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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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63 records · Page 4

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NASA NTRSDate not supplied

Battery Pack Shape Optimization using Transient Heat Conduction Coupled with Cell-Discharge Analysis

Battery electric systems exhibit significant time-dependence, especially when evaluated in the context of an aircraft mission profile with continually changing power demands. Additionally, when evaluating battery-powered aircraft concepts, it is important to accurately compute the temperature of the batteries and properly characterize the thermal response of the system. The temperature of the batteries has a significant impact on cell performance, in addition to safety considerations of maintaining battery temperatures below their operating limit. Because of these considerations, battery models for preliminary design and optimization of aircraft should include the capability to accurately compute the temperature distribution within the battery pack. Furthermore, battery pack designs should be as light-weight as possible to maximize the pack energy density, while also considering battery temperature limits. Here, we demonstrate a simultaneous trajectory and shape optimization of a battery pack concept, using a transient heat transfer finite element model coupled with a time-varying cell-discharge battery model to provide this capability. Including the transient finite element problem in the loop enables accurate temperatures that can be passed back to the cell discharge model, while the cell discharge model can supply the finite element model with time-varying heat boundary conditions to the finite element problem, further benefiting the fidelity of the thermal response of the batteries. We first demonstrate the coupling capability between the battery cell-discharge model and the transient finite-element heat transfer through an optimization which computes the optimal current profile for the battery pack while ensuring the battery temperatures remain below their operational limit. We then build on this optimization by adding shape optimization to the problem, which allows us to consider a composite objective function which also minimizes the mass of the battery pack, while also producing an optimal current discharge profile.

Optimization↗

NASA NTRSDate not supplied

SERENE: Saturn Enceladus Return Explorer with Nuclear Electric Propulsion

A ‘quick’ Enceladus sample return mission concept was developed based on the scientist recommendations at the recent ‘Accelerating Space Science with Nuclear Technology Workshop’. The Nuclear Electric Propulsion spacecraft assumed a follow-on 40 kWe nuclear reactor using the demonstrated 40 kWe Fission Surface Power system, expected in the early 2030s. The NEP vehicle also utilized a set of NEXT-C ion thrusters as well as planned Artemis commercial launchers. By launching the 40 kW NEP vehicle on a Starship and adding the propellants of 15 tankers, the 27t probe could be sent on a direct trajectory to Saturn (no Earth or Jupiter flybys) where NEP was used for Saturn capture, spiral down, spiral up and return to the Earth. A small lander obtained the surface Enceladus sample. Using the 40 kW NEP provided a round-trip time of only 16.5 years. A second option was more attractive from a science perspective whereby the NEP vehicle would deliver a large, 6t chemical lander to low Enceladus orbit where it would grab and return a sample to Earth (similar to the recent Orbilander design but in reverse). After deploying the lander, the NEP vehicle would stay in Saturn space performing a moon tour by orbiting four more moons and mapping the large moon of Titan. This option took slightly longer (18.5 yrs) due to the chemical return leg limitations. Both options demonstrated the agility, payload capability, and sample return goals the workshop recommended. An all-chemical option with two stages was roughly analyzed but took 21.5 yrs and required a Jupiter gravity assist.

Nuclear Electric Propulsion↗

NASA NTRSDate not supplied

Battery Pack Shape Optimization using Transient Heat Conduction Coupled with Cell-Discharge Analysis

Battery electric systems exhibit significant time-dependence, especially when evaluated in the context of an aircraft mission profile with continually changing power demands. Additionally, when evaluating battery-powered aircraft concepts, it is important to accurately compute the temperature of the batteries and properly characterize the thermal response of the system. The temperature of the batteries has a significant impact on cell performance, in addition to safety considerations of maintaining battery temperatures below their operating limit. Because of these considerations, battery models for preliminary design and optimization of aircraft should include the capability to accurately compute the temperature distribution within the battery pack. Furthermore, battery pack designs should be as light-weight as possible to maximize the pack energy density, while also considering battery temperature limits. Here, we demonstrate a simultaneous trajectory and shape optimization of a battery pack concept, using a transient heat transfer finite element model coupled with a time-varying cell-discharge battery model to provide this capability. The transient finite-element analysis is done using TACS, and the cell-discharge battery model uses OpenMDAO and dymos. Including the transient finite element problem in the loop enables accurate temperatures that can be passed back to the cell discharge model, while the cell discharge model can supply the finite element model with time-varying heat boundary conditions, further benefiting the fidelity of the thermal response of the batteries. We first demonstrate the coupling capability between the battery cell-discharge model and the transient finite-element heat transfer through an optimization which computes the optimal current profile for the battery pack while ensuring the battery temperatures remain below their operational limit. We then build on this optimization by adding shape optimization to the problem, which allows us to consider a composite objective function which also minimizes the mass of the battery pack, while also producing an optimal current discharge profile.

