The recent focus on reliability and safety of industrial systems has raised the need for system simulations and analysis. The intrinsic capabilities of modern computational software development have made it possible to predict trends in various system operations more exactly. The present simulation methodology also allows for the analysis of non-measurable numbers, like the Reynolds Number (Re), which hasn’t been taken so often into consideration during past analyses. The main purpose of this article is to overview the system simulation methodology, focusing primarily on System Computer Fluid Dynamic (SCFD) method as the most effective approach to simulate the flow-thermal networks. This article makes use of system simulation models, which depict real industry problems.
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This paper presents an overview of intelligent soft computing techniques within the framework of active control of noise and vibration. Tools considered include genetic algorithms (GAs), neural networks (NNs) and fuzzy logic (FL). The paper highlights associated merits and potential benefits of the approaches in modelling and control of dynamic systems. These are demonstrated in the control of noise in free-field propagation and vibration suppression in 1D and 2D flexible structures. The paper shows that the potential benefits of the individual components can be exploited and approaches for design and development of hybrid soft-computing algorithms devised for modelling and control of dynamic systems. It is demonstrated that significant benefits in terms of performance can be gained with such hybrid algorithms.
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