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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Consistent Second Moment Methods with Scalable Linear Solvers for Radiation Transport

Second moment methods (SMMs) are developed that are consistent with the discontinuous Galerkin spatial discretization of the discrete ordinates (or S\(_N\)) transport equations. The low-order (LO) diffusion system of equations is discretized with fully consistent P\(_1\), local discontinuous Galerkin (LDG), and interior penalty (IP) methods. A discrete residual approach is used to derive SMM correction terms that make each of the LO systems consistent with the high-order discretization. We show that the consistent methods are more accurate and have better solution quality than independently discretized LO systems, that they preserve the diffusion limit, and that the LDG and IP consistent SMMs can be scalably solved in parallel on a challenging, multimaterial benchmark problem.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Visible Scalable Terrain (ViSTa) format

Visible Scalable Terrain is a format for the production, interchange, and display of 3D terrain data that is specifically suited to stereo vision based robotic applications.

ViSTa Visible Scalable Terrain robotic application↗
NASA NTRSDate not supplied

Distributed Spacecraft Autonomy - Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC) measurements. DSA will be demonstrated onboard the NASA Ames Starling mission – a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets. The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasmasphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input dataset for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software, aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements, which define the range of observability for the satellites, as well as exploitative measurements, which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth’s magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions. As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown. Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middleware provides reliable delivery, routing, and topic subscription features over User Datagram Protocol (UDP). Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarm scalability from 2 satellites to 100 satellites is shown.

Distributed Autonomy↗
NASA NTRSDate not supplied

Coupled Reactor Multiphysics and Mass Scalability Assessment for Crewed Megawatt-Class NEP System Architectures

Nuclear Electric Propulsion (NEP) is an in-space propulsion technology capable of enabling opposition and conjunction class crewed Mars missions. NEP subsystems include the reactor for heat generation, a power conversion system (PCS), power management and distribution (PMAD), electric propulsion subsystem (EPS), and a primary heat rejection system. Specific mass, or αe (kg / kWe), is a key performance parameter (KPP) of the propulsion system which is directly scalable with the performance and mass estimates for individual components. To inform technology maturation planning, full system and component level parametric modeling is ongoing to explore the design trade space and illustrate the effect of subsystem design choices on the system KPPs. In this study, scaling of high-assay, low-enriched uranium (HALEU) reactor designs is assessed through coupled reactor physics and thermal hydraulics analyses. Scaling analyses evaluate the impact of system performance parameters (power level, interface temperatures) on mass for direct gas cooled, pumped liquid metal, and passively cooled heat pipe reactor concepts. Each concept requires specific geometries and working fluids to reach the performance goals of PCS interface conditions (temperature, pressure, flow rate) and system mass. The reactor assembly includes the active core (fuel, moderator, cladding, working fluid), axial and radial neutron reflectors, control drums, structural support / pressure vessel, and external radiation shielding. Each of these components are parametrically sized based on performance parameters for a megawatt-class power cycle. Results of this scaling analysis increase NEP propulsion system modeling fidelity and ultimately aim to support technology down-selection along with related technology development planning. The reactor and shield αe are a function of several PCS design choices, and reactor scaling with these parameters must be considered to enable an informed decision on reactor geometry and working fluid combination.

Nuclear Electric Propulsion↗
NASA NTRSDate not supplied

Coupled Reactor Multiphysics and Mass Scalability Assessment for Crewed Megawatt-Class NEP System Architectures

Nuclear Electric Propulsion (NEP) is an in-space propulsion technology capable of enabling opposition and conjunction class crewed Mars missions. NEP subsystems include the reactor for heat generation, a power conversion system (PCS), power management and distribution, electric propulsion system, and heat rejection system. Specific mass, or α (kg/kWe), is a key performance parameter (KPP) of the propulsion system which is directly scalable with the performance and mass predictions for each individual component. To inform technology maturation planning activities, full system and component level parametric modeling is ongoing to explore the design trade space and illustrate the effect of subsystem design choices on the system KPPs. In this study, scaling of high-assay, low-enriched uranium reactor designs is assessed through coupled reactor physics and thermal hydraulics analyses. Scaling analyses evaluate the impact of system performance parameters (power level, interface temperatures) on mass for direct gas cooled, pumped liquid metal, and passively-cooled heat pipe reactor concepts. Each concept requires specific geometries, fluids, and power conversion interface conditions (temperature, pressure, flow rate) to meet desired performance and mass. The reactor assembly includes the active core (fuel, moderator, cladding, working fluid), axial and radial neutron reflectors, control drums, structural support / pressure vessel, and external radiation shielding. Each of these components are parametrically sized based on performance parameters for a megawatt-class power cycle. Results of this scaling analysis increase NEP propulsion system modeling fidelity and ultimately aim to support concept down-selection along with related technology development planning. The reactor and shield α are a function of several PCS and heat rejection system design choices, and reactor scaling with these parameters must be considered to enable an informed decision on an optimal reactor geometry and working fluid combination.

