Special Issue "Reduced Order Models for Computational Fluid Dynamics"

A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Mathematical and Computational Fluid Mechanics".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editor

Dr. Giovanni Stabile
Website
Guest Editor
SISSA, International School for Advanced Studies, Mathematics Area, mathLab, Trieste, Italy
Interests: reduced-order modeling for fluids; machine learning methods for fluid simulations; fluid-structure interaction; uncertainty quantification

Special Issue Information

Dear Colleagues,

Despite the recent increase in the available computation power, there are still several cases in computational fluid dynamics where standard discretization techniques (finite elements, finite volumes, finite differences, spectral elements, etc.) become unaffordable. Such situations occur when a large number of system configurations are in need of being tested, or limited computational time is required. Typical examples of this type are shape optimization, uncertainty quantification, and real-time control. A viable approach to reduce the computational burden is given by reduced-order models. Many different types of reduced order have been developed over the years, and a possible distinction is between those that are intrusive and require the knowledge of the underlying full order model and those that are merely data-driven and therefore non-intrusive. In the first category fall the reduced-basis method, the POD-Galerkin approach, and the proper generalized decomposition. In the second category, one can find truncation-based methods, dynamic mode decomposition, neural networks, and in general all the models based on just input–output data. Possible applications include but are not limited to uncertainty quantification, inverse problems, real-time control, shape optimization, etc.

This Special Issue will publish original research, overviews, and applications on reduced-order models for computational fluid dynamics of both the intrusive and non-intrusive type.

Dr. Giovanni Stabile
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Certified reduced basis method
  • Proper orthogonal decomposition
  • Dynamic mode decomposition
  • Data-driven modeling
  • Hyper-reduction techniques
  • Uncertainty quantification
  • Closure modeling
  • Machine learning for fluids

Published Papers

This special issue is now open for submission.
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