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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Assessment of Various Low Temperature Electrolytes in Prototype Li-Ion Cells Developed for ESMD Applications

Due to their attractive properties and proven success, Li-ion batteries have become identified as the battery chemistry of choice for a number of future NASA missions. A number of these applications would be greatly benefited by improved performance of Li-ion technology over a wider operating temperature range, especially at low temperatures, such as future ESMD missions. In many cases, these technology improvements may be mission enabling, and at the very least mission enhancing. In addition to aerospace applications, the DoE has interest in developing advanced Li-ion batteries that can operate over a wide temperature range to enable terrestrial HEV applications. Thus, our focus at JPL in recent years has been to extend the operating temperature range of Li-ion batteries, especially at low temperatures. To accomplish this, the main focus of the research has been devoted to developing improved lithium-ion conducting electrolytes. In the present paper, we would like to present some of the results we have obtained with six different ethylene carbonate-based electrolytes optimized for low temperature. In addition to investigating the behavior in experimental cells initially, the performance of these promising low temperature electrolytes was demonstrated in large capacity, aerospace quality Li-ion prototype cells, manufactured by Yardney Technical Products and Saft America, Inc. These cells were subjected to a number of performance tests, including discharge rate characterization, charge rate characterization, cycle life performance at various temperatures, and power characterization tests.

Wide Operating Temperature Electrolytes↗
DOE OSTI2025

Ceramic Composite Inert Matrix Fuel Forms in High-Temperature Gas-Cooled Microreactors

Here, this work optimizes micro-prismatic high-temperature gas reactor (HTGR) designs to reduce the energy-normalized mass of spent nuclear fuel (SNF) and high-level waste (HLW) produced. The optimization was performed for the current graphite moderator and an inert matrix fuel (IMF) concept employing different composite moderators in a prismatic design architecture. The fuel matrix is magnesium oxide (MgO) with entrained tristructural-isotropic (TRISO) fuel. The moderator materials, including beryllium oxide (MgO-BeO) and beryllium (MgO-Be) at 40 vol % loading and yttrium hydride (MgO-YH x=1.9 ) and zirconium hydride (MgO-ZrH x=1.9 ) at 15 vol % loading, were entrained within the MgO host matrix. A generic graphite micro-prismatic HTGR is used as the baseline point design where the external dimensions are held constant. The composite moderator designs use 19.9% enriched uranium nitride TRISO fuel and hexagonal assemblies. For each IMF concept, an optimization study was performed to maximize the discharge burnup of the fuel by varying the TRISO packing fraction and the lattice pitch of the assemblies. The mass of SNF and HLW, other waste metrics, fuel cost, environmental impact metrics, and the activity of the SNF and HLW at 100 years and 100 000 years were calculated for the optimized IMF and graphite reference designs. The IMF results were subsequently compared to those of the graphite reference and the values for a light water reactor (LWR) and a small modular LWR. For the SNF and HLW, all the IMF concepts and the graphite reference produced less waste compared to the traditional LWR designs. However, the IMF concepts outperformed the graphite reference regarding the mass of SNF and HLW. For the other waste metrics, the IMF concepts showed reductions in fuel cost with improved environmental metrics relative to the graphite reference. Overall, the IMF concepts significantly reduced the SNF and HLW produced per unit of energy generated compared to traditional LWR designs.

TRISO↗
DOE OSTI2025

CALPHAD-based Bayesian optimization to accelerate alloy discovery for high-temperature applications

Two crucial properties influencing the performance of high-temperature alloys are coefficient of thermal expansion (CTE) and phase constitution. It is desirable to have alloys with low CTE, which reduces CTE mismatch with the surface oxide and the likelihood of oxide spallation. Reducing the amount of brittle intermetallic phases such as Sigma (σ) enhances alloy ductility and processability. Here, we propose a multi-objective Bayesian Optimization (BO) model to simultaneously minimize CTE (at an operational temperature of 1150 °C) and T σ (temperature when the Sigma phase completely dissolves in the metal matrix), properties which are obtained from high-throughput CALculation of PHAse Diagrams (CALPHAD). The model successfully identifies several alloys with CTE ≤ 2 × 10 –5 /K and T σ ≤ 500 °C by exploring just 7% of the nickel–chromium–cobalt–aluminum–iron (Ni–Cr–Co–Al–Fe) composition space. Such multi-objective alloy design frameworks can be used to inform additive manufacturing experiments and accelerate alloy discovery for high-temperature energy applications.

