CN103473061A - System and method for optimizing structural noise uncertainty - Google Patents
System and method for optimizing structural noise uncertainty Download PDFInfo
- Publication number
- CN103473061A CN103473061A CN2013104153853A CN201310415385A CN103473061A CN 103473061 A CN103473061 A CN 103473061A CN 2013104153853 A CN2013104153853 A CN 2013104153853A CN 201310415385 A CN201310415385 A CN 201310415385A CN 103473061 A CN103473061 A CN 103473061A
- Authority
- CN
- China
- Prior art keywords
- file
- optimization
- uncertainty
- module
- user
- 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.)
- Granted
Links
Images
Abstract
Provided is a system and a method for optimizing structural noise uncertainty. The system comprises a system framework program and five sub-modules, namely, structural acoustic characteristic deterministic analysis module, structural acoustic characteristic uncertainty analysis module, structural noise certainty optimization module, structural noise uncertainty optimization module and post processing module. By the aid of the method, structural noise uncertainty optimization design is achieved on the basis of deterministic analysis and uncertainty analysis of structural acoustic characteristics. The method integrates an optimization model of solving an uncertainty problem and various algorithms in the system, and provides a convenient tool for non-professionals to conduct structural noise uncertainty analysis and optimization.
Description
Technical field
The present invention relates to a kind of optimization system of the construct noise towards uncertain problem and method, belong to uncertain Multidisciplinary Optimization technical field.
Background technology
Uncertain Multidisciplinary Optimization technology because of the loop characteristic of inherent data transmission cause the uncertainty analysis calculation of complex and calculated amount larger, uncertain in a plurality of subject subsystems coupling propagate, make practical application based on uncertain Multidisciplinary Optimization become one and there is larger challenging problem.
Present stage, structure is carried out to uncertain noise optimization design and mostly realize by integrated required software in the Multidisciplinary Optimization environment of versatility and algorithm.
The Multidisciplinary Optimization environment refers to according to the Multidisciplinary Optimization flow process, will be distributed in the analytical model of each subject on each computing machine or the designing and calculating environment that Optimized model integrates.Enforcement is integrated with two kinds of technology paths: (a) by adopting distributed computing technology; (b) apply commercial integrated software.In the Multidisciplinary Optimization field, Distributed Calculation mainly adopts CORBA and Java technology.CORBA is an OO Distributed Computing Platform, and it allows can carry out pellucidly interoperability between different programs, sets up the isomery distributed application system.NASA has adopted the analytical model of integrated each subject of CORBA technology in the Multidisciplinary Optimization system for high speed civil aircraft overall design exploitation, and the CDE system of the MDOPT system of Boeing's exploitation and Europe exploitation has also adopted the CORBA technology.Java is an application development platform, and it provides OO programming language and running environment, and its essence is exactly to utilize the types of objects be distributed in network jointly to complete corresponding task.Java medium-long range method call RMI makes to be distributed between two members on the different addresses of network and realizes interoperability.The application Java technology such as Kroo are that the aircraft Multidisciplinary Optimization has been developed an environment of the Multidisciplinary Optimization towards cooperative optimization method.Alzubbi etc. have developed virtual aircraft design and Optimization Framework based on JavaRMI.Recently, because commercial Multidisciplinary Optimization integrated software (as iSIGHT/FIPER, Optimus, Pointer, AML, ModelCenter, DARWIN, IMAGE etc.) is increasingly mature, adopted more and more commercial integrated software when building the MDO environment.For example, Beam etc. has adopted the analytical model of integrated each subsystem of ModelCenter software when the distributed analysis mode environment of exploitation aircraft.Rohrschneider etc. during Mars Aircraft Concept Design environment, have also adopted ModelCenter software to carry out the analytical model of integrated each subsystem when setting up long boat.Zhang Xiaoping has set up pneumatic/structure-integrated design environment of the connection wing with iSIGHT software.
The mode that the Multidisciplinary Optimization environment of employing versatility carries out uncertainty analysis, optimization often is only applicable to studied particular problem, again integrated again for other problems.From current application point, the method is suitable for theoretical research, and is difficult to use in the engineering application.Aspect the multidisciplinary optimization of considering uncertain factor, still do not have ripe practical software available, more do not have specialty to carry out the related software integrated platform of uncertainty analysis, therefore uncertain Multidisciplinary Optimization technical application has been produced to adverse influence.
Summary of the invention
Technology of the present invention is dealt with problems: the deficiency that is difficult to be applicable to the engineering application in order to overcome current construct noise uncertainty optimization method, a kind of construct noise uncertainty optimization system and method is provided, practical, reliable, there is the ability of processing all kinds of construct noise uncertainty optimization design problems, the user needn't carry out the integrated work that just can easily complete uncertain design optimization to all kinds of algorithms, software voluntarily, helps lend some impetus to the application of uncertainty optimization design technology aspect Practical Project.
The technology of the present invention solution: a kind of construct noise uncertainty optimization system, comprise: structural acoustic characteristic deterministic parsing module, construct noise deterministic optimization module, structural acoustic characteristic uncertainty analysis module, construct noise uncertainty optimization module, post-processing module reach the system framework program for integrated above-mentioned module, wherein:
Structural acoustic characteristic deterministic parsing module: for the function of implementation structure acoustic characteristic deterministic parameters calculation, analysis; Resolve each parameter value from the input parameter file, parameter value is write to the numerical evaluation model file; Call the acoustics software for calculation, logarithm value computation model file is solved calculating, obtains the result of calculation file; Parse required response from destination file, comprise design domain acoustic pressure, the total acoustic energy of structure radiation, write the output response file;
Construct noise deterministic optimization module: for the function of implementation structure Noise sourse determination optimal design; Judge whether the deterministic optimization configuration file exists, if do not had input, output file by the deterministic parsing module, parse input parameter and output response, if exist resolved by configuration file, for the user, be optimized setting; The result that arranges by the user to Optimized model, write and distribute rationally in file; The data of resolving in the uncertainty optimization configuration file are imported the Optimization Software template file into, call Optimization Software amended template file is calculated, and optimize and finish, and preserve optimum results and process data;
Structural acoustic characteristic uncertainty analysis module: for carrying out the uncertainty analysis of structural acoustic characteristic, comprise uncertainty propagation and two parts of index calculating; Uncertainty propagation is embodied as: the input file that judges whether to exist uncertainty analysis, parse every input parameter if exist from this document, if there is no from the input parameter file of deterministic parsing, parse every input parameter, for the user, selected and arrange; Setting according to the user to uncertainty quantification and uncertainty propagation, call the sample point that corresponding uncertainty propagation algorithm generates some input parameters, and repeatedly call the deterministic parsing module, obtains corresponding some output response sample points; Call corresponding nondeterministic algorithm, calculated the uncertainty analysis result of response by some response sample points; Index is calculated and is embodied as: judge whether configuration file and uncertain response analysis data wherein exist; Read the uncertainty analysis result of response by configuration file; Call the corresponding index computational algorithm, the reliability met with a response or robustness result of calculation; Result of calculation is write to output file and configuration file;
Construct noise uncertainty optimization module: for the function of implementation structure noise uncertainty optimization design; Judge whether the uncertainty optimization configuration file exists, if do not had input, output file by the uncertainty analysis module, parse input parameter, output response and uncertain index, exist and resolved by configuration file, for the user, be optimized setting; The result that arranges by the user to Optimized model, write and distribute rationally in file; The data of resolving in the uncertainty optimization configuration file are imported the Optimization Software template file into; Call Optimization Software amended template file is calculated, optimize and finish, preserve optimum results and process data;
Post-processing module: this module is used for the variation course of the design parameter of Optimized Iterative process, optimized variable, target function value, sensitivity, robustness physical quantity is displayed, and exports with picture format; Read the numerical value of each physical quantity each step iteration in optimizing process from the process data file, be presented on interface with form; According to user's selection and setting, obtain corresponding certain physical quantity optimizing process data, with the form demonstration of curve map or broken line graph; Preserve the curve map or the broken line graph that demonstrate are exported as to picture format if need;
The system framework program: for transmission and the management of the integrated and data that realize each submodule, major function comprises the foundation of construct noise uncertainty optimization design engineering, opening operation, and calling each submodule; Set up construct noise uncertainty optimization design engineering, according to path and the engineering name of user's input, set up the file with the engineering name name under path; Set up the file corresponding with the deterministic parsing of structural acoustic characteristic, structural acoustic uncertainty analysis, construct noise deterministic optimization, four modules of construct noise uncertainty optimization in file; The template of each module being moved to required file copies under corresponding folder, and the parameter in file is carried out to initialization; During the calling of each submodule, obtain project file folder path, place, and path, optimization system place, desired path called thereby obtain module; Call required file or folder path and command parameter by calling module executive routine path, module, generate order line, use the command line mode calling module.
