Computer Physics Communications

Given the growing emphasis on hypersonic propulsion systems, accurate prediction of thermochemical non-equilibrium phenomena has become paramount. However, the thermodynamic models used to simulate combustion and thermal non-equilibrium flows in hypersonic regimes differ substantially. The simple partition-function model–predominantly applied in thermal non-equilibrium simulations–relies on rigid-rotor and harmonic-oscillator approximations that neglect key molecular energy interactions, whereas polynomial representations of thermodynamic properties–widely employed in combustion simulations–incorporate additional correction factors to account for detailed molecular and atomic energy modes. In this paper, we introduce a novel OpenFOAM-based solver for a unified approach to thermochemical non-equilibrium flows using a polynomial representation. This contrasts with most prior work, which applies polynomial representations to combustion but relies on the simple partition-function model for thermal non-equilibrium flows. Notably, this solver implements a damped Newton–Raphson method to enhance numerical stability and convergence during temperature evaluations. We first analyzed the differences between these thermodynamic models and their respective impacts on predicted properties. Subsequently, we performed numerical simulations for a 0-D heat bath, the LENS-XX double cone, and a shock-induced combustion case. The results demonstrate that, the polynomial representation–incorporating detailed molecular energy modes at lower computational cost–exhibits pronounced differences in thermodynamic properties relative to the simple partition-function model, demonstrating superior suitability for thermochemical non-equilibrium flow analysis.

Deep Frequency Modulation Interferometry (DFMI) is an emerging laser interferometry technique for high-precision metrology, offering picometer-level displacement measurements and the potential for absolute length determination with sub-wavelength accuracy. However, the design and optimization of DFMI systems involve a complex interplay between interferometer physics, laser technology, multiple noise sources, and the choice of data processing algorithm. To address this, we present DeepFMKit, a new open-source Python library for the end-to-end simulation and analysis of DFMI systems. The framework features a high-fidelity physics engine that rigorously models key physical effects such as time-of-flight delays in dynamic interferometers, arbitrary laser modulation waveforms, and colored noise from user-defined 1/fα spectral densities. This engine is coupled with a suite of interchangeable parameter estimation algorithms, including a highly-optimized, frequency-domain Non-linear Least Squares (NLS) for high-throughput batch processing of experimental data, and multiple time-domain Extended Kalman Filter (EKF) implementations for sample-by-sample state tracking, featuring both random walk and integrated random walk (constant velocity) process models. Furthermore, DeepFMKit includes a high-throughput experimentation framework for automating large-scale parameter sweeps and Monte Carlo analyses, enabling systematic characterization of system performance. DeepFMKit’s modular, object-oriented architecture facilitates the rapid configuration of virtual experiments, providing a powerful computational tool for researchers to prototype designs, investigate systematic errors, and accelerate the development of precision interferometry.

We propose a novel method for reconstructing Laurent expansion of rational functions using p-adic numbers. By evaluating the rational functions in p-adic fields rather than finite fields, it is possible to probe the expansion coefficients simultaneously, enabling their reconstruction from a single set of evaluations. Compared with the reconstruction of the full expression, constructing the Laurent expansion to the first few orders significantly reduces the required computational resources. Our method can handle expansions with respect to more than one variables simultaneously. Among possible applications, we anticipate that our method can be used to simplify the integration-by-parts reduction of Feynman integrals in cutting-edge calculations.

