CN117435308B - Modelica model simulation method and system based on parallel computing algorithm - Google Patents
Modelica model simulation method and system based on parallel computing algorithm Download PDFInfo
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Abstract
The invention discloses a Modelica model simulation method and system based on a parallel computing algorithm, which belongs to the technical field of parallel simulation, and the method specifically comprises the following steps: according to the modeling requirement of a pulley rope system, a model of a pulley rope is built by using a model language, parallel computing algorithm is utilized to conduct parallelization processing on the model of the pulley rope, a model simulation tool is used to run the parallelized model of the pulley rope, simulation is conducted on the model of the pulley rope, analysis and evaluation of simulation results are conducted after the parallel simulation is completed, performance optimization is conducted on the parallelized model according to actual conditions, the problem that the model of the pulley rope is divided into a plurality of sub-models and the running speed is low is effectively solved through parallel computing, and the operation efficiency and performance of the model are greatly improved.
Description
Technical Field
The invention belongs to the technical field of parallel simulation, and particularly relates to a Modelica model simulation method and system based on a parallel computing algorithm.
Background
With the maturity of computer technology, the model-based system engineering method is widely applied to the fields of system design and simulation modeling, and has the advantages of reusability, no ambiguity, easy understanding, easy replication and propagation and the like, so that the traditional text-based system engineering method is gradually replaced. As the functional requirements of electromechanical systems become increasingly complex, it often involves many fields and disciplines such as mechanical, electronic, electrical, hydraulic, thermal, and control, making the scale of the systems increasingly large, the structure and behavior increasingly complex, and multi-domain system design and simulation modeling become increasingly important.
The rise of the Modelica language provides convenience for large-scale, time-varying, complex multi-domain system modeling. Modelica has a broad development prospect as a multi-domain system modeling simulation language. It is a new generation of CAE technology for complex engineering system design and has become a de facto standard for engineering system physical modeling. The model has high computational complexity.
For example, chinese patent with the grant publication number CN115293056B discloses a model-oriented multi-objective optimization method, which includes: acquiring each input parameter and each output parameter to be optimized in a Modelica model; generating an initial population formed by N value combinations of each input parameter; substituting individuals of various groups in the current group into the Modelica model for simulation; executing a rapid non-dominant sorting algorithm on the current population to obtain a first non-dominant level and a first crowding degree of each population of individuals, and calculating a first fitness of each population of individuals; generating a new current population and N new value combinations of the corresponding output parameters according to the first fitness and the priority of each input parameter, and returning to the calculation operation of the first non-dominant level and the first crowding degree until a set optimal termination condition is reached; and selecting at least one optimal value combination of each input parameter from the final current population.
For example, chinese patent with the publication number CN111967128B discloses a method for improving the solving efficiency of model simulation model, which includes the following steps: dividing a Modelica simulation model to be processed into a large time scale model and a small time scale model by taking variable variation frequency as a reference; the large time scale model is a discrete system model which is easy to converge, and the small time scale model is a continuous system model which is difficult to converge; modeling a split large time scale model and a split small time scale model respectively; modeling a large time scale model by adopting a discrete system, and modeling a small time scale model by adopting a continuous system; and carrying out model reconstruction on the modeled large time scale model and small time scale model by adopting a continuous-discrete mixed modeling method. According to the invention, the Modelica model is reconstructed, and the Modelica model is deconstructed at a code layer and split into a continuous system model and a discrete system model, so that the operation efficiency of the Modelica model is remarkably improved.
Problems with the above patents are: when the system model is constructed, the system model includes a plurality of sub-models, and when calculation simulation is performed, the calculation efficiency is reduced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a model simulation method and a model simulation system based on a parallel computing algorithm, which are used for establishing a model of a pulley rope by using a model language according to the modeling requirement of a pulley rope system, parallelizing the model of the pulley rope by using the parallel computing algorithm, operating the parallelized model of the pulley rope by using a model simulation tool, simulating the model of the pulley rope, analyzing and evaluating simulation results after the parallel simulation is completed, optimizing the performance of the parallelized model according to actual conditions, effectively solving the problems that the model of the pulley rope is divided into a plurality of sub-models and the operating speed is low by parallel computing, and greatly improving the operation efficiency and the performance of the model of the pulley rope.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A Modelica model simulation method based on a parallel computing algorithm comprises the following specific steps:
Step S1: establishing a Modelica model of the pulley rope by using Modelica language according to modeling requirements of the pulley rope system;
step S2: parallelizing the pulley rope model by using a parallelization calculation algorithm;
step S3: running a parallelized pulley rope model by using a model simulation tool, and simulating the pulley rope model;
Step S4: after the parallel simulation is completed, the simulation result is analyzed and evaluated, and the performance of the parallelized model is optimized according to the actual situation.
