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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Miniaturized Millimeter-Wave Multilayer Filter Design Using Additive Manufacturing

Here, this article presents an innovative, fully additive manufactured approach to millimeter-wave multilayer circuits. An aerosol jet printer is used to fabricate a multilayer stepped-impedance low-pass filter. By leveraging the second layer for miniaturization, we achieve a more compact design. By precisely controlling conductor separation and utilizing 3-D printing technology, we were able to optimize the width of the high- and low-impedance segments for optimal filter performance. Two filter types were successfully fabricated: a low-pass microstrip filter and a low-pass stripline filter, both with a cutoff frequency near 29 GHz and exhibiting acceptable stopband attenuation. The stripline configuration allows for a 36% decrease in the stripline waveguide conductor area while achieving a great passband insertion loss of just 0.62 dB, enabled by aerosol jet printing (AJP). The line loss of both designs was characterized using a microstrip through line and a stripline through line. Both designs demonstrated low overall loss, with the microstrip line exhibiting a loss of 0.26 dB/mm and the stripline having a loss of 0.37 dB/mm at 29 GHz. This work demonstrates a multilayer integration solution and offers an advantage in reducing the size of RF circuits such as filter banks for next-generation integrated RF front ends.

additive manufacturing↗
DOE OSTI2026

Effect of Illumination Area on the Ultrafast Temporal Response of MSM GaN Photodiodes

We investigate the influence of spatial illumination profiles on the temporal response of metal–semiconductor–metal GaN photodiodes. Using both simulation and experimental measurements, we compare the response curves under two scenarios: illumination confined to the active area between electrodes and extended illumination beyond the contacts. The results show that limiting the beam illumination to the active region significantly sharpens the response, reducing the long decay tails associated with slow carrier drift from peripheral regions. As a result, the experimental data closely match simulation predictions, confirming that illumination geometry plays a critical role in optimizing photodiode performance for ultrafast detection applications.

Carrier drift time↗
DOE OSTI2025

Enhanced domain dynamics in alternating current poled rhombohedral Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 single crystals

Abstract The understanding of domain dynamics in ferroelectric materials is crucial for optimizing their performance in piezoelectric and electro‐optic applications. Although previous studies have focused on static domain structures and macroscopic characteristics, the time‐resolved approach of domains remains largely unexplored. In this study, we compare the dynamic responses of direct current (DC) and alternating current (AC) poled [001]‐oriented rhombohedral Pb(Mg 1/3 Nb 2/3 )O 3 –PbTiO 3 (PMN–PT) single crystals using X‐ray photon correlation spectroscopy (XPCS) during the application of external electric fields. Our results demonstrate that the AC‐poled sample exhibit enhanced reconfiguration of domain variants in response to driving fields compared to the DC‐poled counterpart, as evidenced by accelerated correlation decay and faster relaxation time. This phenomenon is attributed to enhanced reversible domain wall motion achieved through AC poling, which facilitates field‐induced domain realignment. These findings provide insight into the relationship between dynamics and macroscopic properties in relaxor‐PT single crystals for high‐performance applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2024

Dynamic sparse x-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst

Tomographic imaging of time-evolving samples is a challenging yet important task for various research fields. At the nanoscale, current approaches face limitations of measurement speed or resolution due to lengthy acquisitions. We developed a dynamic nanotomography technique based on sparse dynamic imaging and 4D tomography modeling. We demonstrated the technique, using ptychographic x-ray computed tomography as its imaging modality, on resolving the in situ hydration process of polymer electrolyte fuel cell (PEFC) catalyst. The technique provides a 40-time increase in temporal resolution compared to conventional approaches, yielding 28 nm half-period spatial and 12 min temporal resolution. The results allow a quantitative characterization of the water intake process inside PEFC catalysts with nanoscale resolution, which is crucial for understanding their electrochemical mechanisms and optimizing their performance. Our technique enables high-speed operando nanotomography studies and paves the way for wider application of dynamic tomography at the nanoscale.