The compressible reacting flow numerical solver is an essential tool in the study of combustion, energy disciplines, as well as in the design of industrial power and propulsion devices. We have established the first JAX-based (a Python library developed by Google for accelerator-oriented array computation and high-performance numerical computing) block-structured adaptive mesh refinement (AMR) framework, called JAX-AMR, and then developed a fully-differentiable solver for compressible reacting flows, named JANC. JANC is implemented in Python and features automatic differentiation capabilities, enabling an efficient integration of the solver with machine learning. Furthermore, benefited by multiple acceleration features such as accelerated linear algebra (XLA)-powered Just-In-Time (JIT) compilation, GPU/TPU computing, parallel computing, and AMR, the computational efficiency of JANC has been significantly improved. In a comparative test of a two-dimensional detonation tube case, the computational cost of the JANC core solver, running on a single A100 GPU, was reduced to 1% of that of OpenFOAM, which was parallelized across 384 CPU cores. When the AMR method is enabled for both solvers, JANC’s computational cost can be reduced to 1-2% of that of OpenFOAM. The core solver of JANC has also been tested for parallel computation on a 4-card A100 setup, demonstrating its convenient and efficient parallel computing capability. JANC also shows strong compatibility with machine learning by combining adjoint optimization to make the whole dynamic trajectory efficiently differentiable. JANC provides a new generation of high-performance, cost-effective, and high-precision solver framework for large-scale numerical simulations of compressible reacting flows and related machine learning research.

Orbital magnetization, a key property arising from the orbital motion of electrons, plays a crucial role in determining the magnetic behavior of molecules and solids. Despite its straightforward calculation in finite systems, the computation in periodic systems poses challenges due to the ill-defined position operator and surface current contributions. The modern theory of orbital magnetization, formulated in the Wannier representation and implemented within the Density Functional Theory (DFT) framework, offers an accurate solution through the “converse approach.” In this paper, we introduce QE-CONVERSE, a refactored and modular implementation of the converse method, designed to replace the outdated routines from Quantum ESPRESSO (version 3.2). QE-CONVERSE integrates recent advancements in computational libraries, including scaLAPACK and ELPA, to enhance scalability and computational efficiency, particularly for large supercell calculations. While QE-CONVERSE incorporates these improvements for scalability, the main focus of this work is provide the community with a performing and accurate first principles orbital magnetization package to compute properties such as Electron Paramagnetic Resonance (EPR) g-tensors and Nuclear Magnetic Resonance (NMR) chemical shifts, specially in systems where perturbative methods fail. We demonstrate the effectiveness of QE-CONVERSE through several benchmark cases, including the NMR chemical shift of 27Al in alumina and 17O and 29Si in α-quartz, as well as the EPR g-tensor of nΣ (n ≥ 2) radicals and substitutional nitrogen defects in silicon. In all cases, the results show excellent agreement with theoretical and experimental data, with significant improvements in accuracy for EPR calculations over the linear response approach. The QE-CONVERSE package, fully compatible with the latest Quantum ESPRESSO versions, opens new possibilities for studying complex materials with enhanced precision.

The scrape-off layer of a tokamak fusion reactor carries the plasma exhaust from the hot core plasma to the material surfaces of the reactor vessel. The heat loads imposed by the exhaust are a critical limit on the performance of fusion power plants. Turbulent transport of the plasma regulates the width of the scrape-off layer plasma and must be modelled to understand the intensity of these heat loads. STORM is a plasma turbulence code capable of simulating three dimensional turbulence across the full scrape-off layer of a tokamak fusion reactor, using a drift reduced, collisional fluid model. STORM uses mostly finite difference schemes, with a staggered grid in the direction parallel to the magnetic field. We describe the model, geometry and initialisation options used by STORM, as well as the numerical methods, which are implemented using the BOUT++ plasma simulation framework. BOUT++ has been enhanced alongside the development of STORM, providing better support for staggered grid methods. We summarise these enhancements, including a detailed explanation of the parallel derivative methods, which underwent a major update for version 4 of BOUT++.

Lethe is an open-source Computational Fluid Dynamics (CFD) software framework with extensive multiphase and multiphysics capabilities. By leveraging the deal.II open-source framework, Lethe finite element solvers scale well on modern high-performance computers while possessing advanced features such as dynamic mesh adaptation, load-balancing, isoparametric high-order capabilities, and a fully-fledged Discrete Element Method (DEM) module. To facilitate contributions from the community, Lethe is extensively tested with continuous integration using over 450 unit and functional tests. Furthermore, Lethe contains 74 fully documented examples with pre-processing and post-processing steps to allow users to learn how to rapidly use and modify the framework. In this article, we give an overview of the simulation models available within Lethe and illustrate these capabilities with a selected list of examples including turbulent and multiphase flows.