Described structural acoustic characteristic deterministic parsing module implementation procedure:
(1) resolve each parameter value from the input parameter file, input parameter file wherein, initial when this module of isolated operation by the user to the selection of input parameter with generation is set, when this module is called by uncertainty analysis module or deterministic optimization module, uncertainty analysis module or deterministic optimization module will be modified to the data in file on demand;
(2) parameter value is written to the relevant position of input file template, generates the numerical evaluation model file;
(3) call the acoustics software for calculation, logarithm value computation model file is solved calculating, obtains the result of calculation file;
(4) according to the locating information of each output response, parse required every response from destination file, response is in the locating information of destination file, by the user, the output response selected, obtained and store during defining operation;
(5) numerical value that respectively responds that will parse writes the output response file.
Described construct noise deterministic optimization module implementation procedure:
(1) judge whether the deterministic optimization configuration file exists.If exist, configuration file content resolved and shown, if there is no, by input, the output file of deterministic parsing module, parse input parameter and output respond and shows, the confession user is optimized setting;
(2) user arranges Optimized model on interface, comprises the selection optimized variable, and Offered target function and constraint condition are selected optimized algorithm and parameter is arranged;
(3) result that arranges to Optimized model by the user, be saved in and distribute rationally in file;
(4) carry out and optimize, call the Optimization Software integrated program, by required information, comprise that optimizing folder path passes to this program;
(5) the Optimized model information that will read from distribute file rationally by API is imported in the Optimization Software template file;
(6) call optimizing Design Software, amended template file is optimized to calculating, will call the deterministic parsing module in computation process and calculate the output response;
(7) after optimizing the calculating end of run, optimum results information is derived and is saved under engineered paths, and send message to optimizing modular program;
(8) program reads optimum results information and is presented on interface, and result is saved in and distributes rationally in file, and process data is saved to the process data file.
Nondeterministic algorithm in described structural acoustic uncertainty analysis module all adopts the method for MATLAB and C Plus Plus hybrid programming to be integrated in software module, and the specific implementation process is as follows:
The uncertainty propagation part:
(1) judge whether to exist the input file of uncertainty analysis, as existed, parse every input parameter and show from this document; If do not existed, from the input parameter file of deterministic parsing, parse every input parameter and show, create configuration file and input file;
(2) user selects the type of uncertain parameters and uncertain parameter from input parameter, input characterising parameter value; Select the uncertainty propagation algorithm types;
(3) uncertain parameters of the user being selected, corresponding uncertain type and characterising parameter value are saved in configuration file;
(4) setting to uncertainty propagation according to the user, carry out selected uncertainty propagation algorithm A selected uncertain parameters be processed into to some groups of sample points, and sample numerical value writes configuration file and shows;
(5), by the numerical value of one group of sample point and definite input parameter value of reading, write corresponding parameter position in deterministic parsing input parameter file from input file;
(6) call deterministic analyzer, obtain the result of calculation of this group sample point, read the numerical value of each response from the output response file of deterministic parsing, be presented on interface and deposit in the configuration file of uncertainty analysis file;
(7) circulation is carried out (5) and (6) until all sample points were all carried out to deterministic parsing;
(8) read some groups of response sample points in Study document, by the uncertainty propagation algorithm, B is calculated sample point, and the uncertainty analysis result met with a response, be saved to configuration file by data;
The index calculating section:
(1) judge that whether configuration file and uncertain response analysis data wherein exist, if exist, continue.
(2) if Ke Kaoxing & in configuration file; The data of robustness index analysis exist, Du Qukekaoxing & from configuration file; The data at robustness index interface also are presented on interface; If do not exist, data that read uncertain response analysis from configuration file, and be presented in interface;
(3) according to user's setting and the data in configuration file, call corresponding index calculating method, result of calculation is presented on interface;
(4) achievement data is saved in configuration file, the index of uncertain response and last group is determined to response is saved in output file simultaneously.
Described construct noise uncertainty optimization module implementation procedure:
(1) judge whether the uncertainty optimization configuration file exists.If exist, configuration file content resolved and shown, if there is no, by input, the output file of uncertainty analysis module, parse input parameter, output response and uncertain index and show, be optimized setting for the user;
(2) user arranges Optimized model on interface, comprises the selection optimized variable, and Offered target function and constraint condition are selected optimized algorithm and parameter is arranged;
(3) result that arranges to Optimized model by the user, be saved in and distribute rationally in file;
(4) carry out and optimize, call the Optimization Software integrated program, required information is comprised to optimizing folder path passes to this program;
(5) the Optimized model information that will read from distribute file rationally by API is imported in the Optimization Software template file;
(6) call optimizing Design Software, amended template file is optimized to calculating, computation process will be called the uncertainty analysis module and calculate output response and uncertain index;
(7) after optimizing the calculating end of run, optimum results information is derived and is saved under engineered paths, and send message to optimizing modular program;
(8) program reads optimum results information and is presented on interface, and result is saved in and distributes rationally in file, and process data is saved to the process data file.
