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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 19 records

Protocol for Engineered Compositional Asymmetry Within Nanodiscs

Membrane proteins remain the most challenging targets for structural characterization, yet their elucidation provides valuable insights into protein function, disease mechanisms, and drug specificity. Structural biology platforms have advanced rapidly in recent years, notably through the development and implementation of nanodiscs—discoidal lipid–protein complexes that encapsulate and solubilize membrane proteins within a controlled, native-like environment. While nanodiscs have become powerful tools for studying membrane proteins, faithfully reconstituting the compositional asymmetry intrinsic to nearly all biological membranes has not yet been achieved. Proper membrane leaflet lipid distribution is critical for accurate protein folding, stability, and insertion. Here, we share a protocol for reconstituting tailored compositional asymmetry within nanodiscs through membrane extraction from giant unilamellar vesicles (GUVs) treated with a leaflet-specific methyl-β-cyclodextrin (mβCD) lipid exchange. Nanodisc asymmetry is verified through a geometric approach: biotin-DPPE-preloaded mβCD engages in lipid exchange with the outer leaflet of POPC GUVs solubilized by the lipid-free membrane scaffold protein (MSP) Δ49ApoA-I to form nanodisc structures. Once isolated, nanodiscs are introduced to the biotin-binding bacterial protein streptavidin. High-speed atomic force microscopy imaging depicts nanodisc–dimer complexes, indicating that biotin-DPPE was successfully reconstituted into a single leaflet of the nanodiscs. This finding outlines the first step toward engineering tailored nanodisc asymmetry and mimicking the native environment of integral proteins—a potentially powerful tool for accurately reconstituting and structurally analyzing integral membrane proteins whose functions are modulated by lipid asymmetry.

Biological and medical sciences↗

Design and validation of a nanosecond short-wave infrared streak camera using gaseous detonation experiments

A custom short-wave infrared (SWIR) galvanometric streak camera was developed to provide nanosecond- scale, time-resolved optical diagnostics. The system was designed as a compact, mechanically driven alternative to traditional tube-based streak cameras, using a galvanometer mirror and multiple-reflection optical path to achieve high temporal resolution while maintaining electronic synchronization for trigger sequencing. Temporal resolution achieved 139.5 ns/px, allowing direct conversion of image-slope gradients to physical wave velocities. The instrument was validated using hydrogen-oxygen-argon detonations within a modular detonation tube, capturing transient emission structures and post-detonation decay behavior with microsecond precision. Although signal levels in filtered configurations were limited by lens transmission and detector sensitivity, the system successfully resolved intensity rise and decay characteristics across 10-60 % argon mixtures. The observed trends demonstrate the feasibility of compact galvanometer-based streak systems for high-speed SWIR imaging in reactive environments!

42 ENGINEERING↗

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗

Design and Analysis of a 25 MWe Supercritical CO2 Turbo Machine

This paper presents the design and analysis of a turbo machine operating with supercritical carbon dioxide (sCO2) in a 25 MWe Recompression Brayton Cycle (RCBC). The work was performed under US Department of Energy (DoE) program DE-EE-0010318. The design process involves the aerodynamic design of the compressor and turbine, including the initial layout of flowpaths and stage configurations to achieve high efficiency and performance. Additionally, a comprehensive rotodynamic analysis is performed to ensure the stability and reliability of the system. Conceptual designs for a high-speed motor and synchronous generator that match turbomachinery requirements are also developed from first principles.

42 ENGINEERING↗

High-Order Methods in NASA’s Next Generation of Computational Fluid Dynamics Tools

