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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Low-loss Si-based Dielectrics for High Frequency Components of Superconducting Detectors

Silicon-based dielectric is crucial for many superconducting devices, including high-frequency transmission lines, filters, and resonators. Defects and contaminants in the amorphous dielectric and at the interfaces between the dielectric and metal layers can cause microwave losses and degrade device performance. Optimization of the dielectric fabrication, device structure, and surface morphology can help mitigate this problem. We present the fabrication of silicon oxide and nitride thin film dielectrics. We then characterized them using Scanning Electron Microscopy, Atomic Force Microscopy, and spectrophotometry techniques. The samples were synthesized using various deposition methods, including Plasma-Enhanced Chemical Vapor Deposition and magnetron sputtering. The film's morphology and structure were modified by adjusting the deposition pressure and gas flow. The resulting films were used in superconducting resonant systems consisting of planar inductors and capacitors. Measurements of the resonator properties, including their quality factor, were performed.

low-loss dielectrics↗
DOE OSTI2025

Cracking the failure of lithium batteries

Lithium batteries that use a solid electrolyte have the potential to improve safety and increase the amount of stored energy . This makes solid-state electrochemical cells a promising option for electric vehicles and wearable devices. However, nonuniform plating or stripping of lithium at the interface between an anode (negative electrode) and the electrolyte during charging and discharging leads to growth of detrimental lithium filaments (dendrites) that short-circuit the battery cell. This problem even occurs when the battery operates at small currents. The underlying mechanism of this failure is not well understood. On page 311 of this issue, Wang et al. (1) report that structural defects accumulate in the lithium metal anode under repeated charging and discharging at a small current. This is similar to mechanical fatigue that is observed over longer periods of intermittent straining of a material. Here, the observation could guide the design of lithium batteries with increased life span.

25 ENERGY STORAGE↗
DOE OSTI2023

Machine learning and LHC event generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Interactions between Surface Chemistry and Gas-Phase Combustion: New Optical Tools for Probing Flame-Wall Interactions and the Heterogeneous Chemistry of Soot Growth and Oxidation in Flame. Final report

Some of the most stubborn and technologically critical problems in combustion are dominated by heterogeneous processes. While purely gas-phase combustion systems have been the subject of intense theoretical and experimental study, combustion phenomena occurring at interfaces are far less understood. This is partly caused by the lack of experimental approaches capable of probing locations very close to an interface, especially in the hostile environment of combustion. For laser-based optical techniques, measurements taken near interfaces are often complicated by laser scattering from the surface interfering with relatively weak signals. Further, for measurements intended to probe molecular species adsorbed at the interface between a gas-phase combustion reaction and a condensed phase material, signals are generally overwhelmed by contributions from the bulk phases, causing the small contribution from the interfacial molecular species to be undetectable. Our goal in this project has been to develop new optical tools for imaging chemical species, temperature, and surface species at and near surfaces or interfaces of relevance to combustion. We have placed focus on the development and refinement of ultrafast techniques such as femtosecond coherent Raman imaging and femtosecond/picosecond sum-frequency generation (SFG) scattering, as well as the models used to simulate such spectra under differing conditions of pressure and chemical speciation. The two physical phenomena initially targeted for study in this project were flamewall interactions, and the growth of particulates in flames.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

(U) SENSMG: First-Order Sensitivities of Neutron Reaction Rates, Reaction-Rate Ratios, Leakage, k eff , α , and Subcritical Multiplication Using PARTISN