Nuclear Electric Propulsion↗
DOE OSTI2024

Scalable Nanoimprint Manufacturing of Functional Multilayer Metasurface Devices

Optical metasurfaces, consisting of subwavelength-scale meta-atom arrays, hold great promise of overcoming the fundamental limitations of conventional optics. Due to their structural complexity, metasurfaces usually require high-resolution yet slow and expensive fabrication processes. Here, using a metasurface polarimetric imaging device as an example, the photonic structures and the Nanoimprint lithography (NIL) processes are designed, creating two separate NIL molds over a patterning area of > 20 mm2 with designed Moiré alignment markers by electron-beam writing, and further subsequently integrate silicon and aluminum metasurface structures on a chip. Uniquely, the silicon and aluminum metasurfaces are fabricated by using the nanolithography and 3D pattern-transfer capabilities of NIL, respectively, achieving nanometer-scale linewidth uniformity, sub-200 nm translational overlay accuracy, and <0.017 rotational alignment error while significantly reducing fabrication complexity and surface roughness. Here, the micro-sized multilayer metasurfaces have high circular polarization extinction ratios as large as ≈20 and ≈80 in blue and red wavelengths. Further, the metasurface chip-integrated CMOS imager demonstrates high accuracy in broad-band, full Stokes parameter analysis in the visible wavelength ranges and single-shot polarimetric imaging. This novel, NIL-based, multilayered nanomanufacturing approach is applicable to the scalable production of large-area functional structures for ultra-compact optic, electronic, and quantum devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Recent Advances in Scalable, High‐Mass Loaded Electrodes for Grid‐Scale Energy Storage

Abstract The increasing electrification of daily life as well as the intermittent characteristic of renewable energy sources require viable solutions for grid‐scale energy storage. Critical considerations for grid storage applications are electrode mass loading and electrode thickness as these features govern battery pack energy density, an important factor in determining manufacturing costs. For this reason, there is increased interest in finding new ways of creating electrodes with high mass loading. In this review, various high‐mass loading fabrication approaches are considered for positive electrode materials used in batteries. The benchmark used for high mass loading is above 20 mg cm −2 , which is higher than the practical limit of conventional tape‐cast electrodes. Several different electrode approaches are described including templating, laser patterning, direct ink writing, and electrodeposition. A variety of materials are covered with the most prominent being LiFe(PO 4 ) (LFP), LiCoO 2 (LCO), and MnO 2 . In research to date, scalable electrochemical performance has been achieved with mass loadings over 100 mg cm −2 . Areal capacities as high as 14.7 mAh cm −2 at 1.82 mA cm −2 have been achieved in non‐aqueous electrolytes and 9.8 mAh cm −2 at 10 mA cm −2 in aqueous electrolytes. These results establish that the mass loading of electrodes can be scaled up without compromising their electrochemical properties.

White, Makena [Department of Materials Science and↗
DOE OSTI2026

Scalable Upcycling of Spent Lithium-Ion Battery Anodic Graphite to Electronic-Grade Graphene

Recycling processes for lithium-ion batteries (LIBs) are imperative to support the sustainable growth of global energy storage systems. This study introduces a scalable method for the upcycling of spent graphite anodes from LIBs to produce electronic-grade graphene nanoplatelets. In addition to comprehensive materials characterization, the electronic quality of the upcycled graphene is demonstrated by formulating it into a screen printing ink that achieves high-resolution patterning and thin-film electrical conductivity exceeding 104 S m−1. This screen printing ink is also used to print planar micro-supercapacitors with exceptional areal capacitance (1.78 mF cm−2), areal energy density (0.247 µWh cm−2), and cycling stability (> 10 000 cycles). Life cycle assessment (LCA) and techno-economic analysis (TEA) highlight the environmental benefits and cost reductions attainable through upcycling of graphite from LIBs. By capturing economic value from spent LIBs, this work fosters a sustainable battery supply chain and provides an abundant and geographically distributed raw material for electronic-grade graphene.

energy storage↗
DOE OSTI2025

Unveiling the Role of Critical Impurities in Spent LiFePO 4 Cathodes for Scalable Direct Regeneration

