Qinglin Wang

Data-driven learning provides a promising route for accelerating computational fluid dynamics, yet transient flow prediction remains challenging because models must simultaneously capture evolving temporal dynamics and cross-scale spatial structures while generalizing across geometries and operating conditions. In this work, we study one-step-ahead prediction of two-dimensional incompressible transient flows from historical snapshots and propose Multi-Scale Spatiotemporal Flow (MuST-Flow), a physics-informed multi-scale spatiotemporal network for recurrent forecasting of velocity and pressure fields. The central idea of MuST-Flow is to couple cross-scale feature extraction with physical regularization, enabling the model to better resolve localized high-gradient flow structures while maintaining physically consistent evolution. Specifically, MuST-Flow adopts a multi-scale spatiotemporal convolutional design with dilated receptive fields to learn hierarchical flow representations and incorporates a hybrid loss derived from the residuals of the incompressible Navier–Stokes equations to encourage mass and momentum consistency during training. To evaluate generalization beyond fixed configurations, the proposed method is assessed on transient hydrofoil flow data under cross-geometry testing with varied operating conditions. Experimental results show that MuST-Flow achieves the lowest mean absolute error, highest structural similarity, and lowest speed and velocity-direction errors among both generic spatiotemporal prediction baselines and flow-oriented neural-operator baselines, while maintaining competitive divergence and Navier–Stokes residuals. These results demonstrate its effectiveness as an efficient surrogate modeling framework for two-dimensional incompressible transient flow prediction.

Large-scale mantle convection simulations repeatedly solve sparse velocity-pressure systems, and the multigrid velocity solver often dominates the total runtime. This paper presents an MT-3000-oriented heterogeneous multigrid solver for CitcomCU on the new-generation Tianhe supercomputer. The solver keeps the original governing equations, finite-element discretization, Uzawa iteration, and geometric multigrid framework, while redesigning the dominant kernels and data movement for the explicitly managed memory hierarchy of MT-3000. The central design is an 8-color block Gauss-Seidel smoother co-designed with ELL-like node-based storage, topology-derived neighbor indexing, AM-aware row-wise streaming, ping-pong double buffering, host-accelerator collaboration, and communication-computation overlap. These techniques expose block-level parallelism, improve locality, and reduce avoidable DMA traffic without increasing the number of multigrid cycles in the tested cases. For the Busse1993 benchmark, the optimized solver remains consistent with the original CitcomCU implementation: after 4000 time steps, the full-domain relative L2 errors are 0.47% for temperature and 2.27% for the three-component velocity vector. Compared with the unmodified production CPU-based CitcomCU baseline, the complete heterogeneous execution mode achieves system-level speedups of up to 4.91x in average multigrid-cycle time and 4.71x in average complete-Uzawa-solve time per simulation step. Weak scaling maintains 59.54% efficiency on 12,800 processes, demonstrating the effectiveness of MT-3000-specific algorithmic and data-movement redesign for matrix-based geodynamic multigrid solvers.