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11 results for “fluid-structure interaction”
Wall Resolved Fluid-Structure Interaction Numerical Simulation of a Modern Wind Turbine Blade
<p>Wall-resolved fluid-structure interaction (FSI) numerical simulations of the NREL 5 MW wind turbine blade<br> are compared using two FSI approaches. The first method is based on high-fidelity Nektar++/SHARPy FSI framework,<br> where the fluid governing equations are solved using high-order spectral/hp element method and the turbulent flow is<br> resolved using Large Eddy Simulation (LES) on thick strips, while large-deformation dynamics of the structure are mod-<br> elled using a geometrically exact nonlinear composite beam finite-element model. Thick strip method for the fluid reduces<br> the computational cost by considering a series of smaller domains, each of which has a finite thickness in the spanwise<br> direction. Hence, the overall flow over the blade is treated with a sectional approach, where in each of these sections,<br> strips, the 3D flow is reconstructed locally. Tip-loss correction is used to compensate for the sectional approach over the<br> blade. The second FSI approach is based on OpenFoam/Calculix coupling, where the second-order unstructured finite<br> volume method approach is used for solving the three-dimensional flow equations and the flow turbulence is captured us-<br> ing the k-ω SST model. The structural dynamics are modeled via second-order finite element method using standard solid<br> elements. Effects of the solution fidelity on the prediction of aerodynamic forces as well as on the full three-dimensional<br> flow modelling over the blade versus sectional representation of flow over the blade while incorporating the local three-<br> dimensionality in each section and tip-correction are discussed. Further, significance of two approaches on modelling<br> the slender blade, one using the beam mode and the other utilizing the full 3D solution of structure is addressed. Finally,<br> assessment of computational cost and scalability of the two approaches are presented and discussed.</p>
Capturing functional relations in fluid-structure interaction via machine learning
<p>While fluid-structure interaction (FSI) problems are ubiquitous in various applications from cell-biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations in solving the Navier-Stokes (N-S) equations. To mimic the effect of fluid on an immersed beam, we have introduced dissipation into the beam model with time-varying forces acting on it. The forces in a discretized setup have been decoupled via an appropriate linear algebraic operation, which generates the ground truth force/moment data for the ML analysis. The adopted ML technique, symbolic regression, generates computationally tractable functional forms to represent the force/moment with respect to space and time. These estimates are fed into the dissipative beam model to generate the immersed beam's deflections over time, which are in conformity with the detailed FSI solutions. Numerical results demonstrate that the ML-estimated continuous force and moment functions are able to accurately predict the beam deflections under different discretizations.</p>
Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach
<p>The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns. This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.</p>
PART-2: Dataset for journal: "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect"
<p>Complementary file for the attached files<br> Written 24-04-2017<br> by Robert Bleischwitz (modellwerft@freenet.de)</p> <p>General Comments</p> <p>0.) This specific upload contains PART-2 of the full dataset</p> <p>1.) The attached data relates to experimental windtunnel measurements on passive membrane wings for MAVs. The data was aquired between 2012-2016 at the University of Southampton, involving Robert Bleischwitz as PhD student, who was supervised by Roeland de Kat and Bharathram Ganapathisubramani.</p> <p>2.) The attached data is given time-resolved and time-synchronised at 800Hz over a imaging-period of 5000 images, involving load measurements via a 6-axis load-cell ATI Nano17 /25N, deformation measurements via Digitial Image Processing (DIC) and planar flow measurements via two side-by-side cameras. </p> <p>3.) More setup and processing details can be found in the paper "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect" (2017) by the authors R. Bleischwitz, R. de Kat, B. Ganapathisubramani<br> Published in the Journal of Fluids and Structures (http://www.sciencedirect.com/science/article/pii/S088997461630370X)</p> <p>4.) All load/deformation/flow folders contain a README.txt(Use 1st) and Instructions.m (Use 2nd) file, which give further supporting details how to illustrate the data</p> <p>5.) This specific upload contains PART-2 of the full dataset, including introduction file + membrane-wing case (Load+DIC+PIV measurements) </p>
