CN114676656B - Consistency measurement method, device, equipment and storage medium of multi-response CFD model - Google Patents

Consistency measurement method, device, equipment and storage medium of multi-response CFD model Download PDF

Info

Publication number
CN114676656B
CN114676656B CN202210588960.9A CN202210588960A CN114676656B CN 114676656 B CN114676656 B CN 114676656B CN 202210588960 A CN202210588960 A CN 202210588960A CN 114676656 B CN114676656 B CN 114676656B
Authority
CN
China
Prior art keywords
model
cfd
response
response vector
test
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210588960.9A
Other languages
Chinese (zh)
Other versions
CN114676656A (en
Inventor
赵娇
陈江涛
肖维
张培红
赵炜
章超
吕罗庚
沈盈盈
周晓军
吴晓军
肖中云
胡向鹏
杨福军
郭勇颜
金韬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Computational Aerodynamics Institute of China Aerodynamics Research and Development Center
Original Assignee
Computational Aerodynamics Institute of China Aerodynamics Research and Development Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Computational Aerodynamics Institute of China Aerodynamics Research and Development Center filed Critical Computational Aerodynamics Institute of China Aerodynamics Research and Development Center
Priority to CN202210588960.9A priority Critical patent/CN114676656B/en
Publication of CN114676656A publication Critical patent/CN114676656A/en
Application granted granted Critical
Publication of CN114676656B publication Critical patent/CN114676656B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Pure & Applied Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Algebra (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Fluid Mechanics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Evolutionary Biology (AREA)
  • Operations Research (AREA)
  • Probability & Statistics with Applications (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention is suitable for the technical field of CFD model confirmation, and provides a consistency measurement method, a device, equipment and a storage medium of a multi-response CFD model, wherein the method comprises the following steps: acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model; acquiring multiple response vectors of the test model based on multiple input samples of the test model; calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model; and determining the consistency of the CFD calculation model and the test model according to the relative energy distance. The method can effectively measure the difference degree between different calculation models and a physical test, avoids the problem that a multi-dimensional joint distribution function needs to be solved in the traditional consistency measurement method, and simultaneously avoids information loss caused by dimension reduction of a multi-dimensional response vector of the CFD calculation model, so that the CFD calculation model comprises all information of multi-dimensional response, and the method is favorable for better confirming the model.

