China
Africa
Explore chapters and articles related to this topic
Motivation and Overview
Published in Naim A. Kheir, Systems Modeling and Computer Simulation, 2018
Basically, simulation is the process by which understanding of the behavior of an already existing (or to be constructed) physical system is obtained by observing the behavior of a model representing the system. Thus, simulation is justly considered the art and science of experimenting with models. A simulation study must have a purpose, and there are many good reasons simulation is valuable. For example, simulation may be performed to check and optimize the design of a system before its construction, helping to avoid costly design errors and ensuring safe, high-quality, and cost-effective products. Other purposes include analysis, performance evaluation, sensitivity analysis, comparison of alternatives, forecasting, safety, human in the loop training, teaching, and decision making.
EPMS for Business Process Analysis
Published in Vivek Kale, Enterprise Process Management Systems, 2018
Simulation is a technique that enables us to define and launch an imitation of the behavior of a certain real system in order to analyze its functionality and performance in detail. For this purpose, real-life input data is required and collected for use in running, observing the system’s behavior over time, and conducting different experiments without disturbing the functioning of the original system. One of the most important properties of the simulation technique is to enable experts to carry out experiments on the behavior of a system by generating various options of “what if” questions. This characteristic of simulation gives a possibility for exploring ideas and creating different scenarios that are based on an understanding of the system’s operation and deep analysis of the simulation output results. This actually represents simulation’s main advantage, which consequently has led to the widespread use of the technique in various fields for both academic and practical purposes.
Modeling and Simulation Tools for Mobile Ad Hoc Networks
Published in Jonathan Loo, Jaime Lloret Mauri, Jesús Hamilton Ortiz, Mobile Ad Hoc Networks, 2016
Kayhan Erciyes, Orhan Dagdeviren, Deniz Cokuslu, Onur Yılmaz, Hasan Gumus
A model is a simplified representation of a system that aids the understanding and investigation of the real system. Simulation is the manipulation of the model of a system that enables one to observe the behavior of the system in a setting similar to real life. By modeling and simulation of a mobile ad hoc network (MANET), it is possible to simplify many difficult real-life problems associated with them. Modeling and simulation of a MANET have limitations, and providing further flexibility in them such that a general MANET without much limitations can be modeled and simulated is an important research topic. In this chapter, we review network models, topology control models, mobility models, and simulators for MANETs by investigating their current limitations and future trends.
Design and management of software development projects under rework uncertainty: a study using system dynamics
Published in Journal of Decision Systems, 2023
Mst Taskia Khatun, Kazuo Hiekata, Yutaka Takahashi, Isaac Okada
As mentioned earlier, rework generation is uncertain, and the amount of rework varies and is not entirely predictable. Hence, several choices were made for each scenario to measure performance and definethe rework generation dynamism. The variations in the choices represent the sensitivity analysis for each scenario. Becauseseveral assumptions weremade in the simulation model, sensitivity analysis helps develop intuition about the model structure, identify variations in assumed information, and study the model’s behaviour to interpret its output depending on input parameters. The choices illustratedinthe Tradespace represent the effects of productivityvariation, error fraction, use of overtime on project completion time, and cost. The corresponding criticality of uncertainty was obtained based on performance variations. Again, the use of the rework cycle in this studysignificantly impactedthe exploration of the rework in detail. The recursive behaviour of the rework cycle repetitively generates and discovers rework, which helps identify the depth of uncertain rework and its impact on project performance. This analysis can be helpful indecision-making by verifying several alternatives. The average results for each scenario are shownin Table 4.
Image regression-based digital qualification for simulation-driven design processes, case study on curtain airbag
Published in Journal of Engineering Design, 2023
Mohammad Arjomandi Rad, Mirza Cenanovic, Kent Salomonsson
During the design processes, simulations have been mainly used in the digital qualification phase to test the performance of a pre-built model. To this end, a wide range of simulations such as rigid body dynamics, finite elements/differences, computational fluid dynamics (CFD), discrete events, and so on are used. However, with so many sources for design iterations, it is not surprising that many companies, on top of well-known and commercially developed tools, attempt to develop in-house qualification tools and accelerate their design evaluation process. Figure 1 shows a process model for our studied design process that is challenged in this noted way. As depicted in the picture, during the development process series of simulations as an example of digital qualification are performed sequentially.
Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion
Published in Virtual and Physical Prototyping, 2021
The modelling and simulations performed at Lawrence Livermore National Laboratory underpin the physics of complex melt flow and defect formation mechanisms in single-material L-PBF (Khairallah et al. 2016; Khairallah and Anderson 2014; Khairallah et al. 2020; King et al. 2015a). This section mainly reports the latest research status of the modelling and simulation of the molten pool behaviour of multi-material L-PBF. The simulations of L-PBF processes can be categorised into three: macroscopic, mesoscopic, and microscopic (Kyogoku and Ikeshoji 2020; Tan, Sing, and Yeong 2020). A limited number of studies are based on the macroscopic (Bandyopadhyay and Heer 2018) and microscopic methods (Mohanty and Hattel 2017) for multi-material L-PBF modelling. Most investigations in this field have been conducted on the mesoscopic scale. These simulations generally include two steps: discrete element modelling (DEM) and computational fluid dynamics (CFD) modelling. A typical integrated DEM–CFD flow for multi-material L-PBF is shown in Figure 3. In contrast to single-material L-PBF modelling, multi-material L-PBF modelling involves two or more materials, and different material physical parameters must be assigned to the corresponding powder particles on the same powder layer (Gu et al. 2020).