Specifically, the pulley rope model in step S1 includes: rope model, pulley and hoisting model and slip pair model.
Specifically, the specific method in step S2 is as follows:
Step S201: setting the parallel computing task of the pulley rope model as a parallel computing state, and distributing the parallel computing task of the pulley rope model to a container of a running client through client management;
Step S202: the method comprises the steps of setting a set of tasks of parallel calculation of pulley rope model as N, setting a set of clients of a server as M, and setting N= { N 1,n2,...,nm},M＝{m1,m2,...,mn }, wherein N m represents the task of parallel calculation of the M-th pulley rope model, M n represents the N-th client, weighting the machine performance of the i-th client, and the calculation formula is as follows:
Wherein, W mi represents the weight of the machine performance of the ith client, W xc represents the calculation capability parameter of the CPU thread of the computer, W kxc represents the idle condition of the CPU thread of the computer, alpha and beta represent correction parameters, and N xc represents the number of processes running on the computer;
Step S203: according to the weight of the performance of the client, the tasks of parallel calculation of the pulley rope model are sequentially carried out, the running time of the task of parallel calculation of the pulley rope model at the j-th client is calculated, and the calculation formula is as follows:
wherein, Representing the running time of the pulley rope Modelica model parallel computing task at the jth client, wherein lambda represents a running time adjusting parameter;
Step S204: the parallel efficiency of the pulley rope Modelica model parallel calculation during task parallel calculation is calculated, and the calculation formula is as follows:
where XL represents the parallel efficiency of the pulley rope Modelica model in task parallel computation, Representing the run time of the task of pulley rope Modelica model parallel computation at the kth client,/>Representing the run time of the task of pulley rope Modelica model parallel computation at the p-th client,/>Representing the running time of the pulley rope model parallel computing task at the q-th client,// representing the sign of the division return integer between integers,% representing the sign of the division return remainder between integers;
step S205: and (3) adjusting the tasks of the parallel calculation of the pulley rope model by using a task adjustment strategy until the parallel efficiency tends to the maximum value.
Specifically, the task adjustment policy in step S205 includes: when m is less than or equal to n, no adjustment is needed, and when m is more than n, the method leads
Specifically, the Modelica simulation tool in step S3 includes: mworks. Sysplorer and Dymola.
A model simulation system based on a parallel computing algorithm, comprising: the modeling requirement analysis module, the parallelization module, the Modelica simulation module, the analysis and evaluation module;
The modeling requirement analysis module is used for establishing a model of the pulley rope by using a model language according to the modeling requirement of the pulley rope system;
the parallelization module is used for parallelizing the pulley rope model by using a parallelization calculation algorithm;
The model simulation module is used for running a parallelized pulley rope model by using a model simulation tool and simulating the pulley rope model;
the analysis and evaluation module is used for analyzing and evaluating simulation results after the parallel simulation is completed, and performing performance optimization on the parallelized model according to actual conditions.
Specifically, the modeling requirement analysis module comprises a modeling requirement analysis unit and a Modelica model establishment unit,
The modeling demand analysis unit is used for analyzing the modeling demand of the pulley rope system;
The Modelica model building unit is used for building the Modelica model of the pulley rope by using Modelica language.
An electronic device comprising a memory storing a computer program and a processor implementing the steps of a model simulation method based on a parallel computing algorithm when executing the computer program.
A computer readable storage medium having stored thereon computer instructions which when executed perform the steps of a model simulation method based on a parallel computing algorithm.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention provides a Modelica model simulation method system based on a parallel computing algorithm, which performs optimization and improvement on architecture, operation steps and flow, and has the advantages of simple flow, low investment and operation cost and low production and working cost.
2. The invention provides a model simulation method based on a parallel computing algorithm, which is characterized in that a model of a pulley rope is built by using a model language according to modeling requirements of a pulley rope system, the parallel computing algorithm is utilized to parallelize the model of the pulley rope, a model simulation tool is used to run the parallelized model of the pulley rope, the model of the pulley rope is simulated, analysis and evaluation of simulation results are carried out after the parallel simulation is completed, performance optimization is carried out on the parallelized model according to actual conditions, and the problems that the model of the pulley rope is divided into a plurality of sub-models and the running speed is low are effectively solved by parallel computing, so that the operation efficiency and performance of the model of the pulley rope are greatly improved.
Drawings
FIG. 1 is a flow chart of a Modelica model simulation method based on a parallel computing algorithm;
FIG. 2 is a schematic diagram of a Modelica model simulation system based on a parallel computing algorithm according to the present invention;
Fig. 3 is a diagram of an electronic device of a model simulation method based on a parallel computing algorithm.
Detailed Description
In order that the technical means, the creation characteristics, the achievement of the objects and the effects of the present invention may be easily understood, it should be noted that in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "a", "an", "the" and "the" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The invention is further described below in conjunction with the detailed description.
