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Fluids
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Radial Basis Functions and their Applications in Fluids
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Radial Basis Functions and their Applications in Fluids

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 7659

Special Issue Editor

Dr. Marco Evangelos Biancolini

E-Mail Website
Guest Editor
Department of Enterprise Engineering, University of Rome "Tor Vergata", Via Politecnico, 1, 00133 Rome, Italy
Interests: high performance computing; fluid Structure Interaction; aeroelasticity; radial basis functions; mesh morphing

Special Issue Information

Dear Colleagues,

Since middle sixties, when Hardy firstly introduced Radial Basis Functions (RBF) as “Multiquadric equations of topography and other irregular surfaces”, such a versatile mathematical method has been explored, evolved and applied to a variety of applications. RBF are nowadays mathematical tool mature enough for consistently handling scientific and engineering applications. RBF adoption to tackle fluids problem is well established and ranges from the adaption/mapping of dataset defined on meshes (pressure mapping, shape morphing) to the meshless approach to solve CFD problems and/or mimic interpolate experimental acquired flow field. The research interest about RBF on fluids is not limited to the mathematical foundation and application of RBF to the specific problems, but also to the acceleration of the RBF solvers which, especially for global supported radial functions, need to tackle large sized dense linear problems.

The aim of this special issue is to collect contributions on the application of RBF in Fluids. Contributions are expected on the following topics:

  • Mesh morphing adaption for shape optimisation including both Free Form problems (RBF coupled with adjoint solvers) and parameter based ones.
  • Innovative paradigms to update Fluid meshes and CAD representations based on RBF
  • RBF interpolation in connection with Reduced Order Models (mesh morphing, data mapping)
  • RBF mapping of quantities (displacements, temperatures, pressures, …) to enable multi-physics simulation
  • RBF interpolation to mimic experimental/numerical fields
  • RBF role on fluid structure interaction
  • HPC implementation of RBF and algorithms to accelerate the solution of large RBF

Listed topic are just a hint. Contributions bringing innovative approaches of RBF in Fluids (in the application and/or in the method) are welcome.

Dr. Marco Evangelos Biancolini
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 submissions that pass pre-check are 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 monthly 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 1800 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.

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Published Papers (4 papers)