Description

Consistency measurement method, device, equipment and storage medium of multi-response CFD model
Technical Field
The invention relates to the technical field of CFD model confirmation, in particular to a method, a device, equipment and a storage medium for measuring consistency of a multi-response CFD model.
Background
The model confirmation is originally originated in the fields of aerospace, nuclear industry and the like and is used for objectively evaluating the reliability of a simulation model, so that a high-precision prediction model is guided to be developed to reduce or even completely replace physical tests. As numerical simulation models are gradually popularized and applied in other industrial fields, model validation issues have attracted much attention in recent years.
In model confirmation, the model consistency measurement is an important step, and is a quantitative expression of the conformity degree of the model simulation result and the real test result. The model consistency measurement provides quantitative basis for evaluating the consistency of the model and the real test data, and is important basis for model selection and credibility evaluation. The CFD (Computational Fluid Dynamics) model has the characteristics of multiple inputs and multiple responses (outputs) and possible correlation among the responses, the difficulty of solving a multidimensional joint distribution function exists in the consistency measurement of the CFD model with multiple responses at present, and the traditional method adopts the Mahalanobis distance to reduce the dimensions of multidimensional responses, but the method can cause response information loss. Therefore, in the consistency measurement of the CFD model, how to avoid solving the multidimensional joint distribution function and include all information of the multidimensional response of the CFD model is a problem that needs to be solved urgently in model validation.
Disclosure of Invention
The invention aims to provide a consistency measurement method of a multi-response CFD model, which can avoid the situation that the multi-dimensional joint distribution function problem needs to be solved in the existing CFD model consistency measurement method, and simultaneously does not need to reduce the dimension of the multi-dimensional response of the CFD model, so that the multi-dimensional response of the CFD model contains all information, and the model can be better confirmed.
In a first aspect, an embodiment of the present invention provides a method for measuring consistency of a multi-response CFD model, including:
s1, acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model;
s2, acquiring a multi-response vector of the test model based on the multi-input sample of the test model;
s3, calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model;
and S4, determining the consistency of the CFD calculation model and the test model according to the relative energy distance.
Further, the CFD calculation model and the test model each comprise at least two input parameters and at least one model parameter.
Furthermore, the multiple input samples of the CFD calculation model and the multiple input samples of the test model are obtained by performing latin hypercube sampling on respective input parameters.
Further, the step of calculating the relative energy distance between the multi-response vector of the CFD computational model and the multi-response vector of the experimental model comprises:
calculating the V statistic of the multi-response vector of the CFD calculation model to obtain the statistical energy of the calculation model; calculating the V statistic of the multi-response vector of the test model to obtain the statistical energy of the test model;
and calculating the multi-response vector of the CFD calculation model and the V statistic of the multi-response vector of the test model, and calculating the relative energy distance together with the calculation model statistical energy and the test model statistical energy based on the V statistic.
Further, the input parameters of the CFD calculation model and the test model satisfy a random distribution.
Further, the multiple input samples and the multiple response vectors of the test model can be obtained through physical experiments.
In a second aspect, an embodiment of the present invention provides a consistency measurement apparatus for a multi-response CFD model, including:
the first acquisition module is used for acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model;
the second acquisition module is used for acquiring multiple response vectors of the test model based on the multiple input samples of the test model;
the calculation module is used for calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model;
and the determining module is used for determining the consistency of the CFD calculation model and the test model according to the relative energy distance.
In a third aspect, an embodiment of the present invention provides a computer device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the method for consistency measurement of a multi-response CFD model when executing the computer program.
In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method for consistency measurement of a multi-response CFD model.
Compared with the prior art, the embodiment of the invention mainly has the following beneficial effects: acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model; acquiring multiple response vectors of the test model based on multiple input samples of the test model; calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model; and determining the consistency of the CFD calculation model and the test model according to the relative energy distance. The method can effectively measure the difference degree between different CFD calculation models and physical tests, avoids the problem that a multi-dimensional joint distribution function needs to be solved in the traditional model consistency measurement method, and simultaneously avoids information loss caused by dimension reduction of multi-dimensional response vectors of the CFD calculation models, so that the CFD calculation models comprise all information of multi-dimensional response, and the method is favorable for better confirming the models.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention or in the description of the prior art will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow diagram of one embodiment of a method for consistency measurement of a multi-response CFD model of the present invention;
FIG. 2 is a schematic block diagram of an embodiment of a consistency measurement apparatus for a multi-response CFD model according to the present invention;
fig. 3 is a schematic diagram of the basic structure of a computer device of the present invention.
Detailed Description
The following description provides many different embodiments, or examples, for implementing different features of the invention. The particular examples set forth below are illustrative only and are not intended to be limiting.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a flowchart of an embodiment of a consistency measurement method for a multi-response CFD model according to the invention, and the consistency measurement method for the multi-response CFD model includes the following steps:
