scholarly journals Research on simulation model validation based on probability relational analysis

2021 ◽  
Vol 39 (5) ◽  
pp. 1158-1167
Author(s):  
Xiaolei NING ◽  
Xin ZHAO ◽  
Yingxia WU ◽  
Junmin ZHAO ◽  
Meibo LYU ◽  
...  
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Model Verification ◽  
Cumulative Distribution ◽  
Consistency Analysis ◽  
Simulation Data ◽  
Dynamic Data ◽  
Flight Data ◽  
Relational Analysis ◽  
Relational Degree

The most basic and direct method for simulation model validation is to compare the consistency of missile flight data and simulation data under the same input conditions. However, the existing dynamic data consistency analysis methods are mainly suitable for the case between 1-D missile flight data and 1-D simulation data, and do not conform to the consistency test of single sample flight data and multi-sample simulation data in equipment qualification/finalization test. To solve this problem, a simulation model validation method based on probabilistic relational analysis is proposed. The consistency of output data is measured from the two scales of probability relational coefficient and probability relational degree. The probability relational coefficient is determined by calculating the cumulative distribution probability value of real missile flight samples in the distribution function constructed by simulation data. The probability correlation degree is calculated by judging whether the probability relational coefficient satisfies the uniform distribution of[0 1]. The consistency analysis problem of a kind of dynamic data association is solved accordingly. The correlation theorem that the probability relational degree must satisfy and its property are proved. Meanwhile the operation steps of simulation model verification based on probability correlation analysis are given. This method can process all multi-dimensional simulation data at the same time, and integrate the random factors in the test process, so it can make full use of the test information under the condition of small sample flight test, and improve precision and the reliability of simulation model verification. The rationality and validity of this method are further verified by numerical tests and application examples.

scholarly journals The General Principles of the Transportation Simulation Model Development and Validation

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Management Strategies ◽  
Model Development ◽  
Simulation Models ◽  
Model Verification ◽  
Microscopic Simulation ◽  
Calibration Methods ◽  
Transportation Simulation ◽  
Development And Validation

Transportation simulation model development allows simulating traveller’s decisions, evaluating various transportation management strategies and complex solutions. The aim of the paper is to set the general principles of the transportation simulation model development and validation. The paper contains the overview of the transportation simulation models types with the examples from the conducted projects for the Riga city. The basic steps of the simulation model development procedure: initial data preparation and analysis, transportation model development and simulation, scenarios planning and evaluation, and simulation models outcomes evaluation are considered. Simulation model verification, validation and calibration definitions are given. The basic checks for the transportation macroscopic and microscopic simulation model validation are listed. A summary of the transportation simulation model validation and calibration methods and parameters is given.

scholarly journals Research on Method of Simulation Model Validation Based on Improved Grey Relational Analysis

2012 ◽  
Vol 25 ◽  
pp. 1118-1125 ◽  
Author(s):  
Baomei Qiu ◽  
Fengjuan Wang ◽  
Yinguo Li ◽  
Wenying Zuo
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Grey Relational Analysis ◽  
Relational Analysis ◽  
Grey Relational

scholarly journals Some Issues in the Testing of Computer Simulation Models

2016 ◽  
Vol 5 (1) ◽  
pp. 1-10
Author(s):  
David Murray-Smith
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Simulation Models ◽  
Model Testing ◽  
Model Verification ◽  
Verification And Validation ◽  
Quality Data ◽  
Model Quality ◽  
Face Validation

The testing of simulation models has much in common with testing processes in other types of application involving software development. However, there are also important differences associated with the fact that simulation model testing involves two distinct aspects, which are known as verification and validation. Model validation is concerned with investigation of modelling errors and model limitations while verification involves checking that the simulation program is an accurate representation of the mathematical and logical structure of the underlying model. Success in model validation depends upon the availability of detailed information about all aspects of the system being modelled. It also may depend on the availability of high quality data from the system which can be used to compare its behaviour with that of the corresponding simulation model. Transparency, high standards of documentation and good management of simulation models and data sets are basic requirements in simulation model testing. Unlike most other areas of software testing, model validation often has subjective elements, with potentially important contributions from face- validation procedures in which experts give a subjective assessment of the fidelity of the model. Verification and validation processes are not simply applied once but must be used repeatedly throughout the model development process, with regressive testing principles being applied. Decisions about when a model is acceptable for the intended application inevitably involve some form of risk assessment. A case study concerned with the development and application of a simulation model of a hydro-turbine and electrical generator system is used to illustrate some of the issues arising in a typical control engineering application. Results from the case study suggest that it is important to bring together objective aspects of simulation model testing and the more subjective face- validation aspects in a coherent fashion. Suggestions are also made about the need for changes in approach in the teaching of simulation techniques to engineering students to give more emphasis to issues of model quality, testing and validation.

