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Comparison of Reduced-Basis techniques for the model order reduction of parametric incompressible fluid flows
Progress in Nuclear Energy ( IF 2.7 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.pnucene.2020.103551
Péter German , Mauricio Tano , Jean C. Ragusa , Carlo Fiorina

Abstract The applicability of two Reduced-Basis techniques to parametric laminar and turbulent incompressible fluid-flow problems in nuclear engineering is studied in this work. The Reduced-Basis methods are used to generate Reduced-Order Models (ROMs) that can accelerate multi-query problems often encountered in design optimization and uncertainty quantification. Both approaches are intrusive and utilize Proper Orthogonal Decomposition (POD) for data compression. The accuracy of the two methods for reconstructing the velocity, pressure, and turbulent eddy viscosity fields is assessed. The methods are classified based on how many reduced equations are involved. The first approach, often referred to as POD-FV-ROM, only solves one equation with additional physics-based approximations to relate the reduced pressure, velocity, and eddy viscosity fields. The second approach solves two equations and relies on a supremizer stabilization technique. Both methods are reviewed in the finite volume setting and are tested using transient problems: a backward facing step case with an inlet and outlet and a molten-salt closed-loop case. Extension of the methods to parametric reduced-order problems for uncertainty quantification purposes is presented. Conclusions, summarizing the advantages and disadvantages of these two approaches, and recommendations are provided.

中文翻译:

参数化不可压缩流体流动模型降阶的简化基技术比较

摘要 本文研究了两种简化基技术在核工程中参数层流和湍流不可压缩流体流动问题中的适用性。降阶基础方法用于生成降阶模型 (ROM),可以加速设计优化和不确定性量化中经常遇到的多查询问题。这两种方法都是侵入式的,并利用适当的正交分解 (POD) 进行数据压缩。评估了用于重建速度、压力和湍流涡粘性场的两种方法的准确性。这些方法根据涉及的简化方程的数量进行分类。第一种方法,通常称为 POD-FV-ROM,只求解一个方程,其中包含额外的基于物理的近似值,以将降低的压力、速度、和涡粘性场。第二种方法求解两个方程并依赖于超大稳定技术。这两种方法都在有限体积设置中进行了审查,并使用瞬态问题进行了测试:具有入口和出口的后向阶梯情况以及熔盐闭环情况。介绍了将方法扩展到用于不确定性量化目的的参数化降阶问题。结论,总结了这两种方法的优缺点,并提供了建议。介绍了将方法扩展到用于不确定性量化目的的参数化降阶问题。结论,总结了这两种方法的优缺点,并提供了建议。介绍了将方法扩展到用于不确定性量化目的的参数化降阶问题。结论,总结了这两种方法的优缺点,并提供了建议。
更新日期:2020-12-01
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