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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Artemis CubeSats Help Advance Exploration of the Moon, Mars, and Beyond

On April 1, NASA launched the Artemis II mission – the first crewed mission of the Artemis campaign. The nine-day lunar flyby launched on the agency’s SLS (Space Launch System) rocket and had four astronauts test the Orion spacecraft in high Earth orbit and in deep space. Four 12U CubeSats also launched on the mission, housed within the SLS’s Orion stage adapter (OSA). The spacecraft were deployed approximately five-and-a-half hours into the flight, following Orion separation and departure from SLS, as the SLS interim cryogenic propulsion stage (ICPS) and the attached OSA were in a high Earth orbit on an atmospheric disposal trajectory. All four CubeSats were successfully deployed from the launch vehicle. Deployment statuses of the spacecraft, along with updates about the SLS rocket and Artemis missions, are reported in this paper. The paper will also discuss opportunities for CubeSat payloads on future Artemis missions.

Russell Lane↗
DOE OSTI2026

Full ribosomal operon sequencing of anaerobic gut fungi (phylum Neocallimastigomycota ): insights on its markers and phylogenetic resolution

The phylogenetic affiliations of anaerobic gut fungi (Neocallimastigomycota) are typically evaluated using single-gene markers. However, this approach often fails to resolve relationships between closely related lineages. To address this issue and identify alternative markers, we created a curated database comprising the complete ribosomal operon sequences of 156 isolates, representing 20 of the 22 recognized genera and two new genus-level clades. Using long-read sequencing, we obtained ~9 kbp operon sequences and developed a robust analysis pipeline. Incorporating both coding genes and non-coding regions (excluding IGS1) improved phylogenetic resolution. This phylogenetic approach successfully resolved the Cyllamyces and Caecomyces clades (hard-to-distinguish genetically), as well as seven analysed Piromyces species. We also scanned the operon for markers that are suitable for short-read sequencing platforms, with the aim of enhancing biodiversity and phylogenetic studies. Notably, the ETS1 genetic region also enabled the distinction between these lineages, indicating its phylogenetic value within the ribosomal operon. The resulting database is a valuable resource for expanding and strengthening phylogenetic frameworks.

High-throughput sequencing↗
NASA NTRS2026

Fuel Heating System Design and Thermal Modeling for Aviation Combustion Research

Aviation fuels offer substantial heat-sink capability that can be applied to aircraft thermal-management strategies and advanced propulsion-system concepts. Accurate control of fuel temperature is critical for understanding combustion behavior and supporting high temperature fuel experimental studies. This work describes the modeling and experimental validation of a portable, high temperature fuel-conditioning system designed for multiple NASA Glenn Research Center’s combustion facilities. The system utilizes a circulating-oil heating unit and modular tube-in-tube heat exchangers capable of conditioning Jet-A to temperatures approaching 600 K. A Thermal Desktop model was created to analyze heat exchanger configurations and predict performance across a range of operating conditions. Model predictions were compared with measurements collected in the CE-13C test facility, showing good agreement across the tested flow range except at low fuel flow rate around 2 kg/hr. Results provide guidance for sizing future fuel heating system for combustion research applications.

Jet fuel heating system↗
DOE OSTI2026

Feedstock Choices for Advancing the Bioeconomy in the US and Policy Implications

Biomass feedstocks will be critical for scaling up the bioeconomy in the United States to meet multiple demands for biofuels, sustainable aviation fuel, bioproducts, and biochemicals. There is a wide range of choices of feedstocks, including a variety of dedicated energy crops and crop residues. There are substantial differences in the yields, costs of production, and carbon intensity of these feedstocks and for each feedstock across locations. These feedstocks also differ in the trade-offs they offer among multiple environmental impacts. We discuss the economic factors that will influence the production of these feedstocks and the implications of alternative biofuel and low-carbon policies for the mix of feedstocks that will be incentivized.