Data transmission between described system framework program and each module all adopts the mode of read-write XML file, and its implementation is as follows:
Set up construct noise uncertainty optimization design engineering:
(1) according to path and the engineering name of user's input, set up the file with the engineering name name under path;
(2) set up the file corresponding with the deterministic parsing of structural acoustic characteristic, structural acoustic uncertainty analysis, construct noise deterministic optimization, four modules of construct noise uncertainty optimization in file;
(3) template of each module being moved to required file copies under corresponding folder, and the parameter in file is carried out to initialization;
Calling of each submodule:
(1) obtain current engineering place folder path and path, optimization system place;
(2) current engineering place folder path, add and just can obtain the title of current calling module corresponding folder module and call required folder path, is the path of certain file as required, adds that this document name gets final product;
(3) path, optimization system place adds the wherein position of calling module, obtains this module executive routine path;
(4) call required file or folder path and command parameter by calling module executive routine path, module, generate order line;
(5) use the command line mode calling module.
A kind of construct noise uncertainty optimization method, implementation step is as follows:
(1) the system framework program is set up construct noise uncertainty optimization design engineering according to arranging of user, set up the project file folder under specified path, and set up four files corresponding with the deterministic parsing of structural acoustic characteristic, construct noise deterministic optimization, the uncertainty analysis of structural acoustic characteristic, four modules of construct noise uncertainty optimization under this document folder, when each module is used, the file copy of indispensability is to relevant position, and the parameters in file is carried out to initialization, in order to the operation of each module in optimizing process;
(2) by the numerical evaluation model file of user's choice structure initial model in structural acoustic characteristic deterministic parsing module, and result of calculation file, and therefrom select input parameter and output response, according to user's selection with arrange and generate input parameter file corresponding to this module and output response file, wherein store each parameter, the title of response, numerical value, locating information, carry out reading of numerical value in order to subsequent step, resolve and retouching operation, generate according to arranging of user the autoexec that calls acoustical predictions software, carry out the structural acoustic property calculation for calling software, while carrying out the structural acoustic property calculation, structural acoustic characteristic deterministic parsing module writes the parameters value in the input parameter file relevant position of numerical evaluation model file, call acoustical predictions software, generate the result of calculation file, locating information according to the output response, reading out each response shows and is stored in the output response file,
(3) user's optimized variable, objective function, constraint condition to deterministic optimization in construct noise deterministic optimization module is defined, and optimized algorithm is selected and arranged.Arrange the configuration information of Optimized model is stored to the deterministic optimization configuration file according to the user.Carry out while optimizing, information in the deterministic optimization configuration file is delivered in the Optimization Software template file, generate new optimization file, this document comprises whole Optimized model, calling Optimization Software is calculated Optimized model, computation process comprises the several times iteration, and iterative process is for the first time: each optimized variable initial value is write to deterministic parsing input parameter file; Call structural acoustic characteristic deterministic parsing module, obtain corresponding output response file, resolve the output response from file, be delivered to Optimization Software, calculated each optimized variable value of next iteration by optimized algorithm, iterative process thereafter is: each optimized variable value of this iteration is write to deterministic parsing input parameter file; Call structural acoustic characteristic deterministic parsing module, obtain corresponding output response file, resolve the output response from file, be delivered to Optimization Software; Judge whether target restrains, whether meet constraint condition, if meet, optimize and finish, export optimum results and process data, otherwise calculated each optimized variable value of next iteration by optimized algorithm, carry out next iteration;
(4) user carries out the setting of selection, setting and the propagation algorithm of uncertain parameters in structural acoustic characteristic uncertainty analysis module.Setting according to the user, part input parameter is uncertain parameter, these parameters can obtain some groups of sample points by the uncertainty propagation algorithm, parameters value in each group sample point is write to deterministic parsing input parameter file, call structural acoustic characteristic deterministic parsing module, obtain corresponding output response file, resolve the output response from file, obtain one group of corresponding response.Obtain some groups of response sample points after having calculated whole sample points, calculate the uncertainty analysis result of output response through the uncertainty propagation algorithm, uncertainty analysis result by response, the index computational algorithm that calls appointment is set according to the user, calculate the desired value of selected response, desired value and other responses are deposited in to the output file of uncertainty analysis;
(5) user's optimized variable, objective function, constraint condition to uncertainty optimization in construct noise uncertainty optimization module is defined, and optimized algorithm is selected and arranged.Arrange the configuration information of Optimized model is stored to the uncertainty optimization configuration file according to the user.Carry out while optimizing, information in the uncertainty optimization configuration file is delivered in the Optimization Software template file, generate new optimization file, this document comprises whole Optimized model, calling Optimization Software is calculated Optimized model, computation process comprises the several times iteration, and iterative process is for the first time: the input file that each optimized variable initial value is write to uncertainty analysis; Call structural acoustic characteristic uncertainty analysis module, obtain corresponding output file, resolve output response and index value from file, be delivered to Optimization Software, calculated each optimized variable value of next iteration by optimized algorithm, iterative process thereafter is: each optimized variable value of this iteration is write to uncertainty analysis output parameter file; Call structural acoustic characteristic uncertainty analysis module, obtain corresponding output file, from file, resolve and respectively export response and index value, be delivered to Optimization Software; Judge whether target restrains, whether meet constraint condition, if meet optimize and finish, export optimum results and process data, otherwise calculated each optimized variable value of next iteration by optimized algorithm, carry out next iteration;
(6) after the optimization in step (3) or (5) finishes, read process data by post-processing module, and with form, all data are shown, according to user's selection and setting, the mode by the change procedure of a certain physical quantity with broken line graph or curve map shows; If the user selects to preserve picture, post-processing module is pressed picture format by this figure and is derived.
The present invention's advantage compared with prior art is: all kinds of algorithms that will process uncertain problem due to the present invention are integrated among system, comprise general Optimized model in simultaneity factor, with existing uncertainty optimization method, compare, do not need to re-establish Optimized model while being optimized, algorithm is write, integrated, only need be arranged Optimized model, algorithm is selected to get final product, for those are theoretical to uncertainty optimization and the method shortage is understood in depth engineering staff carries out the construct noise uncertainty analysis, a kind of instrument easily that provides is provided, be applicable to the engineering application.Due to relatively independent between each module of this system, each module has separately independently input, output file, is convenient to revise and safeguard.
The accompanying drawing explanation
Fig. 1 is system architecture diagram of the present invention;
Fig. 2 is the structural acoustic characteristic deterministic parsing module implementation procedure in the present invention;
Fig. 3 is the structural acoustic characteristic uncertainty analysis module implementation procedure in the present invention;
Fig. 4 is that in construct noise deterministic optimization module in the present invention and construct noise uncertainty optimization module, integration optimizing software is carried out the implementation procedure of optimizing;
Fig. 5 is the construct noise deterministic optimization module implementation procedure in the present invention;
Fig. 6 is the construct noise uncertainty optimization module implementation procedure in the present invention;
Fig. 7 is the post-processing module implementation procedure in the present invention.