Science & Technology - Other Topics↗
DOE OSTI2025

A gravity-based mounting approach for large-scale cryogenic calorimeter arrays

Cryogenic calorimeters are among the leading technologies for searching for rare events. The CUPID experiment is exploiting this technology to deploy a tonne-scale detector to search for neutrinoless double-beta decay of 100 Mo. The CUPID collaboration proposed an innovative approach to assembling cryogenic calorimeters in a stacked configuration, held in position solely by gravity. This gravity-based assembly method is unprecedented in the field of cryogenic calorimeters and offers several advantages, including relaxed mechanical tolerances and simplified construction. To assess and optimize its performance, we constructed a medium-scale prototype hosting 28 Li 2 MoO 4 crystals and 30 Ge light detectors, both operated as cryogenic calorimeters at the Laboratori Nazionali del Gran Sasso (Italy). Despite an unexpected excess of noise in the light detectors, the results of this test proved (i) a thermal stability better than ±0.5 mK at 10 mK, (ii) a good energy resolution of Li 2 MoO 4 cryogenic calorimeters, (6.6 ± 2.2) keV FWHM at 2615 keV, and (iii) a Li 2 MoO 4 light yield measured by the closest light detector of 0.36 keV/MeV, sufficient to guarantee the particle identification requested by CUPID.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Pushing the limits of pulse shape discrimination in a large liquid xenon detector

Abstract The LUX-ZEPLIN (LZ) experiment is a direct-detection dark matter experiment, optimized to search for weakly interacting massive particles (WIMPs) through WIMP-nucleon interactions. The main challenge in dark matter detection is differentiating between WIMP signals and background events. In LZ, the ratio of ionization to scintillation signals (charge-to-light) is the primary method for rejecting electronic recoil (ER) background. Pulse shape discrimination (PSD) offers a method for additional ER backgrounds rejection in liquid xenon detectors. In this paper, the discrimination power of PSD with the LZ experiment is discussed. To precisely characterize the scintillation pulse shape, an analysis framework is developed to reconstruct the detection time of individual photons. Using LZ calibration data, the photon-timing tail fraction discriminator is optimized and achieves ER leakage as low as $$15\%$$ 15 % . For specific background processes such as $$^{124}$$ 124 Xe double electron capture, the leakage is reduced further to about $$5\%$$ 5 % . PSD is combined with charge-to-light to form two-factor discrimination (TFD). The optimized TFD performance is compared with the performance of the charge-to-light method, with the corresponding false positive rate reduced by up to a factor of two for large scintillation pulses. Finally, PSD and TFD are applied to data from LZ’s WS2024 run and their performance is summarized.

Akerib, D. S. [SLAC National Accelerator Laborator↗
DOE OSTI2025

Future Circular Collider Feasibility Study Report

Volume 3 of the FCC Feasibility Report presents studies related to civil engineering, the development of a project implementation scenario, and environmental and sustainability aspects. The report details the iterative improvements made to the civil engineering concepts since 2018, taking into account subsurface conditions, accelerator and experiment requirements, and territorial considerations. It outlines a technically feasible and economically viable civil engineering configuration that serves as the baseline for detailed subsurface investigations, construction design, cost estimation, and project implementation planning. Additionally, the report highlights ongoing subsurface investigations in key areas to support the development of an improved 3D subsurface model of the region. The report describes the development of the project scenario based on the ‘avoid-reduce-compensate’ iterative optimisation approach. The reference scenario balances optimal physics performance with territorial compatibility, implementation risks, and costs. Environmental field investigations covering almost 600 hectares of terrain—including numerous urban, economic, social, and technical aspects—confirmed the project’s technical feasibility and contributed to the preparation of essential input documents for the formal project authorisation phase. The summary also highlights the initiation of public dialogue as part of the authorisation process. The results of a comprehensive socio-economic impact assessment, which included significant environmental effects, are presented. Even under the most conservative and stringent conditions, a positive benefit-cost ratio for the FCC-ee is obtained. Finally, the report provides a summary of the studies conducted to document the current state of the environment.

Benedikt, M. [European Organization for Nuclear Re↗
DOE OSTI2024

DeepOpt

DeepOpt is a simple and easy-to-use library for performing Bayesian optimization, leveraging the powerful capabilities of BoTorch. Its key feature is the ability to use neural networks as surrogate functions during the optimization process, allowing Bayesian optimization to work smoothly even on large datasets and in many dimensions. DeepOpt also provides simplified wrappers for BoTorch fitting and optimization routines.

