Multi-physics Modeling of Technological Systems
It is a fact that the massive use of modeling and numerical simulation is now spreading to all life stages of a technological system. It is an effective way to limit risks, optimize performance, and reduce development time (getting it right the first time). In this logic, the commercial offer of system-level simulation environments continues to grow, to diversify, and to be more accessible to the non-specialist. We would almost believe that the simulation environment used gives us immediate competence to create value and facilitate decision-making in response to engineering needs.
In modeling, the first principles of conservation to adopt a network vision were formulated in the 18th and 19th centuries. The 20th century saw the emergence of the notions of “across” and “through” variables, domain analogies, and then bond graph to provide a methodological framework for the modeling and analysis of energy transfer systems. In the 1960s, electronics gave rise to commercial analog simulation solutions, which largely contributed to the promotion of block-diagram representation. The methods of numerical integration that appeared with Newton, since the end of the 18th century, have continued to develop before and after the advent of digital computers. Modern digital solvers are now able to deal with stiff, discontinuous and algebro-differential problems. They even have a layer of expertise that adapts them permanently and automatically to the mathematical nature of the model to integrate. The programming languages have evolved at the same time to standardize the definition of models and the implementation of solvers. Recent acausal languages like Modelica even support the sorting of non-oriented equation for computation, allowing the symbolic manipulations, and relieving many constraints of assembly or inversion of models. Finally, the libraries of generic models that are offered enable building a model by a series of drags and drops, without even writing a line of code. On these findings, we would indeed be tempted to consider that with a modern simulation business environment, the simulation of a multi-physics technology system no longer presents any difficulty. This is not the case for several reasons.
The initial training of technicians and engineers has long been constrained by its structuring in hermetic silos (mechanics, electrotechnics, fluid mechanics, etc.). At the end of the 1970s, it first aimed to cover the needs related to fine sizing via local modeling (solid finite elements and later computational fluid mechanics, 2D or 3D electromagnetism). It is only in the last two decades that the multidisciplinary system simulation has really developed. However, training and bibliographic resources have mostly remained associated with specific technology areas or software. For its part, the application field of the system simulation is no longer limited only to the study of the dynamic response, particularly for the needs of the control. It extends to growing concerns about performance (power consumption, power requirements, failure response, thermal equilibrium, system integration, etc.). It must and must take advantage of advanced inverse sizing methods, optimization, robust design, and verification/validation.
Considering these new skill needs, the authors propose in this book an applied vision that comes from many years of experience of higher education and research related to the industry. Modeling is treated here in a broad sense: simplification just sufficient for the development of the model, implementation, simulation of the model taking into account the constraints of the solvers, and exploitation of the simulation for the effective creation of value. Through the many topical examples they develop, the authors effectively disseminate good practices and facilitate the acquisition of know-how in multi-physics modeling of technological systems.
Pr. Jean-Charles Maré
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|September 5, 2019|
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