s1, acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model;
and S2, acquiring a multi-response vector of the test model based on the multi-input sample of the test model.
In an embodiment of the present invention, each of the CFD calculation model (i.e., the CFD model, hereinafter referred to as a calculation model) and the test model at least includes two input parameters and at least one model parameter, and multiple input samples of the corresponding model can be obtained corresponding to the at least two input parameters, and specifically, the multiple input samples of the model can be obtained by performing latin hypercube sampling on the input parameters of the model.
For example, the above calculation model can be expressed by the following formula:
Figure 425472DEST_PATH_IMAGE001
Figure 873771DEST_PATH_IMAGE002
accordingly, the above test model can be simulated using the following formula:
Figure 292989DEST_PATH_IMAGE003
Figure 891461DEST_PATH_IMAGE004
wherein the content of the first and second substances,
Figure 236991DEST_PATH_IMAGE005
、
Figure 200399DEST_PATH_IMAGE006
two input parameters representing the model are provided,
Figure 670695DEST_PATH_IMAGE007
、
Figure 135174DEST_PATH_IMAGE008
representing the two output responses of the computational model described above,
Figure 709113DEST_PATH_IMAGE009
、
Figure 905739DEST_PATH_IMAGE010
two output responses of the test model are shown,
Figure 925648DEST_PATH_IMAGE011
model parameters that represent the computational model are then calculated,
Figure 69184DEST_PATH_IMAGE012
model parameters representing a test model; it should be noted that the number of the input parameters and the model parameters is only illustrative, and specifically, the number of the input parameters and the model parameters may be two or more of the multiple parameters of the CFD model, and the corresponding output response may also be multiple (i.e., multiple responses), and taking the CFD model as a turbulence model of an aircraft airfoil as an example, the model mainly includes 9 parameters
Figure 795832DEST_PATH_IMAGE013
Model parameters
Figure 756834DEST_PATH_IMAGE011
、
Figure 655519DEST_PATH_IMAGE012
May be one or more of them, in the present invention
Figure 133904DEST_PATH_IMAGE011
、
Figure 308534DEST_PATH_IMAGE012
Taking fixed values to represent different models, inputting parameters
Figure 784646DEST_PATH_IMAGE005
、
Figure 716830DEST_PATH_IMAGE006
Or one or more thereof; value of the above model input parameters
Figure 61223DEST_PATH_IMAGE005
、
Figure 464261DEST_PATH_IMAGE006
Satisfy a random distribution, such as a normal distribution, or other distributions (e.g., a uniform distribution, a t-distribution, etc.), for example
Figure 439170DEST_PATH_IMAGE014
、
Figure 920967DEST_PATH_IMAGE015
Therefore, the model can adapt to different input parameters and model parameters, and the generalization capability of the model measurement is improved. The present invention is to measure the consistency between the above calculation model and the test model, i.e. the difference between the two models.
In order to verify the correctness of the CFD model consistency measurement method of the present invention, a CFD comparison model may be further constructed in this embodiment, and is expressed by the following formula:
Figure 944417DEST_PATH_IMAGE016
Figure 500164DEST_PATH_IMAGE017
further, sampling from input parameters by Latin hypercube
Figure 973870DEST_PATH_IMAGE018
、
Figure 51286DEST_PATH_IMAGE019
The random distribution of the test model is randomly sampled to obtain a random sample of the input parameters of the test model
Figure 675165DEST_PATH_IMAGE020
Obtaining a random sample of the input parameters of the CFD calculation model
Figure 413314DEST_PATH_IMAGE021
And obtaining random samples of the input parameters of the CFD comparison model
Figure 933288DEST_PATH_IMAGE022
。
It should be noted that, in the following description,
Figure 61781DEST_PATH_IMAGE023
and
Figure 551668DEST_PATH_IMAGE024
、
Figure 518225DEST_PATH_IMAGE025
the values of (A) may be the same or different,
Figure 5838DEST_PATH_IMAGE011
and
Figure 683944DEST_PATH_IMAGE025
the values cannot be the same, e.g. can be taken
Figure 118468DEST_PATH_IMAGE026
，
Figure 503313DEST_PATH_IMAGE027
(ii) a Can also take
Figure 224144DEST_PATH_IMAGE028
，
Figure 769307DEST_PATH_IMAGE029
，
Figure 538680DEST_PATH_IMAGE030
And the more the model parameters are theoretically close to the model parameters of the test model as can be seen from the calculation formulas of the models
Figure 105927DEST_PATH_IMAGE012
The higher the consistency of the two models, such as when
Figure 873026DEST_PATH_IMAGE031
In time, the calculation model and the test model will be completely consistent,
Figure 463407DEST_PATH_IMAGE023
and
Figure 98788DEST_PATH_IMAGE024
the larger the difference, the larger the difference between the calculation model and the test model.
Correspondingly, random samples of each model input parameter are input into corresponding model calculation formulas to respectively obtain respective multi-response vectors, namely test model response vectors
Figure 894443DEST_PATH_IMAGE032
Computing model response vectors
Figure 894760DEST_PATH_IMAGE033
And CFD contrast model response vector
Figure 441279DEST_PATH_IMAGE034
。
And S3, calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model.
Specifically, the step S3 includes:
calculating the V statistic of the multi-response vector of the CFD calculation model to obtain the statistical energy of the calculation model; calculating the V statistic of the multi-response vector of the test model to obtain the statistical energy of the test model;
and calculating the multi-response vector of the CFD calculation model and the V statistic of the multi-response vector of the test model, and calculating the relative energy distance together with the statistical energy of the calculation model and the statistical energy of the test model based on the V statistic.
In the embodiment of the invention, the computational model multi-response vector obtained in the steps is
Figure 145930DEST_PATH_IMAGE036
Is available as independent co-distributed vectors
Figure 297557DEST_PATH_IMAGE037
Representing, testing model multiple response vectors
Figure 796672DEST_PATH_IMAGE039
Is available as independent co-distributed vectors
Figure 594601DEST_PATH_IMAGE040
Is shown, i.e. can be selected from
Figure 447150DEST_PATH_IMAGE041
And
Figure 515601DEST_PATH_IMAGE042
to obtain corresponding
Figure 920037DEST_PATH_IMAGE041
、
Figure 909990DEST_PATH_IMAGE043
The above-mentioned calculation of the V statistic of the multi-response vector of the CFD calculation model obtains the statistical energy of the calculation model
Figure 861503DEST_PATH_IMAGE044
Multiple response vectors, particularly by taking computational models
Figure 112356DEST_PATH_IMAGE036
And its independent same distribution vector
Figure 625377DEST_PATH_IMAGE037
The euclidean distance norm of (a) is obtained, i.e.:
Figure 837046DEST_PATH_IMAGE045
Figure 421612DEST_PATH_IMAGE046
the V statistic of the multiple response vectors of the test model is calculated to obtain the statistical energy of the test model
Figure 464654DEST_PATH_IMAGE047
In particular by taking the multiple response vectors of the test model
Figure 578935DEST_PATH_IMAGE039