The missions of the National Aeronautics and Space Administration (NASA) routinely produce unique requirements and challenges for development and application of Computational Fluid Dynamics (CFD) methods. NASA presently embodies four distinct Mission Directorates: Aeronautics Research, Exploration Systems, Science, and Space Operations. These missions generate requirements for systems that operate in a wide variety of environments. They range from the high-speed flight of aerodynamically optimized vehicles operating in the earth’s atmosphere to spacecraft designed for missions that don’t favor aerodynamic optimization, some operating in the atmosphere of planets and planetary moons such as Mars and Venus or Saturn’s moon Titan. Systems supporting these vehicles, such as rocket and jet propulsion, reaction control systems, fluid and thermal transfer systems, etc. can also generate their own unique set of flow phenomena that challenge today’s CFD methodology. Through the NASA Engineering and Safety Center (NESC), NASA annually conducts state-of-the-discipline assessments in fifteen distinct engineering disciplines. These assessments are performed by the NASA Technical Fellows that lead Technical Discipline Teams (TDT) of recognized experts in these fifteen areas. In the Aerosciences discipline, three topics have been identified as the top challenges for the discipline: aero-plume interaction prediction, unsteady separated flows, and aerothermodynamic prediction. These challenge areas are defined by the Agency’s high-risk projects and problems on which the NESC is requested to perform independent testing, analysis, and assessments. When viewed as a whole, these tests, analyses, and assessments provide a clear view of the recurring technical challenges facing Agency engineers and researchers and can be used to guide future research and technology development. The present state-of-the-art in the application of CFD at NASA is the use of Reynolds-Averaged Navier- Stokes (RANS) solvers, primarily executed in a steady-state mode of operation. In isolated cases, Unsteady RANS (URANS) solvers have been employed when steady RANS solutions produce poorly converging or oscillating results or in cases, such as aeroelastic analysis, which require unsteady aerodynamic simulation. For most traditional external and internal aerodynamic flows, structured overset grids or unstructured grids are employed to minimize geometric modeling and grid generation times. Grid adaptation, primarily as a series of coarse-grain intermediate processing steps is also seeing use on particularly complex flow problems and configurations. In the case of aerothermodynamic flows, engineers have been forced to continue to employ structured grid techniques as the present unstructured grid methodology has proven inadequate in the prediction of surface heating. In the area of aero-plume interaction modeling, two-gas, frozen chemistry simulation is generally the state-of-the- art, with some production solvers capable of predicting flows with only a single gas component. Prediction of flows falling into the afore-mentioned top Aerosciences technical challenges have severely stressed the present state-of-the-art in CFD prediction and for some problems, such as unsteady separated flows and aero-plume interaction cases, engineers have begun employing Large Eddy Simulation (LES) and Hybrid RANS/LES techniques. In some isolated aero-propulsion interaction cases, chemically reacting flow simulations have been applied. These methods are highly evolutionary and engineers have little experience in their application, so they cannot be heavily relied upon in today’s application environment. Therefore, this leads one to muse over which numerical technologies will be included in the CFD tools that will be employed 30 years in the future. This presentation will describe specific technical problems that have stressed NASA’s traditional CFD methods to their breaking point and will link these issues to the Agency’s top Aerosciences technical challenges. The discussion will then shift to the characteristics of future CFD solvers that will be required to attack these challenges and how these characteristics differ from the present state-of-the- art. High-order methods certainly appear to have a place in the development of future CFD tools and some of the physical characteristics of our most challenging problems suggest that high-order methods are the only way to effectively solve them. But there are some relatively severe implementation issues that face these methods, particularly in the area of general applicability and robust operation as an engineering tool. Desired characteristics of next-generation CFD solvers will be discussed and the author’s view of which emerging numerical technologies might be employed to address these attributes will also be presented

David M Schuster↗

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗

Assessment of the Loss of Vacuum on the Local Metal Temperature in the TEX Experiment Assembly

A four-part assessment using analytical methods and semi-analytical simulation tools finds that the maximum temperature variation in the highly enriched uranium (HEU) Jemima plate assembly is less than ten Kelvin (< 10 K) in an abrupt loss of vacuum scenario. The temperature variation in the metals is likely to be one to four Kelvin (1 K – 4 K) based on the most realistic conditions in the assessment. The four assessment parts include: (1) the peak gas temperature bound for the initial influx of the ambient air; (2) the high-heat transfer limit during the high-speed forced convection period in an insulated HEU plate; (3) the high-heat transfer limit during the high-speed forced convection period with aluminum spacers; and (4) the longer-time thermal equilibration period between the air and metal stack. The four-part assessment serves as conservative upper bound on the temperature variation in the metal stack and remains well below the 40 Kelvin limit where the temperature-induced stresses could lead to permanent, plastic deformation.

