Summary
Fluid-structure interaction (FSI) is a ubiquitous phenomenon in industry such as ocean engineering, biomedicine, aerospace and a driven idea for hydrokinetic energy conversion. Strong nonlinear interactions between flow and structures, as well as turbulent flow bring a huge challenge for understanding and dynamics prediction of the FSI systems. Facing with this challenge and the rising motivation of harnessing clean energy from natural fluids, SMARTFLUIDS aims to build a framework of developing Reduced Order Models (ROMs) with low complexity but retaining dominant physics for FSI, which will bring a deep understanding and efficient prediction of FSI.
A low-dimensional subspace of the FSI will be extracted using modal analysis through Variational Autoencoders (VAE) based on deep learning (DL) of FSI data. The FSI data will be obtained by performing high-fidelity computational fluids dynamics (CFD) simulations of classic FSI problems. The ROMs are developed by both physics-informed mapping of governing equations onto the low-dimensional subspace and data-driven techniques to deal with nonlinear and unresolved parts of the FSI. Physical constrains are incorporated and sparse measurements of the FSI will be used in building the ROMs. Dynamics and future states of the FSI, hydro- or aerodynamic loads on the structures and structural responses can be predicted based on the ROMs. SMARTFLUIDS will provide an innovative and systematic view of FSI by focusing on a few dominant features and enrich the knowledge of FSI physics by adopting latest DL techniques. The project will bring a novel solution to reduce time and cost of CFD and experiments in predicting FSI dynamics and structural responses for engineering design of pipelines, cables, wind turbines blades, airplane wings and promote the renewable energy development.
A low-dimensional subspace of the FSI will be extracted using modal analysis through Variational Autoencoders (VAE) based on deep learning (DL) of FSI data. The FSI data will be obtained by performing high-fidelity computational fluids dynamics (CFD) simulations of classic FSI problems. The ROMs are developed by both physics-informed mapping of governing equations onto the low-dimensional subspace and data-driven techniques to deal with nonlinear and unresolved parts of the FSI. Physical constrains are incorporated and sparse measurements of the FSI will be used in building the ROMs. Dynamics and future states of the FSI, hydro- or aerodynamic loads on the structures and structural responses can be predicted based on the ROMs. SMARTFLUIDS will provide an innovative and systematic view of FSI by focusing on a few dominant features and enrich the knowledge of FSI physics by adopting latest DL techniques. The project will bring a novel solution to reduce time and cost of CFD and experiments in predicting FSI dynamics and structural responses for engineering design of pipelines, cables, wind turbines blades, airplane wings and promote the renewable energy development.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101165107 |
| Start date: | 01-01-2025 |
| End date: | 31-12-2029 |
| Total budget - Public funding: | 1 499 159,00 Euro - 1 499 159,00 Euro |
Cordis data
Original description
Fluid-structure interaction (FSI) is a ubiquitous phenomenon in industry such as ocean engineering, biomedicine, aerospace and a driven idea for hydrokinetic energy conversion. Strong nonlinear interactions between flow and structures, as well as turbulent flow bring a huge challenge for understanding and dynamics prediction of the FSI systems. Facing with this challenge and the rising motivation of harnessing clean energy from natural fluids, SMARTFLUIDS aims to build a framework of developing Reduced Order Models (ROMs) with low complexity but retaining dominant physics for FSI, which will bring a deep understanding and efficient prediction of FSI.A low-dimensional subspace of the FSI will be extracted using modal analysis through Variational Autoencoders (VAE) based on deep learning (DL) of FSI data. The FSI data will be obtained by performing high-fidelity computational fluids dynamics (CFD) simulations of classic FSI problems. The ROMs are developed by both physics-informed mapping of governing equations onto the low-dimensional subspace and data-driven techniques to deal with nonlinear and unresolved parts of the FSI. Physical constrains are incorporated and sparse measurements of the FSI will be used in building the ROMs. Dynamics and future states of the FSI, hydro- or aerodynamic loads on the structures and structural responses can be predicted based on the ROMs. SMARTFLUIDS will provide an innovative and systematic view of FSI by focusing on a few dominant features and enrich the knowledge of FSI physics by adopting latest DL techniques. The project will bring a novel solution to reduce time and cost of CFD and experiments in predicting FSI dynamics and structural responses for engineering design of pipelines, cables, wind turbines blades, airplane wings and promote the renewable energy development.
Status
SIGNEDCall topic
ERC-2024-STGUpdate Date
15-11-2024
Images
No images available.
Geographical location(s)
