FEniCS-HPC: Automated Predictive High-Performance Finite Element Computing with Applications in Aerodynamics

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• 关键词：FEM ; Adaptive ; Turbulence
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9573
• 期：1
• 页码：356-365
• 全文大小：1,087 KB
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  5.Hoffman, J., Jansson, J., Jansson, N., Vilela De Abreu, R.: Towards a parameter-free method for high reynolds number turbulent flow simulation based on adaptive finite element approximation. Comput. Meth. Appl. Mech. Eng. 288, 60–74 (2015)MathSciNet CrossRef
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• 作者单位：Johan Hoffman (19) (20)
  Johan Jansson (19) (20)
  Niclas Jansson (19)
  
  19. Computational Technology Laboratory, School of Computer Science and Communication, KTH, Stockholm, Sweden
  20. BCAM - Basque Center for Applied Mathematics, Bilbao, Spain
  
• 丛书名：Parallel Processing and Applied Mathematics
• ISBN：978-3-319-32149-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Developing multiphysics finite element methods (FEM) and scalable HPC implementations can be very challenging in terms of software complexity and performance, even more so with the addition of goal-oriented adaptive mesh refinement. To manage the complexity we in this work present general adaptive stabilized methods with automated implementation in the FEniCS-HPC automated open source software framework. This allows taking the weak form of a partial differential equation (PDE) as input in near-mathematical notation and automatically generating the low-level implementation source code and auxiliary equations and quantities necessary for the adaptivity. We demonstrate new optimal strong scaling results for the whole adaptive framework applied to turbulent flow on massively parallel architectures down to 25000 vertices per core with ca. 5000 cores with the MPI-based PETSc backend and for assembly down to 500 vertices per core with ca. 20000 cores with the PGAS-based JANPACK backend. As a demonstration of the power of the combination of the scalability together with the adaptive methodology allowing prediction of gross quantities in turbulent flow we present an application in aerodynamics of a full DLR-F11 aircraft in connection with the HiLift-PW2 benchmarking workshop with good match to experiments.