Kur, Eugene↗
DOE OSTI2025

Maskman

SAND2025-04369O Maskman is a user-friendly tool designed to create hex masks, which are essential for optimizing application performance in high-performance computing environments. By converting a list of integers into binary and then hex masks, Maskman simplifies the process of setting application affinity. This ensures that software runs efficiently on specific nodes within a computing cluster. Ideal for researchers and developers, Maskman streamlines the preparation of inputs for HPC schedulers, enhancing resource management and improving overall system performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Pase, Douglas [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Nanoindentation’s imprint on an advanced society: Toward application conditions at the extremes

Abstract As society strives for a more sustainable future, key technologies depend on advanced materials development. Nanoindentation enables highly localized nanoscale measurement of mechanical properties, crucial for optimizing the performance of functional materials. Coupled with chemical charging, variable temperatures, or high deformation speeds, it offers insights into pressing materials science challenges necessary for sustainable energy transitions. This article explores the role of nanoindentation in advancing vital next-generation technologies. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mechano-chemical understanding of NaSICON for aqueous redox-flow batteries

Redox-flow batteries utilizing a sodium superionic conductor (NaSICON) can be cost-effective systems for grid energy storage by combining high sodium selectivity with reliance on abundant, low-cost elements. However, improving membrane toughness while maintaining a sufficiently thin membrane for ion conduction is needed. Addressing this issue requires deeper insight into the mechanical properties of NaSICON, its interactions with aqueous chemistries, and the chemo-mechanical degradation mechanisms that arise at the intersection of these phenomena. Here, we provide a framework for understanding these problems, strategies to address them, and highlight the potential of unconventional sintering and thin-film fabrication to optimize the performance of NaSICON in practical flow cells.

Grid energy storage↗
DOE OSTI2024

RESONANT MATCHING SECTION FOR CEBAF ENERGY UPGRADE

In order to prevent emittance growth during long stores of the proton beam at the future Electron-Ion Collider (EIC), we need to have some mechanism to provide fast cooling of the dense proton beams. One promising method is coherent electron cooling (CeC), which uses an electron beam to both ?measure? the positions of protons within the bunch and then apply energy kicks which tend to reduce their longitudinal and transverse actions. In this work, we discuss the under- lying physics of this process. We then discuss simulations which constrain the electrons to move only longitudinally in order to perform fast optimizations and long-term track- ing of the bunch evolution, and benchmark these results against fully 3D codes. Additionally, we discuss practical challenges, including the necessity of a high-quality elec- tron beam and sub-micron alignment of the electrons and protons.

Deitrick, K.↗
DOE OSTI2024

Optimization of a Welding Procedure for Making Critical Aluminum Welds on the LBNF Absorber Core Block

The LBNF Absorber consists of thirteen 6061-T6 aluminum core blocks. The core blocks are water cooled with de-ionized (DI) water which becomes radioactive during beam operations. The cooling water flows through gun-drilled channels in the core blocks. A weld quality optimization was performed to produce National Aeronautical Standard 1514 Class I quality welds on the aluminum core blocks. This was not successful in all cases. An existing Gas Tungsten Arc Welding Procedure Specification was fine tuned to minimize, in most cases, and eliminate detect-able tungsten inclusions in the welds. All the weld coupons, however passed welding inspection as per the piping code: ASME B31.3 Normal Fluid Service. Tungsten electrode diameter, type, and manufacturer were varied. Some of the samples were pre-heated and others were not. It was observed that larger diameter electrodes, 5/32 in., with pre-heated joints resulted in welds with the least number of tungsten inclusions. It is hypothesized that thinner electrodes breakdown easily and get lodged into the weld pool during the welding process. This breakdown is further enhanced by the large temperature differential between the un-preheated sample and the hot electrode.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Optimization of a Welding Procedure for Making Critical Aluminum Welds on the LBNF Absorber Core Block

he LBNF Absorber consists of thirteen 6061-T6 aluminum core blocks. The core blocks are water cooled with de-ionized (DI) water which becomes radioactive during beam operations. The cooling water flows through gun-drilled channels in the core blocks. A weld quality optimization was performed to produce National Aeronautical Standard (NAS) 1514 Class I quality welds on the aluminum core blocks. This was not successful in all cases. An existing Gas Tungsten Arc Welding (GTAW) Welding Procedure Specification (WPS) was fine tuned to minimize, in most cases, and eliminate detectable tungsten inclusions in the welds. All the weld coupons however, passed welding inspection as per the piping code: ASME B31.3 Normal Fluid Service. Tungsten electrode diameter, type, and manufacturer were varied. Some of the samples were pre-heated and others were not. It was observed that larger diameter electrodes, 5/32 in., with pre-heated joints resulted in welds with the least number of tungsten inclusions. It is hypothesized that thinner electrodes breakdown easily and get lodged into the weld pool during the welding process. This breakdown is further enhanced by the large temperature differential between the un-preheated sample and the hot electrode.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