Embodiment
System architecture figure as shown in Figure 1 is known, construct noise uncertainty optimization system and method for the present invention, the system framework program reached for integrated above-mentioned module by structural acoustic characteristic deterministic parsing module, construct noise deterministic optimization module, structural acoustic characteristic uncertainty analysis module, construct noise uncertainty optimization module, post-processing module forms.Whole implementation procedure is as follows:
(1) structural acoustic characteristic deterministic parsing module: for the function of implementation structure acoustic characteristic deterministic parameters calculation, analysis; Resolve each parameter value from the input parameter file, parameter value is write to the numerical evaluation model file; Call the acoustics software for calculation, logarithm value computation model file is solved calculating, obtains the result of calculation file; Parse required response from destination file, comprise design domain acoustic pressure, the total acoustic energy of structure radiation, write the output response file;
(2) construct noise deterministic optimization module: for the function of implementation structure Noise sourse determination optimal design; Judge whether the deterministic optimization configuration file exists, if do not had input, output file by the deterministic parsing module, parse input parameter and output response, if exist resolved by configuration file, for the user, be optimized setting; The result that arranges by the user to Optimized model, write and distribute rationally in file; The data of resolving in the uncertainty optimization configuration file are imported the Optimization Software template file into, call Optimization Software amended template file is calculated, and optimize and finish, and preserve optimum results and process data;
(3) structural acoustic characteristic uncertainty analysis module: for carrying out the uncertainty analysis of structural acoustic characteristic, comprise uncertainty propagation and two parts of index calculating; Uncertainty propagation is embodied as: the input file that judges whether to exist uncertainty analysis, parse every input parameter if exist from this document, if there is no from the input parameter file of deterministic parsing, parse every input parameter, for the user, selected and arrange; Setting according to the user to uncertainty quantification and uncertainty propagation, call the sample point that corresponding uncertainty propagation algorithm generates some input parameters, and repeatedly call the deterministic parsing module, obtains corresponding some output response sample points; Call corresponding nondeterministic algorithm, calculated the uncertainty analysis result of response by some response sample points; Index is calculated and is embodied as: judge whether configuration file and uncertain response analysis data wherein exist; Read the uncertainty analysis result of response by configuration file; Call the corresponding index computational algorithm, the reliability met with a response or robustness result of calculation; Result of calculation is write to output file and configuration file;
(4) construct noise uncertainty optimization module: for the function of implementation structure noise uncertainty optimization design; Judge whether the uncertainty optimization configuration file exists, if do not had input, output file by the uncertainty analysis module, parse input parameter, output response and uncertain index, exist and resolved by configuration file, for the user, be optimized setting; The result that arranges by the user to Optimized model, write and distribute rationally in file; The data of resolving in the uncertainty optimization configuration file are imported the Optimization Software template file into; Call Optimization Software amended template file is calculated, optimize and finish, preserve optimum results and process data;
(5) post-processing module: this module is used for the variation course of the design parameter of Optimized Iterative process, optimized variable, target function value, sensitivity, robustness physical quantity is displayed, and exports with picture format; Read the numerical value of each physical quantity each step iteration in optimizing process from the process data file, be presented on interface with form; According to user's selection and setting, obtain corresponding certain physical quantity optimizing process data, with the form demonstration of curve map or broken line graph; Preserve the curve map or the broken line graph that demonstrate are exported as to picture format if need;
(6) system framework program: for transmission and the management of the integrated and data that realize each submodule, major function comprises the foundation of construct noise uncertainty optimization design engineering, opening operation, and calling each submodule; Set up construct noise uncertainty optimization design engineering, according to path and the engineering name of user's input, set up the file with the engineering name name under path; Set up the file corresponding with the deterministic parsing of structural acoustic characteristic, structural acoustic uncertainty analysis, construct noise deterministic optimization, four modules of construct noise uncertainty optimization in file; The template of each module being moved to required file copies under corresponding folder, and the parameter in file is carried out to initialization; During the calling of each submodule, obtain project file folder path, place, and path, optimization system place, desired path called thereby obtain module; Call required file or folder path and command parameter by calling module executive routine path, module, generate order line, use the command line mode calling module.
The implementation procedure of above-mentioned each module is as follows:
1. structural acoustic characteristic deterministic parsing module realizing method
The implementation procedure of this module is as shown in Figure 2:
(1) resolve each parameter value from the input parameter file.About the input parameter file, when initial this module of operation, need to carry out following steps: in the deterministic parsing file during the input template file copy that the user is selected presss from both sides to project file; The input parameter of selecting according to the user, deposit the sequence number of all input parameters, title, numerical value, locating information unification in the input parameter file of deterministic parsing, and the user can directly revise parameter.
(2) parameter value in the input parameter file is write to the relevant position of template file, generate the required input file of solver.
(3) the operation autoexec, call solver input file calculated, and generates the result of calculation file.The wherein generation of autoexec: the solver of selecting according to the user when module is moved at first, obtain calling the required autoexec of this program, the user can change solver as required, regenerates autoexec.
(4) be output file by result of calculation, read the numerical value of correspondence position according to the locating information of each output response in template, and then obtain the output response, for the response obtained by other calculation of parameter,, according to expression formula, the substitution parameter value is calculated, and obtains response.Obtain required information about response, when initial this module of operation, need carry out following steps: in the deterministic parsing file during the output template file copy that the user is selected presss from both sides to project file; The output response of selecting according to the user and the response defined by self-defining function, the sequence number of all responses, title, numerical value, locating information unification are deposited in the output response file of deterministic parsing, if the output defined by self-defining function response also comprises the expression formula of calculating this response during storage.
(5) all responses are write to the output response file.Read input parameter file and the every data of output in response file and realize the demonstration of input/output data.
2. the realization of structural acoustic characteristic uncertainty analysis module
This module realizes that roughly as shown in Figure 3, nondeterministic algorithm wherein all adopts the method for MATLAB and C Plus Plus hybrid programming to be integrated in software module to process, and this module comprises that uncertainty propagation and index calculate two parts, and the specific implementation process is as follows:
The uncertainty propagation part:
(1) whether have input file in search uncertainty analysis file, some words therefrom read parameter and show; If no, from the deterministic parsing file, copy and come, then reading displayed, create configuration file and input file.
(2) user selects the type of uncertain parameters and uncertain parameter from input parameter, input characterising parameter value, and select the uncertainty propagation algorithm types.
(3) uncertain parameters of the user being selected, corresponding uncertain type and characterising parameter value are saved in configuration file.
(4) uncertainty propagation completes setting, after the user clicks operation, carries out selected algorithm A selected uncertain parameters is processed into to some groups of sample points, and sample numerical value writes configuration file.
(5) structural response calculates and shows the input parameter sample, carries out subsequent step after the user clicks operation.
(6) by the numerical value of one group of sample point and definite input parameter value of reading, write deterministic parsing input parameter file from input file.