42 ENGINEERING↗

Demonstration of Grid Services Using Mixed Grid-Forming and Grid-Following Technologies at the Wheatridge Renewable Energy Facility (Final Technical Report)

This Final Technical Report summarizes the analytical, modeling, and engineering work performed to evaluate grid-forming (GFM) inverter capabilities within a large hybrid renewable energy facility. The project focused on assessing the ability of advanced inverter-based resources to provide essential reliability services through coordinated operation of grid-forming (GFM) and grid-following (GFL) technologies. During Budget Period 1, the project developed a comprehensive framework of GFM performance metrics, including angle support, voltage regulation, frequency response, damping behavior, and current-limiting performance. Extensive electromagnetic transient (EMT) studies and hardware-in-the-loop (HIL) testing were conducted to validate GFM behavior under a range of operating conditions, including weak-grid scenarios, voltage disturbances, and multi-resource interactions. The project also established validated modeling approaches, plant-level control integration strategies, commissioning frameworks, and high-speed measurement infrastructure to support future field demonstration. Although the project concluded prior to field demonstration, the results provide a utility-scale foundation for evaluating, modeling, and deploying grid-forming technologies. The methodologies and tools developed contribute to industry understanding of inverter-based resource behavior and support future power system reliability under increasing renewable penetration.

14 SOLAR ENERGY↗

Design and Optimization of an Open Rotor Stage

Open rotor propulsors enable greatly increased effective bypass ratios over current aircraft engines, resulting in significantly improved efficiencies. One of the most promising of these geometries is the “open fan”, which consists of a single rotor followed by a single stator. This configuration will be referred to as an “open rotor stage” for design work at NASA. In this work we consider the multidisciplinary design and optimization of an open rotor stage using a variety of 2D and 3D techniques. The 2D aerodynamic techniques used for design include a 2D Trefftz plane method, axisymmetric throughflow, and quasi-3D blade-to-blade CFD. Aerodynamic performance is prioritized, while structural requirements are met and some acoustic considerations are included. Between the rotor, stator, and hub profiles, a total of 101 design variables are used. Differences in the process of designing an open rotor stage vs. designing other high-speed propellers are also discussed. The current design, the NASA Optimized Propulsor for Efficiency and Noise (N-OPEN-1), achieves 84.7% net efficiency as predicted by 3D CFD, with further improvements expected to be possible. This design makes effective use of a slower rotor and a less tip-loaded work distribution than typical single-rotation propellers, allowing the stator to produce 14.8% of the thrust. Laminar flow on the blades was responsible for a 1.7% efficiency benefit and warrants further study.

Open rotors↗

Design and Optimization of an Open Rotor Stage

Open rotor propulsors enable greatly increased effective bypass ratios over current aircraft engines, resulting in significantly improved efficiencies. One of the most promising of these geometries is the “open fan”, which consists of a single rotor followed by a single stator. This configuration will be referred to as an “open rotor stage” for design work at NASA. In this work we consider the multidisciplinary design and optimization of an open rotor stage using a variety of 2D and 3D techniques. The 2D aerodynamic techniques used for design include a 2D Trefftz plane method, axisymmetric throughflow, and quasi-3D blade-to-blade CFD. Aerodynamic performance is prioritized, while structural requirements are met and some acoustic considerations are included. Between the rotor, stator, and hub profiles, a total of 101 design variables are used. Differences in the process of designing an open rotor stage vs. designing other high-speed propellers are also discussed. The current design, the NASA Optimized Propulsor for Efficiency and Noise (N-OPEN-1), achieves 84.7% net efficiency as predicted by 3D CFD, with further improvements expected to be possible. This design makes effective use of a slower rotor and a less tip-loaded work distribution than typical single-rotation propellers, allowing the stator to produce 14.8% of the thrust. Laminar flow on the blades was responsible for a 1.7% efficiency benefit and warrants further study.

turbomachinery↗

Time-resolved spray characterization via unified optical flow and binarization technique

