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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Underwater thermomagnetic generator for remote marine thermal energy harvesting and sensing

Thermomagnetic generators offer a promising approach for sustainable power generation in remote marine environments. Here, this study presents the design, thermal modeling, and experimental validation of a passively driven underwater thermomagnetic generator developed for powering ocean observation and monitoring sensors. The generator was evaluated under varying working fluids, thermal boundary conditions, and extended operation to assess real-world applicability. Two fluids, deionized water and silicone-based Thermal C5, were tested under simulated shallow- and deep-ocean conditions. Deionized water outperformed Thermal C5, especially in colder environments (~5°C), achieving a peak output of 2.7 mW due to larger temperature gradients and enhanced convective-evaporative heat transfer. Long-duration tests revealed a transient evaporation-condensation cycle that temporarily reduced rotor immersion and performance before stabilizing. The generator powered commercial marine sensors for over 6 h without external batteries, demonstrating the viability of compact, passively cooled thermomagnetic systems for autonomous, off-grid marine sensing.

13 HYDRO ENERGY↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Energy efficiency in industrial drying: A hybrid ultrasonic system with a novel dynamic optimization framework

Drying processes are among the most energy-consuming operations in industrial and manufacturing settings, demanding strategic selection, design, and control for enhanced efficiency. Advancing drying technologies is critical for improving sustainability, lowering energy use, reducing carbon emissions, and minimizing waste. This study explores two innovative strategies aimed at transforming drying processes into sustainable, low-carbon systems by reducing energy consumption, minimizing waste, and maintaining a strong emphasis on preserving product quality. The first strategy showcases a sub-pilot scale hybrid ultrasonic-convective dryer for agrifood products. This technology, powered by electricity (process electrification), integrates non-thermal ultrasonic dehydration with convective heating and is presented as a sustainable and energy-efficient solution that enhances eco-friendly practices. The second strategy involves introducing and implementing a novel, multiobjective, mixed integer dynamic optimization technique to determine the optimal time-dependent process parameter values for the drying operation. This optimization technique yields operating conditions that are piecewise constant in time aiming to maximize the energy efficiency of the hybrid ultrasonic-convective dryer while ensuring strict adherence to product quality constraints. By adopting the hybrid ultrasonic-convective dryer, a notable 35% improvement in energy efficiency was achieved compared to conventional hot-air drying systems for drying apple slices. The proposed optimization framework further enhanced energy efficiency by nearly 14% over the most efficient process on the identical testbed, under static operating conditions. The reported enhancements have been experimentally validated. Regarding drying time (thereby improving production yield), the developed hybrid ultrasonic-convective dryer demonstrates as much as a 41% reduction in total processing time, which is further optimized by an additional 10% using our proposed optimization framework. The research outcomes have profound implications for the design and operation of drying systems, encompassing crucial aspects such as process electrification, cost-effectiveness, energy savings, time efficiency, product yield, product quality, and process automation.

Dynamic optimization↗
DOE OSTI2024

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. Further, an optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g -1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg -1 S) and high S-loading cathodes (>4 mg cm -2 ) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg -1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