36 MATERIALS SCIENCE↗
NASA NTRS1980

Optimal thermionic energy conversion with established electrodes for high-temperature topping and process heating

Applied research-and-technology (ART) work reveals that optimal thermionic energy conversion (TEC) with approximately 1000 K to approximately 1100 K collectors is possible using well established tungsten electrodes. Such TEC with 1800 K emitters could approach 26.6% efficiency at 27.4 W/sq cm with approximately 1000 K collectors and 21.7% at 22.6 W/sq cm with approximately 1100 K collectors. These performances require 1.5 and 1.7 eV collector work functions (not the 1 eV ultimate) with nearly negligible interelectrode losses. Such collectors correspond to tungsten electrode systems in approximately 0.9 to approximately 6 torr cesium pressures with 1600 K to 1900 K emitters. Because higher heat-rejection temperatures for TEC allow greater collector work functions, interelectrode loss reduction becomes an increasingly important target for applications aimed at elevated temperatures. Studies of intragap modifications and new electrodes that will allow better electron emission and collection with lower cesium pressures are among the TEC-ART approaches to reduced interelectrode losses. These solutions will provide very effective TEC to serve directly in coal-combustion products for high-temperature topping and process heating. In turn this will help to use coal and to use it well.

Morris, J. F.↗
DOE OSTI2021

Modeling in High Temperature Corrosion: A Review and Outlook

Realizing higher operating temperatures to increase efficiency of future applications for energy conversion and storage while minimizing cost is a challenge for development of high-temperature materials. Simultaneous optimization of mechanical properties and corrosion resistance continues to be a difficult task but is essential due to the need to significantly accelerate the transition between technology readiness levels in the future. Furthermore, oxidation-induced degradation will be a critical life-limiting mechanism at increased operating temperatures. Suitable high-temperature materials cannot be solely identified by time-consuming experiments and reliable computational methods incorporating the relevant physics of processes must be considered to complement the experimental efforts. In the present work, a review of the methods employed to model oxidation-induced material degradation described in literature will be discussed. Furthermore, their capability to predict lifetime and aid in material selection will be evaluated.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
DOE OSTI2026

Influence of the as-built microstructure on the recrystallization of an additively manufactured Inconel939 Ni-based superalloy

This study investigates the influence of the as-built microstructure on the recrystallization (RX) behavior and mechanical properties of the Ni-based superalloy Inconel 939 produced by laser powder bed fusion (PBF-LB/M). Two distinct as-built microstructures were obtained by varying the hatch distance (h d ): a columnar, strongly textured condition (h d =50, termed h d 50) and an equiaxed, weakly textured condition (h d =70, termed h d 70)). Both were subjected to nine solution treatments combining three temperatures (1100, 1150, and 1200 °C) and three holding times (1, 4, and 8 h). Comprehensive microstructural characterization was conducted to assess grain morphology, texture, grain boundary character, dislocation density, and precipitate distribution. Recrystallization was found to be significantly slower than in cast counterparts, requiring higher temperatures and longer times for completion. The initial microstructure plays a decisive role: full RX was achieved only in hd70 specimens after treatment at 1200 °C for 8 h, whereas hd50 samples exhibited delayed and incomplete RX under identical conditions. This behavior is attributed to the finer grain size and higher fraction of high-angle grain boundaries in hd70, which promote recrystallization. Mechanical testing revealed that hd70 samples subjected to a 1200 °C/8 h treatment followed by standard double ageing show higher yield and tensile strengths across the investigated temperature range than both printed and cast Inconel939 processed under conventional conditions, albeit with slightly reduced ductility. The enhanced mechanical performance is attributed to the larger grain size, which limits grain boundary sliding. These results demonstrate the critical importance of controlling the as-built microstructure and tailoring post-processing strategies to optimize high-temperature performance of PBF-LB/M Inconel939.