Mechanical Performance Simulation Model Based on PDM

2013 ◽  
Vol 300-301 ◽  
pp. 32-35
Author(s):  
Xiao Wen Zeng ◽  
Cheng Zeng ◽  
Bing Han
Keyword(s):  
Simulation Model ◽  
Mechanical Performance ◽  
Product Structure ◽  
Dynamics Simulation ◽  
Simulation Data ◽  
Performance Simulation ◽  
Collaborative Simulation ◽  
Complex Simulation ◽  
Mechanical Dynamics ◽  
Relationship Of

In order to manage the complex simulation data management in the process of mechanical dynamics simulation, a new management model was presented which is Performance Simulation Model(PSM). The model was based on PDM product structure and the concepts and elements of PSM were defined in this paper. Furthermore, the functional framework of PSM was proposed which based on the hierarchical relationship of product structure and the data stream relationship of data structure matrix. Finally, PSM was applied on ship planetary reducer collaborative simulation platform. The result indicates that the simulation data in mechanical collaborative simulation are managed by PSM, and the problem of interaction between collaborative simulation and PDM is solved.

Assessment of urban flooding by dual drainage simulation model RisUrSim

2005 ◽  
Vol 52 (5) ◽  
pp. 257-264 ◽  
Author(s):  
T.G. Schmitt ◽  
M. Thomas ◽  
N. Ettrich
Keyword(s):  
Simulation Model ◽  
Surface Flow ◽  
Water Levels ◽  
Model Verification ◽  
Urban Drainage ◽  
Urban Flooding ◽  
Insurance Company ◽  
Drainage Systems ◽  
Engineering Research ◽  
Urban Drainage Systems

The European research project in the EUREKA framework, RisUrSim is presented with its overall objective to develop an integrated planning tool to allow cost effective management for urban drainage systems. The project consortium consisted of industrial mathematics and water engineering research institutes, municipal drainage works as well as an insurance company. The paper relates to the regulatory background of European Standard EN 752 and the need of a more detailed methodology to simulate urban flooding. The analysis of urban flooding caused by surcharged sewers in urban drainage systems leads to the necessity of a dual drainage modeling. A detailed dual drainage simulation model is described based upon hydraulic flow routing procedures for surface flow and pipe flow. Special consideration is given to the interaction between surface and sewer flow during surcharge conditions in order to most accurately compute water levels above ground as a basis for further assessments of possible damage costs. The model application is presented for a small case study in terms of data needs, model verification and first simulation results.

Power Electronic Converters Simulation Model Verification for Grid Code Compliance Testing

2021 ◽  
Author(s):  
Hossein Hafezi ◽  
Hannu Laaksonen ◽  
Kimmo Kauhaniemi ◽  
Panu Lauttamus ◽  
Stefan Strandberg
Keyword(s):  
Simulation Model ◽  
Model Verification ◽  
Power Electronic ◽  
Power Electronic Converters ◽  
Compliance Testing ◽  
Code Compliance

Development of a Simulation Model for Optimization of Business Process

2018 ◽  
pp. 26-42
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Business Process ◽  
Research Question ◽  
Future Research ◽  
The Novel ◽  
Model Based ◽  
Model Validity ◽  
Proposed Model ◽  
New Research

Optimization of business process assists in efficient organization of business process. For the success of optimization of business process, a simulation model based on gap processes for the analysis of buyers' burstiness in business process has been proposed. However, the model has to be validated. The aim of the research is to implement a validation approach to the simulation model based on gap processes for the optimization of business process underpinning elaboration of a new research question on the model validity. The meaning of the key concepts of “validation,” “model validation,” and “model validation approach” is studied. The results of the present research show that the application of real system measurements validates the simulation model for the optimization of business process. The novel contribution of the manuscript is revealed in the newly created research question on the proposed model validity. Directions of future research are proposed.

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