And its independent same distribution vector
Figure 74638DEST_PATH_IMAGE040
The euclidean distance norm of (a) is obtained, i.e.:
Figure 197315DEST_PATH_IMAGE048
Figure 298126DEST_PATH_IMAGE049
multiple response vectors of the above computational model
Figure 887371DEST_PATH_IMAGE036
And multiple response vectors of the test model
Figure 463846DEST_PATH_IMAGE039
The V statistic of (c) is:
Figure 560852DEST_PATH_IMAGE050
Figure 250591DEST_PATH_IMAGE051
then the relative energy distance between the multi-response vector of the computational model and the multi-response vector of the experimental model is:
Figure 541895DEST_PATH_IMAGE052
the relative energy distance between the multi-response vector of the experimental model and the multi-response vector of the CFD comparison model can also be obtained as follows:
Figure 340087DEST_PATH_IMAGE053
it is worth mentioning that the multi-input samples and multi-response vectors of the test model can also be obtained by physical experiments, such as corresponding data directly generated by real physical experiments in turbulence experiments of the wing profile of the aircraft, so that the full information of the real response data can be included and can be used for better confirming the model.
And S4, determining the consistency of the CFD calculation model and the test model according to the relative energy distance.
In the embodiment of the present invention, in the case where the model of the experimental model and the model parameter of the calculation model are the same, for example, the model parameter is taken
Figure 679932DEST_PATH_IMAGE054
，
Figure 785029DEST_PATH_IMAGE055
After the above calculation steps, can obtain
Figure 309551DEST_PATH_IMAGE056
，
Figure 267143DEST_PATH_IMAGE057
It can be seen that the relative energy distances of the multi-response vectors of the CFD computational model and the experimental model
Figure 410680DEST_PATH_IMAGE058
Relative energy distance close to 0 and much smaller than the multi-response vector of the experimental model and the multi-response vector of the CFD contrast model
Figure DEST_PATH_IMAGE059
In accordance with the theoretical expectation of the invention, i.e. calculation of the model
Figure 402906DEST_PATH_IMAGE011
The closer the model parameters of (A) are to those of the test model
Figure 737810DEST_PATH_IMAGE012
The smaller the relative energy distance between the two models is, the higher the consistency between the two models is, when
Figure 651540DEST_PATH_IMAGE060
The time calculation model and the test model are completely consistent, and the larger the relative energy distance between the two models is, the larger the difference between the calculation model and the test model is, such as the test model and the comparison model in the invention. Therefore, the model consistency measurement method can effectively measure the difference degree between different calculation models and a physical test (or a test model) through the energy distance between the calculation models, avoids the difficult problem that a multidimensional joint distribution function needs to be solved in the traditional consistency measurement method, and simultaneously avoids the loss of information caused by dimension reduction of a multidimensional response vector of the calculation model, so that the calculation model comprises all information of multidimensional response, thereby being beneficial to better confirming the model.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the drawings may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time or on the same device or machine, but may be performed at different times and different places, which are not necessarily performed in sequence, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
In a second aspect, as shown in fig. 2, as an implementation of the consistency measurement method for the multi-response CFD model shown in fig. 1, the present invention provides an embodiment of a consistency measurement apparatus for a multi-response CFD model, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 1, and the consistency measurement apparatus for a multi-response CFD model can be applied in various computer devices.
Fig. 2 is a schematic structural diagram illustrating an apparatus for consistency measurement of a multi-response CFD model according to an embodiment of the present invention, where the apparatus 200 specifically includes:
a first obtaining module 201, configured to obtain a multi-response vector of the CFD computational model based on a multi-input sample of the CFD computational model;
a second obtaining module 202, configured to obtain multiple response vectors of the test model based on multiple input samples of the test model;
a calculating module 203, configured to calculate a relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model;
a determining module 204, configured to determine a consistency between the CFD calculation model and the test model according to the relative energy distance.
The consistency measurement device for the multi-response CFD model provided in the embodiment of the present invention can implement each implementation manner in the embodiment of the method of fig. 1 and corresponding beneficial effects, and is not described herein again to avoid repetition.
In a third aspect, an embodiment of the present invention provides a computer device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the method for consistency measurement of a multi-response CFD model when executing the computer program.
In a fourth aspect, the present invention is a computer-readable storage medium, which stores a computer program, and the computer program is used for implementing the steps of the method for consistency measurement of a multi-response CFD model when being executed by a processor.
Specifically, referring to fig. 3, fig. 3 is a schematic diagram of a basic structure of a computer device according to an embodiment of the present invention. The computer device 300 includes a memory 301, a processor 302, and a network interface 303 communicatively coupled to each other via a system bus. It is noted that only computer device 300 having components 301-303 is shown, but it is understood that not all of the illustrated components are required and that more or fewer components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 301 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 301 may be an internal storage unit of the computer device 300, such as a hard disk or a memory of the computer device 300. In other embodiments, the memory 301 may also be an external storage device of the computer device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 300. Of course, the memory 301 may also include both internal and external storage devices of the computer device 300. In this embodiment, the memory 301 is generally used for storing an operating system installed on the computer device 300 and various types of application software, such as program codes of a consistency measurement method of a multi-response CFD model. In addition, the memory 301 may also be used to temporarily store various types of data that have been output or are to be output.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (such as a ROM/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (8)