In this study, we introduce Fire, an open-source adaptive mesh refinement (AMR) solver for supersonic reacting flows, and conduct theoretical analyses on the efficiency of AMR methods. Fire is developed within the AMR framework of ECOGEN (Schmidmayer et al., 2020). To accurately model compressible multi-component reacting flows, the Fire solver employs the thermally perfect gas model for multi-species gaseous mixtures, mixture-averaged transport models for viscous fluxes, and detailed finite-rate chemistry for combustion processes. The solver utilizes the Harten-Lax-van Leer Contact approximate Riemann solver with low-Mach number correction to evaluate inviscid fluxes, demonstrating its superiority over the traditional Harten-Lax-van Leer Contact solver on detonation simulations. Moreover, we deduce the theoretical speedup ratio (denoted as η_the) of AMR methods over uniform-grid methods by analyzing the advancing procedures. This theoretical analysis is well-supported by the numerical speedup ratio (denoted as η_num) given by numerical tests. To further enhance computational efficiency, we propose a three-stage AMR strategy specifically tailored to the characteristics of inert flows, flame fronts, and shock-flame interactions. Comprehensive validation tests, encompassing unsteady convection and diffusion, planar deflagration, inert and reacting shock-bubble interactions, planar detonations, and detonation cellular structures, confirm the accuracy and efficiency of Fire in simulating supersonic combustions. We anticipate that this work will not only serve as a valuable numerical tool for supersonic reacting flows research but also contribute to a deeper understanding and improvement of AMR methodologies.

The symmetry-constrained response tensors on transport, optical, and electromagnetic effects are of central importance in condensed matter physics because they can guide experimental detections and verify theoretical calculations. These tensors encompass various forms, including polar, axial, i-type (time-reversal even), and c-type (time-reversal odd) matrixes. The commonly used magnetic groups, however, fail to describe the phenomena without the spin-orbit coupling (SOC) effect and cannot build the analytical relationship between magnetic orders with response tensors in magnetic materials. Developing approaches on these two aspects is quite demanding for theory and experiment. In this paper, we use the magnetic group, spin group, and extrinsic parameter method comprehensively to investigate the symmetry-constrained response tensors, then implement the above method in a platform called "TensorSymmetry". With the package, we can get the response tensors disentangling the effect free of SOC and establish the analytical relationship with magnetic order, which provides useful guidance for theoretical and experimental investigation for magnetic materials.

The concept of representative volume (REV, or RVE in material sciences) is the cornerstone of continuum scale models – one needs to get the volume big enough so that it could be represented with an averaged value (scalar, vector or tensorial, etc.). While REV should be established in all larger scale simulations, this is rarely done in practice despite widespread adoption of 3D imaging devices in all research areas starting from petroleum engineering, material sciences and spanning to biology. Sometimes REV analysis is performed in the form of very simple procedures, such as a check for convergence of porosity or surface area, which is technically identical to omitting it entirely. The main reason for this to happen is the poor understanding of REV concept in general (mainly its connection to spatial stationarity and necessity for vector metrics with high information content) and unavailability of open-source robust solutions. In this work we present PARSE library that solves exactly this problem – we developed an easy to use and well-documented code based on rigorous research carried out recently in explaining the “dark sides” of representativity. In addition to REV, our code allows spatial stationarity analysis and comparison of samples (with subsequent clusterization into different groups) based on vector metrics – correlation functions, persistence diagrams and pore-network statistics, that altogether possess high information content which is critical in establishing stationarity and REV. We test our library on images produced by known statistical processes, such as Poisson spheres. After verification, we show how to compare different samples and group them depending on their “structural DNA”. All solutions explained in the paper are represented by Jupiter notebooks that can be used to perform similar analysis, moreover, the class structure of PARSE library allows painless modifications to be implemented. We believe that such an open-source library will be useful in numerous fields and will become an invaluable tool for 3D image analysis.