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Towards p-Adaptive Spectral/hp Element Methods for Modelling Industrial Flows

Moxey, D. and Cantwell, C. D. and Mengaldo, G. and Serson, D. and Ekelschot, D. and Peiró, J. and Sherwin, S. J. and Kirby, R. M. (2017) Towards p-Adaptive Spectral/hp Element Methods for Modelling Industrial Flows. In: Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016. Lecture Notes in Computational Science and Engineering. No.119. Springer , Cham, Switzerland, pp. 63-79. ISBN 978-3-319-65869-8. http://resolver.caltech.edu/CaltechAUTHORS:20171128-091234987

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Abstract

There is an increasing requirement from both academia and industry for high-fidelity flow simulations that are able to accurately capture complicated and transient flow dynamics in complex geometries. Coupled with the growing availability of high-performance, highly parallel computing resources, there is therefore a demand for scalable numerical methods and corresponding software frameworks which can deliver the next-generation of complex and detailed fluid simulations to scientists and engineers in an efficient way. In this article we discuss recent and upcoming advances in the use of the spectral/hp element method for addressing these modelling challenges. To use these methods efficiently for such applications, is critical that computational resolution is placed in the regions of the flow where it is needed most, which is often not known a priori. We propose the use of spatially and temporally varying polynomial order, coupled with appropriate error estimators, as key requirements in permitting these methods to achieve computationally efficient high-fidelity solutions to complex flow problems in the fluid dynamics community.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-319-65870-4_4DOIArticle
https://link.springer.com/chapter/10.1007%2F978-3-319-65870-4_4PublisherArticle
http://rdcu.be/zKUKPublisherFree ReadCube access
Additional Information:© 2017 Springer International Publishing AG. First Online: 17 August 2017. D.M. acknowledges support from the EU Horizon 2020 project ExaFLOW (grant 671571) and the PRISM project under EPSRC grant EP/L000407/1. D.S. is grateful for the support received from CNPq (grant 231787/2013–8) and FAPESP (grant 2012/23493-0). D.E. acknowledges support from the EU ITN project ANADE (grant PITN-GA-289428). S.J.S. acknowledges Royal Academy of Engineering support under their research chair scheme. R.M.K. acknowledges support from the US Army Research Office under W911NF1510222 (overseen by Dr. M. Coyle). Computing resources supported by the UK Turbulence Consortium (EPSRC grant EP/L000261/1) and the Imperial College HPC service.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)671571
Engineering and Physical Sciences Research Council (EPSRC)EP/L000407/1
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)231787/2013-8
Fundação de Amparo à Pesquisa do Estado de Sao Paulo (FAPESP)2012/23493-0
European CommissionPITN-GA-289428
Royal Academy of EngineeringUNSPECIFIED
Army Research Office (ARO)W911NF1510222
Engineering and Physical Sciences Research Council (EPSRC)EP/L000261/1
Imperial College LondonUNSPECIFIED
Record Number:CaltechAUTHORS:20171128-091234987
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20171128-091234987
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83489
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Nov 2017 17:27
Last Modified:28 Nov 2017 17:31

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