1. A consistency measurement method of a multi-response CFD model is characterized by comprising the following steps:
s1, acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model;
s2, acquiring a multi-response vector of the test model based on the multi-input sample of the test model;
s3, calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model;
s4, determining the consistency of the CFD calculation model and the test model according to the relative energy distance;
the step of calculating the relative energy distance of the multi-response vector of the CFD calculation model and the multi-response vector of the test model comprises:
calculating the V statistic of the multi-response vector of the CFD calculation model to obtain the statistical energy of the calculation model; calculating the V statistic of the multi-response vector of the test model to obtain the statistical energy of the test model;
and calculating the multi-response vector of the CFD calculation model and the V statistic of the multi-response vector of the test model, and calculating the relative energy distance together with the statistical energy of the calculation model and the statistical energy of the test model based on the V statistic.
2. The method of claim 1, wherein the CFD computational model and the test model each include at least two input parameters and at least one model parameter.
3. The method of claim 2, wherein the multiple-input samples of the CFD computational model and the multiple-input samples of the test model are each obtained by latin hypercube sampling of the respective input parameters.
4. The method of claim 3, wherein input parameters of the CFD computational model and the trial model satisfy a random distribution.
5. The method of claim 4, wherein the multiple-input samples and multiple-response vectors of the test model are further obtainable by physical experimentation.
6. An apparatus for consistency measurement of a multi-response CFD model, comprising:
the first acquisition module is used for acquiring a multi-response vector of the CFD calculation model based on the multi-input sample of the CFD calculation model;
the second acquisition module is used for acquiring multiple response vectors of the test model based on the multiple input samples of the test model;
the calculation module is used for calculating the relative energy distance between the multi-response vector of the CFD calculation model and the multi-response vector of the test model;
a determining module for determining the consistency of the CFD calculation model and the test model according to the relative energy distance;
the step of calculating the relative energy distance of the multi-response vector of the CFD calculation model and the multi-response vector of the experimental model comprises:
calculating V statistics of the multi-response vectors of the CFD calculation model to obtain the statistical energy of the calculation model; calculating the V statistic of the multi-response vector of the test model to obtain the statistical energy of the test model;
and calculating the multi-response vector of the CFD calculation model and the V statistic of the multi-response vector of the test model, and calculating the relative energy distance together with the statistical energy of the calculation model and the statistical energy of the test model based on the V statistic.
7. A computer device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the steps in the method for consistency metrics of a multi-response CFD model according to any of the claims 1 to 5 when executing the computer program.
8. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for consistency metrics of a multi-response CFD model according to any one of the claims 1 to 5.
CN202210588960.9A 2022-05-27 2022-05-27 Consistency measurement method, device, equipment and storage medium of multi-response CFD model Active CN114676656B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210588960.9A CN114676656B (en) 2022-05-27 2022-05-27 Consistency measurement method, device, equipment and storage medium of multi-response CFD model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210588960.9A CN114676656B (en) 2022-05-27 2022-05-27 Consistency measurement method, device, equipment and storage medium of multi-response CFD model

