The project advanced data-driven modeling and high-fidelity simulation of turbulent, multiphase, and thermal flows, integrating machine learning across the three work packages.
WP1 focused on wall modeling and turbulent flow simulations using a Deep Reinforcement Learning (DRL) framework coupled with an in-house CFD solver, enabling two-way interaction between simulations and ML-based boundary conditions. Extensive studies of turbulent, viscoelastic, and shear-thinning flows investigated near-wall physics, grid independence, and minimal channel configurations. Post-processing tools generated ML-ready databases. High-fidelity DNS and LES simulations were validated against experiments and reference simulations, producing an open-source LES database for zero- and adverse-pressure-gradient thermal boundary layers. The framework integrates advanced inflow/outflow treatments, pressure-gradient imposition, and thermal coupling, ensuring full reproducibility and ML readiness. Additional studies addressed multiphase compressible flows, including shock-induced cavitation in non-spherical droplets, enhancing post-processing tools and resulting in a journal paper under review. WP1 delivered validated tools, datasets, and ML-integrated workflows advancing wall modeling and multiphase simulations.
WP2 developed ML-derived subgrid-scale (SGS) surrogate models and advanced numerical tools for multiphase and cavitation-driven flows. A multi-fidelity Gaussian Process surrogate predicted maximum wall pressures during bubble-cluster collapse using data across grid resolutions and simplified single-bubble cases, demonstrating strong cross-fidelity correlations. Open-source releases included JAX-Fluids for fully resolved bubble-cluster simulations and the AMReX-based 3phaseSolver for pressure-wave propagation in multi-material bodies. Additional progress included ML-training databases, reproducible agglomerate geometries for medical applications, ultrasound-driven bubble dynamics in soft tissues with real-fluid thermodynamics and fluid–structure interaction, and continuous-adjoint optimization for unsteady VoF multiphase flows with phase change. WP2 delivered validated frameworks, surrogate models, and open-source tools significantly enhancing multiphase and cavitation modeling.
WP3 targeted open-source, fully differentiable solvers and ML-enhanced tools for cavitating flows. JAX-Fluids was extended for DNS of cavitation, incorporating level-set generation from 3D STL and 2D DXF geometries, and weakly compressible and barotropic models, validated on a 3D cavitation tunnel and released in Deliverable D3.1. Complementary work included data-driven modeling for cavitating propellers and complex geometries, development of a boundary graph neural network for 2D airfoil pressure prediction, and pooling strategies for Graph-U-Net architectures applied to 3D ship hulls. Machine-learning solvers based on PINNs and ConvLSTM were explored, with a validated single-phase PINN solver extended toward two-phase cavitating flows and ConvLSTM-based surrogate solvers accelerating iterative CFD schemes via temporal correction learning from coarse-grid data.
Overall, the project delivered validated, reproducible, and open-source tools, high-fidelity datasets, ML-integrated solvers, and surrogate models, establishing a robust, extensible platform for state-of-the-art simulation and modeling of turbulent, multiphase, and cavitating flows. These results enable advanced research, optimization, and industrial applications in thermal systems, propulsion, biomedical therapies, and fluid–structure interactions, providing a scalable framework for combining machine learning with high-fidelity CFD.