(7) call deterministic analyzer, obtain the result of calculation of this group sample point, read the numerical value of each response from the output response file of deterministic parsing, be presented on interface and deposit in the configuration file of uncertainty analysis file.
(8) circulation is carried out (6) and (7) until all sample points were all carried out to deterministic parsing.
(9) some groups of response datas that read in configuration file are calculated by uncertainty propagation algorithm B, obtain the uncertainty analysis result data of output response, and point is preserved data are saved to configuration file.
The index calculating section:
(1) judge whether configuration file exists, if there is no, show miscue, close interface; If exist, continue.Judge in configuration file, whether uncertain response analysis data exist.If there is no, show miscue, close interface; If exist, continue.
(2) if Ke Kaoxing & in configuration file; The data that the robustness index is calculated exist, Du Qukekaoxing & from configuration file; The data that the robustness index is calculated also are presented on interface.If do not exist, from configuration file, read the data of uncertain response analysis, and be presented in interface.
(3) by user's selection algorithm, or the change calculating parameter, according to user's setting and the data in configuration file, call corresponding index calculating method, result of calculation is presented on interface.
(4) achievement data is saved in configuration file, the index of uncertain response and last group is determined to response is saved in output file simultaneously.
3. the realization of construct noise deterministic optimization module and uncertainty optimization module
Construct noise deterministic optimization module and uncertainty optimization module are carried out the process optimized as shown in Figure 4:
(1) judge whether to exist configuration file.If exist, read and distribute file content rationally and show on interface; If there is no, read the input parameter fileinfo, and in interface display.
(2) user arranges Optimized model on interface, comprises the selection optimized variable, and Offered target function and constraint condition are selected optimized algorithm and parameter arranged etc.
(3) Optimized model information is saved in and distributes rationally in file.
(4) carry out and optimize.Call the Optimization Software integrated program, required information is comprised to the optimization folder path, pass to this program.
(5) the Optimized model information that will read from distribute file rationally by API is imported in the Optimization Software template file.
(6) call optimizing Design Software, amended template file is optimized to calculating.After optimizing the calculating end of run, optimum results information is derived and is saved under engineered paths, and send message to optimizing modular program.
(7) program reads optimum results information, is presented on interface, and result is saved in and distributes rationally in file.
Determinacy optimization and uncertainty optimization for construct noise, carry out the mode of optimization realization all as indicated above, substantially similar, difference only is to carry out while optimizing, deterministic optimization calls the executive routine of deterministic parsing module, and uncertainty optimization is called the executive routine of uncertainty analysis module.
The implementation procedure of deterministic optimization module as shown in Figure 5, is carried out Optimization Steps as follows:
(1) the input parameter initial value data user arranged writes the input file of deterministic parsing.Carry out deterministic parsing, obtaining result of calculation is output file, parses corresponding data in output file.
(2) calculate Next iteration point by optimized algorithm.
(3) data of next iteration point are write to the input file of deterministic parsing, carry out deterministic parsing, obtain output file, parse corresponding data in output file.Judge that whether numerical value meet constraint, whether target restrains, if meet constraint, optimum results is exported in the target convergence, otherwise calculates next iteration point, carries out this step and finishes to optimizing.
The implementation procedure of uncertainty optimization module as shown in Figure 6, is carried out Optimization Steps as follows:
(1) the input parameter initial value data user arranged writes the input file of uncertainty analysis.According to the setting of carrying out in the uncertainty analysis module, carry out the uncertainty propagation flow process, obtain the uncertainty description parameter of each response, carry out index calculating, obtain index value, generate the output file that comprises response and index value.Parse corresponding data in output file.
(2) calculate Next iteration point by optimized algorithm.
(3) data of next iteration point are write to the input file of uncertainty analysis, carry out uncertainty analysis, obtain output file, parse corresponding data in output file.Judge that whether numerical value meet constraint, whether target restrains, if meet constraint, optimum results is exported in the target convergence, otherwise calculates next iteration point, carries out this step and finishes to optimizing.
4. the realization of post-processing module
From Fig. 5 and Fig. 6, construct noise deterministic optimization module and construct noise uncertainty optimization module, in optimizing process, have the process data Presentation Function, and this partial function is realized by post-processing module.Performing step is as follows:
(1) read the process data file and be presented on interface with form.Optimizing file, (deterministic optimization is the deterministic optimization file to process data file wherein, uncertainty optimization is the uncertainty optimization file) under, carry out while optimizing and by optimizing module, the data of the optimized variable of each step in optimizing process, objective function, constraint are deposited in this document, this document is the Excel file, and data are stored with form.
(2) select by the user optimized variable, objective function or the constraint condition that need to check, the form by the variation course of user-selected variable with broken line graph or curve map is presented on interface.
(3) if the user selects to be preserved generating figure, selection and setting according to the user, be saved to assigned address by the curve map of generation or broken line graph with picture format.
5. the realization of system framework program
The system framework program mainly comprise construct noise uncertainty optimization design engineering foundation, each submodule the function such as call, the data transmission between this program and each module all adopts the mode of read-write XML file, implementation method is as follows:
Set up construct noise uncertainty optimization design engineering:
(1) according to path and the engineering name of user's input, set up the file with the engineering name name under path.
(2) set up the file corresponding with the deterministic parsing of structural acoustic characteristic, structural acoustic uncertainty analysis, construct noise deterministic optimization, four modules of construct noise uncertainty optimization in file;
(3) template of each module being moved to required file copies under corresponding folder, and the parameter in file is carried out to initialization;
Calling of each submodule:
(1) obtain current engineering place folder path and path, optimization system place;
(2) current engineering place folder path, add and just can obtain the title of current calling module corresponding folder module and call required folder path, is the path of certain file as required, adds that this document name gets final product;
(3) path, optimization system place adds the wherein position of calling module, obtains this module executive routine path;
(4) call required file or folder path and command parameter by calling module executive routine path, module, generate order line;
(5) use the command line mode calling module.
Non-elaborated part of the present invention belongs to techniques well known.
The above; be only part embodiment of the present invention, but protection scope of the present invention is not limited to this, in the technical scope that any those skilled in the art disclose in the present invention; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.
