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Fluids
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Challenges and Directions in Fluid Structure Interaction
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Challenges and Directions in Fluid Structure Interaction

A special issue of Fluids (ISSN 2311-5521).

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 6170

Special Issue Editors

Dr. Pengtao Yue

E-Mail Website
Guest Editor
Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24060, USA
Interests: computational fluid dynamics; complex fluids; interfacial flows; fluid–structure interaction
Dr. Fang-Bao Tian

E-Mail Website
Guest Editor
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia
Interests: computational biomechanics; biofluid mechanics; fluid-structure interaction; immersed boundary method
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fluid–structure interactions are ubiquitous in nature and many engineering systems. Examples of these interactions include flag flapping, insect flight, fish swimming, arterial flows, particle flows and wind turbines. These problems typically involve an unsteady interplay among hydrodynamic, elastic, damping and inertial forces as well as other forces due to control techniques. Due to their universality and importance, great efforts have been made to study these fluid–structure interaction problems, with a particular focus on developing numerical methods and experimental techniques, elucidating the associated physics and promoting these applications in engineering designs. This Special Issue seeks to (1) highlight recent advances in numerical simulations and experimental techniques, (2) identify and address existing challenges and (3) propose future directions in fluid–structure interaction research. We welcome articles on themes including, but not limited to: numerical methods, experimental techniques, flow physics, multiprocess coupling, flexible structure controls and applications of optimization and machine learning.

Dr. Pengtao Yue
Dr. Fang-Bao Tian
Guest Editors

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.

Keywords

  • fluid–structure interaction
  • numerical method
  • experimental technique
  • flow control
  • machine learning

Published Papers (4 papers)