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Research

15 pages, 6336 KiB  
Article
Advanced RBF Methods for Mapping Aerodynamic Loads onto Structures in High-Fidelity FSI Simulations
by Andrea Chiappa, Andrea Lopez and Corrado Groth
Fluids 2024, 9(6), 137; https://doi.org/10.3390/fluids9060137 - 6 Jun 2024
Viewed by 905
Abstract
The reliable exchange of data is a crucial issue for the loose coupling of computational fluid dynamics (CFD) and computational structural mechanics (CSM) modules in fluid–structure interaction (FSI) applications. This paper presents a comparison between two methods for mapping the traction field across [...] Read more.
The reliable exchange of data is a crucial issue for the loose coupling of computational fluid dynamics (CFD) and computational structural mechanics (CSM) modules in fluid–structure interaction (FSI) applications. This paper presents a comparison between two methods for mapping the traction field across mismatching grids, namely the RIBES method and the preCICE algorithm, both based on radial basis function (RBF) interpolation. The two methods demonstrate different degrees of control over balance preservation during mapping, with the RIBES algorithm exhibiting greater efficacy. Test benches are a parametric double curved geometry and a wind tunnel mock-up. In this second case, forces from mapping are used to load a CSM model to retrieve stress and displacement fields. Differences in FEM results are appreciable although not significant, showing a correlation between the accuracy of balance preservation during data mapping and the structural output. Full article
(This article belongs to the Special Issue Radial Basis Functions and their Applications in Fluids)
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21 pages, 2507 KiB  
Article
A Parametric 3D Model of Human Airways for Particle Drug Delivery and Deposition
by Leonardo Geronzi, Benigno Marco Fanni, Bart De Jong, Gerben Roest, Sasa Kenjeres, Simona Celi and Marco Evangelos Biancolini
Fluids 2024, 9(1), 27; https://doi.org/10.3390/fluids9010027 - 18 Jan 2024
Cited by 3 | Viewed by 2169
Abstract
The treatment for asthma and chronic obstructive pulmonary disease relies on forced inhalation of drug particles. Their distribution is essential for maximizing the outcomes. Patient-specific computational fluid dynamics (CFD) simulations can be used to optimize these therapies. In this regard, this study focuses [...] Read more.
The treatment for asthma and chronic obstructive pulmonary disease relies on forced inhalation of drug particles. Their distribution is essential for maximizing the outcomes. Patient-specific computational fluid dynamics (CFD) simulations can be used to optimize these therapies. In this regard, this study focuses on creating a parametric model of the human respiratory tract from which synthetic anatomies for particle deposition analysis through CFD simulation could be derived. A baseline geometry up to the fourth generation of bronchioles was extracted from a CT dataset. Radial basis function (RBF) mesh morphing acting on a dedicated tree structure was used to modify this baseline mesh, extracting 1000 synthetic anatomies. A total of 26 geometrical parameters affecting branch lengths, angles, and diameters were controlled. Morphed models underwent CFD simulations to analyze airflow and particle dynamics. Mesh morphing was crucial in generating high-quality computational grids, with 96% of the synthetic database being immediately suitable for accurate CFD simulations. Variations in wall shear stress, particle accretion rate, and turbulent kinetic energy across different anatomies highlighted the impact of the anatomical shape on drug delivery and deposition. The study successfully demonstrates the potential of tree-structure-based RBF mesh morphing in generating parametric airways for drug delivery studies. Full article
(This article belongs to the Special Issue Radial Basis Functions and their Applications in Fluids)
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15 pages, 1325 KiB  
Article
Radial Basis Function Surrogates for Uncertainty Quantification and Aerodynamic Shape Optimization under Uncertainties
by Varvara Asouti, Marina Kontou and Kyriakos Giannakoglou
Fluids 2023, 8(11), 292; https://doi.org/10.3390/fluids8110292 - 30 Oct 2023
Viewed by 1905
Abstract
This paper investigates the adequacy of radial basis function (RBF)-based models as surrogates in uncertainty quantification (UQ) and CFD shape optimization; for the latter, problems with and without uncertainties are considered. In UQ, these are used to support the Monte Carlo, as well [...] Read more.
This paper investigates the adequacy of radial basis function (RBF)-based models as surrogates in uncertainty quantification (UQ) and CFD shape optimization; for the latter, problems with and without uncertainties are considered. In UQ, these are used to support the Monte Carlo, as well as, the non-intrusive, Gauss Quadrature and regression-based polynomial chaos expansion methods. They are applied to the flow around an isolated airfoil and a wing to quantify uncertainties associated with the constants of the γ−R˜eθt transition model and the surface roughness (in the 3D case); it is demonstrated that the use of the RBF-based surrogates leads to an up to 50% reduction in computational cost, compared with the same UQ method that uses CFD computations. In shape optimization under uncertainties, solved by stochastic search methods, RBF-based surrogates are used to compute statistical moments of the objective function. In applications with geometric uncertainties which are modeled through the Karhunen–Loève technique, the use on an RBF-based surrogate reduces the turnaround time of an evolutionary algorithm by orders of magnitude. In this type of applications, RBF networks are also used to perform mesh displacement for the perturbed geometries. Full article
(This article belongs to the Special Issue Radial Basis Functions and their Applications in Fluids)
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8 pages, 349 KiB  
Article
Calculation of Thermodynamic Characteristics and Sound Velocity for Two-Dimensional Yukawa Fluids Based on a Two-Step Approximation for the Radial Distribution Function
by Ilnaz I. Fairushin and Anatolii V. Mokshin
Fluids 2023, 8(2), 72; https://doi.org/10.3390/fluids8020072 - 17 Feb 2023
Cited by 2 | Viewed by 1592
Abstract
We propose a simple two-step approximation for the radial distribution function of a one-component two-dimensional Yukawa fluid. This approximation is specified by the key parameters of the system: coupling parameter and screening parameter. On the basis of this approximation, analytical expressions are obtained [...] Read more.
We propose a simple two-step approximation for the radial distribution function of a one-component two-dimensional Yukawa fluid. This approximation is specified by the key parameters of the system: coupling parameter and screening parameter. On the basis of this approximation, analytical expressions are obtained for the same thermodynamic quantities as internal energy, internal pressure, excess entropy in the two-particle approximation, and also longitudinal sound velocity. The theoretical results show an agreement with the results obtained in the case of a true radial distribution function. Full article
(This article belongs to the Special Issue Radial Basis Functions and their Applications in Fluids)
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