Claims (7)
1. a construct noise uncertainty optimization system is characterized in that comprising: structural acoustic characteristic deterministic parsing module, construct noise deterministic optimization module, structural acoustic characteristic uncertainty analysis module, construct noise uncertainty optimization module, post-processing module and for the system framework program of integrated above-mentioned module; Wherein:
Structural acoustic characteristic deterministic parsing module: for the function of implementation structure acoustic characteristic deterministic parameters calculation, analysis; Resolve each parameter value from the input parameter file, parameter value is write to the numerical evaluation model file; Call the acoustics software for calculation, logarithm value computation model file is solved calculating, obtains the result of calculation file; Parse required response from destination file, comprise design domain acoustic pressure, the total acoustic energy of structure radiation, write the output response file;
Construct noise deterministic optimization module: for the function of implementation structure Noise sourse determination optimal design; Judge whether the deterministic optimization configuration file exists, if do not had input, output file by the deterministic parsing module, parse input parameter and output response, if exist resolved by configuration file, for the user, be optimized setting; The result that arranges by the user to Optimized model, write and distribute rationally in file; The data of resolving in the uncertainty optimization configuration file are imported the Optimization Software template file into, call Optimization Software amended template file is calculated, and optimize and finish, and preserve optimum results and process data;
Structural acoustic characteristic uncertainty analysis module: for carrying out the uncertainty analysis of structural acoustic characteristic, comprise uncertainty propagation and two parts of index calculating; Uncertainty propagation is embodied as: the input file that judges whether to exist uncertainty analysis, parse every input parameter if exist from this document, if there is no from the input parameter file of deterministic parsing, parse every input parameter, for the user, selected and arrange; Setting according to the user to uncertainty quantification and uncertainty propagation, call the sample point that corresponding uncertainty propagation algorithm generates some input parameters, and repeatedly call the deterministic parsing module, obtains corresponding some output response sample points; Call corresponding nondeterministic algorithm, calculated the uncertainty analysis result of response by some response sample points; Index is calculated and is embodied as: judge whether configuration file and uncertain response analysis data wherein exist; Read the uncertainty analysis result of response by configuration file; Call the corresponding index computational algorithm, the reliability met with a response or robustness result of calculation; Result of calculation is write to output file and configuration file;
Construct noise uncertainty optimization module: for the function of implementation structure noise uncertainty optimization design; Judge whether the uncertainty optimization configuration file exists, if do not had input, output file by the uncertainty analysis module, parse input parameter, output response and uncertain index, exist and resolved by configuration file, for the user, be optimized setting; The result that arranges by the user to Optimized model, write and distribute rationally in file; The data of resolving in the uncertainty optimization configuration file are imported the Optimization Software template file into; Call Optimization Software amended template file is calculated, optimize and finish, preserve optimum results and process data;
Post-processing module: this module is used for the variation course of the design parameter of Optimized Iterative process, optimized variable, target function value, sensitivity, robustness physical quantity is displayed, and exports with picture format; Read the numerical value of each physical quantity each step iteration in optimizing process from the process data file, be presented on interface with form; According to user's selection and setting, obtain corresponding certain physical quantity optimizing process data, with the form demonstration of curve map or broken line graph; Preserve the curve map or the broken line graph that demonstrate are exported as to picture format if need;
The system framework program: for transmission and the management of the integrated and data that realize each submodule, major function comprises the foundation of construct noise uncertainty optimization design engineering, opening operation, and calling each submodule; Set up construct noise uncertainty optimization design engineering, according to path and the engineering name of user's input, set up the file with the engineering name name under path; Set up the file corresponding with the deterministic parsing of structural acoustic characteristic, structural acoustic uncertainty analysis, construct noise deterministic optimization, four modules of construct noise uncertainty optimization in file; The template of each module being moved to required file copies under corresponding folder, and the parameter in file is carried out to initialization; During the calling of each submodule, obtain project file folder path, place, and path, optimization system place, desired path called thereby obtain module; Call required file or folder path and command parameter by calling module executive routine path, module, generate order line, use the command line mode calling module.
2. a kind of construct noise uncertainty optimization system according to claim 1 is characterized in that: described structural acoustic characteristic deterministic parsing module implementation procedure:
(1) resolve each parameter value from the input parameter file, output parameter file wherein, initial when this module of isolated operation by the user to the selection of input parameter with generation is set, when this module is called by uncertainty analysis module or deterministic optimization module, uncertainty analysis module or deterministic optimization module will be modified to the data in file on demand;
(2) parameter value is written to the relevant position of input file template, generates the numerical evaluation model file;
(3) call the acoustics software for calculation, logarithm value computation model file is solved calculating, obtains the result of calculation file;
(4) according to the locating information of each output response, parse required every response from destination file, response is in the locating information of destination file, by the user, the output response selected, obtained and store during defining operation;
(5) numerical value that respectively responds that will parse writes the output response file.
3. according to a kind of construct noise uncertainty optimization system described in claim 1, it is characterized in that: described construct noise deterministic optimization module implementation procedure:
(1) judge whether the deterministic optimization configuration file exists.If exist, configuration file content resolved and shown, if there is no, by input, the output file of deterministic parsing module, parse input parameter and output respond and shows, the confession user is optimized setting;
(2) user arranges Optimized model on interface, comprises the selection optimized variable, and Offered target function and constraint condition are selected optimized algorithm and parameter is arranged;
(3) result that arranges to Optimized model by the user, be saved in and distribute rationally in file;
(4) carry out and optimize, call the Optimization Software integrated program, by required information, comprise that optimizing folder path passes to this program;
(5) the Optimized model information that will read from distribute file rationally by API is imported in the Optimization Software template file;
(6) call optimizing Design Software, amended template file is optimized to calculating, will call the deterministic parsing module in computation process and calculate the output response;
(7) after optimizing the calculating end of run, optimum results information is derived and is saved under engineered paths, and send message to optimizing modular program;
(8) program reads optimum results information and is presented on interface, and result is saved in and distributes rationally in file, and process data is saved to the process data file.
4. a kind of construct noise uncertainty optimization system according to claim 1, it is characterized in that: the nondeterministic algorithm in described structural acoustic uncertainty analysis module all adopts the method for MATLAB and C Plus Plus hybrid programming to be integrated in software module, and the specific implementation process is as follows:
The uncertainty propagation part:
(1) judge whether to exist the input file of uncertainty analysis, as existed, parse every input parameter and show from this document; If do not existed, from the input parameter file of deterministic parsing, parse every input parameter and show, create configuration file and input file;
(2) user selects the type of uncertain parameters and uncertain parameter from input parameter, input characterising parameter value; Select the uncertainty propagation algorithm types;
(3) uncertain parameters of the user being selected, corresponding uncertain type and characterising parameter value are saved in configuration file;
(4) setting to uncertainty propagation according to the user, carry out selected uncertainty propagation algorithm A selected uncertain parameters be processed into to some groups of sample points, and sample numerical value writes configuration file and shows;
(5), by the numerical value of one group of sample point and definite input parameter value of reading, write corresponding parameter position in deterministic parsing input parameter file from input file;
(6) call deterministic analyzer, obtain the result of calculation of this group sample point, read the numerical value of each response from the output response file of deterministic parsing, be presented on interface and deposit in the configuration file of uncertainty analysis file;
(7) circulation is carried out (5) and (6) until all sample points were all carried out to deterministic parsing;
(8) read some groups of response sample points in configuration file, by the uncertainty propagation algorithm, B is calculated sample point, and the uncertainty analysis result met with a response, be saved to configuration file by data;
The index calculating section:
(1) judge that whether configuration file and uncertain response analysis data wherein exist, if exist, continue;
(2) if Ke Kaoxing & in configuration file; The data of robustness index analysis exist, Du Qukekaoxing & from configuration file; The data at robustness index interface also are presented on interface; If do not exist, data that read uncertain response analysis from configuration file, and be presented in interface;
(3) according to user's setting and the data in configuration file, call corresponding index calculating method, result of calculation is presented on interface;
(4) achievement data is saved in configuration file, the index of uncertain response and last group is determined to response is saved in output file simultaneously.
