Computer Physics Communications

GHWC is a GPU-accelerated gyrofluid code for simulating quasi-two-dimensional turbulence with consistent finite Larmor radius (FLR) effects in magnetized plasmas. The simulation setup allows for fundamental studies of FLR effects on isothermal resistive drift waves, turbulence, and zonal flows. It includes the standard Hasegawa-Wakatani model in the limit of cold ions. It is a GPU-accelerated update of GHW, rewritten in CUDA. This version has already been used in a recent publication on zonal-flow merging.

One major shortcoming of classical and mixed quantum-classical approaches devoted to large helium nanodroplets (HNDs) is the lack of superfluidity. A method, recently published in Chemical Physics Letters, is implemented in DynHeMat to enable the user to take into account helium superfluidity on a phenomenological manner by imposing projectiles colliding with HNDs to maintain their velocity at the critical Landau velocity when they move within the HND. Projectiles can thus enter deeply inside the droplet and, for instance, the stability of weakly-bound complexes in helium can be investigated. Moreover, the database provided with DynHeMat, called ZPAD_DB, contains a new file with the positions and velocities of He_70000 equilibrated at T = 0.37 K for 1.5 ns with the mPL He-He pseudopotential. Output files collecting the HND center-of-mass position and linear momentum as well as the HND total angular momentum are supplied for each trajectory and output files gathering energetic data are somewhat changed. Finally, a few error messages displayed at DynHeMat execution are slightly modified.

Efficient simulation of photon propagation in highly scattering, fluorescent multi-layer media remains challenging due to the need to simultaneously model scattering, absorption, and emission. Existing open-source codes, developed primarily for biomedical and sensing applications, cannot handle multi-layer configurations with multiple fluorescent and non-fluorescent (e.g., white filler) inclusions. There is a need for user-friendly open-source tools capable of modeling such complex structures, with applications in radiative cooling, energy harvesting, and solid-state lighting. Additionally, accommodating uncertainty in optical properties is a valuable feature currently lacking in existing codes. In this paper, we present an open-source code utilizing a Python-based, parallelized Monte Carlo algorithm that handles photon propagation in multi-layer media with fluorescent and non-fluorescent inclusions, simulating the following spectral radiative properties: reflectance, spectral fluorescence, absolute and normalized radiosity, total and different types of absorptances, and transmittance. This work serves as a built function on FOS, a previous open-source code developed by our group for non-fluorescent media. The Fluor-FOS open-source code enables efficient simulation of spectral radiative properties for fluorescent media in radiative cooling applications, LED packages, and energy harvesting systems. The proposed software has been validated against an open-source code and two experimental cases with different configurations and light sources, ensuring the fidelity of the proposed modified Monte Carlo algorithm.

In recent years, the concept of kirigami has been used in creating deployable structures for various scientific and technological applications. While high-fidelity Finite Element Analysis (FEA) is the standard for analyzing stress distributions and material deformation, it is computationally intensive and often ill-suited for the rapid exploration of vast kinematic configuration spaces. In this work, we develop PyKirigami, a lightweight, open-source Python framework for the efficient deployment simulation of kirigami structures. Unlike continuum mechanics solvers, PyKirigami models tessellations as articulated rigid-body networks, allowing for the real-time simulation of global deployment trajectories and volumetric transformations. The tool incorporates collision detection and interactive actuation, enabling users to validate folding paths and identify geometric locking states in both 2D and 3D topologies. This framework serves as a fast kinematic prototyping tool for kirigami structures, allowing researchers to verify deployment mechanics and self-contacts prior to performing detailed mechanical analysis or physical fabrication.

We present HYMOR (Hypersonic Modal/non-modal, and Receptivity), an open-source computational framework for the linear stability analysis of high-enthalpy hypersonic flows. The toolkit includes MATLAB and Julia implementations and is released under the MIT license. HYMOR provides global modal, non-modal, and receptivity analyses capable of capturing interactions among spatially separated physical mechanisms that are inaccessible to traditional local methods. A shock-fitting formulation is employed to treat the bow shock as a sharp discontinuity, ensuring that the interaction of infinitesimal disturbances with the shock recovers the response predicted by linear interaction analysis. The code also solves the nonlinear equations for base-flow computation and automatically linearizes the resulting discrete operators for the stability analyses. Several thermochemical models are available for the treatment of real-gas effects in high-enthalpy regimes. The numerical implementation is verified against a collection of benchmark cases that demonstrate the accuracy and capabilities of the toolkit across its modal, non-modal, and receptivity analysis modes.

In this paper, we present HyperPrecision, a Mathematica package for high-precision numerical evaluation of general Horn-type multivariate hypergeometric functions and their Laurent expansions in a small parameter ϵ. Such functions appear widely in physics and mathematics, with applications ranging from quantum field theory and string theory to number theory and statistics. Their high-precision numerical evaluation, however, remains challenging, since their defining series converge only in restricted domains and analytic continuation beyond these domains is, in general, non-trivial. HyperPrecision addresses this problem by automatically constructing the Pfaffian system of partial differential equations for a given hypergeometric function and restricting it to a one-dimensional contour in the space of variables connecting the starting point to the target point. The resulting ordinary differential equation is then solved by the Frobenius method, with the boundary conditions analytically determined by the defining series. We illustrate the use of the package by evaluating commonly occurring multivariate hypergeometric functions, including the Appell F1, F2, F3, and F4 functions, the Horn G- and H-series, and the Lauricella FA, FB, FC, and FD functions, as well as by considering applications to angular integrals, Feynman integrals, and cosmological and holographic correlators.