This work leverages an unsupervised machine learning and advanced image processing techniques to characterize the breakup of fuel sprays in a small-scale combustor under reacting conditions, providing valuable insights into near-nozzle flow phenomenology. The proposed methodology integrates an improved optical flow model on a convolutional neural network to extract flow vectors with a binarization technique to assess droplets’ size and shape across the region of interest. The velocimetry approach demonstrates superior performance compared to a state-of-the-art optical flow model when applied to high-speed X-ray phase contrast spray images, achieving more accurate and reliable flow predictions. Moreover, breakup processes are quantified by breakup length and sphericity in accordance with velocity estimations, allowing a more complete characterization of the flow. This study establishes a robust methodology for analyzing spray morphology and primary breakup in compact combustors, contributing valuable means of understanding and optimizing fuel spray behavior in advanced combustion systems.

42 ENGINEERING↗

Background-Oriented Schlieren Velocimetry of Helium Coolant Flow in Additively Manufactured Channels

High-pressure helium gas cooling is an attractive solution for thermal management of the fusion blanket first wall, as this coolant is chemically and neutronically inert and separable from hydrogenic species. However, due to the low thermal mass of helium, geometric optimization of these channels is required to provide sufficient cooling at manageable flow rates and pumping burdens. Increasingly, analysis and optimization of these coolant channels rely on computational fluid dynamics (CFD) simulations, and these require relevant experimental data for turbulence model validation. Toward this end, a high-pressure helium gas flow visualization system has been employed to image the flow of helium in flow channels with one-sided heating, mimicking the blanket first wall environment. Flow of helium at 4 MPa pressure and flow rates up to 68 g/s (Reynolds number 57 000) is supplied to rectangular channel test sections, with uniform heating applied to the bottom wall of the channel at heat fluxes varied between roughly 50 and 130 kW/m2. A high-speed camera is used to image index of refraction (IOR) gradients in the fluid via background-oriented schlieren (BOS), and temperature and pressure instrumentation are used to characterize thermal-hydraulic performance of each channel. Cross correlation of time-resolved BOS images is then used to calculate time-averaged 2-D helium velocity fields. Flow in additively manufactured (AM) channels is examined in this manner, including both featureless channels and those containing baffling as a heat transfer enhancement. The flow distribution seen in the featureless case differs significantly from that seen in prior simulations, whereas the flow in the baffled case shows the predicted behavior of flow forced along the heated wall. This augmented flow distribution is seen to increase the heat transfer coefficient in the baffled test section. Here, strategies are discussed for ongoing and future validation of these simulations, with the aim of model deployment for blanket cooling design and optimization.

Additive manufacturing↗

Flow pattern and void fraction characterization in nitrogen/water flows through a diamond-type triply periodic minimal surface lattice

Here, this study presents, to the authors knowledge, the first experimental investigation on the void fraction and flow patterns in two-phase flows through a diamond-type Triply Periodic Minimal Surface (TPMS) lattice. An additively manufactured TPMS structure was tested under upward co-current flow of a water/nitrogen mixture. Superficial velocities varied for a total of 42 test conditions (gas: 0.01–2.4 m/s; liquid: 0.01–2.4 m/s; mass flux: 20–2370 kg/m 2 ·s). High-speed video and X-ray imaging enabled time-averaged void fraction measurements and identified six distinct flow regimes which were used to develop a flow pattern map. Comparison of the void fraction data with correlations from literature demonstrated the Rouhani and Axelsson (1970) [49] correlation modified by Steiner (1993) [52] provided the best agreement, which was improved with empirically fit coefficients. This approach predicted the void fraction with errors < ±20% for 64% of the data, with a mean absolute percent deviation (MAPD) of 23%. The measured frictional pressure drop was compared to correlations from literature which captured the observed trends but did not provide good accuracy. The best agreement was found after optimizing the empirical coefficients of the Muller-Steinhagen & Heck (1986) [56] correlation; this approach captured 33% of the data within ±20% with a MAPD of 46% over the full range and captured 71% of the data within ±20% a MAPD of 13.3% at mass fluxes >1100 kg/m 2 -s. These results provide foundational insight into TPMS two-phase flow behavior and inform modeling and design of advanced heat exchange components incorporating TPMS geometries.

