arXiv subjects

Xingxin Yang

Publications and source records attributed to Xingxin Yang.

3 recordsLinked to original sources

arXiv2026

VATO: A Vortex-Force-Aware Transformer Operator for Unsteady Separated Aerofoil Flows

Accurate prediction of unsteady separated flows is challenging because the aerodynamic loads depend on nonlinear separation and vortex-shedding dynamics. Although high-fidelity CFD resolves these mechanisms, its cost limits repeated use in design and control. Standard field-level surrogate training, however, does not distinguish the flow regions that contribute most strongly to the aerodynamic loads. We introduce VATO (Vortex-Force-Aware Transformer Operator), which couples the Vortex Force Map (VFM) method to a geometry-aware neural operator through two complementary mechanisms. VATO-S adds training-only supervision of the local VFM force-contribution field, with no increase in model size or inference cost. VATO-A uses VFM contribution and sensitivity fields to prioritise force-relevant source locations for residual cross attention. The methods are evaluated on unsteady CFD data for double-edged-plate aerofoils over 54 trajectories from nine geometries. Over lead times of 1-20~ms, VATO-S reduces velocity, pressure, and vorticity errors by 10.4\%, 1.0\%, and 15.6\%, respectively, while VATO-A achieves reductions of 15.8\%, 7.5\%, and 31.2\%. VATO-S gives the lowest VFM-derived drag error, whereas VATO-A gives the lowest pressure-derived lift and drag errors. Over lead times extending 50\% beyond the training range, VATO-A retains a 26.9\% reduction in vorticity error and larger improvements in all four force readouts, despite reduced gains in velocity and pressure. These results show that force-aware operator learning can improve both flow-field prediction and aerodynamic functional accuracy in unsteady separated flows.

cs.LG↗

arXiv2026

Deforming Vortex Force Mapping for Undulating Swimmers

Undulatory propulsion is commonly interpreted through reactive added-mass loading associated with body kinematics and vortex-induced loading, but their relative contributions to the streamwise force have not been systematically quantified across swimming kinematics. Moreover, existing vortex-based descriptions do not resolve the signed spatial contribution of vortical flow to the streamwise force. We extend vortex force map (VFM) attribution to deforming bodies, demonstrated for prescribed two-dimensional undulating swimmers at $Re=5000$ across a range of Strouhal numbers and dimensionless wavelengths. The deforming-body VFM organises the streamwise force as a positive reactive added-mass scale with a signed vortex-pressure modification. Across the 80 cases, vortex pressure opposes the reactive contribution in 76 cases. Reference-configuration maps show that the near-body vortex-pressure contribution is consistently adverse, whereas the wake contribution changes sign across regimes and becomes favourable at high thrust. Net vortex-pressure assistance occurs only for the high-thrust anguilliform swimmer. A matched high-thrust pair further shows that the case with the higher peak downstream velocity has the smaller signed wake contribution, demonstrating that wake intensity alone does not determine its force effect.

physics.flu-dyn↗

arXiv2026

STCO: Conditional Neural Operators for Time-Dependent PDEs

Neural operators have emerged as efficient surrogates for time-dependent physical systems governed by partial differential equations (PDEs), but their future-state predictions are often conditioned only on observed states and static problem descriptors. For control or optimization, however, body motion, inflow, or forcing are prescribed for the query without being determined solely by the observed state. We introduce the Spatiotemporal Conditional Operator (STCO) for prescribed-condition operator learning (PCOL), a common interface that supplies prescribed target-time condition fields to heterogeneous backbone architectures while retaining their architecture-specific core computation and context pathways. Its condition interface combines Flow-Aware Graph Leaf (FAGL) with Dual-Site Feature-wise Linear Modulation (DSFiLM). Non-learned FAGL uses vorticity from the final observed frame to construct a fixed-cardinality adaptive partition, then co-locates the observed history and target-time condition fields at its regional coordinates. DSFiLM injects separate motion, inflow, and force routes before and after operator computation through current-feature-driven slot- and channel-wise gates. We evaluate twelve matched backbone architectures with different existing physical and temporal inputs. The immersed-boundary computational fluid dynamics (CFD) benchmark spans prescribed motion, inflow disturbances, body-force actuation, and morphology. Across twelve matched backbones, three regimes, and two lead ranges, STCO yields mean paired reductions of 31.1% in relative-L2 field error and 24.7% in normalized pressure-derived load error. It also lowers longer-lead field error for 11 backbones, while interventions on individual condition groups produce measurable prediction changes for every group evaluated.

cs.AI↗