A fully explicit Lagrangian-Lagrangian fluid-structure interaction solver with GPU acceleration based on a physically consistent moving particle hydrodynamics
Year: 2025
Type: article
Abstract: Understanding fluid–structure interaction is essential across many engineering applications. This work presents a fully explicit Lagrangian–Lagrangian solver based on the Moving Particle Hydrodynamics method that treats fluids and structures within a unified analytical framework. A weakly compressible fluid model and a hyperelastic structural model are combined, and the coupling is achieved with a fluid–structure acceleration–based approach that enforces consistent momentum exchange at the interface. Verification and validation include the two-dimensional Turek–Hron benchmarks and three-dimensional dam-break simulations involving elastic obstacles. The solver reproduces structural oscillations, free-surface evolution, and stress distributions in close agreement with analytical references, experimental measurements, and results from established fluid–structure interaction solvers. Complex behaviors such as splash formation and large elastic deformations are captured without ad hoc stabilization. A scalability study demonstrates efficient use of graphics processing units for simulations up to twelve million particles, while reduced efficiency appears for very small particle counts due to memory-allocation overhead. Overall, the solver exhibits physical consistency, strong scalability for large-scale problems, and robustness in highly nonlinear conditions. These capabilities make it a practical, parameter-free tool for high-fidelity prediction in nuclear safety, structural impact assessment, and free-surface flow problems.
Source: Computers & Structures
Authors: Ryo Yokoyama, Kai Wang, Shuichiro Miwa, Koji Okamoto
Institution: The University of Tokyo
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,200
Award: 破壊・相変化を伴う大規模固液混相流解析手法
OpenAlex ID: W7102784256
