories may contain some same data and some different data, and the difference is due to the difference in the case category to which the physical module belongs, which is another prerequisite condition in addition to the dependency relationship. Based on this, in the embodiment of the present application, the rule data sets corresponding to each function category under M categories can be compared to obtain and output the difference part of the rule data sets of M categories under the same function category. Accordingly, one feasible way of step S106 is to determine the case configuration auxiliary data corresponding to each function category according to the rule data sets corresponding to each function category under M categories and the difference part of the rule data sets of M categories under the same function category. In this way, the accuracy of the case configuration auxiliary data can be further improved.

[0097] Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. The electronic device can run Figure 1 The data processing method for CFD simulation, as shown in Figure 5 The electronic device includes an internal communication bus 501, a processor 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, and a communication port 505. When applied to a personal computer, the electronic device can also include a hard disk 506. The internal communication bus 501 can realize data communication between the components of the electronic device. The processor 502 can make judgments and issue prompts. In some embodiments, the processor 502 can be composed of one or more processors. The communication port 505 can realize data communication between the electronic device and the outside. In some embodiments, the electronic device can send and receive information and data from the network through the communication port 505. The electronic device can also include different forms of program storage units and data storage units, such as the hard disk 506, the read-only memory (ROM) 503, and the random access memory (RAM) 504, which can store various data files used for computer processing and / or communication, and possible program instructions executed by the processor 502. The processor 502 executes these instructions to implement the main part of the method. The results processed by the processor 502 are transmitted to the electronic device through the communication port 505 and displayed on the user interface.

[0098] The processor 502 can be of any type suitable to the local technical network, and can include, by way of non-limiting examples, one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multi-core processor architectures, as non-limiting examples. The electronic device can have multiple processors such as, for example, a special purpose integrated circuit chip that is clocked in time with a clock that synchronizes the main processor.

[0099] The processor 502 can be configured to perform the following steps: obtaining a plurality of characteristic cases for CFD simulation; identifying each characteristic case according to a preset dimension to determine a case category of each characteristic case; performing parameter processing on the characteristic cases contained in each case category to obtain an original case data set corresponding to each case category, the parameter processing including CFD input parameter extraction and parameter function division, the original case data set including a function category of each parameter; dividing parameter data in the original case data set into a plurality of data subsets according to the function category, one data subset corresponding to one function category; identifying a hierarchical relationship between the parameter data in each data subset, and converting each data subset into a structured parameter set corresponding to each function category according to the hierarchical relationship, one function category corresponding to one structured parameter set; obtaining a dependency relationship between the parameter data in each structured parameter set, and converting each structured parameter set into a rule data set according to the dependency relationship; and determining case configuration auxiliary data corresponding to each function category according to the rule data set.

[0100] The above data processing method for CFD simulation can be implemented as a computer program, saved in the hard disk 506, and loaded into the processor 502 for execution to implement the data processing method for CFD simulation of the present application.

[0101] In general, the various embodiments of the application can be implemented in hardware or special-purpose circuits, software, logic or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software which can be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the application are illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein can be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

[0102] The present application also provides at least one computer program product tangibly embodied on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, for example, instructions included in program modules, executed by devices on target real or virtual processors to perform the steps described above with respect to Figure 1The processes described can be generally implemented on one or more computer systems, which can interact with one or more human computer interface devices. Such devices can include a keyboard, a mouse, touch input device, a scanner, a camera, a microphone, a speech recognition device, a speech output device, a display screen, a speaker, a printer, a network interface, a storage device, a memory device, and the like. The computer systems can also interact with one or more other computer systems or devices over one or more networks. The computer systems can include one or more processing units, one or more memory devices, one or more storage devices, and one or more input / output devices. The computer systems can also include one or more operating systems, one or more compilers, and one or more interpreters. The computer systems can also include one or more program modules, which can include routines, programs, libraries, objects, classes, components, data structures, and the like. In various embodiments, the functionality of the program modules can be combined or separated into other program modules according to the needs of the computer systems. Machine executable instructions for the program modules can be executed within a local or distributed device. In a distributed device, the program modules can be located in local and remote storage media.

[0103] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flow charts and / or block diagrams to be implemented. The program code can be entirely on the machine, partially on the machine, partially on a remote machine, or entirely on the remote machine or server.

[0104] In the context of the present application, computer program code or related data can be carried by any suitable carrier for enabling the device, apparatus, or processor to perform the various processes and operations as described above. Examples of carriers include signals, computer readable media, and the like.

[0105] The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0106] As used in this application, the terms "comprises", "comprising", "includes", "including", "has", "having" or the like are intended to be inclusive and not

[0107] Also, certain terminology has been used in the present application for the purpose of reference only, and thus use of such terms does not limit the scope of the present application. For example, the terms "one embodiment," "an embodiment," "some embodiments," and / or "one alternative" are used to describe a particular embodiment, instance, or implementation. Thus, the above-described embodiments should not be construed as limiting the scope of the present application, but merely as describing possible implementations, alternative implementations, equivalent implementations, various features, and / or examples.