The GW method is a widely employed approach for calculating quasiparticle energies and band structures of materials. The static Coulomb hole plus screened exchange (COHSEX) approximation, which corresponds to the static limit of the GW self-energy, provides a computationally efficient alternative. In systems with spin-orbit coupling (SOC), it is conventional practice to treat SOC as a perturbation and apply GW corrections separately to obtain quasiparticle energies incorporating SOC effects. However, this approach may be inadequate for accurately describing these effects due to its initial neglect of spin degrees of freedom. To address this limitation, we implement the spinor COHSEX and GW formalism in KSSOLV, a MATLAB toolbox for electronic structure calculations using Kohn-Sham density functional theory. Our implementation enables meticulous treatment of spinor in COHSEX and GW calculations. We validate the correctness and computational efficiency of our approach by calculating quasiparticle energies for various molecular and periodic solids with different spin-orbit coupling strengths.

We present SPARC-atomSFE, a spectral finite-element package for accurate and efficient atomic structure calculations within the framework of Kohn-Sham density functional theory. The package supports both all-electron and norm-conserving pseudopotential calculations across a broad hierarchy of exchange-correlation approximations, spanning local, semilocal, and nonlocal functionals, within a spin-unpolarized, non-relativistic, spherically symmetric atomic framework. The nonlocal functionals include hybrid functionals and the many-body random phase approximation (RPA); for hybrid functionals, we implement both the generalized Kohn–Sham and optimized effective potential (OEP) approaches, while RPA is treated within the OEP framework. SPARC-atomSFE also includes support for fractional orbital occupations and charged atoms. Spatial discretization is based on an adaptive grid with element nodes distributed according to the Legendre–Gauss–Lobatto scheme, high-order C^0-continuous Lagrange polynomial basis functions, and Gauss–Legendre quadrature for numerical integration. We present systematic convergence studies and identify the computational parameters required to achieve target accuracies. We validate the accuracy of SPARC-atomSFE through representative calculations spanning the various exchange-correlation approximations, obtaining results that are in very good agreement with values reported in the literature. We further demonstrate two representative applications: accuracy testing of pseudopotentials for advanced exchange-correlation, and machine learning of the exact exchange OEP potential.

Accurate simulations of combustion phenomena require solving stiff ordinary differential equations (ODEs) governing chemical reactions, which often constitute a substantial fraction of the total simulation time. However, realistic chemical reaction mechanisms typically involve numerous species and exhibit high levels of mathematical stiffness. While implicit solvers with adaptive step sizes are essential for handling stiff systems, their implementation on Graphics Processing Units (GPUs) faces significant challenges due to thread divergence arising from adaptive time-stepping and error control mechanisms. As a result, existing GPU-accelerated chemical ODE solvers have predominantly focused on explicit methods or simple implicit methods, leaving a significant gap in efficient GPU-accelerated implicit solvers for stiff ODEs requiring high-order accuracy. Here we introduce GPSODE (GPU-accelerated Parallel Stiff ODE solver), a CUDA-centric package (kernels and device-side scheduling in CUDA; Python/C utilities in the repository) that implements an enhanced state-detection-based SEULEX (SD-SEULEX) algorithm specifically optimized for GPU architectures. The SD-SEULEX method enables the software to dynamically adjust the set of active grid points to mitigate GPU thread divergence while maintaining the numerical accuracy and stability of the classical SEULEX method. Floating-point work, state tagging, and index compaction for clustering run on the GPU; the host launches kernels and reads compact counters. Validation tests demonstrate excellent agreement with reference solutions for zero-dimensional homogeneous reactors using H2, CH4, and C7H16 mechanisms, as well as one-dimensional hydrogen-air premixed flames. Performance evaluations show that SD-SEULEX achieves a two-fold performance improvement compared to the baseline SEULEX algorithm. When integrated into OpenFOAM, GPSODE achieves a 172x speedup in the chemical solver compared to a single CPU core, with multi-GPU scaling tests showing 18.4x speedup in the chemical solver and 10.1x overall acceleration versus a 32-core CPU node. C-linkable entry points are provided for integration with OpenFOAM and other CFD stacks.

Phonon calculations are pivotal for understanding the thermodynamic and dynamical properties of materials, yet density functional theory (DFT) approaches often incur prohibitive computational costs for large-scale systems such as moiré superlattices. We introduce NEPHONON, an open-source high-performance package designed to quickly calculate phonon properties of extended systems with near-DFT accuracy. By leveraging the computational efficiency of machine-learned Neuroevolution potentials, NEPHONON enables the rapid generation of second-order force constants even in systems with thousands of atoms. Beyond standard phonon band structures, density of states, and group velocity, NEPHONON implements the simulation of isofrequency phonon surfaces and inelastic neutron scattering spectra S(Q,E). Furthermore, the code provides access to full phonon eigenvectors, facilitating advanced analyses of topological chiral phonons and visualization of vibrational modes. Through benchmarks on twisted bilayer phosphorene, graphene, and copper, we demonstrate the package's capabilities, confirming that NEPHONON is a powerful tool for rapidly exploring complex phononic phenomena in materials.