SENSMG is a tool for computing first-order sensitivities of neutron reaction rates, reaction-rate ratios, leakage, k eff , α, and subcritical multiplication using the PARTISN multigroup discrete-ordinates code. SENSMG computes sensitivities to all of the transport cross sections and data (total, fission, a nu, chi, and all scattering moments), two edit cross sections (absorption and capture), and the density for every nuclide and energy group. It also computes sensitivities to the mass density for every material and derivatives with respect to all interface locations and outer boundaries. It computes sensitivities to user specified reactions whose cross sections are available in a user-supplied NJOY output file. The tool can be used for one-dimensional spherical and slab (r) and two-dimensional cylindrical (r-z) geometries. The tool can be used for fixed-source and eigenvalue problems. For most responses, the tool implements Generalized Perturbation Theory (GPT) as discussed by Williams and Stacey. The tool is thus limited to computing sensitivities only for GPT-allowable responses. For subcritical multiplication, the tool implements sensitivities derived by O’Brien and Clark. SENSMG has a similar role as the old SWANLAKE (Ref. 8), FORSS (Ref. 9), and SENSIT (Ref. 10) codes. It has capabilities similar to those of SUSD3D (Refs. 11 and 12), which also uses PARTISN. Section II of this report describes the theory behind adjoint-based sensitivities, gives the equations that SENSMG solves, and defines the sensitivities that are output. Section III describes the user interface, including the input file and command line options. Section IV describes the output. Section V gives some notes about the coding that may be of interest. Section VI presents some sample problems and discusses verification, which is ongoing. Section VII lists needs and ideas for future work. Appendix A lists most of the input files whose results are presented in Sec. VI. Appendix B provides some useful details on one of the cross-section libraries that SENSMG supports.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. In this work, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors ( 2 χ n , topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Implementing a unified solver for nonlinearly constrained optimization

SQP and interior-point methods (also referred to as Lagrange-Newton methods) typically share key algorithmic components, such as strategies for computing descent directions and mechanisms that promote global convergence. Building on this insight, we introduce a unifying framework with eight building blocks that abstracts the workflows of Lagrange-Newton methods. We then present Uno, a modular C++ solver that implements our unifying framework and allows the automatic combination of a wide range of strategies with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno’s software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTE collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno and via its C, Julia, Python, Fortran, and AMPL interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Using micro-XRF to characterize chloride ingress through cold joints in 3D printed concrete

Digital fabrication methods with concrete have been rapidly developing, with many problems related to component production and material control being solved in recent years. These processes produce inherently layered cementitious components that are anisotropic, and in many cases, produces a weak interface between layers, which are generally referred to as cold joints. While material strength at these interfaces has been well studied in recent years, durability has received less attention, even though cold joints can function as channels for aggressive agents, such as chlorides. This work presents a method using micro-X-ray fluorescence (μXRF) to image chloride ingress into layer interfaces of 3D printed fine-grained concrete specimens produced with varying layer deposition time intervals, and also compares it to neutron imaging of moisture uptake. The results show that cold joints formed after a 1 day time interval are highly susceptible to chloride ingress, and that curing conditions play a major role in how quickly interfacial transport can take place. The μXRF method is also shown to be useful for study of transport of chlorides in cold joints, due to its spatial resolution and direct analysis of an aggressive species of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2023