25 ENERGY STORAGE↗
DOE OSTI2024

Relative permeabilities for two-phase flow through wellbore cement fractures

Multiple fluids are likely to exist in fractures and flow paths associated with leaky wellbores, including liquids (e.g., crude oil) and gases (e.g., gas exsolved from liquid). These fluids occupy and move through different portions of the pore spaces within the fractures depending on many factors, including fluid properties, fracture size, and the amount of the different fluids. Upward leakage of any phase, through the fracture, can contaminate water-bearing formations, create hazardous surface conditions, and compromise the functionality of the wellbore. Early signs of wellbore leaks may be expressed by anomalous pressure behavior at surface monitoring points on cavern storage wells. These pressure anomalies are difficult to interpret, necessitating knowledge of the factors that affect the multiphase flow in fractures and porous media. These parameters are critical to modeling multiphase flow in fractures. This insight can guide further diagnosis and maximize leak remediation. Here, our study focuses on the relationship of the liquid–gas relative permeabilities for representative variable-aperture wellbore cement fracture. To obtain the relative permeability of each phase, two-phase flow tests were conducted where both fluids were flowing simultaneously through a fractured wellbore cement specimen under a range of factors, namely (1) aperture size, (2) capillary numbers, and (3) viscosity ratio. The flow experiments were conducted under a range of confining stresses and flow velocities, using nitrogen gas and silicone oils (of different viscosities) in a specially designed pressure vessel. The sum of gas and oil relative permeabilities were found to be less than one under all conditions, which indicates that the presence of one phase affects the permeability of the other phase, and vice versa. Since the gas phase flow conditions include a significant inertial flow component in addition to viscous flow, the inertial flow coefficients at different saturation states are presented. The factors affecting the relationship between the relative permeabilities are discussed in detail. A new mathematical model for estimating the relative permeability of wellbore cement fracture is presented and experimentally validated.

58 GEOSCIENCES↗
DOE OSTI2024

A multiphase flow model of water droplets dielectrophoretic-induced air dehumidification phenomena

Air humidity in indoor spaces plays a critical role in human comfort and health. Dehumidification systems are used for building humidity controls, but they can take significant energy consumption, especially in geographic locations with high outdoor humidity and warm climates. Consequently, there is a growing demand for innovative dehumidification processes that consume minimal energy. Dielectrophoretic air dehumidification represents one such promising approach. However, it has not garnered significant attention due to the absence of engineering models and simulation tools capable of evaluating its performance and limitations at large-scale airflows. A new numerical multiphase CFD model, which is also experimentally validated, is developed in a customized Reacting Foam solver based on OpenFOAM® version 9. The newly developed model seeks to decrease substantial energy consumption and lower costs by leveraging the dielectrophoretic phenomenon to regulate moisture levels in the air. The solver integrates a hybrid Eulerian-Lagrangian framework to track the droplet's trajectory and growth rate while solving the continuum equations for the moist air. An electrospray produces electrically charged droplets, which grow during their in-flight trajectories as water vapor condenses onto their surfaces. The role of electrostatic forces in promoting vapor condensation within a high-gradient electrical field is investigated, and the dielectrophoretic vapor nucleation process on charged water droplets is discussed. The CFD model was validated against results from the literature and from proof-of-concept experiments conducted by the authors, which showed a 2 % air dehumidification with a single electrospray and airflow rate of 5 cubic feet per minute. The simulation results indicated that augmenting the number of electrically charged spray droplets increased the dehumidification of the air to 25 %. The initial mean droplet diameter, the orientation of the injector and relative humidity significantly influence the assessment of dehumidification. As a result, scaling up this approach to larger airflow volumes is identified as a potential future research direction.

42 ENGINEERING↗
DOE OSTI2024

Solid face sheets enable lattice metamaterials to withstand high-amplitude impulsive loading without yielding

Owing to their ability to provide tunable mechanical responses, lattice materials are frequently studied to elucidate their response to static and dynamic loads. However, these roles are typically in opposition: static loads must be supported sufficiently far away from the onset of buckling or yielding, whereas dynamic loads are typically ameliorated by crushing of the lattice, which provides excellent energy-absorption due to the large plastic deformation accompanying densification. In contrast, this work considers the octet truss as an exemplar topology, in a structural role where it must simultaneously support static loads while enduring high-amplitude impulsive loads. This study focuses on the ability to withstand impulsive loads without yielding, an essential prerequisite to enduring dual loading. Computational studies using the ALE3D hydrocode were performed to examine the response of the octet truss under a short temporal width impulse shape associated with laser-driven shocks. A key finding was that covering the lattice with a solid face sheet and treating this face sheet thickness as a design variable allows the Taylor-like pulse to be attenuated prior to entering the weaker lattice, at the cost of added mass up front. Experimental validation was accomplished by laser-driven shock testing, using octet trusses printed out of Ti-5Al-5V-5Mo-3Cr. The results show that for a given quantity of mass, the attenuation is maximized when as much mass as possible is moved into the face sheet, leaving a more slender lattice structure. The effect of placing mass in the face sheet rather than lattice beams dominates the effect of relative density, to the point where a low-mass structure with most of the mass concentrated in the face sheet can outperform a high-mass structure with most of the mass in the lattice. Finally, by further understanding the propagation of short pulse width waves within under-dense structures, this study expand the domain of applicability of such structures, including lattice materials, to challenging dual-loading regimes spanning decades of strain rates.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Origin of replication discovery for environmentally isolated Pantoea strain enables expression of heterologous proteins, pathways and products

