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| − | Published in ''Journal of Computational Physics'' Vol. 352, pp. 142-171, 2018<br /> | + | Published in ''Journal of Computational Physics'', Vol. 352, pp. 142-171, 2018<br /> |
DOI: 10.1016/j.jcp.2017.09.060 | DOI: 10.1016/j.jcp.2017.09.060 | ||
== Abstract == | == Abstract == | ||
In this work we put the method proposed by van Hinsberg et al. to the test, highlighting its accuracy and efficiency in a sequence of benchmarks of increasing complexity. Furthermore, we explore the possibility of systematizing the way in which the method's free parameters are determined by generalizing the optimization problem that was considered originally. Finally, we provide a list of worked-out values, ready for implementation in large-scale particle-laden flow simulations. | In this work we put the method proposed by van Hinsberg et al. to the test, highlighting its accuracy and efficiency in a sequence of benchmarks of increasing complexity. Furthermore, we explore the possibility of systematizing the way in which the method's free parameters are determined by generalizing the optimization problem that was considered originally. Finally, we provide a list of worked-out values, ready for implementation in large-scale particle-laden flow simulations. | ||
Latest revision as of 12:57, 26 April 2019
Published in Journal of Computational Physics, Vol. 352, pp. 142-171, 2018
DOI: 10.1016/j.jcp.2017.09.060
Abstract
In this work we put the method proposed by van Hinsberg et al. to the test, highlighting its accuracy and efficiency in a sequence of benchmarks of increasing complexity. Furthermore, we explore the possibility of systematizing the way in which the method's free parameters are determined by generalizing the optimization problem that was considered originally. Finally, we provide a list of worked-out values, ready for implementation in large-scale particle-laden flow simulations.
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Published on 01/01/2018
DOI: 10.1016/j.jcp.2017.09.060
Licence: CC BY-NC-SA license
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