Khanna, Madhu (ORCID:0000000349944451)↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
NASA NTRS2026

Smart Process Planning for Automated Fiber Placement

Many industries, including aerospace, automotive, wind energy, maritime, and sporting goods, rely on strong, lightweight materials called composites. These materials are made by layering fibers, which can come in the form of narrow strips or wider sheets, and setting them in a polymer matrix. One of the most advanced ways to make these parts is through automated fiber placement, where a machine lays down the fibers in precise patterns. This method can create very efficient and strong designs, but it is complex, expensive, and often depends heavily on the experience of skilled engineers. Today, the design, manufacturing, and inspection stages of composite production are usually handled separately. This separation means that important information, such as how a part will be built or what defects might occur, is not always shared between stages. As a result, parts may not be as lightweight, strong, or defect-free as possible, and the process can take longer and cost more. This research develops a smart process planning system that connects design, manufacturing, and inspection into one continuous process. Built as software that works with existing tools, the system can automatically plan how the fibers are placed, predicting and reducing defects while improving both manufacturability and strength. The system optimizes not only individual layers but also how defects are distributed across all layers, preventing them from stacking up in ways that weaken the final part. It also uses inspection results from completed parts to improve future designs, creating a feedback loop where each stage informs the others. The system was tested by designing a composite panel using this new approach and comparing it to a panel made with state-of-the-art manual planning methods. The results showed that the system could intentionally control where defects appeared and increase the efficiency of the planning process. By unifying design, manufacturing, and inspection, this research shows a way to make advanced composite manufacturing more efficient, consistent, and cost-effective. This approach lowers the barrier to using automated fiber placement and opens the door for its wider adoption not only in aerospace but also in industries such as automotive, wind energy, maritime, and sporting goods, where strong and lightweight structures are essential.

Computer-Aided Process Planning↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2024

RMCProfile7 : reverse Monte Carlo for multiphase systems

This work introduces a completely rewritten version of the programRMCProfile(version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature ofRMCProfile7is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available.RMCProfile7is freely available for download at https://rmcprofile.ornl.gov/.

Chemistry↗
DOE OSTI2026

Calibration of the Nonlinear Viscoelastic SPECtacular Model for 828-CTBN/DEA/GMB and 828/Z/AlOx

Nonlinear viscoelastic model calibrations are developed for two filled epoxies: an adduct of Epon 828 DGEBA and CTBN cross-linked with diethanolamine (DEA) filled with glass microballoons, called 828-CTBN/DEA/GMB, and Epon 828 DGEBA cross-linked with Ancamine Z and filled with alumina, called 828/Z/AlOx. The thermo-mechanical responses of these materials are represented using the SPECtacular constitutive model, an extension of the Simplified Potential Energy Clock (SPEC) model. The model couples

Cundiff, Kenneth Noel [Sandia National Laboratorie↗
NASA NTRS1973

Battery cell thermal-conductive coating increases efficiency

Thin coating of high-temperature epoxy resin provides necessary electrical insulation, as well as good thermal conductivity between battery cells. Insulation increases efficiency of nickel-cadmium battery, as it would any multicell battery assembly in which cell-to-cell thermal balance is critical.

Doyle, H. M.↗
NASA NTRS1977

A nickel-cadmium battery reconditioning circuit

The circuit presented is simple and small enough to be included in a typical battery charge/power control assembly, yet provides the advantage of a complete ground-type battery reconditioning discharge. Test results on the circuit when used to recondition two 24 cell, 20 A-h nickel-cadmium batteries are given. These results show that a battery reconditioned with this circuit returns to greater than 90 percent of its original capacity (greater than nameplate capacity) and follows a typical new battery degradation curve even after over 20,000 simulated orbital cycles for a 4 year period. Applications of the circuit are considered along with recommendations relative to its use. Its application in low voltage (22 to 36 Vdc) power systems and in high voltage (100 to 150 Vdc) power systems is discussed. The implications are that the high voltage systems have a greater need for battery reconditioning than their low voltage counterparts, and that using these circuit techniques, the expected life of a battery in low Earth orbit can be up to 5 years.

Lanier, R.↗
DOE OSTI2026

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
NASA NTRS1963

The Detection of Water Vapor on Mars

We have found eleven weak lines of water vapor on a high dispersion near-infrared spectrogram of Mars taken at the coudè focus of the Mount Wilson 100-inch reflector on April 12/13,1963. We exposed 43 hours on ammoniated IV-N emulsion with a dispersion of 5.6 A/mm; the plate is well exposed at the poles and somewhat overexposed at the center of the planet’s disk. The Martian H 2 O lines in the X 8200 water-vapor band are displaced 0.42 A longward of their telluric counterparts due to the relative velocities of the earth and Mars: AV = +15 km/sec on that date. The Martian lines are strongest over the poles of the planet-we set the spectrograph slit daost north-south to cover both polar regions. This apparent polar strengthening of the Martian H 2 O lines is at least partially due to the increased air mass through the line of sight to high latitudes.

MARS /PLANET/↗
DOE OSTI2025

Electrocatalytic grafting of polyvinyl chloride plastics

Polyvinyl chloride (PVC) plastics require high loadings of plasticizers and stabilizers to achieve commercially useful bulk properties. However, these non-covalent additives leach from PVC over time, resulting in the loss of their tailored functionality. This work details the electrocatalytic functionalization of PVC to covalently graft plasticizing additives directly onto the polymer backbone. Here, mechanistic insights guided the design of electrocatalysts capable of modifying C–Cl bonds of PVC under mild conditions with high selectivity while suppressing side reactions such as elimination and chain scission. Functional groups that mimic PVC plasticizers are covalently installed into the backbone of PVC to create new materials with distinct bulk properties from the original polymer. The degree of polymer grafting is easily controlled by simply changing the redox capacity that is passed during electrolysis. This strategy is employed to create chemically- and leach-resistant PVC materials by directly electrolyzing mixtures of consumer PVC products

Zackasee, Jordan L.S.↗
NASA NTRS2016

International Space Station Lithium-Ion Battery

The International Space Station (ISS) Electric Power System (EPS) currently uses Nickel-Hydrogen (Ni-H2) batteries to store electrical energy. The batteries are charged during insolation and discharged during eclipse. The Ni-H2 batteries are designed to operate at a 35 depth of discharge (DOD) maximum during normal operation in a Low Earth Orbit. Since the oldest of the 48 Ni-H2 battery Orbital Replacement Units (ORUs) has been cycling since September 2006, these batteries are now approaching their end of useful life. In 2010, the ISS Program began the development of Lithium-Ion (Li-ion) batteries to replace the Ni-H2 batteries and concurrently funded a Li-ion cell life testing project. This paper will include an overview of the ISS Li-Ion battery system architecture and the progress of the Li-ion battery design and development.

Battery↗
NASA NTRSDate not supplied

In-Situ Heat Rejection to the Martian Atmosphere

In order to provide in-situ heat rejection to the thin Martian atmosphere, approaches relying on forced convection and buoyancy flow are conceptualized. A convective radiator concept utilizing a fan to draw local atmosphere through an expanded metal foam heat exchanger panel is developed. To provide a pumped fluid loop interface, flow tubes are embedded in the heat exchanger panel and heat is transferred from the tubes to the foam via conduction and subsequently convectively to the environment. To forgo the use of a specialized high throughput fan, a concept utilizing a buoyant chimney tower to draw atmospheric flow through a low pressure drop heat exchanger is also considered. Both concepts may best align with moderate- to high-temperature heat loads that provide a significant delta temperature relative to the ambient atmosphere temperature. While eliminating dust accumulation on radiator surfaces, some type of serviceable filtration or screening may be required to prevent accumulation of dust inside the expanded metal foam heat exchanger for both concepts.

Heat Rejection↗
DOE OSTI2026

X-ray tomography of damage dynamics in advanced materials using a laser wakefield accelerator

Additively manufactured (AM) metals offer the potential for customizable, cost-effective components, but qualification and certification are crucial. Key to this process is understanding pore dynamics under stress, typically analyzed using micro-computed tomography. This study introduces laboratory-scale “betatron” x-rays from laser wakefield acceleration as a high-throughput alternative for x-ray tomography of advanced materials, such as AM AlSi10Mg alloys. Coupled with 3D finite element modeling, this method provides detailed insights into stress-porosity interactions. The approach delivers high-resolution scans, revealing that pore shape and local triaxiality significantly influence fracture dynamics, supporting advanced material characterization. This work also demonstrates the potential and versatility of laser-betatron x-ray μCT for generating large datasets to accelerate our understanding of the stochastic, process-specific nature of pore formation in AM alloys.

Senthilkumaran, Vigneshvar↗