Leveraging predicted origin sequences from a previously characterized groundwater plasmidome, we constructed a barcoded plasmid library to screen for previously unknown origins. Testing this library against a panel of representative bacterial strains led to the identification of 3 previously unknown origins that replicate in gram-negative bacteria not previously associated with these origin sequences. Experimental validation confirmed that a plasmid bearing origin 6911 as the sole origin could replicate with a copy number of 9 (±2) in Pantoea sp. MT58, a fast growing and metal tolerant, environmentally important bacterium. Plasmids based on this new origin were used to express the reporter protein GFP, and non-native metabolite pathways for the natural product indigoidine and the terpenoid compound isoprenol. Functional previously unknown origins of replication in such non-model organisms can expand the toolkit for genetic manipulations of both model and less-studied bacteria.

molecular biology↗
DOE OSTI2024

Novel Z-DNA binding domains in giant viruses

Z-nucleic acid structures play vital roles in cellular processes and have implications in innate immunity due to their recognition by Zα domains containing proteins (Z-DNA/Z-RNA binding proteins, ZBPs). Although Zα domains have been identified in six proteins, including viral E3L, ORF112, and I73R, as well as, cellular ADAR1, ZBP1, and PKZ, their prevalence across living organisms remains largely unexplored. In this study, we introduce a computational approach to predict Zα domains, leading to the revelation of previously unidentified Zα domain-containing proteins in eukaryotic organisms, including non-metazoan species. Our findings encompass the discovery of new ZBPs in previously unexplored giant viruses, members of the Nucleocytoviricota phylum. Through experimental validation, we confirm the Zα functionality of select proteins, establishing their capability to induce the B-to-Z conversion. Additionally, we identify Zα-like domains within bacterial proteins. While these domains share certain features with Zα domains, they lack the ability to bind to Z-nucleic acids or facilitate the B-to-Z DNA conversion. Our findings significantly expand the ZBP family across a wide spectrum of organisms and raise intriguing questions about the evolutionary origins of Zα-containing proteins. Moreover, our study offers fresh perspectives on the functional significance of Zα domains in virus sensing and innate immunity and opens avenues for exploring hitherto undiscovered functions of ZBPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A high-volume resonator for L-band DNP-NMR

DNP-NMR and EPR experiments that operate at or greater than L-band (i.e., ν 0 (e – ) = 1–2 GHz) are typically limited to maximum sample volumes of several hundred µL. These experiments rely on well-known resonator designs for DNP/EPR irradiation such as the loop-gap resonator and Alderman-Grant coil, where their maximum volumes limit further application to imaging experiments and high-throughput screening beyond L-band. Herein, we demonstrate a birdcage (BC) resonator design that can accommodate several mL of sample while operating around 1.5 GHz. The sample volume is maximized by using two identical BC resonators in a stacked configuration. Simulations are used to optimize the BC design and the performance is validated experimentally with liquid-state Overhauser-DNP-NMR experiments. This BC design exploits just the parasitic capacitance of conductive rings and features no fixed tuning capacitors. An enhancement of –77 is achieved on a 10 mM 4-Amino-TEMPO in H 2 O sample for a 5 mL sample volume. Finally, the associated sample heating is minimal due to the low-E-fields generated and the large sample mass with +3.4 K when driving 100 W for several seconds.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Solid-state batteries enabled by ultra-high-frequency self-heating