5. according to a kind of construct noise uncertainty optimization system described in claim 1, it is characterized in that: described construct noise uncertainty optimization module implementation procedure:
(1) judge whether the uncertainty optimization configuration file exists.If exist, configuration file content resolved and shown, if there is no, by input, the output file of uncertainty analysis module, parse input parameter, output response and uncertain index and show, be optimized setting for the user;
(2) user arranges Optimized model on interface, comprises the selection optimized variable, and Offered target function and constraint condition are selected optimized algorithm and parameter is arranged;
(3) result that arranges to Optimized model by the user, be saved in and distribute rationally in file;
(4) carry out and optimize, call the Optimization Software integrated program, required information is comprised to optimizing folder path passes to this program;
(5) the Optimized model information that will read from distribute file rationally by API is imported in the Optimization Software template file;
(6) call optimizing Design Software, amended template file is optimized to calculating, computation process will be called the uncertainty analysis module and calculate output response and uncertain index;
(7) after optimizing the calculating end of run, optimum results information is derived and is saved under engineered paths, and send message to optimizing modular program;
(8) program reads optimum results information and is presented on interface, and result is saved in and distributes rationally in file, and process data is saved to the process data file.
6. according to a kind of construct noise uncertainty optimization system described in claim 1, it is characterized in that: the data transmission between described system framework program and each module all adopts the mode of read-write XML file, and implementation method is as follows:
Set up construct noise uncertainty optimization design engineering:
(1) according to path and the engineering name of user's input, set up the file with the engineering name name under path;
(2) set up the file corresponding with the deterministic parsing of structural acoustic characteristic, structural acoustic uncertainty analysis, construct noise deterministic optimization, four modules of construct noise uncertainty optimization in file;
(3) template of each module being moved to required file copies under corresponding folder, and the parameter in file is carried out to initialization;
Calling of each submodule:
(1) obtain current engineering place folder path and path, optimization system place;
(2) current engineering place folder path, add and just can obtain the title of current calling module corresponding folder module and call required folder path, is the path of certain file as required, adds that this document name gets final product;
(3) path, optimization system place adds the wherein position of calling module, obtains this module executive routine path;
(4) call required file or folder path and command parameter by calling module executive routine path, module, generate order line;
(5) use the command line mode calling module.
7. a construct noise uncertainty optimization method is characterized in that performing step is as follows:
(1) the system framework program is set up construct noise uncertainty optimization design engineering according to arranging of user, set up the project file folder under specified path, and set up four files corresponding with the deterministic parsing of structural acoustic characteristic, construct noise deterministic optimization, the uncertainty analysis of structural acoustic characteristic, four modules of construct noise uncertainty optimization under this document folder, when each module is used, the file copy of indispensability is to relevant position, and the parameters in file is carried out to initialization, in order to the operation of each module in optimizing process;
(2) by the numerical evaluation model file of user's choice structure initial model in structural acoustic characteristic deterministic parsing module, and result of calculation file, and therefrom select input parameter and output response, according to user's selection with arrange and generate input parameter file corresponding to this module and output response file, wherein store each parameter, the title of response, numerical value, locating information, carry out reading of numerical value in order to subsequent step, resolve and retouching operation, generate according to arranging of user the autoexec that calls acoustical predictions software, carry out the structural acoustic property calculation for calling software, while carrying out the structural acoustic property calculation, structural acoustic characteristic deterministic parsing module writes the parameters value in the input parameter file relevant position of numerical evaluation model file, call acoustical predictions software, generate the result of calculation file, locating information according to the output response, read out each response, be presented on interface and be stored in the output response file,
(3) user's optimized variable, objective function, constraint condition to deterministic optimization in construct noise deterministic optimization module is defined, and optimized algorithm is selected and arranged; Arrange the configuration information of Optimized model is stored to the deterministic optimization configuration file according to the user; Carry out while optimizing, information in the deterministic optimization configuration file is delivered in the Optimization Software template file, generate new optimization file, this document comprises whole Optimized model, calling Optimization Software is calculated Optimized model, computation process comprises the several times iteration, and iterative process is for the first time: each optimized variable initial value is write to deterministic parsing input parameter file; Call structural acoustic characteristic deterministic parsing module, obtain corresponding output response file, resolve the output response from file, be delivered to Optimization Software, calculated each optimized variable value of next iteration by optimized algorithm, iterative process thereafter is: each optimized variable value of this iteration is write to deterministic parsing input parameter file; Call structural acoustic characteristic deterministic parsing module, obtain corresponding output response file, resolve the output response from file, be delivered to Optimization Software; Judge whether target restrains, whether meet constraint condition, if meet, optimize and finish, export optimum results and process data, otherwise calculated each optimized variable value of next iteration by optimized algorithm, carry out next iteration;
(4) user carries out the setting of selection, setting and the propagation algorithm of uncertain parameters in structural acoustic characteristic uncertainty analysis module; Setting according to the user, part input parameter is uncertain parameter, these parameters can obtain some groups of sample points by the uncertainty propagation algorithm, parameters value in each group sample point is write to deterministic parsing input parameter file, call structural acoustic characteristic deterministic parsing module, obtain corresponding output response file, resolve the output response from file, obtain one group of corresponding response; Obtain some groups of response sample points after having calculated whole sample points, calculate the uncertainty analysis result of output response through the uncertainty propagation algorithm, uncertainty analysis result by response, the index computational algorithm that calls appointment is set according to the user, calculate the desired value of selected response, desired value and other responses are deposited in to the output file of uncertainty analysis;