Inconel939↗
DOE OSTI2024

Room-temperature fabrication of garnet-type solid-electrolyte: Optimizing particle size for high ionic conductivity

Dense and uniform Li 6.25 Al 0.25 La 3 Zr 2 O 12 (Al-doped LLZO) solid-electrolyte film of ~24 µm thickness is successfully fabricated by room temperature aerosol deposition (AD) method. The process optimization study revealed that careful control of particle size and morphology is one of critical determinants in the development of a compact AD membrane. Notably, our method facilitated an impressive ionic conductivity of approximately 10 –5 S cm –1 , bypassing the necessity for post-annealing processes, a milestone in itself. However, it is hypothesized that the attained conductivity is somewhat restrained by factors such as smaller grain size and potential surface degradation due to moisture exposure during fabrication, indicating avenues for further research. Looking forward, detailed investigations into the film's microstructure and its impact on transport properties will be a focal point, alongside potential enhancements through post-annealing and particle coating strategies. Finally, this research hints at a promising trajectory for the development of high-efficiency solid-state battery technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Thermophotovoltaic efficiency of 40%

Thermophotovoltaics (TPVs) convert predominantly infrared wavelength light to electricity via the photovoltaic effect, and can enable approaches to energy storage and conversion that use higher temperature heat sources than the turbines that are ubiquitous in electricity production today. Since the first demonstration of 29% efficient TPVs (Fig. 1a) using an integrated back surface reflector and a tungsten emitter at 2,000°C (ref. 10), TPV fabrication and performance have improved. However, despite predictions that TPV efficiencies can exceed 50%, the demonstrated efficiencies are still only as high as 32%, albeit at much lower temperatures below 1,300 C (refs. 13,14,15). Here we report the fabrication and measurement of TPV cells with efficiencies of more than 40% and experimentally demonstrate the efficiency of high-bandgap tandem TPV cells. The TPV cells are two-junction devices comprising III–V materials with bandgaps between 1.0 and 1.4 eV that are optimized for emitter temperatures of 1,900–2,400°C. The cells exploit the concept of band-edge spectral filtering to obtain high efficiency, using highly reflective back surface reflectors to reject unusable sub-bandgap radiation back to the emitter. A 1.4/1.2 eV device reached a maximum efficiency of (41.1 ± 1)% operating at a power density of 2.39 W cm –2 and an emitter temperature of 2,400°C. A 1.2/1.0 eV device reached a maximum efficiency of (39.3 ± 1)% operating at a power density of 1.8 W cm–2 and an emitter temperature of 2,127°C. These cells can be integrated into a TPV system for thermal energy grid storage to enable dispatchable renewable energy. This creates a pathway for thermal energy grid storage to reach sufficiently high efficiency and sufficiently low cost to enable decarbonization of the electricity grid.

14 SOLAR ENERGY↗
DOE OSTI2025

Understanding the Effect of Sample Geometry on Temperature Distribution during Optical Floating Zone Crystal Growth in Vacuum Environment through Heat Transfer Modeling

Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Temperature Data Assimilation with Salinity Corrections: Validation for the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 1993-1998

The NASA Seasonal-to-Interannual Prediction Project (NSIPP) has developed an Ocean data assimilation system to initialize the quasi-isopycnal ocean model used in our experimental coupled-model forecast system. Initial tests of the system have focused on the assimilation of temperature profiles in an optimal interpolation framework. It is now recognized that correction of temperature only often introduces spurious water masses. The resulting density distribution can be statically unstable and also have a detrimental impact on the velocity distribution. Several simple schemes have been developed to try to correct these deficiencies. Here the salinity field is corrected by using a scheme which assumes that the temperature-salinity relationship of the model background is preserved during the assimilation. The scheme was first introduced for a zlevel model by Troccoli and Haines (1999). A large set of subsurface observations of salinity and temperature is used to cross-validate two data assimilation experiments run for the 6-year period 1993-1998. In these two experiments only subsurface temperature observations are used, but in one case the salinity field is also updated whenever temperature observations are available.

