arXiv subjects

Artur K. Lidtke

Publications and source records attributed to Artur K. Lidtke.

3 recordsLinked to original sources

arXiv2026

Sharp-interface Simulations of Energetic Multiphase Flows with Large Density and Viscosity Ratios

Flows with high density ratios, such as wave breaking and air entrainment in maritime applications, remain challenging to simulate due to their energetic and strongly nonlinear nature. In such regimes, maintaining numerical robustness is difficult when using the commonly adopted velocity-based formulation. The Consistent Mass-Momentum (CMOM) transport framework improves numerical robustness by enforcing fundamental physical properties, most notably momentum conservation and semi-discrete energy-conserving. However, CMOM replaces the advection of a continuous velocity field with that of a discontinuous momentum field. When combined with sharp interface methods, this leads to severe momentum shocks, for which conventional shock-capturing schemes are ineffective. To reconcile physical fidelity with numerical robustness, this work proposes a Synchronized Donor-Region of Momentum fluxes (SynDRoM) that enforces monotonicity of the transported velocity field. The resulting algorithm effectively eliminates spurious velocity oscillations without sacrificing physical fidelity, as demonstrated through scalar transport and interfacial shear instability test cases. Beyond difficulties from large density ratio, improper estimation of viscosity in the vicinity of the interface can introduce numerical instabilities at finite time steps, thereby undermining overall robustness. To address this issue, a viscosity limiter based on the bounded kinetic viscosity concept is introduced and validated using a gravity-driven plane shear flow. Finally, a breaking wave simulation is performed to assess the combined performance of the proposed physics-preserving numerical schemes for multiphase flows.

physics.flu-dyn↗

arXiv2026

Acceleration of an algebraic multigrid pressure solver using graph neural networks

Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities. This work introduces a data-driven algebraic multigrid (AMG) smoother that uses a modified graph convolutional isomorphism network (GCIN). The graph neural network predicts optimal polynomial coefficients to construct a sparse pseudo-inverse operator across diverse grid topologies. The coefficients are optimized to reduce the residual after each V-cycle iteration. By directly capturing the algebraic structure of the system from the sparse coefficient matrix, the proposed method maintains the solver's linearity while adapting to local anisotropies in unstructured grids. Our framework demonstrates significant performance gains by reducing the number of V-cycles required for a given tolerance and delivering wall-clock speedups from 4% to 37% across diverse benchmarks. Notably, the model exhibits robust generalization by maintaining efficiency on meshes up to 128 times larger than those seen in training, and by accelerating the solver's convergence on unseen industry-relevant problems such as the AirfRANS dataset.

physics.comp-ph↗

arXiv2025

Predicting airfoil pressure distribution using boundary graph neural networks

Surrogate models are essential for fast and accurate surface pressure and friction predictions during design optimization of complex lifting surfaces. This study focuses on predicting pressure distribution over two-dimensional airfoils using graph neural networks (GNNs), leveraging their ability to process non-parametric geometries. We introduce boundary graph neural networks (B-GNNs) that operate exclusively on surface meshes and compare these to previous work on volumetric GNNs operating on volume meshes. All of the training and evaluation is done using the airfRANS (Reynolds-averaged Navier-Stokes) database. We demonstrate the importance of all-to-all communication in GNNs to enforce the global incompressible flow constraint and ensure accurate predictions. We show that supplying the B-GNNs with local physics-based input-features, such as an approximate local Reynolds number $\mathrm{Re}_x$ and the inviscid pressure distribution from a panel method code, enables a $83\%$ reduction of model size and $87\%$ of training set size relative to models using purely geometric inputs to achieve the same in-distribution prediction accuracy. We investigate the generalization capabilities of the B-GNNs to out-of-distribution predictions on the S809/27 wind turbine blade section and find that incorporating inviscid pressure distribution as a feature reduces error by up to $88\%$ relative to purely geometry-based inputs. Finally, we find that the physics-based model reduces error by $85\%$ compared to the state-of-the-art volumetric model INFINITY.

physics.flu-dyn↗