Example 1
Referring to fig. 1, an embodiment of the present invention is provided: a Modelica model simulation method based on a parallel computing algorithm comprises the following specific steps:
Step S1: establishing a Modelica model of the pulley rope by using Modelica language according to modeling requirements of the pulley rope system;
step S2: parallelizing the pulley rope model by using a parallelization calculation algorithm;
step S3: running a parallelized pulley rope model by using a model simulation tool, and simulating the pulley rope model;
Step S4: after the parallel simulation is completed, the simulation result is analyzed and evaluated, and the performance of the parallelized model is optimized according to the actual situation.
The biggest feature of Modelica is the support of physical modeling. By physical modeling is meant that a modeling approach is employed that is as close as possible to the engineering system design process and that is consistent with the design habits of the engineer. This means that the engineer does not need to cross mathematical equations during modeling, only processes components and parameters. Modelica provides complete support for direct physical modeling through typical features such as object-oriented modeling, multi-domain modeling, declarative non-causal modeling, continuous-discrete hybrid modeling, and the like.
The pulley rope model in said step S1 comprises: rope model, pulley and hoisting model and slip pair model.
The specific method of the step S2 is as follows:
Step S201: setting the parallel computing task of the pulley rope model as a parallel computing state, and distributing the parallel computing task of the pulley rope model to a container of a running client through client management;
Step S202: the method comprises the steps of setting a set of tasks of parallel calculation of pulley rope model as N, setting a set of clients of a server as M, and setting N= { N 1,n2,...,nm},M＝{m1,m2,...,mn }, wherein N m represents the task of parallel calculation of the M-th pulley rope model, M n represents the N-th client, weighting the machine performance of the i-th client, and the calculation formula is as follows:
wherein, The weight of the machine performance of the ith client is represented, W xc represents a computer CPU thread computing capability parameter, W kxc represents a computer CPU thread idle condition, alpha and beta represent correction parameters, and N xc represents the number of processes running on a computer;
Step S203: according to the weight of the performance of the client, the tasks of parallel calculation of the pulley rope model are sequentially carried out, the running time of the task of parallel calculation of the pulley rope model at the j-th client is calculated, and the calculation formula is as follows:
wherein, Representing the running time of the pulley rope Modelica model parallel computing task at the jth client, wherein lambda represents a running time adjusting parameter;
Step S204: the parallel efficiency of the pulley rope Modelica model parallel calculation during task parallel calculation is calculated, and the calculation formula is as follows:
where XL represents the parallel efficiency of the pulley rope Modelica model in task parallel computation, Representing the run time of the task of pulley rope Modelica model parallel computation at the kth client,/>Representing the run time of the task of pulley rope Modelica model parallel computation at the p-th client,/>Representing the running time of the pulley rope model parallel computing task at the q-th client,// representing the sign of the division return integer between integers,% representing the sign of the division return remainder between integers;
step S205: and (3) adjusting the tasks of the parallel calculation of the pulley rope model by using a task adjustment strategy until the parallel efficiency tends to the maximum value.
The task adjustment strategy in step S205 includes: when m is less than or equal to n, no adjustment is needed, and when m is more than n, the method leads
The Modelica simulation tool in the step S3 comprises: mworks. Sysplorer and Dymola.
The main functions and characteristics of the MWORKS. Sysplorer are as follows: 1) Modeling a system in the field of multi-process; 2) A multi-view multi-document modeling environment; 3) A multi-form modeling support; 4) A customizable model library; 5) Physical unit derivation and inspection; 6) Automatically generating a simulation code; 7) Analyzing and post-processing results; 8) Hardware in-loop simulation; 9) Good scalability.
Example 2
Referring to fig. 2, another embodiment of the present invention is provided: a model simulation system based on a parallel computing algorithm, comprising: the modeling requirement analysis module, the parallelization module, the Modelica simulation module, the analysis and evaluation module;
The modeling requirement analysis module is used for establishing a model of the pulley rope by using a model language according to the modeling requirement of the pulley rope system;
the parallelization module is used for parallelizing the pulley rope model by using a parallelization calculation algorithm;
The model simulation module is used for running a parallelized pulley rope model by using a model simulation tool and simulating the pulley rope model;
the analysis and evaluation module is used for analyzing and evaluating simulation results after the parallel simulation is completed, and performing performance optimization on the parallelized model according to actual conditions.
The modeling requirement analysis module comprises a modeling requirement analysis unit and a Modelica model establishment unit,
The modeling demand analysis unit is used for analyzing the modeling demand of the pulley rope system;
The Modelica model building unit is used for building the Modelica model of the pulley rope by using Modelica language.