Optimization↗

DOE OSTI2026

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity (1 mS cm –1 ), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid‐state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ machine learning interatomic potentials to construct large‐scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal‐anion tetrahedra of various lengths. By analyzing the residence time of alkali cations migrating around tetrahedrally‐coordinated metals, we find that oxygen anions limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct‐particles correlations, while alkali transport is dictated by uncorrelated self‐diffusion. By extending this analysis to chemical compositions AMX 2.5 O 0.75 , spanning different alkaline (A = Li, Na, K), metallic (M = Al, Ga, In), and halogen (X = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic isovalent chemistry.

amorphous materials↗

DOE OSTI2026

Multitask graph neural networks for elastoplastic response prediction in dual-phase polycrystals

Microstructure-sensitive prediction of elastoplastic response remains a recurring bottleneck in multiscale damage and fatigue modeling, where large ensembles of statistically distinct polycrystals are required to quantify variability and extreme-value behavior. In this work, we develop a multitask graph neural network (GNN) surrogate that maps dual-phase ferrite–martensite polycrystal microstructures to Statistical Volume Element (SVE)-level elastoplastic Quantities of Interest (QoIs). Each SVE is represented as a grain-adjacency graph, with node features encoding phase, geometry, and crystallographic orientation, and edge features encoding relative misorientation. A message-passing graph convolution generates node embeddings, which are pooled into a graph representation and passed to a multitask regression head that jointly predicts 10 scalar QoIs and vector-valued stress–strain responses in orthogonal loading directions across multiple martensite volume fractions and SVE sizes. Results show high accuracy for scalar QoIs and strong agreement for full stress–strain trajectories, with population envelopes reproducing both median behavior and finite-SVE variability across compositions and partition scales. A unified model trained on pooled volume-fraction data preserves most within-regime accuracy relative to regime-specific models while also capturing the broader cross-regime variation reflected in the pooled test set. Distributional comparisons further demonstrate that the surrogate preserves heterogeneity under SVE partitioning, enabling statistically consistent block-wise random-field construction for mesoscale analyses. Overall, the proposed grain-graph surrogate provides a practical pathway to accelerate ensemble-based studies of SVE-level constitutive variability in dual-phase polycrystals.

Crystal plasticity↗

DOE OSTI2026

Seasonal variation of the surface cross-shelf exchange in the northern South China Sea: a Lagrangian perspective

Previous studies on cross-shelf exchange, predominantly adopted an Eulerian perspective, struggled to identify water sources and pathways. Using a high-resolution regional ocean modeling system (ROMS) and Lagrangian particle tracking, this study systematically investigates the seasonal variation and dynamics of surface cross-shelf exchange in the northern South China Sea (NSCS) from a Lagrangian perspective. Based on daily released 30-day drifter trajectories we identify the key pathways, source regions for surface cross-shelf exchange, revealing pronounced seasonal variability. Results show the surface cross-shelf exchange generally following monsoon-driven Ekman transport. However, local dynamics, especially topographic modulation, can outweigh the expected Ekman-driven transport, producing surface exchange patterns opposite to that predicted from the prevailing winds. Topographic effects vary across different scales. In the coastal waters of western Guangdong during winter, despite downwelling-favorable winds, the modulation of alongshore currents by island topography induces an eastward pressure gradient. This gradient, through geostrophic balance, drives offshore flows opposite to wind-driven Ekman onshore transport. Furthermore, the eastern widened shelf exhibits a distinct seasonal variation of cross-shelf exchange, with strong offshore transport (opposite to the direction of Ekman transport) in winter and exceedingly weak exchange in summer. Analysis of the underlying mechanisms reveals that this winter offshore transport is primarily attributed to geostrophic flows driven by surface pressure gradient that is jointly modulated by the Kuroshio intrusion and local widened shelf topography, and enhanced by cumulative submesoscale processes. In summer, a persistent and strong along-isobath jet acts as a dynamic barrier, effectively suppressing the exchange. These findings highlight the important role of topography in regulating surface material transport, and have important implications for predicting the advection and dispersion of plankton or oil spills over the continental shelf influenced by monsoon.

Hao, Dongliang↗

DOE OSTI2024

Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes (“tadpoles” and “boomerangs”), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2025

Enhancing Nasa Sounding Rocket Capabilities Through Composite Material Adoption

Sounding rockets are a suborbital research platform employed by NASA for heliophysics, astrophysics, and geospace science investigations, and for technology development. They follow semi-parabolic trajectories and are launched using surplus military, and commercial, solid-propellant rocket motors. NASA sounding rocket payloads are comprised of standard, modular subassemblies, designed to perform specific functions critical to achieving mission success criteria. Since the program’s inception, payload length and mass have trended upwards, limiting the capability of the platform to reach exospheric apogees and decreasing the time above critical altitudes to observe solar and celestial targets. To offset this trend, composite materials may be of use in payload and subsystem design to decrease payload mass. This investigation seeks to understand if there is a practical benefit to introducing composite materials on the NSRP by assessing mission specific impacts to those missions conducted in fiscal year 2024. To accurately estimate the mass of a composites-based payload, a standard subsystem (NIACS) underwent structural reconfiguration. Materials were selected that align with NSRP design, manufacturing, and testing standards. NIACS reconfiguration resulted in a mass reduction of 19.6% which was then applied to the payload system in entirety. Using vehicle performance carpet plots, delta apogee and time above the Kármán line were determined. Performance improvements were then evaluated against that mission’s success criteria to determine if that mission would have benefitted from using composite materials. On average, each mission would have gained 59.8 kilometers apogee and 62.9 seconds above the Kármán line. However, only nine of the seventeen missions would have benefitted from these performance improvements. Two of the missions could have used a lower-impulse launch vehicle to achieve mission success. Upleg stability met programmatic criteria; downleg instability and dynamic pressure improved, showing that ballast would not offset the mass reduction of using composite materials. Cost analysis showed that production costs will increase 39%. For the two missions that could have used a lower-impulse launch vehicle, the cost of the launch vehicle would have offset payload production costs. In summary, composite materials are not recommended for universal use on NASA sounding rocket payloads due to unclear programmatic benefits and added cost. However, they are justified for optical astrophysics and heliophysics missions, high-apogee (exosphere) requirements, or when they reduce payload costs versus higher impulse launch vehicles. Reflown, recoverable subsystems should use composites to enhance apogee and observation time.

Robert Henry Burth↗

NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