Direct regeneration offers a promising alternative to recycling End-of-Life (EoL) batteries by restoring metal elements and preserving the material structure, yet scaling these technologies to handle practical cathode black mass (CBM) with impurities remains challenging. Here, this study investigates the evolution of impurities, including aluminum (Al), polyvinylidene difluoride (PVDF) binder, and residual carbon (C), during direct recycling of spent LiFePO 4 (LFP) cathodes and their impact on electrochemical performance. Using various ex situ and in situ analyses, it is shown that the formation of lithium fluoride (LiF) during the traditional direct recycling process hinders lithium diffusion and deteriorates the reversible capacity. To address this major challenge, the combination of pH-controlled hydrothermal purification and the two-step sintering process is proposed effectively to regenerate spent LFP cathodes, eliminating the negative effect of Al and fluorine (F) impurities while mitigating any potential impacts of carbon residuals. The regenerated LFP from spent CBM achieves superior performance, retaining 152.5 mAh g −1 at 0.1 C and 133 mAh g −1 at 1 C with 98.7% capacity retention after 200 cycles. This approach is further validated using three distinct waste feedstocks from battery modules, enhancing impurity management and scalability in direct recycling. These findings present a sustainable and economically viable solution for large-scale LFP regeneration.

25 ENERGY STORAGE↗
DOE OSTI2024

Scalable Graphene Oxide Hollow Fiber Membranes for Dye Desalination Enabled by Multi‐Purpose Polyamine Functionalization

2D nanosheets such as graphene oxide (GO) can be stacked to construct membranes with fine-tuned nanochannels to achieve molecular sieving ability. These membranes are often thin to achieve high water permeance, but their fabrication with consistent nanostructures on a large scale presents an enormous challenge. Herein, GO-based hollow fiber membranes (HFMs) are developed for dye desalination by synergistically combining chemical etching to form in-plane nanopores (10–30 nm) to increase water permeance and polyamine functionalization to improve underwater stability and enable facile large-scale production using existing membrane manufacturing processes. HFM modules with areas of 88 cm 2 and GO layer thicknesses of ≈500 nm are fabricated, and they exhibited a stable dye water permeance of 75 L m −2 h −1 bar −1 , rejection of >99.5% for Direct red and Congo red, and Na 2 SO 4 /dye separation factor of 300–500, superior to state-of-the-art commercial membranes. Furthermore, the versatility of this approach is also demonstrated using different short polyamines and porous substrates. This study reveals a scalable way of designing 2D materials into high-performance robust membranes for practical applications.

amine functionalization↗
DOE OSTI2025

Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN

We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) computations in both training scale and data diversity. It abstracts over message passing algorithms, allowing both reproduction of and comparison across algorithmic innovations that define nearest-neighbor convolution in GNNs. This work discusses a series of optimizations that have allowed scaling up the GFMs training to tens of thousands of GPUs on datasets consisting of hundreds of millions of graphs. Our GFMs use multitask learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on two state-of-the-art US Department of Energy (US-DOE) supercomputers, namely the Perlmutter petascale system at the National Energy Research Scientific Computing Center and the Frontier exascale system at Oak Ridge Leadership Computing Facility. The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2000 GPUs on Perlmutter and 16,000 GPUs on Frontier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uncovering grain and subgrain microstructure at the scale of additive manufacturing melt tracks with a scalable cellular automaton solidification model

Metal additive manufacturing, characterized by rapid solidification, yields refined grains with a distinctive cellular subgrain microstructure that plays a pivotal role in determining material properties. Due to the significant computational expense demanded to simulate the required physics with submicron spatial resolution, their numerical simulations have been limited to proof-of-concept studies to either 2D or small subregions of a melt pool. In this study, an open-source, scalable, solidification code, muMatScale, based on the cellular automaton method, has been developed to predict the grain and the underlying subgrain microstructure over an entire melt pool. The model incorporates flexible parallelization schemes, utilizing MPI and OpenMP GPU Offloading, in addition to appropriate multi-physics specific to non-equilibrium rapid solidification in AM. The impact of nucleation parameters on grain microstructures was investigated with a focus on grain size variations and morphology transitions. With selected nucleation parameters, the simulation predicted the grain size, subgrain morphology, crystallographic orientation, and microsegregation aligned with experimental measurements. The model demonstrates that epitaxial grain growth is a dominant factor at the melt pool boundary, influencing grain size variation under different grain sizes in the build plate while maintaining consistent primary dendrite arm spacing under identical thermal conditions. Here, the highly efficient numerical model enables large-scale simulations with a spatial resolution of 100 nm or less, unveiling unprecedented insights into thermal and solutal diffusion driven grain growth, and the subgrains with microsegregation within grains in 3D across scales. muMatScale will enable the linking of submicron length-scale microstructure to part-level material behavior by investigating fundamental solidification problems at the intercellular scale in many-track and many-layer builds.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A time-parallel method for scalable heat transfer simulations of additive manufacturing

