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Reduction and predictive control design for a computational fluid dynamics model

机译:计算流体动力学模型的约简和预测控制设计

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Many models of commercial industrial applications are based on computational fluid dynamics (CFD) models. The models are usually of high order that it becomes infeasible to control or to optimize them. In the paper, it is shown that CFD models can be reduced very effectively by applying proper orthogonal decomposition. The resulting reduced CFD model has a state space structure and therefore enables application of many well-known control designs, including model predictive controllers.
机译:商业工业应用的许多模型都基于计算流体动力学(CFD)模型。这些模型通常是高阶的,因此无法控制或优化它们。本文表明,通过应用适当的正交分解可以非常有效地减少CFD模型。所得的简化的CFD模型具有状态空间结构,因此可以应用许多众所周知的控制设计,包括模型预测控制器。

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