Publications (2)

Publication Number Publication Date
CN114676656A CN114676656A (en) 2022-06-28
CN114676656B true CN114676656B (en) 2022-08-23

Family

ID=82080978

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210588960.9A Active CN114676656B (en) 2022-05-27 2022-05-27 Consistency measurement method, device, equipment and storage medium of multi-response CFD model

Country Status (1)

Country Link
CN (1) CN114676656B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105183975A (en) * 2015-09-01 2015-12-23 中国空气动力研究与发展中心高速空气动力研究所 Trailing vortex field transfer based numerical simulation method for multi-aircraft flight formation
CN111538487A (en) * 2020-04-17 2020-08-14 中国空气动力研究与发展中心计算空气动力研究所 Distributed parallel grid generation software framework
CN112377936A (en) * 2020-11-18 2021-02-19 哈尔滨锅炉厂有限责任公司 Method for reducing resistance of boiler flue gas duct system

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11983467B2 (en) * 2020-02-11 2024-05-14 United States Of America As Represented By The Administrator Of Nasa Rapid aero modeling for computational experiments (RAM-C)
CN112016253A (en) * 2020-09-09 2020-12-01 中国空气动力研究与发展中心计算空气动力研究所 High-fidelity chaotic polynomial correction method suitable for CFD uncertainty quantification
CN113627098B (en) * 2021-07-23 2023-12-08 北京理工大学 CFD model confirmation method and product design method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105183975A (en) * 2015-09-01 2015-12-23 中国空气动力研究与发展中心高速空气动力研究所 Trailing vortex field transfer based numerical simulation method for multi-aircraft flight formation
CN111538487A (en) * 2020-04-17 2020-08-14 中国空气动力研究与发展中心计算空气动力研究所 Distributed parallel grid generation software framework
CN112377936A (en) * 2020-11-18 2021-02-19 哈尔滨锅炉厂有限责任公司 Method for reducing resistance of boiler flue gas duct system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
CFD模拟方法的发展成就与展望;阎超等;《力学进展》;20110925(第05期);全文 *
计算流体力学数值模拟验证与确认策略;赵小鹏等;《渭南师范学院学报》;20171220(第24期);全文 *

Also Published As

Publication number Publication date
CN114676656A (en) 2022-06-28

Similar Documents

Publication Publication Date Title
CN112884092B (en) AI model generation method, electronic device, and storage medium
WO2019129060A1 (en) Method and system for automatically generating machine learning sample
US8204714B2 (en) Method and computer program product for finding statistical bounds, corresponding parameter corners, and a probability density function of a performance target for a circuit
US9589096B1 (en) Method and apparatus for integrating spice-based timing using sign-off path-based analysis
US9195222B2 (en) Systems and methods for evaluating stability of software code for control systems
CN110347582B (en) Buried point testing method and device
Wills et al. Parameter estimation for discrete-time nonlinear systems using EM
CN115841046B (en) Accelerated degradation test data processing method and device based on wiener process
US20230066703A1 (en) Method for estimating structural vibration in real time
Lee et al. The delta expansion for the transition density of diffusion models
Bradde et al. A scalable reduced-order modeling algorithm for the construction of parameterized interconnect macromodels from scattering responses
CN114676656B (en) Consistency measurement method, device, equipment and storage medium of multi-response CFD model
CN114692529B (en) CFD high-dimensional response uncertainty quantification method and device, and computer equipment
Wang et al. Variable selection for the partial linear single-index model
US20170140080A1 (en) Performing And Communicating Sheet Metal Simulations Employing A Combination Of Factors
Khokhlov et al. Data security and quality evaluation framework: Implementation empirical study on android devices
CN113255769A (en) Compound attribute prediction model training method and compound attribute prediction method
EP4248339A1 (en) Method and system for accelerating the convergence of an iterative computation code of physical parameters of a multi-parameter system
CN111090554A (en) Method, device, equipment and storage medium for predicting server performance test time
CN110765006A (en) Flow testing method and device, computer readable storage medium and electronic device
CN113641848B (en) Online assembly connection guiding method and system for electronic product
US20230142773A1 (en) Method and system for real-time simulations using convergence stopping criterion
CN117435452A (en) Data processing method and device, electronic equipment and storage medium
CN115983076A (en) Modelica system reduced model generation method, device, equipment and medium
CN116049733A (en) Neural network-based performance evaluation method, system, equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant