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Numerical Modeling and Simulation
Published in Yogesh Jaluria, Design and Optimization of Thermal Systems, 2019
System simulation refers to the process of obtaining quantitative information on the behavior and characteristics of the real system by analyzing, studying, or examining a model of the system. The model may be a physical, scaled-down version of the given system, derived on the basis of the similarity principles outlined in Chapter 3. Such a model is subjected to a variety of operating and environmental conditions and the performance of the system is determined in terms of variables such as pressure, flow rate, temperature, energy input/output, and mass transfer rate that are of particular interest in thermal systems. The results from such a simulation may be expressed in terms of correlating equations derived by curve-fitting techniques. Physical modeling and testing of full-size components such as compressors, pumps, and heat exchangers are often used to derive the performance characteristics of these components. This approach is rarely used for the entire system because of the typically high cost and effort involved in fabrication and experimentation.
Selected Case Studies
Published in Clement Kleinstreuer, Theory and Applications, 2017
One could define a model as a (mathematical) representation of the real process; the actual operation of the model, e.g., the computer program, is the simulation. Another definition for system simulation is representation of the system's behavior by moving it from state to state in accordance with well-defined operating rules and subsequent observation of its dynamic performance, using computers. Alternatively, some authors categorized modeling of a physical or biochemical process as direct analysis, reconstruction, or identification. In direct analysis, the goal is to determine the output for a given set of input and system parameters. In turn, reconstruction implies determination of a system’s input parameters; this is also called “inverse problem modeling.” The identification problem is finding the system parameters when the system’s input and output are given. In two-phase flow modeling we typically proceed with a direct analysis.
Elements of Continuum Mechanics
Published in Clement Kleinstreuer, Biofluid Dynamics, 2016
One could define a model as a (mathematical) representation of the real process; the actual operation of the model, e.g., the computer program, is the simulation. Another definition for system simulation is representation of the system’s behavior by moving it from state to state in accordance with well-defined operating rules and subsequent observation of its dynamic performance, using computers. Alternatively, some authors categorized modeling of a physical or biochemical process as direct analysis, reconstruction, or identification. In direct analysis, the goal is to determine the output for a given set of input and system parameters. In turn, reconstruction implies determination of a system’s input parameters; this is also called “inverse problem modeling.” The identification problem is finding the system parameters when the system’s input and output are given. In two-phase flow modeling we typically proceed with a direct analysis.
Development of a comprehensive transient fuel cell-battery hybrid system model and rule-based energy management strategy
Published in International Journal of Green Energy, 2023
Yahan Xu, Zirong Yang, Kui Jiao, Dong Hao, Qing Du
System simulation technology is a powerful tool for product design and development, component matching and optimization, system coupling and integration, etc. Meanwhile, it can also play an important role in exploring the energy management of the whole vehicle, transient responses, and failure mechanism analysis. Based on the methodology, many researches have been conducted on the hybrid power system of FCEVs. Liu et al. (Liu and Liu 2015) developed a powertrain system model with adjustable fuel cell and battery sizes. The optimal control strategy based on Pontryagin’s minimum principle was proposed. Results showed that the battery lifetime was improved, and the fuel consumption was reduced. Fathabadi (Fathabadi 2018) established the zero-dimensional equivalent circuit hybrid power system model considering fuel cell, battery, and supercapacitor. Zhang et al. (Zhang, Li, and Liu et al. 2019) developed a novel configuration of fuel cell-battery power system, consisting of three fuel cell stacks and a single battery pack. The output power of a single stack was fixed, and the activation time was controlled by a switch strategy. It was found that the durability was increased by 11.8 times, 4.8 times, and 6.9 times for city, highway, and combined city-highway driving cycles, respectively. Saman et al. (Ahmadi, Bathaee, and Hosseinpour 2018) designed a powertrain model composed of fuel cell, battery, and supercapacitor. An intelligent control technique based on fuzzy logic control was proposed to achieve the energy management. Li et al. (Li, Liu, and Ding 2018) established the model predictive control framework for fuel cell hybrid construction equipment, including powertrain model, fuel cell stack model, supercapacitor model, and DC/DC converter model. Wang et al (Wang et al. 2019). (Wang, Sun, and Chen 2019) developed the vehicle framework model composed of ultracapacitor, fuel cell, and vehicle dynamics to compare the differences of PID control strategy and rule-based power allocation strategy. The proposed energy management strategy was further verified by experiments with real physical systems. Similar studies were conducted by Sun et al (Sun, Wang, and Chen et al. 2020), which were also focused on the online power allocation problem.