Here, a major challenge in simulating the thermal behavior in additive manufacturing processes is the disparate length and time scales between transport phenomena occurring in the melt pool and the component. A common simulation approach relies on spatial decomposition for parallel computing, but due to the nature of heat transfer in AM, where most of the computational expenditure is localized near the melt pool, the computational speedup from spatial parallelization saturates quickly. Therefore, additional parallelism by means of time-domain decomposition is needed to fully take advantage of high-performance computing (HPC) resources. This work introduces a time-parallel method to improve the computational scalability of additive manufacturing simulations on HPC systems, while maintaining high temporal resolution of heat transfer near the melt pool. The method, inspired by the nonlinear paraexp formalism, performs an iterative superposition of nonlinear solutions to the initial value problem, integrating the heat equation across overlapping time-parallel intervals. For a single layer of the NIST AMB2018–01 L7 benchmark problem, the method achieves a 38.51x speedup in wall-clock time with a maximum error in the global temperature solution of 0.99%. This reduces the total solution time from 196.72 min to 5.11 min on 128 nodes of the ORNL Frontier supercomputer. The tradeoff between accuracy and total wall-clock time is investigated and recommendations for time-parallel deployment for AM problems are made.

Additive manufacturing↗
DOE OSTI2025

Separation of terbium from proton-irradiated gadolinium oxide targets – development of an effective, scalable and automatable process

This work reports an effective and scalable radiochemical separation process for isolating terbium from Gd 2 O 3 . The separation process uses three commercially available extraction chromatography resin columns, has been implemented on a computer-controlled chemistry module, and tested with 100 mg quantities of proton-irradiated nat Gd 2 O 3 . Here, the 4-hour separation procedure isolated radioterbium in 1.3 mL of 0.01 M HCl with 80 ±8% radiochemical yield and a Gd decontamination factor >(1.2 ± 0.3)·10 5 .

Adjacent lanthanide separation↗
DOE OSTI2025

One-dimensional heterocyclic carbene–Au metal–organic frameworks bridging ultra-high vacuum models and scalable liquid-phase growth

The controlled design of molecule–metal interfaces is central to the development of functional nanomaterials for catalysis, sensing, and molecular electronics. Here we show that the adsorption of a Janus-type diimidazolium precursor on gold yields one-dimensional (1D) N-heterocyclic carbene (NHC)–Au–NHC metal organic frameworks (MOFs) featuring positively charged gold nodes. Using synchrotron X-ray photoemission spectroscopy (XPS), near edge X-ray adsorption fine structure (NEXAFS) spectroscopy and scanning tunnelling microscopy (STM), we demonstrate that thermal activation promotes counterion removal and drives the formation of extended 1D arrays, characterized by ∼1.0 nm Au–Au spacing and adatom densities up to 0.6 atom nm −2 (∼4% of surface atoms). Importantly, we translate this ultra-high vacuum (UHV) benchmark into a scalable solution-phase protocol in ethanol, enabling 1D-MOF growth under mild, base-free, open-air conditions. The resulting films retain structural and electronic signatures of UHV-grown systems, bridging model studies and practical synthesis. This approach establishes NHC–metal frameworks as accessible, tunable platforms for catalysis and materials design.

Gold adatoms↗
DOE OSTI2025

Scalable fabrication of a tough and recyclable spore-bearing biocomposite thermoplastic polyurethane

Thermoplastic polyurethanes (TPUs) are a class of versatile thermoplastic elastomers, but most of their products lack a proper recycling strategy or have no end-of-life solutions. To pursue a sustainable end-of-life solution for TPU-based products, self-disintegrating biocomposite TPUs have recently been developed by embedding spores of TPU-degrading bacteria into TPUs via melt extrusion. Herein, we improve upon spore-bearing biocomposites and demonstrate industrially relevant manufacturing conditions for fabricating biocomposite TPUs. To minimize the coloration of biocomposite TPUs, spore production was modified. The innate brown color of the resulting materials was diminished by reducing FeSO 4 in sporulation media, generating white spores without compromising spore productivity, viability, morphology or heat-shock tolerance. Biocomposite TPUs containing white spores displayed a 45 % increase in toughness compared to TPUs without spores, while retaining ∼ 90 % spore viability post processing. Furthermore, biocomposite TPU fabrication was demonstrated using a scalable continuous extruder followed by injection molding. Biocomposite TPUs generated by these industry-relevant processes exhibited comparable toughness improvement and spore viability to biocomposite TPU prepared using a lab scale microcompounder, while enhancing productivity by 30-fold. Finally, spore addition significantly improved the recyclability of biocomposite TPUs, enabling 80 % toughness retention after 5 rounds of iterative melt processing. Additionally, no negative effect on the lifespan of the generated TPUs was observed over 1 year of storage. Overall, this study confirms that spore-bearing biocomposite TPUs are promising for practical applications, offering an accessible method to enhance toughness and sustainability of commercial TPUs through the incorporation of spore-based living fillers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. Here, an extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.

36 MATERIALS SCIENCE↗