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The Direct Simulation Monte Carlo (DSMC) method was widely used to simulate low density gas flows with large Knudsen numbers. However, DSMC encounters limitations in the regime of lower Knudsen numbers (Kn < 0. 05). In such cases, approaches from classical computational fluid dynamics (CFD) relying on the continuum assumption are preferred, offering accurate solutions at acceptable computational costs. In experiments aimed at imaging aerosolized nanoparticles in vacuo a wide range of Knudsen numbers occur, which motivated the present study on the analysis of the advantages and drawbacks of DSMC and CFD simulations of rarefied flows in terms of accuracy and computational effort. Furthermore, the potential of hybrid methods is evaluated. For this purpose, DSMC and CFD simulations of the flow inside a convergent–divergent nozzle (internal expanding flow) and the flow around a conical body (external shock generating flow) were carried out. CFD simulations utilize the software OpenFOAM and the DSMC solution is obtained using the software SPARTA. The results of these simulation techniques are evaluated by comparing them with experimental data (1), evaluating the time-to-solution (2) and the energy consumption (3), and assessing the feasibility of hybrid CFD-DSMC approaches (4). • Accuracy and performance evaluation of flow predictions using CFD and DSMC. • Low density internal expanding flow and external shock-generating flow. • Sensitivity study of the DSMC simulations concerning various parameters. • Strong/weak scaling studies analyzing the comput. speedup and energy consumption. • Exploitation of an optimal parametric setup for hybrid DSMC/CFD simulations. [ABSTRACT FROM AUTHOR]

Recently, a unified stochastic particle (USP) method [14] has been developed for the simulation of multiscale nonequilibrium gas flows. Compared with the conventional stochastic particle methods, such as the direct simulation Monte Carlo (DSMC) method, USP can be applied with much larger time steps and cell sizes as it couples the effects of particle movements and collisions. To extend the applicability of USP to complex nonequilibrium gas flows, we develop a USP solver referred to as SPARTACUS within the framework of SPARTA, which is a widely used software based on the DSMC method for the simulation of rarefied gas flows. Inheriting from SPARTA, SPARTACUS is parallelized with the message passing interface (MPI), and it is open-source under the GNU General Public License (GPL) and released in an online repository with essential documentation, tutorial examples, and benchmark cases. To evaluate the performance of SPARTACUS, the test cases cover from one to three dimensional flows with a wide range of the Knudsen number and Mach number, including Couette flow, lid-driven cavity flow, hypersonic flow past a cylinder, and supersonic flow around a blunt body. The results obtained by SPARTACUS in the rarefied and continuum regimes are in good agreement with DSMC and computational fluid dynamics (CFD) results, respectively. Moreover, SPARTACUS has significant computational efficiency advantage over traditional DSMC solvers, especially for three-dimensional nonequilibrium gas flows. In addition, SPARTACUS shows comparable parallel efficiency with SPARTA, so it is promising to be applied with high performance computing for the simulation of complex multiscale gas flows encountered in engineering problems. Program Title: SPARTACUS CPC Library link to program files: https://doi.org/10.17632/pyb343w7zv.1 Developer's repository link: https://github.com/KKFeng/spartacus Licensing provisions: GNU General Public License version 2 Programming language: C++ External routines/libraries: SPARTA (http://sparta.sandia.gov/) Nature of problem: Multiscale simulation of nonequilibrium gas flows using unified stochastic particle method. Solution method: Unified Stochastic Particle (USP) method. • An open-source unified stochastic particle solver, SPARTACUS, was developed for the simulation of multiscale gas flows. • The results obtained by SPARTACUS in both rarefied and continuum regimes agree well with DSMC or CFD results. • SPARTACUS has significant computational efficiency advantage over traditional DSMC solvers. [ABSTRACT FROM AUTHOR]

Direct Simulation Monte Carlo (DSMC) method is gradually being applied to simulate near-continuum flowfields. However, the large amount of computation hinders its application. Parallelization is the primary way to solve this problem. Nevertheless, when the Knudsen number (Kn) is less than 1.0 × 10 − 2 , it is hard to gain good load balance while parallelized since the overall computational load of DSMC is challenging to determine and also changes with cases. This paper proposed a fine-grain parallel strategy based on the shared memory architecture. This strategy is designed to gain good load balance through reasonably decoupling computational load, which means that the load of each module of the program is treated separately, not as a whole. Implemented through Open Multi-Processing (OpenMP), a "dual-decomposition" method is adopted to achieve the load-decoupling strategy. In the "dual-decomposition" method, the computation domain is decomposed with a different weight according to whether one module is particle-dominated or collision-dominated. Besides, a multi-segment partition algorithm with low overhead is proposed. Two cases, including one unsteady shock-bubble interaction (SBI) case and the other steady supersonic jet case, are used to verify our strategy's correctness and test the performance under different conditions. The results show the strategy newly proposed can reduce more than 70% load imbalance relative to our previous P a r t P l u s C o l l strategy when K n < 1.0 × 10 − 2. Simultaneously, the results prove that our load-decoupling strategy can improve parallel efficiency by about 11.17 % − 21.80 % , compared with Sparta [1]. This strategy sets the fundamental for improving parallel efficiency of large-scale parallel computing of DSMC. [ABSTRACT FROM AUTHOR]