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Reverse Engineering the Organizational Processes: A Multiformalism Approach
Published in Wasim Ahmed Khan, Ghulam Abbas, Khalid Rahman, Ghulam Hussain, Cedric Aimal Edwin, Functional Reverse Engineering of Machine Tools, 2019
There is a recent advent of several academic and commercial modeling and simulation (M&S) platforms that support multimodeling. The Command and Control Wind Tunnel (C2WT) (Hemingway et al., 2012) developed by Vanderbilt University and the Service Oriented Architecture for Socio-Cultural Systems (SORASCS) (Garlan et al., 2009) developed by Carnegie Mellon University are examples of multimodeling capable platforms developed in academia. While the first provides a federated approach, utilizing the High-Level Architecture (HLA) standard and the meta-programmable Generic Modeling Environment (GME) (Davis, 2003); the second employs service-oriented architecture techniques in providing model interoperation capabilities. The list of commercially available multimodeling platform is growing as more and more M&S software offer extensions and provisions for interoperating multiple models within a single execution environment. Noted among them is AnyLogic’s multimethod simulation modeling tool. It supports agent-based, discrete event, and system dynamics simulation methodologies within a single M&S environment. Another notable application is called ModelCenter, developed by Phoenix Integration, Inc. ModelCenter is a software package that allows integration of multiple analysis models in support of design and optimization of systems. A recent effort by Laskey et al. (2018) presents a multimodeling approach to inference enterprise modeling (MIEM). MIEM combines multiple models to generate multiple predictions of inference enterprise performance. These predictions are combined into an overall estimate of performance with error bounds. The approach is implemented as a Semantic Testbed for Inference Enterprise Modeling (STIEM). The STIEM approach is useful to a broad set of areas that involve multiple analysis models – examples include defense/national security, homeland security, intelligence operations, etc.
New approach for the design and optimization of a quarter-car suspension system
Published in Journal of the Chinese Institute of Engineers, 2022
Amir Guizani, Hassen Trabelsi, Fatma Abid, Moncef Hammadi, Maher Barkallah, Mohamed Haddar
The objective of this part was to have a full idea about the variation ranges of the different design variables affecting the dynamic response of the Macpherson suspension. This study would therefore be very useful for limiting the search space of variables that have a negligible impact on the behavior of the car suspension. The curves drawn in Figures 2, 3 show the maximum displacements Y2max and ψmax (with Y2max = max y2(t) and ψmax = max ψ(t)) of the suspension system according to the design parameters (ƞCont,k2,k1, m2, m1, and ƞ). These curves were obtained thanks to the coupling of the model developed in Matlab, on the one hand, with the multi-physics simulation tool ModelCenter, on the other.