Troccoli, Alberto↗
NASA NTRS2009

Electrolytes for Low-Temperature Operation of Li-CFx Cells

A report describes a study of electrolyte compositions selected as candidates for improving the low-temperature performances of primary electrochemical cells that contain lithium anodes and fluorinated carbonaceous (CFx) cathodes. This study complements the developments reported in Additive for Low-Temperature Operation of Li-(CF)n Cells (NPO- 43579) and Li/CFx Cells Optimized for Low-Temperature Operation (NPO- 43585), which appear elsewhere in this issue of NASA Tech Briefs. Similar to lithium-based electrolytes described in several previous NASA Tech Briefs articles, each of these electrolytes consisted of a lithium salt dissolved in a nonaqueous solvent mixture. Each such mixture consisted of two or more of the following ingredients: propylene carbonate (PC); 1,2-dimethoxyethane (DME); trifluoropropylene carbonate; bis(2,2,2-trifluoroethyl) ether; diethyl carbonate; dimethyl carbonate; and ethyl methyl carbonate. The report describes the physical and chemical principles underlying the selection of the compositions (which were not optimized) and presents results of preliminary tests made to determine effects of the compositions upon the low-temperature capabilities of Li-CFx cells, relative to a baseline composition of LiBF4 at a concentration of 1.0 M in a solvent comprising equal volume parts of PC and DME.

Smart, Marshall C.↗
DOE OSTI2023

Multi-Objective Optimization of a Bi-Metal High-Temperature Recuperator for Application in Concentrating Solar Power

Supercritical CO 2 closed Brayton cycles are a major candidate for future power cycle designs in concentrating solar power applications, with high-temperature recuperators playing an essential role in realizing their high thermal efficiency. Here, printed circuit heat exchangers (PCHEs) are often chosen for this role due to their thermal-hydraulic and mechanical performance at high temperatures and pressures, all while remaining compact. However, PCHEs can be costly because of the high-performance materials demanded in these applications, and the heat exchanger internal geometry is restricted by their manufacturing process. Additively manufactured heat exchangers can address both of these shortcomings. This work proposes a modular bi-metal high-temperature recuperator with integrated headers to be produced with additive manufacturing. Beginning with existing PCHE channel geometries, a 1D heat exchanger model is developed. Then, multi-objective optimization is used to maximize the heat transfer effectiveness of a lab-scale device while limiting its size. Two distinct channel geometries emerge from the optimization. Optimal designs achieve up to 88% effectiveness with negligible pressure drop. Deterioration of effectiveness due to axial conduction of heat in the heat exchanger walls is found to be a notable problem for lab-scale PCHEs, and the optimal designs obtained here minimize its detrimental effects. A sensitivity analysis reveals that the effectiveness of the recuperator is much less sensitive to variation in mass flowrate in off-design operation when axial conduction is significant, while increasing the length of the device easily increases effectiveness.

14 SOLAR ENERGY↗
DOE OSTI2025

Analysis and Validation of PMT s Waveforms in ICARUS LArTPC Using Monte Carlo Simulations

ICARUS (Imaging Cosmic and Rare Underground Signals) serves as the Far Detector in the Short Baseline Neutrino (SBN) program at Fermilab, playing a central role in investigating the potential existence of sterile neutrinos in the eV squared mass range. The detector consists of two large Liquid Argon Time Projection Chambers (LArTPCs) with a total capacity of 760 tons of liquid Argon. A key component of the system is its array of 360 Photo-Multiplier Tubes (PMTs), which detect the scintillation light produced by charged particles in liquid Argon; the fast scintillation signal enables accurate event timing, triggering, and reconstruction. Together with the TPC and CRT systems, the PMTs ensure precise interaction time measurements, which are crucial for distinguishing neutrino interactions from cosmic-ray backgrounds. ICARUS uses Hamamatsu R5912-MOD PMTs, optimized for cryogenic temperatures, with high quantum efficiency, excellent timing resolution, low dark current (around 10 nA at 1500 V), and broad spectral sensitivity (300–650 nm). These characteristics are crucial for the efficient detection of scintillation light. Analyzing the waveforms of PMT signals allows for a detailed comparison between experimental data and Monte Carlo simulations. This analysis is fundamental for improving the accuracy of neutrino event reconstruction, enhancing detector calibration, and optimizing the detector's performance for current and future operations.

