Search arXivSearch

arXiv · 1711.06211

An Efficient Algorithm for the Multicomponent Compressible Navier-Stokes Equations in Low- and High-Mach Number Regimes

Abstract

The goal of this study is to develop an efficient numerical algorithm applicable to a wide range of compressible multicomponent flows. Although many highly efficient algorithms have been proposed for simulating each type of the flows, the construction of a universal solver is known to be challenging. Extreme cases, such as incompressible and highly compressible flows, or inviscid and highly viscous flows, require different numerical treatments in order to maintain the efficiency, stability, and accuracy of the method. Linearized block implicit (LBI) factored schemes are known to provide an efficient way of solving the compressible Navier-Stokes equations implicitly, allowing us to avoid stability restrictions at low Mach number and high viscosity. However, the methods' splitting error has been shown to grow and dominate physical fluxes as the Mach number goes to zero. In this paper, a splitting error reduction technique is proposed to solve the issue. A novel finite element shock-capturing algorithm, proposed by Guermond and Popov, is reformulated in terms of finite differences, extended to the stiffened gas equation of state (SG EOS) and combined with the LBI factored scheme to stabilize the method around flow discontinuities at high Mach numbers. A novel stabilization term is proposed for low Mach number applications. The resulting algorithm is shown to be efficient in both low and high Mach number regimes. The algorithm is extended to the multicomponent case using an interface capturing strategy with surface tension as a continuous surface force. Numerical tests are presented to verify the performance and stability properties for a wide range of flows.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Roman Frolov. 2018-10-02. An Efficient Algorithm for the Multicomponent Compressible Navier-Stokes Equations in Low- and High-Mach Number Regimes. https://doi.org/10.1016/j.compfluid.2018.10.001

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bi-Hamiltonian in Semiflexible Polymers built upon Overdamping Process

Quantifying the interaction between a system of interest and its ambient conditions, the memory effect links the states of two distinct Hamiltonians: one for the target system and one for the environment. In this paper, we propose the diffusion process derived from the Smoluchowski equation that can derive the evolution process described by the memory effect integration in a non Markovian regime. The Smoluchowski picture, within the framework of stochastic thermodynamics, justifies a diffusion process incorporated into the equations of motion, and the result of the derivation enables a coarse-grained molecular dynamics simulation with the modified equation of motion to reproduce attenuation from collisions between single walled carbon nanotubes (SWCNTs) under far from equilibrium conditions. The results of the numerical experiments on the collision confirm that heat diffusion compensates for the correlated momentum arising from the memory effect between the two Hamiltonians in both equilibrium and far from equilibrium states.

physics.comp-ph

Translation of transient acoustic fields

A method is presented for the translation of acoustic field data from a source to a target region. Field data are represented as spherical harmonic expansions on spheres surrounding the source and target regions respectively and expansions are translated using a ``point and shoot'' method using the Kirchhoff--Helmholtz integral to carry out an axial translation from one sphere to the other. The principal motivation for the method is its use in a time-domain Fast Multipole Method, and test cases reflective of this application are presented. The method converges to six digits for appropriate values of parameters and for the values of $N$ considered here computational effort scales approximately as $N^{2}$ where $N$ is the order of spherical harmonic expansion for the field data. The method is causal and thus avoids artifacts generated in methods which are not based on intrinsically causal formulations.

physics.comp-ph

Learning continuous reaction paths for transition-state prediction

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.

physics.comp-ph