【摘 要】
：
In the context of multidisciplinary design optimization, the major issue for numerical simulations consists in obtaining a good compromise between accuracy
【机 构】
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DepartmentofAppliedMathematics,NorthwesternPolytechnicalUniversity,Xi'an,Shaanxi710072,PRChinaLabor
【出 处】
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ASMDO 2012(Forth International Conference on Multidisciplina
论文部分内容阅读
In the context of multidisciplinary design optimization, the major issue for numerical simulations consists in obtaining a good compromise between accuracy and computational effort.Reduced-order models such as Proper Orthogonal Decomposition (POD) constitute an economical and efficient option to decrease the cost of the high fidelity numerical simulations (finite elements, finite volumes, etc.).The idea is to represent a physical field as a linear combination of restricted modes.However, the truncation of the POD basis implies an error in the calculation of the global values used as objectives and constraints, which in turn might bias the optimization results, furthermore, it also makes it difficult to execute the model based on POD in the case of great number of constraints.In this work, a novel constrained proper orthogonal decomposition method (called CPOD2) is thoroughly presented, along with its mathematical justification.This technique does not only produce an alternative orthonormal basis, but also addresses the precision request.To illustrate the efficiency of the proposed approach, the CPOD2 method is applied to approximate the representation of aerodynamical flow field in two test cases.A detailed analysis and comparison with three reduced-order models demonstrate the advantages of CPOD2.
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