Example 3
Referring to fig. 3, an electronic device includes a memory and a processor, where the memory stores a computer program, and the processor implements steps of a model simulation method based on a parallel computing algorithm when executing the computer program.
A computer readable storage medium having stored thereon computer instructions which when executed perform the steps of a model simulation method based on a parallel computing algorithm.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are all within the protection of the present invention.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The Modelica model simulation method based on the parallel computing algorithm is characterized by comprising the following specific steps of:
Step S1: establishing a Modelica model of the pulley rope by using Modelica language according to modeling requirements of the pulley rope system;
step S2: parallelizing the pulley rope model by using a parallelization calculation algorithm;
step S3: running a parallelized pulley rope model by using a model simulation tool, and simulating the pulley rope model;
Step S4: after the parallel simulation is completed, analyzing and evaluating a simulation result, and performing performance optimization on the parallelized model according to actual conditions;
the specific method of the step S2 is as follows:
Step S201: setting the parallel computing task of the pulley rope model as a parallel computing state, and distributing the parallel computing task of the pulley rope model to a container of a running client through client management;
Step S202: the method comprises the steps of setting a set of tasks of parallel calculation of pulley rope model as N, setting a set of clients of a server as M, and setting N= { N 1,n2,...,nm},M＝{m1,m2,...,mn }, wherein N m represents the task of parallel calculation of the M-th pulley rope model, M n represents the N-th client, weighting the machine performance of the i-th client, and the calculation formula is as follows:
wherein, The weight of the machine performance of the ith client is represented, W xc represents a computer CPU thread computing capability parameter, W kxc represents a computer CPU thread idle condition, alpha and beta represent correction parameters, and N xc represents the number of processes running on a computer;
Step S203: according to the weight of the performance of the client, the tasks of parallel calculation of the pulley rope model are sequentially carried out, the running time of the task of parallel calculation of the pulley rope model at the j-th client is calculated, and the calculation formula is as follows:
wherein, Representing the running time of the pulley rope Modelica model parallel computing task at the jth client, wherein lambda represents a running time adjusting parameter;
Step S204: the parallel efficiency of the pulley rope Modelica model parallel calculation during task parallel calculation is calculated, and the calculation formula is as follows:
where XL represents the parallel efficiency of the pulley rope Modelica model in task parallel computation, Representing the run time of the task of pulley rope Modelica model parallel computation at the kth client,/>Representing the run time of the task of pulley rope Modelica model parallel computation at the p-th client,/>Representing the running time of the pulley rope model parallel computing task at the q-th client,// representing the sign of the division return integer between integers,% representing the sign of the division return remainder between integers;
Step S205: the task of parallel calculation of the pulley rope Modelica model is adjusted by utilizing a task adjustment strategy until the parallel efficiency tends to the maximum value;
the task adjustment strategy in step S205 includes: when m is less than or equal to n, no adjustment is needed, and when m is more than n, the method leads
2. The model simulation method based on the parallel computing algorithm as claimed in claim 1, wherein the pulley rope model in step S1 comprises: rope model, pulley and hoisting model and slip pair model.
3. The Modelica model simulation method based on the parallel computing algorithm according to claim 2, wherein the Modelica simulation tool in step S3 comprises: mworks. Sysplorer and Dymola.
4. A model simulation system based on a parallel computing algorithm, which is implemented based on the model simulation method based on the parallel computing algorithm as claimed in any one of claims 1-3, and is characterized by comprising: the modeling requirement analysis module, the parallelization module, the Modelica simulation module, the analysis and evaluation module;
The modeling requirement analysis module is used for establishing a model of the pulley rope by using a model language according to the modeling requirement of the pulley rope system;
the parallelization module is used for parallelizing the pulley rope model by using a parallelization calculation algorithm;
The model simulation module is used for running a parallelized pulley rope model by using a model simulation tool and simulating the pulley rope model;
the analysis and evaluation module is used for analyzing and evaluating simulation results after the parallel simulation is completed, and performing performance optimization on the parallelized model according to actual conditions.
5. A Modelica model simulation system based on a parallel computing algorithm as claimed in claim 4, wherein the modeling requirement analysis module comprises a modeling requirement analysis unit and a Modelica model building unit,
The modeling demand analysis unit is used for analyzing the modeling demand of the pulley rope system;
The Modelica model building unit is used for building the Modelica model of the pulley rope by using Modelica language.
6. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of a model simulation method based on a parallel computing algorithm as claimed in any one of claims 1-3.
7. A computer readable storage medium having stored thereon computer instructions which when run perform the steps of a model simulation method based on a parallel computing algorithm as claimed in any of claims 1-3.
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| 基于扩充Modelica的复杂系统多层次并行仿真;贺东京 等;北京航空航天大学学报;20120930;第38卷(第9期);正文第1240-1244页 * |
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