Brio, V. [Catania U.] (ORCID:0009000088807391)↗
DOE OSTI2025

Phase-field modeling of diffusion bonding in 316H stainless steel: Impact of processing conditions on grain morphology and bonding quality

A novel multi-phase, multi-component phase‐field model is presented to study the diffusion bonding of 316H stainless steel. Combined with targeted experimental investigations, this model simulates the bond-growth process and predicts the bonding quality. Unlike previous models, our approach captures the simultaneous evolution of voids and grain structures, while quantifying bonding quality using defined bonding ratio. A comprehensive analysis of bond process control is performed by changing temperature, pressure and surface roughness observing the resulting bond structure, which is consistent with experimental observations and analytical predictions. Temperature is determined to be the dominant factor, with the transition from a flat to a robust bond occurring between 1000 °C and 1050 °C. At the ideal bonding temperature of 1050 °C, a surface roughness exceeding 0.6 μm or an applied stress below 4 MPa results in poor bonding quality. Beyond this, higher pressures and smoother surfaces reduce void size, accelerate void shrinkage, and lead to improved bond integrity. This diffuse-interface model can be extended to other material systems if supplied with appropriate thermodynamic and kinetic data. In conclusion, this makes it an effective modeling platform for optimizing high-temperature diffusion bonding and developing reliable bonded components such as compact heat exchangers.

Diffusion bonding↗
DOE OSTI2025

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗
DOE OSTI2024

Magnetism and finite-temperature effects in UZr 2 : A density functional theory analysis

The structure and the thermophysical properties of d-UZr 2 are investigated using 0 K density functional theory and ab initio molecular dynamics (AIMD). Modeling the true paramagnetic state of this intermetallic compound has been challenging using first-principles calculations. For the first time, we find that the generalized gradient approximation method without applying an on-site Coulomb interaction term (Hubbard U) can result in a ground state that is antiferromagnetic (AFM). We believe that this weak AFM ground state is the closest to the real paramagnetic state. We found that structure optimization at finite temperatures using AIMD is necessary to achieve this ground state instead of the non-magnetic and ferromagnetic states previously reported in the literature. Our findings indicate that applying the Hubbard U on uranium f-orbitals in this metallic system is unnecessary and not recommended, as it leads to a large overestimation of the volume and introduces an unphysical strong spin polarization. Finally, our approach results in atomic volume, thermal expansion, and heat capacities that have strong agreement with experiments.

36 MATERIALS SCIENCE↗
NASA NTRS2006

Studying the MLT by a Combined Analysis of SABER/TIMED and Lidar Measurements

The SABER instrument on board the TIMED Satellite is a limb scanning infrared radiometer designed to measure temperature and minor constituent vertical profiles and energetics parameters in the mesosphere and lower thermosphere (MLT). The measurements have been performed continuously since January 25, 2002 to provide excellent coverage for both hemispheres. The Leibniz-Institute of Atmospheric Physics (LAP) at Kuehlungsborn, Germany (54N, 12E) operates two lidar instruments, using three different temperature measurement methods, optimized for three altitude ranges. The total altitude range of the lidar installation lies from 1 to 105 km. Another instrument used for intercomparison is the ALOMAR RMR lidar, located at Andoya, Norway (69N, 16E). We have searched the SABER and lidar datasets for coincidental common volume measurements within plus or minus 1 degree in latitude, plus or minus 2 degrees in longitude and approx. 1 hour in time for the sake of (a) comparison of measured temperatures; (b) validation of the models used in SABER data analysis; and (c) extracting new information about MLT parameters. In this work we applied the non-LTE ALI-ARMS code designed to calculate the nonequilibrium radiance in different viewing geometries to the analysis of measurements which satisfied these search criteria. The results of this analysis (a) support the application of higher value of CO2-O quenching rate (6e-12 cubic centimeters per second) by the non-LTE temperature retrievals from the SABER 15 micrometer limb radiance data, and (b) demonstrate the importance of accounting for the vibrational-vibrational energy exchange among the CO2 isotopes for accurate temperature retrievals. Using temperature profiles obtained in lidar measurements as inputs for the retrieval algorithm we also retrieved the nighttime CO2 densities from the SABER 15 micrometer limb radiances and compared them with the model and climatology CO2 data used in the SABER nighttime temperature retrievals.

Feofilov, A. G.↗