OPTOM: Optimization of Parabolic Trough - Operations & Maintenance

The US Department of Energy’s SunShot goals look to reduce the cost of Concentrating Solar Power (CSP) technology to 5¢/kWh for baseload plants. This is about a 50% reduction from current costs. To achieve this cost target, a significant reduction in operation and maintenance (O&M) costs of 40 to 50% is likely needed. Advances are needed in the O&M practices of CSP plants if the technology is to achieve the SunShot cost goals. Digitization of plant performance and O&M data has become a new best practice in the world of renewable energy asset management. Owners and operators of large photovoltaic and wind power plants are working to digitize performance and O&M data at their existing assets, to improve their management of the facilities, to increase performance, reduce O&M costs, and lower the overall life cycle cost of ownership. CSP power plants are behind the curve of other technologies on the digitization of plant information to aid in the plant asset management. This project directly addresses the objective of digitizing the O&M data of the solar field, focusing on three areas: 1) creating a framework for sharing data and information, 2) creating a system for monitoring and managing the maintenance of the solar field collectors, and 3) developing analytic tools to identify issues in the solar field. According to the NREL CSP Best Practices Study, the current practice at many CSP plants is to rely on paper lists, spreadsheets, and email for monitoring and managing problems and maintenance in the solar field. The key element to digitize solar field O&M is the creation of a centralized data archive that all users and systems can interface with. This project developed a centralized relational database framework that allows users and applications to access and share data. Conventional power plants utilize Computerized Maintenance Management Systems (CMMSs) to track the corrective, preventive (scheduled), and predictive maintenance of equipment and subsystems in the power plant. CSP plants use these systems in the power block, but while these systems specialize at tracking maintenance on up to thousands of pieces of equipment, they are not well suited for tracking the tens or hundreds of thousands of components in large commercial CSP or photovoltaic solar fields. In this project we developed a new software application referred to as FieldStatus (TM). This is a specialized database program that is used to track the status of each collector and its components. This application is designed to complement the existing CMMS to enable improved tracking and management of maintenance activities in the solar field. One of the major maintenance tasks for solar fields is maintaining the cleanliness of the mirrors. Although seemingly a relatively straight forward task, it has often proven challenging to maintain high levels of cleanliness in an efficient and cost-effective manner. This project developed new tools and metrics for monitoring and optimizing solar field cleaning resources and overall solar field cleanliness.

14 SOLAR ENERGY↗
DOE OSTI2020

Corrosion Mitigation and Increased Discharge Capacity in Aqueous Processed Thick Cathodes using Phosphoric Acid Additives

Aqueous processed cathodes for lithium-ion batteries are favorable for both cost and environmental reasons; however, these electrodes still face significant problems with increasing areal capacities (i.e. thickness). Highly basic slurry conditions (pH in excess of 12) corrode the current collector surface and evolve hydrogen gas. Consequently, bubbling at the electrode interface causes substantial damage to the dried electrode. As the loading of these electrodes is increased, damage becomes severe and results in lack of adhesion and cohesion. Here introduction of phosphoric acid to combat the rise in pH and suppress the corrosion at the current collector surface is investigated. Phosphoric acid was added in increments of 0.5, 1.0, and 1.5 wt% and the subsequent effects on slurry rheology, particle size, adhesion, and electrochemical cycling were investigated. A technique is reported for obtaining thick (6-8 mAh/cm2) cathodes that exhibit reduced surface cracking and improved rate performance as compared to control samples.

Kukay, Alexander↗
DOE OSTI2023

Evolving efforts to maintain and improve XPS analysis quality in an era of increasingly diverse uses and users

Based on literature analysis, X-ray photoelectron spectroscopy (XPS) use continues to increase exponentially. This increased use is accompanied by anecdotal reports and systematic analyses indicating a growing presence of significantly flawed data analyses. Recognition of this problem within the surface analysis community has increased with an understanding that both inexperienced users and increased use of XPS outside the surface analysis community contribute to the problem. The XPS community has initiated several efforts to help address the problem, which is not unique to XPS. This paper describes some of the specific problems identified and some of the community efforts intended to address them. Here, we describe activities focused on three specific issues: (i) requests for detailed guides and protocols and bite-sized versions of information for non-experts, (ii) incomplete data and analysis reporting, and (iii) the high rate of peak fitting problems. A 2019 survey identified the need for guides, protocols, and standards to assist XPS users. One set of such guides has been published, and another is being assembled. Providing incremental bites of useful information is the goal of a series of papers on specific challenges to surface analysis with example solutions has been initiated as Notes and Insights papers in Surface and Interface Analysis. Examination of XPS-containing papers finds that information to establish the credibility and reproducibility of XPS results is often very incomplete. Unfortunately, ISO and ASTM standards require an amount of parameter reporting that seems excessive and unrealistic for many research publications. Initial approaches to develop and distribute a graded approach to parameter reporting are briefly described. Multiple efforts are underway to address the high rate of problems associated with photoelectron peak fitting. These include guides to peak fitting, guides to peak identification and fitting for specific elements, and the development of a peak fitting social network. The fitting social network is designed to facilitate interactions between new and experienced XPS users; analysts trying to fit XPS data (for publication or other reasons) can ask questions and establish dynamic conversations. Encouraging and enabling high-quality XPS analysis and reporting requires several different types of effort from all members of the surface and interface analysis community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Bipolar Membranes for Ion Management in (Photo)Electrochemical Energy Conversion