PART-1: Dataset for journal: "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect"
<p>Complementary file for the attached files<br> Written 24-04-2017<br> by Robert Bleischwitz (modellwerft@freenet.de)</p> <p>General Comments</p> <p>0.) This specific upload contains PART-1 of the full dataset</p> <p>1.) The attached data relates to experimental windtunnel measurements on passive membrane wings for MAVs. The data was aquired between 2012-2016 at the University of Southampton, involving Robert Bleischwitz as PhD student, who was supervised by Roeland de Kat and Bharathram Ganapathisubramani.</p> <p>2.) The attached data is given time-resolved and time-synchronised at 800Hz over a imaging-period of 5000 images, involving load measurements via a 6-axis load-cell ATI Nano17 /25N, deformation measurements via Digitial Image Processing (DIC) and planar flow measurements via two side-by-side cameras. </p> <p>3.) More setup and processing details can be found in the paper "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect" (2017) by the authors R. Bleischwitz, R. de Kat, B. Ganapathisubramani<br> Published in the Journal of Fluids and Structures (http://www.sciencedirect.com/science/article/pii/S088997461630370X)</p> <p>4.) All load/deformation/flow folders contain a README.txt(Use 1st) and Instructions.m (Use 2nd) file, which give further supporting details how to illustrate the data</p> <p>5.) This specific upload contains PART-1 of the full dataset, including introduction file + rigid flat-plate case (Load+PIV measurements) as reference to membrane wing case (PART-2)</p>
Full dataset for journal: "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect"
<p>0.) Version: 18.May 2017</p> <p>1.) The attached data relates to experimental windtunnel measurements on passive membrane wings for MAVs. The data was aquired between 2012-2016 at the University of Southampton, involving Robert Bleischwitz as PhD student, who was supervised by Roeland de Kat and Bharathram Ganapathisubramani.</p> <p>2.) The attached data is given time-resolved and time-synchronised at 800Hz over a imaging-period of 5000 images, involving load measurements via a 6-axis load-cell ATI Nano17 /25N, deformation measurements via Digitial Image Processing (DIC) and planar flow measurements via two side-by-side cameras. </p> <p>3.) More setup and processing details can be found in the paper "On the fluid-structure interaction of flexible membrane wings for MAVs in and out of ground-effect" (2017) by the authors R. Bleischwitz, R. de Kat, B. Ganapathisubramani<br> Published in the Journal of Fluids and Structures (http://www.sciencedirect.com/science/article/pii/S088997461630370X)</p> <p>4.) All load/deformation/flow folders contain a README.txt(Use 1st) and Instructions.m (Use 2nd) file, which give further supporting details how to illustrate the data</p> <p>5.) This specific upload is zipped and contains all necessary files to reconstruct the time-resolved dataset.</p>
Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks - Supporting Dataset
<div>Supporting dataset for the numerical experiments in the manuscript <em>Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks</em>, consisting of a tar.gz archive containing the following directories:</div> <h3>learnext_dataset</h3> <div>Dataset used to train the DeepONet mesh motion model. For one period of structure deformation in the FSI benchmark problem 2 of Turek and Hron (2006), contains the harmonic mesh motion in input and biharmonic mesh motion in output, relative to the undeformed domain.</div> <h3>mesh</h3> <div>Mesh of the FSI benchmark problem 2 used to run FSI simulations to test DeepONet mesh motion.</div> <h3>Warmstart checkpoint</h3> <div>State checkpoint of FSI benchmark problem 2 run for 15 simulation seconds with trained DeepONet mesh motion. Used to warmstart the FSI simulations to verify quantities of interest produced from DeepONet mesh motion by comparing it with ones from biharmonic mesh motion.</div> <h3>grav-test</h3> <div>Dataset used in gravity-driven deformation test of DeepONet mesh motion.</div> <h3>best_run_model</h3> <div>Saved, pretrained branch and trunk networks from the best run of the hyperparameter study and problem-file needed to build the DeepONet mesh motion from it.</div>
Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework: code and data
<p>These files accompany the publication</p><p>N. Delaissé, T. Demeester, R. Haelterman and J. Degroote. Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework.<i> Archives of Computational Methods in Engineering</i>, Vol.<strong> </strong>30, 3271-3300, 2023. doi: <a href="https://doi.org/10.1007/s11831-023-09907-y">10.1007/s11831-023-09907-y</a></p><p>In this work, the performance of multiple quasi-Newton methods are compared in terms of memory requirements and computational time. The results are generated for the well-known flexible tube example case, using the open-source code <a href="http://github.com/pyfsi/coconut">CoCoNuT</a>. This code, developed at Ghent University, is Python-based and has the capability to couple existing solvers, both open-source and commercial solvers.</p><p>This archive consists of the following files.</p><ul><li><strong>coconut.tar.gz: </strong>the specific CoCoNuT version used (sep-2022), including the Python flow and structure solvers for the flexible tube and modifications for monitoring memory requirements</li><li><strong>compare_coupling_algorithms.tar.gz: </strong>the scripts to set up the cases and perform the calculations and post-processing</li><li><strong>results.tar.gz:</strong> the generated result data</li></ul><p>For requirements to run CoCoNuT, refer to the <a href="http://pyfsi.github.io/coconut/">documentation</a>. Additionally, the Python package guppy3 is required for monitoring the memory use. In this work the data were generated with Andaconda3-2022.05 and the package guppy3-3.1.2.</p><p>Before running the provided scripts, make sure the parent directory of the "coconut" folder is added to the PYTHONPATH. The calculations can be started with "python run.py". For the cases which names contain "_m" followed by a number, e.g. "_m100", the number refers to the number of discretization points on the interface. The cases with suffix "_c" are distinct from those without, as they don't perform the time consuming memory monitoring and are therefore used for measuring computational time.</p>
Bio-inspired forward and backward swimming gaits resulting from fluid-structure interactions
Open the record for dataset details and reuse information.
Aneurysmal haemodynamics: A three-dimensional fluid-structure interaction approach
Open the record for dataset details and reuse information.
Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.
<p>Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.</p> <p> </p> <p>This is the abstract:</p> <p>Congenital bicuspid aortic valve (BAV) consists of two fused cusps and represents a major risk factor for calcific valvular stenosis. Herein, a fully coupled fluid-structure interaction (FSI) BAV model was developed from patient-specific magnetic resonance imaging (MRI) and compared against in vivo 4-dimensional flow MRI (4D Flow). FSI simulation compared well with 4D Flow, confirming direction and magnitude of the flow jet impinging onto the aortic wall as well as location and extension of secondary flows and vortices developing at systole: the systolic flow jet originating from an elliptical 1.6 cm<sup>2</sup> orifice reached a peak velocity of 252.2 cm/s, 0.6% lower than 4D Flow, progressively impinging on the ascending aorta convexity. The FSI model predicted a peak flow rate of 22.4 L/min, 6.7% higher than 4D Flow, and provided BAV leaflets mechanical and flow-induced shear stresses, not directly attainable from MRI. At systole, the ventricular side of the non-fused leaflet revealed the highest wall shear stress (WSS) average magnitude, up to 14.6 Pa along the free margin, with WSS progressively decreasing towards the belly. During diastole, the aortic side of the fused leaflet exhibited the highest diastolic maximum principal stress, up to 322 kPa within the attachment region. Systematic comparison with ground-truth non-invasive MRI can improve the computational model ability to reproduce native BAV hemodynamics and biomechanical response more realistically, and shed light on their role in BAV patients' risk for developing complications; this approach may further contribute to the validation of advanced FSI simulations designed to assess BAV biomechanics.</p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.