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Research

21 pages, 4781 KiB  
Article
A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients
by Amirhossein Mollaali, Izzet Sahin, Iqrar Raza, Christian Moya, Guillermo Paniagua and Guang Lin
Fluids 2023, 8(12), 323; https://doi.org/10.3390/fluids8120323 - 18 Dec 2023
Viewed by 1647
Abstract
In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge, this paper proposes a deep operator learning-based framework that requires [...] Read more.
In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge, this paper proposes a deep operator learning-based framework that requires a limited high-fidelity dataset for training. We introduce a novel physics-guided, bi-fidelity, Fourier-featured deep operator network (DeepONet) framework that effectively combines low- and high-fidelity datasets, leveraging the strengths of each. In our methodology, we begin by designing a physics-guided Fourier-featured DeepONet, drawing inspiration from the intrinsic physical behavior of the target solution. Subsequently, we train this network to primarily learn the low-fidelity solution, utilizing an extensive dataset. This process ensures a comprehensive grasp of the foundational solution patterns. Following this foundational learning, the low-fidelity deep operator network’s output is enhanced using a physics-guided Fourier-featured residual deep operator network. This network refines the initial low-fidelity output, achieving the high-fidelity solution by employing a small high-fidelity dataset for training. Notably, in our framework, we employ the Fourier feature network as the trunk network for the DeepONets, given its proficiency in capturing and learning the oscillatory nature of the target solution with high precision. We validate our approach using a well-known 2D benchmark cylinder problem, which aims to predict the time trajectories of lift and drag coefficients. The results highlight that the physics-guided Fourier-featured deep operator network, serving as a foundational building block of our framework, possesses superior predictive capability for the lift and drag coefficients compared to its data-driven counterparts. The bi-fidelity learning framework, built upon the physics-guided Fourier-featured deep operator, accurately forecasts the time trajectories of lift and drag coefficients. A thorough evaluation of the proposed bi-fidelity framework confirms that our approach closely matches the high-fidelity solution, with an error rate under 2%. This confirms the effectiveness and reliability of our framework, particularly given the limited high-fidelity dataset used during training. Full article
(This article belongs to the Special Issue Challenges and Directions in Fluid Structure Interaction)
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19 pages, 8979 KiB  
Article
Simulating Flow in an Intestinal Peristaltic System: Combining In Vitro and In Silico Approaches
by Xinying Liu, Chao Zhong, David F. Fletcher and Timothy A. G. Langrish
Fluids 2023, 8(11), 298; https://doi.org/10.3390/fluids8110298 - 10 Nov 2023
Viewed by 1362
Abstract
Transport and mixing in the gastric duct occur via peristaltic flow. In vivo data are hard to collect and require strict ethical approval. In contrast, both in vitro and in silico studies allow detailed investigation and can be constructed to answer specific questions. [...] Read more.
Transport and mixing in the gastric duct occur via peristaltic flow. In vivo data are hard to collect and require strict ethical approval. In contrast, both in vitro and in silico studies allow detailed investigation and can be constructed to answer specific questions. Therefore, the aim of this study was to design a new elastic thermoplastic polyurethane (TPU) intestine model and to compare the flow patterns observed experimentally with those predicted by a Fluid Structure Interaction (FSI) simulation. Here, we present complementary studies that allow feedback to improve both techniques and provide mutual validation. The experimental work provides direct measurement of mixing, and the simulation allows the experimental setup to be studied to determine the impacts of various parameters. We conclude by highlighting the utility of this approach. Full article
(This article belongs to the Special Issue Challenges and Directions in Fluid Structure Interaction)
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17 pages, 9231 KiB  
Article
An Interface-Fitted Fictitious Domain Finite Element Method for the Simulation of Neutrally Buoyant Particles in Plane Shear Flow
by Yi Liang, Cheng Wang and Pengtao Sun
Fluids 2023, 8(8), 229; https://doi.org/10.3390/fluids8080229 - 12 Aug 2023
Viewed by 922
Abstract
In this paper, an interface-fitted fictitious domain finite element method is developed for the simulation of fluid–rigid particle interaction problems in cases of rotated particles with small displacement, where an interface-fitted mesh is employed for the discrete scheme to capture the fluid–rigid particle [...] Read more.
In this paper, an interface-fitted fictitious domain finite element method is developed for the simulation of fluid–rigid particle interaction problems in cases of rotated particles with small displacement, where an interface-fitted mesh is employed for the discrete scheme to capture the fluid–rigid particle interface accurately, thereby improving the solution accuracy near the interface. Moreover, a linearization and decoupling process is presented to release the constraint between velocities of fluid and rigid particles in the finite element space, and to make the developed numerical method easy to be implemented. Our numerical experiments are carried out using two different moving interface-fitted meshes; one is obtained by a rotational arbitrary Lagrangian–Eulerian (ALE) mapping, and the other one through a local smoothing process among interface-cut elements. A unified velocity is defined in the entire domain based on the fictitious domain method, making it easier to develop an interface-fitted mesh generation algorithm in a fixed domain. Both show that the proposed method has a good performance in accuracy for simulating a neutrally buoyant particle in plane shear flow. This approach can be easily extended to fluid–structure interaction problems involving fluids in different states and structures in different shapes with large displacements or deformations. Full article
(This article belongs to the Special Issue Challenges and Directions in Fluid Structure Interaction)
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13 pages, 3382 KiB  
Article
Experimental Observations on Flow Characteristics around a Low-Aspect-Ratio Wall-Mounted Circular and Square Cylinder
by Seyed M. Hajimirzaie
Fluids 2023, 8(1), 32; https://doi.org/10.3390/fluids8010032 - 15 Jan 2023
Cited by 4 | Viewed by 1489
Abstract
The mean wake structures of a cube (square cylinder) and circular cylinder of height-to-width aspect ratio 1.0, at a Reynolds number of 1.78 × 104 based on the obstacle width, were investigated experimentally. The boundary-layer thickness was 0.14 of the obstacle height. [...] Read more.
The mean wake structures of a cube (square cylinder) and circular cylinder of height-to-width aspect ratio 1.0, at a Reynolds number of 1.78 × 104 based on the obstacle width, were investigated experimentally. The boundary-layer thickness was 0.14 of the obstacle height. The study was performed using thermal anemometry and two-dimensional digital particle image velocimetry (DPIV). Streamwise structures observed in the mean wake for both cylinders included well-known tip- and horseshoe (HS)-,vortex pairs as well as additional structures akin to the base vortices. In addition to tip-, base-, and HS-vortices, in the near wake of the cube, two more counter-rotating pairs of streamwise structures, including upper and inboard vortices, were observed. The existence of base vortices formed in the near wake for both obstacles is a unique observation and has not been previously reported for such low-aspect-ratio obstacles in thin boundary-layers. A model of arch-vortex evolution was proposed, in which arch structures were deformed by the external shear-flow to explain the observed base-vortices in the cylinder wake. A weak dominant-frequency of St = f0D/U∞ = 0.114 was observed across the height for the cube, while no discernible spectral peaks were apparent in the wake of the cylinder. Cross-spectral analysis revealed the shedding to be symmetric (in-phase) arch-type for the cylinder and predominantly anti-symmetric (out-of-phase) Karman-type for the cube. The study makes fundamental contributions to the understanding of the flow-field surrounding low-aspect-ratio cylinders. Full article
(This article belongs to the Special Issue Challenges and Directions in Fluid Structure Interaction)
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