Conspectus (Photo)electrochemical energy conversion is important in the development of a carbon-neutral energy economy because it can provide a pathway for mitigating the intermittency of renewable energy sources such as wind and solar. In order to operate efficiently, these technologies, which include photoelectrochemical cells, water and CO 2 electrolyzers, fuel cells, and redox flow batteries, require fast charge transfer kinetics at the electrode/electrolyte interface as well as robust ion management in the electrolyte. In conventional electrolyzers and fuel cells, the electrolyte is strongly acidic or basic and ionic current is carried by H + or OH – ions. In contrast, photoelectrodes and electrocatalysts for water splitting are often studied in buffered solutions. The question of ion balance in these systems led us to analyze the polarization losses due to ion concentration gradients in cells that employed various buffer–membrane combinations. Continuously driving the buffer ions across an ionomer membrane not only lowers the buffer capacity of an aqueous electrolyte but also introduces pH gradients that result in significant energy losses. To address the problem, we and other groups have studied the use of reverse-biased bipolar membranes (BPMs) in (photo)electrolytic cells. BPMs consist of an anion exchange layer (AEL) laminated with a cation exchange layer (CEL) and are usually equipped with a catalytic layer in between to accelerate the water dissociation reaction. At the AEL/CEL interface, water dissociates into protons and hydroxide ions, which replenish those consumed at the cathode and anode. Compared to conventional water electrolyzers with proton/anion exchange membranes (PEM/AEM), BPM electrolyzers provide the unique advantage of continuously operating the cathode and anode under different pH conditions, which is desirable when the two electrode reactions have different pH requirements. BPMs also enable the use of buffered electrolytes at pH values that are optimized for electrode stability and product selectivity in applications such as CO 2 electrolysis. Product crossover losses and CO 2 pumping can be dramatically reduced in BPM-based CO 2 electrolyzers, relative to conventional alkaline membranes, by electrostatic repulsion (of anionic products) and electroosmotic drag (for neutral products). BPM-based gas fed CO 2 electrolyzers can achieve high current density, but they suffer from low Faradaic efficiency (FE) due to the acidic local environment of the CEL. This problem can be mitigated by adding an aqueous buffering layer or by creating a weak acid cation exchange film on the CEL face of the membrane. The use of BPMs in fuel cells and redox flow batteries offers some interesting advantages. Configurations with both reverse and forward bias have been studied, but forward bias has been favored due to material compatibility, reaction kinetics, and thermodynamic considerations. The net reaction at the AEL/CEL interface is the acid–base neutralization reaction, which has a high inherent reaction rate constant, but in the BPM is limited to a nanometric space-charge layer and requires efficient catalysis to achieve high current density. Understanding the mechanism of the acid–base neutralization and the opposite process, the water dissociation reaction, will be essential for improving the performance of forward-biased BPMs. In conclusion, this Account reviews our current understanding of the working mechanisms of BPMs and discusses how we can use them to effectively manage ions for various (photo)electrochemical applications.

Yan, Zhifei↗
DOE OSTI2024

Poromechanical cohesive interface element with combined Mode I-II cohesive zone elastoplasticity for simulating fracture in fluid-saturated porous media

