Model Granularity Control Oriented Multi-Resolution Modeling

Abstract

Abstract: Multi-resolution modeling and simulation has been widely applied in various fields, but the current multi-resolution modeling and simulation methods cannot effectively, systematically and comprehensively address four key issues: resolution control, model consistency, information loss and cross resolution interaction. Addressing these four issues, Model Granularity Control Oriented Multi-Resolution Modeling was proposed. This modeling approach has two core modeling components: resolution state chart and resolution connecting bridge, the former is to use UML state diagram to control granularity changes triggered by cross-resolution interaction, human-computer interaction and resource optimization model, and the latter is to model the relationship between different resolution models independently and thoroughly, and to realize consistency maintenance based on information difference. The system variable order problem was used to validate the modeling method.

Key words: multi-resolution modeling, resolution control, resolution state chart, resolution connecting bridge, consistency

CLC Number:

TP391.9

Mao Huachao, Peng Gongzhuang, Zhang Heming, Zhao Jiaxin, Li Minghui. Model Granularity Control Oriented Multi-Resolution Modeling[J]. Journal of System Simulation, 2015, 27(11): 2630-2637.

References

[1] Werther J, Heinrich S, Dosta M, et al. The ultimate goal of modeling-simulation of system and plant performance[J]. Particuology (S1674-2001), 2011, 9(4): 320-329.
[2] Moreira F A, Mart J R, Zanetta Jr L C, et al. Multirate simulations with simultaneous-solution using direct integration methods in a partitioned network environment[J]. Circuits and Systems I: Regular Papers, IEEE Transactions on (S1549-8328), 2006, 53(12): 2765-2778.
[3] Petty M D, Franceschini R W, Panagos J.Engineering Principles of Combat Modeling and Distributed Simulation[M]. Hoboken, USA: Wiley, 2012.
[4] Zhang L, Ma J.A Study on Multi-Resolution Modeling of Mesoscopic-Microscopic Traffic Simulation Model[C]// CICTP 2012@sMultimodal Transportation Systems-Convenient, Safe, Cost-Effective, Efficient. ASCE, 2012: 695-706.
[5] Dada J O, Mendes P.Multi-scale modelling and simulation in systems biology[J]. Integrative Biology (S1536-2310), 2011, 3(2): 86-96.
[6] Wirth B D, Odette G R, Marian J, ,et al. Multiscale modeling of radiation damage in Fe-based alloys in the fusion environment [J]. Journal of Nuclear Materials. Multiscale modeling of radiation damage in Fe-based alloys in the fusion environment [J]. Journal of Nuclear Materials (S0022-3115), 2004, 329-333(A): 103-111.
[7] Horstemeyer M F.Multiscale modeling: a review[M]// Practical aspects of computational chemistry. Germany: Springer, 2010: 87-135.
[8] Davis P K.An introduction to variable-resolution modeling[J]. Naval Research Logistics (NRL)(S0894-069X), 1995, 42(2): 151-181.
[9] Rao D M, Wilsey P.Multi-resolution network simulations using dynamic component substitution[C]// Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2001. Proceedings. Ninth International Symposium on. USA: IEEE, 2001: 142-149.
[10] Faulds A, McGraw R M. Using MRMAide to create wrappers for mixed resolution modeling[C]// AeroSense 2003. International Society for Optics and Photonics, 2003: 321-328.
[11] Powell D R.Control of entity interactions in a hierarchical variable resolution simulation [R]. USA: Los Alamos National Lab., NM(United States), 1997.
[12] Davis P K, Bigelow J H.Experiments in MRM [R]// RAND MR-100-DARPA, 1998. USA: RAND, 1998.
[13] Natrajan A.Consistency maintenance in concurrent representations [D]. USA: University of Virginia, 2000.
[14] Drewry D T, Reynolds Jr P F, Emanuel W R. Optimization as a tool for consistency maintenance in multi-resolution simulation [R]. USA: Virginia Univ. Charlottesville Dept. of Computer Science, 2006.
[15] 刘宝宏. 多分辨率建模的理论与关键技术研究 [D]. 长沙: 国防科学技术大学, 2003.
[16] 李元. 多分辨率建模形式化与通用方法研究 [D]. 北京: 北京航空航天大学, 2011.
[17] Brooks R J, Tobias A M.Choosing the best model: Level of detail, complexity, and model performance[J]. Mathematical and computer modelling (S0895-7177), 1996, 24(4): 1-14.
[18] Kim T G, Lee C, Christensen E R, et al. System entity structuring and model base management[J]. Systems, Man and Cybernetics, IEEE Transactions on (S1083-4427), 1990, 20(5): 1013-1024.
[19] Caughlin D, Sisti A F.A Summary of Model Abstraction Techniques. Enabling Technology for Simulation Science (II)[C]// Proceeding of SPIE AeoroSense, 1998. USA: SPIE, 1998.