The overset grid method is widely employed to solve moving boundary problems in numerical simulations. However, the heavy and inevitable communication resulting from boundary movements severely impedes the improvement of parallel efficiency. This paper proposes a Motion Trace Decomposition (MTD) method to alleviate this issue. The MTD method minimizes communication overhead between processors by decomposing sub-grids and distributing them according to the object motion trajectory, negating the need to reproduce communication areas when boundaries move. Various tests were conducted to evaluate the MTD method, incorporating diverse motion types, such as displacement and rotation. Results from experimental simulations with 1.9 × 106 grid cells indicate that the proposed method enhances the parallel efficiency of the assembly process by up to 20.35% using 72 processors. These findings showcase the significant potential of the MTD method in alleviating communication challenges associated with simulating moving boundary problems using overset grids.

Due to the complex geometry and turbulent flow characteristics, it is hard to simulate the process of steam dumping of the pressurizer relief tank (PRT). In this study, we develop a compressible fluid solver PRTFOAM to numerically study the turbulent flow dynamics from a PRT. The PRTFOAM is implemented based on the OpenFOAM and designed to be capable of integrating various turbulence models. Two representative Reynolds-averaged Navier–Stokes (RANS) models and a Smagorinsky–Lilly SGS model based on Large Eddy Simulation (LES) are coupled and tested with PRTFOAM. The case of a flow past a circular cylinder (Re = 3900) is tested and analyzed comprehensively as a benchmark case. Then, the turbulent steam dumping process in the full geometry of a PRT is analyzed and compared with ANSYS CFX and literature reports. In addition, we tested the WALE model based on the PRT steam dumping process. The results show that SST k-$\omega$ model and Smagorinsky–Lilly SGS model-based LES approach are more appropriate than the LRR model for PRT simulations. Moreover, it shows that the simulation results of Smagorinsky–Lilly SGS model and WALE model are basically consistent under the condition of PRT steam dumping process. Under this condition, the drawbacks of Smagorinsky–Lilly SGS model are not obvious. Furthermore, the comparison with CFX showed that our open source solver could be used to obtain better results in complex engineering cases. The design and testing results would provide guidance for further analysis of thermal-hydraulics in reactors based on open source codes.

The Overset Grid method is a promising computational approach for tackling the challenging moving boundary problems in Computational Fluid Dynamics (CFD) simulations. The computational efficiency and accuracy of the method are critically dependent on the effectiveness of the Overset Grid Assembly (OGA) process. However, the OGA process is plagued by unavoidable issues of load imbalance and communication overheads, which adversely impact the parallel efficiency of the method, particularly when dealing with sub-grids in motion. This paper proposes an improved parallel assembly approach as an effective alternative to address these challenges. Specifically, we introduce a Balanced Merging After Decomposition (BMAD) approach, which ensures that each processor possesses a uniform number of cells from each sub-grid after partitioning and a consistent donor search time. In addition, we deploy a fine-grained list to reduce the data transfer domain, thereby minimizing communication redundancy and cost. We validate the efficiency of our approach in the case of a moving Autonomous Underwater Vehicle (AUV). Experimental results in 3 × 106 grid cells indicate that the proposed approach reduces the parallel computational cost of the OGA process by an average of 21.9% and the speedup has increased by 23.9% with 128 processors. Additionally, it demonstrated equally effective and stable performance in tests using 6 × 106 grid cells, especially achieving the highest speedup of 55.0 with 256 processors.

The resolution of interfacial dynamics in multi-material flows critically relies on Riemann solvers, while recent advances in physics-informed neural networks (PINNs) offer transformative potential for solving multi-material Riemann problems. This paper presents a novel PINNs-based multi-material high-accuracy Riemann solver (PINN-MHRS) designed to solve multi-material Riemann problems involving complex equations of state (EOS) without requiring labeled data. PINN-MHRS determines wave system structures through entropy condition analysis and constructs physics-constrained loss functions. A hard constraints strategy enforces continuity conditions, wave speed ordering, and positivity of pressure/density, significantly simplifying optimization and accelerating convergence. The PINN-MHRS is integrated with the modified ghost fluid method via a Fortran-Python hybrid framework, enabling numerical simulations of multi-material interactions. Time advancement is achieved through a cyclic training-prediction alternating strategy that dynamically adapts to evolving interface conditions. Comprehensive validation demonstrates PINN-MHRS's exceptional performance. For one-dimensional shock tube cases with ideal gas EOS, it achieves errors at least five orders of magnitude lower than Harten–Lax–van Leer Contact (HLLC). When handling the complex Jones-Wilkins-Lee (JWL) EOS for explosives, PINN-MHRS exhibits three orders of magnitude higher accuracy (error: 2.90×10−1) compared to HLLC (error: 2.70×102). In two-dimensional air–helium shock–bubble interaction simulations, PINN-MHRS maintains low-error predictions while accurately solving interface evolution details. PINN-MHRS establishes a new paradigm for high-accuracy, stable Riemann solvers in complex multi-material flow simulations. The Fortran-Python hybrid framework successfully bridges machine learning and traditional computational fluid dynamics (CFD). Its ability to handle complex EOS without labeled data and to maintain physical consistency suggests broad applicability to other interfacial dynamics problems.

Solving fluid–structure interaction (FSI) problems using traditional methods poses significant challenges in the field of numerical simulation. The multiphysics coupling library precise code interaction coupling environment (preCICE), renowned for its robust multiphysics coupling capabilities, offers a promising solution for FSI problems. It supports various open/closed source software and commercial computational fluid dynamics solvers in a black box manner. However, preCICE currently mainly supports coupling schemes of mesh-based methods as well as few meshless methods. This paper proposes a critical grid interpolation (CGI) method as an intermediate medium to enable the particle method to interface with preCICE. The CGI method leverages particles and critical meshes to interpolate displacement and force, thereby allowing the pure Lagrangian smoothed particle hydrodynamics (SPH) method to solve FSI problems. The critical mesh serves as a coupling interface between particles and meshes, theoretically resolving the mesh mismatch issue inherent in particle methods when connecting to preCICE. To validate the performance of the proposed CGI method, we conducted experiments where the fluid and the structure were discretized using SPH and the finite element method, respectively. The results demonstrate the effectiveness of the CGI method in solving FSI problems. Additionally, we have made our source code for the SPH fluid adapter open-source and available on (https://github.com/terrylongsifan/AdapterSPH), facilitating further development of preCICE compatibility with more meshless methods. This study significantly advances the application of SPH in FSI problems by integrating it with preCICE, thereby expanding the versatility and applicability of multiphysics simulations.