A combined Mode I-II cohesive zone (CZ) elasto-plastic constitutive model, and a two-dimensional (2D) cohesive interface element (CIE) are formulated and implemented at small strain within an ABAQUS User Element (UEL) for simulating 2D crack nucleation and propagation in fluid-saturated porous media. Here, the CZ model mitigates problems of convergence for the global Newton-Raphson solver within ABAQUS, which when combined with a viscous stabilization procedure allows for simulation of post-peak response under load control for coupled poromechanical finite element analysis, such as concrete gravity dam stability analysis. Verification examples are presented, along with a more complex ambient limestone-concrete wedge fracture experiment, water-pressurized concrete wedge experiment, and concrete gravity dam stability analyses. A calibration procedure for estimating the CZ parameters is demonstrated with the limestone-concrete wedge fracture process. For the water-pressurized concrete wedge fracture experiment it is shown that the inherent time-dependence of the poromechanical CIE analysis provides a good match with experimental force versus displacement results at various crack mouth opening rates, yet misses the pore water pressure evolution ahead of the crack tip propagation. This is likely a result of the concrete being partially-saturated in the experiment, whereas the finite element analysis assumes fully water saturated concrete. For the concrete gravity dam analysis, it is shown that base crack opening and associated water uplift pressure leads to a reduced Factor of Safety, which is confirmed by separate analytical calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Improved Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently scale (both strong and weak cases) on distributed memory due to the irregular memory-access driven nature (with little or no computations) of the methods. The structure of graphs and their relative distribution over the processing elements poses another level of complexity, making it difficult to attain sustainable scalability across platforms. In this paper, we discuss enhancements to TriC, a distributed-memory implementation of graph triangle counting using Message Passing Interface (MPI), which was featured in the 2020 Graph Challenge competition. We have made some incremental enhancements to TriC, primarily adopting a user-defined buffering strategy to overcome the startup problem for large graphs (by fixing the memory for intermediate data), and experimenting with probabilistic data structures such as bloom filter to improve the query response time for assessing edge existence, at the expense of increasing the overall false positive rate. These adjustments have led to a modest improvements in most cases, as compared to the previous version.

Graph Analytics, HPC↗
DOE OSTI2021

Matrix Graphite Material Models In Pebbles and Compacts For Bison

The cores and reflectors in high-temperature gas-cooled reactors (HTGRs) are made of graphite materials, with the graphite acting as a moderator, a fuel host matrix, or the foundation for various structural components. This study aims to survey the models in the literature for graphite materials being used as host matrices in pebble/fuel compacts and to implement those surveyed models into Bison to conduct an early assessment of graphite's thermo-mechanical response under various reactor conditions. In this study, thermal (e.g., thermal conductivity, and specific heat capacity) and mechanical (e.g., elastic properties, thermal expansion, irradiation-induced dimensional changes, and irradiation-induced creep) material models for various graphite grades (e.g., H-451, IG-110, G-348, 2020, A3-3, and A3-27) are incorporated into Bison. Two benchmark problems are then exercised utilizing these new graphite-related capabilities: (1) modeling an Advanced Gas Reactor (AGR)-2 fuel compact, and (2) modeling the debonding of a particle-matrix interface.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Cholla-MHD: An Exascale-capable Magnetohydrodynamic Extension to the Cholla Astrophysical Simulation Code

Abstract We present an extension of the massively parallel, GPU native, astrophysical hydrodynamics code Cholla to magnetohydrodynamics (MHD). Cholla solves the ideal MHD equations in their Eulerian form on a static Cartesian mesh utilizing the Van Leer + constrained transport integrator, the HLLD Riemann solver, and reconstruction methods at second and third order. Cholla’s MHD module can perform ≈260 million cell updates per GPU-second on an NVIDIA A100 while using the HLLD Riemann solver and second order reconstruction. The inherently parallel nature of GPUs combined with increased memory in new hardware allows Cholla’s MHD module to perform simulations with resolutions ∼500 3 cells on a single high-end GPU (e.g., an NVIDIA A100 with 80 GB of memory). We employ GPU direct Message Passing Interface to attain excellent weak scaling on the exascale supercomputer Frontier, while using 74,088 GPUs and simulating a total grid size of over 7.2 trillion cells. A suite of test problems highlights the accuracy of Cholla’s MHD module and demonstrates that zero magnetic divergence in solutions is maintained to round off error. We also present new testing and CI tools using GoogleTest, GitHub Actions, and Jenkins that have made development more robust and accurate and ensure reliability in the future.

Astronomy & Astrophysics↗