Solid-state batteries (SSBs) are promising next-generation batteries due to their high energy density and enhanced thermal stability and safety. However, their sluggish kinetics and transport at room temperature result in high internal impedance and critically reduce the attainable discharge energy density. Taking advantage of their strong temperature-dependent ionic conductivity, here we introduce ultra-high-frequency (greater than 105 Hz) self-heating (UHFSH) of SSBs, which can rapidly warm up the batteries from room temperature to operating temperature (∼65°C) in less than a minute. As proof of concept, UHFSH experiments were conducted on symmetric solid-state cells with lithium aluminum germanium phosphate electrolyte in different configurations. Using an experimentally validated model, pack-level simulations predict fast heating (50 K/min) and minimized heating energy consumption (less than 4%). Without any modification of the materials or structure of the batteries, our non-intrusive self-heating strategy potentially enables SSBs to discharge more than 2-fold energy in 25°C ambient.

pulse heating↗
DOE OSTI2024

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

25 ENERGY STORAGE↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2026

Enhancing microsegregation during rapid directional solidification through ternary microalloying

The nano-cellular dendritic microstructure formed during rapid directional solidification in powder bed fusion additive manufacturing creates unique properties such as simultaneous improvement in strength and ductility. However, process control of microsegregation features remains challenging due to low sensitivity of critical solidification mechanisms to process parameters. This study leverages microalloying to achieve large changes in dendrite composition, microstructure, and interdendritic zone width during laser powder bed fusion without modifying process parameters. CALPHAD simulations predict that the addition of Zr significantly steepens the solidus line of the dilute Cu-Cr alloy system, leading to enhanced Cr rejection into the melt and greater than 95% reduction in solubility of Cr in the solidified Cu matrix. Experimental validation using time-of-flight secondary ion mass spectrometry and Kelvin probe force microscopy reveals that the ternary alloy containing 0.01 wt% Zr exhibited wider interdendritic regions compared to the binary, a significantly higher number of Cr-rich particles within interdendritic regions, near-complete ejection of oxygen impurities from the matrix, and greater nanoscale work function contrast. These features indicate more aggressive Cr segregation in the presence of Zr and a purer Cu matrix and provide a potentially robust method for engineering the nano-cellular dendritic solidification microstructure.

CALPHAD↗
DOE OSTI2026

Controlling homogenization length scales and microstructure in additively manufactured Ti-Ta functionally graded materials

Materials with smooth compositional gradients or functionally grade materials (FGMs) produced via additive manufacturing (AM), enables joining dissimilar materials and optimizing multiple properties in advanced engineering applications. However, as-printed AM microstructures exhibit micro-segregation and solidification defects which, when combined with controlling macroscale gradient properties, complicates necessary post-processing. Here, we use CALPHAD-informed diffusion modelling to design post-processing heat treatments for lightweight to refractory FGMs. Ti-Ta (0 to 85 at. % Ta) FGMs were fabricated using laser-based directed energy deposition AM. Post-processing heat treatments at 1000° C and 1500° C were designed to promote homogenization across specific length scales and experimentally validated. Investigation of chemical segregation and microstructures demonstrated that the length scale of homogenization is controlled as a function of time, temperature, and local composition. Ta-rich regions exhibited incomplete homogenization compared to Ti-rich layers. Unmelted Ta particles were found to completely dissolve at 1500 °C. By controlling cooling rate (200 °C/min), martensitic structures were produced between 14–36 at. % Ta, consistent with martensite-start temperatures calculations, while furnace cooling (2 °C/min) produced α+β morphologies. This work establishes a validated predictive framework for designing post-processing to tailor microstructure and chemical architecture in AM FGMs, facilitating their deployment in demanding environments.