42 - ENGINEERING↗

Molten pool dynamics and humping suppression in high-speed laser welding via tailored beam configurations

High-speed laser welding is essential for increasing the production rate of fuel cell fabrication. However, when the welding speed exceeds a critical limit, humping occurs and reduces the weld quality. In this study, two tailored beam configurations, including an adjustable ring mode and a dual-beam configuration, were employed to suppress humping. Computational fluid dynamics simulations were performed to elucidate the underlying suppression mechanisms. Here, the results show that, in the adjustable ring mode, humping mitigation arises from a reduced backward cross-sectional melt flow rate and a more stable molten pool. In the dual-beam configuration, humping suppression is attributed to the deceleration of melt flow, the conduction-mode behavior of the trailing beam, and the widening of the molten pool induced by the trailing laser. Furthermore, because the dual-beam configuration directly modifies the trailing molten pool dynamics, it achieves more effective humping suppression, extending the welding speed limit to 1.50 m/s, compared with 1.00 m/s for the adjustable ring mode.

08 HYDROGEN↗

Flame stabilization in DME spray flames under engine-relevant conditions characterized by OH* chemiluminescence and formaldehyde laser-induced fluorescence

The transient and quasi-steady flame structures of Dimethyl Ether (DME) fuel sprays, produced by a single-hole injector (Spray D), were investigated using Planar Laser-Induced Fluorescence (PLIF) and chemiluminescence imaging in a constant-volume chamber under Engine Combustion Network (ECN) Spray A conditions (900 K ambient temperature, 60 bar ambient pressure, 1500 bar injection pressure, and 22.8 kg/m 3 ambient density). Low-temperature chemical reaction zones were visualized using formaldehyde (CH 2 O) PLIF with 355 nm excitation, while high-temperature flame regions were captured via chemiluminescence imaging of excited-state hydroxyl radicals (OH*). Both transient and quasi-steady flame structures clearly show the transition from CH 2 O to OH*, highlighting the progression from low- to high-temperature combustion, while the position of the flame is displaced for DME compared to reference hydrocarbon n-dodecane. Homogeneous reactor calculations with detailed chemistry and using adiabatic mixing for initial temperature show that CH 2 O peaks are significantly higher for DME at the same equivalence ratio, with a higher heat-release during the cool-flame regime with respect to the fuel heating value. Thus, the cool-flame dynamic as a precursor to high-temperature combustion and flame stabilization exhibit distinct behavior for DME relative to conventional hydrocarbons, and these phenomena are effectively resolved through the soot-free nature of DME and the high-speed, time-resolved diagnostics.

CH2O laser-induced fluorescence↗

350 ps Ultrafast room-temperature scintillation realized on CsPbBr 3 -based single crystals via Br 2 over-doping

Ultrafast scintillators are essential for next-generation radiation detection, positron emission tomography, and high-speed medical imaging. All-inorganic CsPbBr 3 perovskites are attractive candidates because of their high stopping power, and excellent optical quality, yet their long carrier lifetimes result in slow scintillation responses on the order of hundreds of nanoseconds. Here, we demonstrate that controlled over-doping with Br 2 produces CsPbBr 3.03 single crystals with sub-nanosecond scintillation at room temperature while preserving crystal quality. Single crystals grown by the Bridgman method exhibit high transparency and maintain the orthorhombic perovskite structure. Br 2 over-doping induces a slight lattice expansion (about 0.42% increase in unit-cell volume) while maintaining the orthorhombic perovskite phase and high optical transparency. Optical absorption reveals a slight redshift of the absorption edge after Br 2 introduction, indicating a modified defect landscape. Time-resolved photoluminescence and radioluminescence measurements show that Br 2 doping creates dense and efficient recombination centers that reduce the scintillation decay time from more than 100 ns in undoped crystals to 350 ps under 5.486 MeV α-particle excitation, and the scintillation decay time decreases by two orders of magnitude. The doped crystals also achieve a spatial resolution of 12 lp mm −1 in X-ray imaging. These results reveal a defect-engineering route for achieving ultrafast scintillation in halide perovskites and highlight the potential of Br 2 -modified CsPbBr 3 for fast timing applications.

Li, Zongxiao [Chinese Academy of Sciences (CAS), N↗

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