[0108] The relative arrangement of components and steps, the numerical expressions, and numerical values set forth in the Examples are not meant to limit the scope of the present application unless otherwise specifically stated. Also, it is to be understood that the drawings are not necessarily drawn to scale and that, for the purposes of simplicity and clarity, not all components are to scale or some components are drawn drawing to exaggerated scale. Technical, methods, and apparatus known to those of ordinary skill have not been described in detail in order to not unnecessarily obscure the present application. Any examples in the present disclosure are intended to be illustrative only and are not intended to be limiting in any way. Thus, additional examples of the exemplary embodiments can have different values for, or can omit, one or more of the features set forth in the Examples. It should be noted that like reference numerals and letters refer to like items in the following figures and no further discussions of such items shall occur in the following description.

[0109] In addition, while the terms used in the present specification are selected from generally known and used terms, some of the terms mentioned in the description of the present application can be created by the applicant in his or her judgment among the concepts of the technical to be described in the present application and can be completely defined in accordance with the description made herein. Also, it should be noted that the terms of the present application are just used to describe specific embodiments, and thus should not be limiting to the present application. Therefore, it is required that the scope of the present application be informed based on the descriptions made herein rather than the used terminology.

[0110] In addition, although operations are described in a particular, sequential order, this should not be understood as requiring that the operations be performed in that order. Rather, it should be understood that the operations can be performed in different order or simultaneously, that they might be omitted in other embodiments, and that additional operations might be added.

[0111] Although the present application has been described with reference to the current embodiments, it will be understood that the above description is meant to be illustrative only and that changes or modifications can be made to the embodiments without departing from the spirit of the application.

Claims

1. A data processing method for CFD simulation, characterized in that, The method comprises the following steps: obtaining a plurality of characteristic cases for CFD simulation; identifying each characteristic case according to a preset dimension to determine the case category of each characteristic case; performing parameter processing on the characteristic cases contained in each case category to obtain an original case data set corresponding to each case category, wherein the parameter processing includes CFD input parameter extraction and parameter function division, and the original case data set includes the function category of each parameter; dividing the parameter data in the original case data set into a plurality of data subsets according to the function category, one data subset corresponding to one function category; identifying the hierarchical relationship between the parameter data in each data subset, and converting each data subset into a structured parameter set corresponding to each function category according to the hierarchical relationship, one function category corresponding to one structured parameter set; obtaining the dependency relationship between the parameter data in each structured parameter set, and converting each structured parameter set into a rule data set according to the dependency relationship; determining the case configuration auxiliary data corresponding to each function category according to the rule data set.

2. The method of claim 1, wherein, The process of CFD input parameter extraction for any characteristic case includes: reading the dictionary file of the target folder of the any characteristic case in sequence, extracting the case basic information and detailed setting parameters from the dictionary file, and the target folder includes 0, constant and system folders.

3. The method of claim 1, wherein, The preset dimension includes a plurality of dimension categories under each preset dimension, and the case category of any characteristic case is identified according to the preset dimension, which includes: identifying the target dimension category to which the any characteristic case belongs under each preset dimension according to the key field in the any characteristic case; determining the case category of the any characteristic case based on the target dimension category to which the any characteristic case belongs under each preset dimension.

4. The method of claim 1, wherein, Any case category contains N characteristic cases, and parameter extraction is performed on the N characteristic cases to obtain an original case data set corresponding to the any case category, which includes: performing parameter extraction on the N characteristic cases under any case category in sequence; comparing the parameter extraction results corresponding to the N characteristic cases to obtain the difference part of the parameter extraction results between the N characteristic cases; outputting the difference part.

5. The method of claim 1, wherein, Each structured parameter set includes any one or more of the following information: a module field representing the function category to which it belongs, a hierarchical parameter, an example value corresponding to the hierarchical parameter, and a field type corresponding to the hierarchical parameter.

6. The method of claim 1, wherein, The dependency relationship includes any one or more of the following: a linkage relationship, a conditional activation, and a mutual exclusion relationship.

7. The method of claim 1, wherein, The preset dimension includes a calculation scenario, a series-parallel relationship, a turbulence model, and a solution algorithm.

8. The method of claim 1, wherein, The function category includes a geometry module, a grid module, a physics module, and a solution module.

9. The method of claim 1, wherein, The preset dimension includes a plurality of dimension categories under each preset dimension, and the method further comprises: obtaining the target dimension category to which the case configuration auxiliary data belongs under each preset dimension; storing the case configuration auxiliary data and the dimension label matching the target dimension category in association in a parameter template library; determining a simulation scenario to be simulated; Obtain and display target case configuration auxiliary data matching the simulation scene from the parameter template library.

10. An electronic device, comprising: Comprise: at least one processor; and at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor, alone or in combination, cause the electronic device to perform the method of any one of claims 1-9.

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