Materials science↗
DOE OSTI2026

A robust alloy design (RAD) strategy for next-generation (IV) nuclear fission reactors

Next-generation nuclear reactors demand structural materials capable of withstanding extreme conditions, including high temperatures, intense neutron flux, and corrosive environments. Multi-Principal Element Alloys (MPEAs) have emerged as promising candidates due to their exceptional radiation tolerance, thermal stability, and compositional flexibility. This study introduces a versatile and customizable Robust Alloy Design (RAD) strategy for systematically designing MPEAs for GEN-IV reactor fuel cladding. The RAD framework integrates nuclear-relevant selection criteria, empirical parameter assessments, and high-throughput CALPHAD simulations to efficiently narrow compositional space and identify stable alloys. A unified RAD score developed for the first time, combines key performance metrics, including fuel-clad chemical interaction (FCCI), neutron absorption cross-section (NAC), valence electron configuration (VEC), and melting point factor (MPF), into a flexible ranking system adaptable to reactor-specific priorities. Among 724 candidates, V555(5Al–5Cr–5Fe–85V) emerged as the top alloy, validated experimentally with a homogeneous single-phase BCC microstructure and superior mechanical properties (nano-indentation: 3.389 ± 0.258 GPa; Vickers hardness: 240 ± 6.7 HV), significantly outperforming Zircaloy-4 and V-4Cr-4Ti. Importantly, the RAD strategy is not limited to nuclear applications; its customizable weighting system enables scalability to other extreme environments. This adaptability positions RAD strategy as a versatile tool for advanced materials design across multiple industries.

Alloy design↗
DOE OSTI2025

CALPHAD-based ICME design of single-step aging to enhance mechanical strength of WAAM Haynes 282

To match the strength of wire-arc additive manufactured Haynes 282 to its wrought counterpart via a single-step aging heat treatment, the CALPHAD (Calculation of Phase Diagrams) method is integrated with physics-based process-structure-property models and experimental validation. The integrated computational materials engineering (ICME) framework simulates the effects of aging on γ′ and M 23 C 6 precipitation and the resulting yield strength. To improve simulation reliability, the interfacial energies between γ/γ′ and γ/M 23 C 6 carbides were estimated by comparison with precipitation kinetic modeling and measured precipitate sizes. γ′ and M23C6 were found to precipitate simultaneously between 640 and 860 °C, producing microstructures similar to those produced by two-step aging. The optimal γ′ size for peak yield stress was calculated to be 20–23 nm. WAAM Haynes 282 aged at 780 °C for 50 h exceeded the mechanical performance of its wrought counterpart subjected to two-step aging, though desired properties can also be achieved at 800 °C for 16 h or less. The error in yield strength is less than 20 MPa, demonstrating good agreement between the modeling framework and experiments. Creep studies showed that WAAM Haynes 282 exceeded the calculated rupture time, reaching 481 h. This proposed methodology can accelerate the design of aging heat treatments for any γ′-strengthened nickel-base alloy, minimizing the resources required for trial-and-error experiments.

CALPHAD↗
DOE OSTI2026

Enhancers that direct gene expression to central nervous system vascular endothelial cells in vivo

CNS vascular endothelial cells (ECs) exhibit a distinctive gene expression program that is foundational for the blood-brain barrier (BBB). Previous research identified candidate cis-regulatory elements (CREs) that were hypothesized to control this program. In this work, transgenic mice and recombinant adeno-associated virus (rAAV) vectors have been used to interrogate these candidate CREs in vivo. These experiments show that an 850 bp genomic DNA segment ∼60 kb 5′ of Slc2a1 possesses enhancer activity that is (1) specific for BBB+ CNS ECs and (2) both necessary and sufficient for BBB+ EC gene expression. A screen of >8,000 genomic DNA segments from CNS EC-specific CRE candidates reveals several hundred with enhancer activity. Transcription factors ERG and LEF1 are shown to occupy sites in brain ECs that are highly enriched in candidate and experimentally validated CREs, lending strong support to a model in which canonical Wnt signaling activates the BBB program via LEF1.

CUT&RUN↗