(5) user's optimized variable, objective function, constraint condition to uncertainty optimization in construct noise uncertainty optimization module is defined, and optimized algorithm is selected and arranged; Arrange the configuration information of Optimized model is stored to the uncertainty optimization configuration file according to the user; Carry out while optimizing, information in the uncertainty optimization configuration file is delivered in the Optimization Software template file, generate new optimization file, this document comprises whole Optimized model, calling Optimization Software is calculated Optimized model, computation process comprises the several times iteration, and iterative process is for the first time: the input file that each optimized variable initial value is write to uncertainty analysis; Call structural acoustic characteristic uncertainty analysis module, obtain corresponding output file, resolve output response and index value from file, be delivered to Optimization Software, calculated each optimized variable value of next iteration by optimized algorithm, iterative process thereafter is: each optimized variable value of this iteration is write to uncertainty analysis output parameter file; Call structural acoustic characteristic uncertainty analysis module, obtain corresponding output file, from file, resolve and respectively export response and index value, be delivered to Optimization Software; Judge whether target restrains, whether meet constraint condition, if meet optimize and finish, export optimum results and process data, otherwise calculated each optimized variable value of next iteration by optimized algorithm, carry out next iteration;
(6) after the optimization in step (3) or (5) finishes, read process data by post-processing module, and with form, all data are shown, according to user's selection and setting, the mode by the change procedure of a certain physical quantity with broken line graph or curve map shows; If the user selects to preserve picture, post-processing module is pressed picture format by this figure and is derived.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310415385.3A CN103473061B (en) | 2013-09-12 | 2013-09-12 | A kind of construct noise uncertainty optimization system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310415385.3A CN103473061B (en) | 2013-09-12 | 2013-09-12 | A kind of construct noise uncertainty optimization system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103473061A true CN103473061A (en) | 2013-12-25 |
| CN103473061B CN103473061B (en) | 2016-09-21 |
Family
ID=49797933
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310415385.3A Expired - Fee Related CN103473061B (en) | 2013-09-12 | 2013-09-12 | A kind of construct noise uncertainty optimization system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103473061B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105571483A (en) * | 2014-10-14 | 2016-05-11 | 睿励科学仪器（上海）有限公司 | Method and apparatus for optimizing parameter of optical system |
| CN105975767A (en) * | 2016-05-03 | 2016-09-28 | 北京航空航天大学 | Method of plate cavity system acoustic characteristic prediction based on interval perturbation analysis theory |
| CN106407586A (en) * | 2016-09-28 | 2017-02-15 | 北京航空航天大学 | Aircraft structure function integrated design platform system based on uncertain optimization |
| CN106960043A (en) * | 2017-03-30 | 2017-07-18 | 中国航空工业集团公司西安飞机设计研究所 | The processing method of initial data in a kind of Aircraft Load design |
| CN112487045A (en) * | 2020-10-20 | 2021-03-12 | 广西盛源行大数据科技有限公司 | Management platform based on big data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070248193A1 (en) * | 2006-04-20 | 2007-10-25 | The Boeing Company | Method for fine timing acquisition |
| CN101271022A (en) * | 2008-05-15 | 2008-09-24 | 上海交通大学 | Transmission path detecting system for vehicle system structure vibration and noise |
| CN101733676A (en) * | 2008-11-17 | 2010-06-16 | 深圳市大族激光科技股份有限公司 | Method for identifying and controlling noise of machine tool structure |
| CN102999678A (en) * | 2012-12-26 | 2013-03-27 | 北京航空航天大学 | Nonlinear multi-target range robust optimization based automobile noise reduction method |
-
2013
- 2013-09-12 CN CN201310415385.3A patent/CN103473061B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070248193A1 (en) * | 2006-04-20 | 2007-10-25 | The Boeing Company | Method for fine timing acquisition |
| CN101271022A (en) * | 2008-05-15 | 2008-09-24 | 上海交通大学 | Transmission path detecting system for vehicle system structure vibration and noise |
| CN101733676A (en) * | 2008-11-17 | 2010-06-16 | 深圳市大族激光科技股份有限公司 | Method for identifying and controlling noise of machine tool structure |
| CN102999678A (en) * | 2012-12-26 | 2013-03-27 | 北京航空航天大学 | Nonlinear multi-target range robust optimization based automobile noise reduction method |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105571483A (en) * | 2014-10-14 | 2016-05-11 | 睿励科学仪器（上海）有限公司 | Method and apparatus for optimizing parameter of optical system |
| CN105571483B (en) * | 2014-10-14 | 2018-06-26 | 睿励科学仪器（上海）有限公司 | A kind of method and apparatus for being used to optimize optical system parameter |
| CN105975767A (en) * | 2016-05-03 | 2016-09-28 | 北京航空航天大学 | Method of plate cavity system acoustic characteristic prediction based on interval perturbation analysis theory |
| CN105975767B (en) * | 2016-05-03 | 2018-10-02 | 北京航空航天大学 | A method of the plate chamber system acoustics prediction based on Interval Perturbation analysis theories |
| CN106407586A (en) * | 2016-09-28 | 2017-02-15 | 北京航空航天大学 | Aircraft structure function integrated design platform system based on uncertain optimization |
| CN106407586B (en) * | 2016-09-28 | 2019-08-13 | 北京航空航天大学 | A kind of Flight Vehicle Structure function integration design platform system based on uncertain optimization |
| CN106960043A (en) * | 2017-03-30 | 2017-07-18 | 中国航空工业集团公司西安飞机设计研究所 | The processing method of initial data in a kind of Aircraft Load design |
| CN112487045A (en) * | 2020-10-20 | 2021-03-12 | 广西盛源行大数据科技有限公司 | Management platform based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103473061B (en) | 2016-09-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103605662B (en) | Distributed computation frame parameter optimizing method, device and system | |
| CN103473061A (en) | System and method for optimizing structural noise uncertainty | |
| US20160085738A1 (en) | Cloud-Based Parallel Computation Using Actor Modules | |
| CN105095092A (en) | Static analysis and dynamic operation based detection of atomic violation of JS (JavaScript) code in Web application | |
| US9015608B2 (en) | Regenerating a user interface area | |
| CN104834599A (en) | WEB security detection method and device | |
| Lehtinen | Derivational robustness and indirect confirmation | |
| CN105117430B (en) | A kind of iterative task process discovery method based on equivalence class | |
| Amparore | A new GreatSPN GUI for GSPN editing and CSL TA model checking | |
| CN112394982B (en) | Method, device, medium and electronic equipment for generating voice recognition system | |
| Li et al. | Automated functional scenarios-based formal specification animation | |
| Javed et al. | Model Based Testing for Web Applications: A Literature Survey Presented. | |
| CN111444635B (en) | System dynamics simulation modeling method and system based on XML language | |
| US10922208B2 (en) | Observer for simulation test and verification | |
| WO2023160402A1 (en) | Data modeling method and apparatus, and device and storage medium | |
| CN109344050B (en) | Interface parameter analysis method and device based on structure tree | |
| US11782948B2 (en) | Processing cartesian columns in analytics applications | |
| KR101901310B1 (en) | System for providing user-oriented interconnection application integration | |
| CN113190582B (en) | Data real-time interactive mining flow modeling analysis system | |
| Roungas et al. | Harnessing Web 3.0 and R to Mitigate Simulation Validation Restrictions. | |
| US8694293B2 (en) | Simulation support method, computer-readable storage medium storing simulation support program, and simulation support apparatus | |
| CN103324524A (en) | Processing method for COMSOL MULTIPHYSICS computing program | |
| Arora et al. | Applying variable chromosome length genetic algorithm for testing dynamism of web application | |
| Do et al. | Assessing resource provisioning and allocation of ensembles of in situ workflows | |
| Jansen et al. | The COMICS tool-Computing minimal counterexamples for discrete-time Markov chains |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160921 Termination date: 20170912 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |