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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Precise 𝐾* ⁡(892) → 𝐾⁡𝜋 branching fractions

Although discovered more than sixty years ago, direct measurement of the 𝐾*⁡(892) → 𝐾⁡𝜋 branching fractions is a formidable challenge that has not been attempted. Typically they are assumed to obey the isospin limit in hundreds of particle data measurements. We show that an abundance of recent amplitude analyses and other data, however, enables recovery of the ratios ℬ⁡(𝐾 *+ → 𝐾 + ⁢𝜋 0 )/ℬ⁡(𝐾 *+ → 𝐾$^0_S$ ⁢𝜋 + ) and 4⁢ℬ⁡(𝐾 *0 → 𝐾$^0_S$ ⁢𝜋 0 )/ℬ⁡(𝐾 *0 → 𝐾 +⁢ 𝜋 − ) at ∼5% precision.

Gligorov, Vladimir V. [Sorbonne Université, Paris ↗
DOE OSTI2024

Hardware Fuzzing with An Emulator

Bugs in digital logic have led to some significant security vulnerabilities. Hardware bugs are particularly troublesome since they cannot be easily patched. Additionally, if the bug is in the root of trust, all trust built upon it can be vulnerable. Traditional testing either require a deep knowledge of the system, creative attack vectors and lots of human interaction. This is not scalable as there are very few engineers that can wear the hat of a designer, a verification engineer, and a cybersecurity expert. Hardware fuzzing is a relatively new research area in dynamic hardware testing. It has proven to be an effective method for discovering bugs, unexpected behaviors, and security vulnerabilities in software. While hardware fuzzing is new to the hardware domain, it has a strong track record in software testing. Fuzzing is a testing technique that randomly mutates the input data to uncover bugs or vulnerabilities in the design. It is especially good at finding corner cases that test engineers can not envision. Another advantage over other dynamic testing techniques is that, if done well, deep knowledge of the design is not required. Additionally, fuzzing scales well. If the system is set up correctly, it can run unsupervised for weeks if necessary. In this work, we propose using hardware fuzzing to improve the input vector generation for an information flow tracking tool. To get reasonable throughput of test vectors, an emulator is targeted as the execution platform. Efficient emulator execution has some specific requirements.

42 ENGINEERING↗
DOE OSTI2025

Uncertainty Quantification for Smooth Functional Data with Application to Material Properties

This document outlines a method for processing functional output (i.e., curves) for the ultimate purpose of sampling curves under specified input conditions for use in modeling and simulation uncertainty quantification (UQ) studies. A set of benchmark curves sufficiently representative of the relevant scenario(s) being simulated are provided to the process and formatted as described in Section 1. Principal Component Analysis (PCA) is utilized to discover the components of uncertainty in the benchmark curves and is outlined in Section 2. Section 3 describes the application of uncertainty quantification to the PCA results for the purpose of sampling curves to be used in UQ analysis. Section 4 applies these techniques to an example benchmark dataset. Concluding remarks are provided in the final section.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The quest to discover supersymmetry at the ATLAS experiment

The search for supersymmetry with the ATLAS experiment at the CERN Large Hadron Collider intensified after the discovery of the Higgs boson in 2012. The search programme expanded in both breadth and depth, profiting from the increased integrated luminosity and higher centre-of-mass energy for the collision data collected between 2015 and 2018, and gaining new sensitivity to unexplored areas of supersymmetry parameter space through the use of novel experimental signatures and innovative analysis techniques. This report summarises the supersymmetry searches at ATLAS using up to 140 fb −1 of pp collisions at $\sqrt{s}$ = 13 TeV, including the limits set on the production of gluinos, squarks, and electroweakinos for scenarios with or without R-parity conservation, and including models where some of the supersymmetric particles are long-lived.

Koeck, Daniela↗
DOE OSTI2026

Audible Noise Modeling of Hydrogen Release Sonic Hazards in Rail Maintenance Facilities

This study implemented validated literature models to predict audible noise due to pressurized gaseous hydrogen releases through a thermally-activated pressure relief device (TPRD) and attached vent stack. A literature survey discovered limited hydrogen-specific noise prediction models validated by experiments. However, empirical noise prediction models for air flowing through pipes and valves were identified. These empirical models were used to predict noise levels and compared against hydrogen noise data reported in two studies: one experimental study of noise from hydrogen leaking through a pipe and another which modeled hydrogen flowing through a solenoid valve during a fuel cell vehicle refueling. The valve flow model was then applied to predict noise for hydrogen releases through a TPRD. Results show that hydrogen releases through a TPRD can produce harmful noise levels varying from 134 to 150 dB. However, further model validation and additional experimental data are needed to improve prediction confidence and accuracy.

08 HYDROGEN↗
DOE OSTI2025

High throughput screening of high entropy spinel electrolytes for multivalent batteries

High-entropy (HE) design emerged as a promising path for discovering multivalent superionic conductors. This work provides a computational exploration of the synthesizability of HE spinel-based electrolytes among the typical chemical space. Design principles have been established, while experimental synthesis has supported the stability rules predicted by computational data.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning BPS spectra and the gap conjecture

We explore statistical properties of Bogomol’nyi-Prasad-Sommerfield q-series for strongly coupled supersymmetric theories that correspond to a particular family of three-manifolds. We discover that gaps between exponents in the -series are statistically more significant at the beginning of the -series compared to gaps that appear in higher powers of. Our observations are obtained by calculating saliencies of -series features used as input data for principal component analysis, which is a standard example of an explainable machine learning technique that allows for a direct calculation and a better analysis of feature saliencies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Unveiling the Arsenal of Apple Bitter Rot Fungi: Comparative Genomics Identifies Candidate Effectors, CAZymes, and Biosynthetic Gene Clusters in Colletotrichum Species

The bitter rot of apple is caused by Colletotrichum spp. and is a serious pre-harvest disease that can manifest in postharvest losses on harvested fruit. In this study, we obtained genome sequences from four different species, C. chrysophilum, C. noveboracense, C. nupharicola, and C. fioriniae, that infect apple and cause diseases on other fruits, vegetables, and flowers. Our genomic data were obtained from isolates/species that have not yet been sequenced and represent geographic-specific regions. Genome sequencing allowed for the construction of phylogenetic trees, which corroborated the overall concordance observed in prior MLST studies. Bioinformatic pipelines were used to discover CAZyme, effector, and secondary metabolic (SM) gene clusters in all nine Colletotrichum isolates. We found redundancy and a high level of similarity across species regarding CAZyme classes and predicted cytoplastic and apoplastic effectors. SM gene clusters displayed the most diversity in type and the most common cluster was one that encodes genes involved in the production of alternapyrone. Our study provides a solid platform to identify targets for functional studies that underpin pathogenicity, virulence, and/or quiescence that can be targeted for the development of new control strategies. With these new genomics resources, exploration via omics-based technologies using these isolates will help ascertain the biological underpinnings of their widespread success and observed geographic dominance in specific areas throughout the country.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Discovery of additional ancient genome duplications in yeasts

Whole-genome duplication (WGD) has had profound macroevolutionary impacts on diverse lineages, preceding adaptive radiations in vertebrates, teleost fish, and angiosperms. In contrast to the many known ancient WGDs in animals, and especially plants, we are aware of evidence for only four WGDs in fungi. The oldest of these occurred ∼100 million years ago (mya) and is shared by ∼60 extant Saccharomycetales species, including the baker’s yeast Saccharomyces cerevisiae. Notably, this is the only known ancient WGD event in the yeast subphylum Saccharomycotina. The dearth of ancient WGD events in fungi remains a mystery. Some studies have suggested that fungal lineages that experience chromosome and genome duplication quickly go extinct, leaving no trace in the genomic record, while others contend that the lack of known WGDs is due to an absence of data. Under the second hypothesis, additional sampling and deeper sequencing of fungal genomes should lead to the discovery of more WGD events. Coupling hundreds of recently published genomes from nearly every described Saccharomycotina species, with three additional long-read assemblies, we discovered three novel WGD events. Although the functions of retained duplicate genes originating from these events are broad, they bear similarities to the well-known WGD that occurred in the Saccharomycetales. In conclusion, our results suggest that WGD may be a more common evolutionary force in fungi than previously believed.

convergent evolution↗
DOE OSTI2025

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

Nichol, J. Jake [Univ. of New Mexico, Albuquerque,↗
DOE OSTI2025

Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here, in this study, we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibrium, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB2 alloys. Specifically, we computationally predict and then experimentally validate an AB 2 alloy series (z < 0.6 for Ti 2−z Zr z CrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.

08 HYDROGEN↗
DOE OSTI2024

ARPA-E Grid Optimization (GO) Competition Challenge 3

Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public. The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023). For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below.

ACOPF↗
DOE OSTI2025

Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.

Materials↗
DOE OSTI2024

Global protein turnover quantification in Escherichia coli reveals cytoplasmic recycling under nitrogen limitation

Protein turnover is critical for proteostasis, but turnover quantification is challenging, and even in well-studied E. coli, proteome-wide measurements remain scarce. Here, we quantify the turnover rates of ~3200 E. coli proteins under 13 conditions by combining heavy isotope labeling with complement reporter ion quantification and find that cytoplasmic proteins are recycled when nitrogen is limited. We use knockout experiments to assign substrates to the known cytoplasmic ATP-dependent proteases. Surprisingly, none of these proteases are responsible for the observed cytoplasmic protein degradation in nitrogen limitation, suggesting that a major proteolysis pathway in E. coli remains to be discovered. Lastly, we show that protein degradation rates are generally independent of cell division rates. Thus, we present broadly applicable technology for protein turnover measurements and provide a rich resource for protein half-lives and protease substrates in E. coli, complementary to genomics data, that will allow researchers to study the control of proteostasis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

X-Ray Imaging and Spectroscopy Mission

The X-Ray Imaging and Spectroscopy Mission (XRISM) is a joint mission between the Japan Aerospace Exploration Agency (JAXA) and the National Aeronautics and Space Administration (NASA) in collaboration with the European Space Agency (ESA). In addition to the three space agencies, universities and research institutes from Japan, North America, and Europe have joined to contribute to developing satellite and onboard instruments, data-processing software, and the scientific observation program. XRISM is the successor to the ASTRO-H (Hitomi) mission, which ended prematurely in 2016. Its primary science goal is to examine astrophysical problems with precise, high-resolution X-ray spectroscopy. XRISM promises to discover new horizons in X-ray astronomy. It carries a 6 × 6 pixelized X-ray microcalorimeter on the focal plane of an X-ray mirror assembly (Resolve) and a co-aligned X-ray CCD camera (Xtend) that covers the same energy band over a large field of view. XRISM utilizes the Hitomi heritage, but all designs were reviewed. The attitude and orbit control system was improved in hardware and software. The spacecraft was launched from the JAXA Tanegashima Space Center on 2023 September 6 (UTC). During the in-orbit commissioning phase, the onboard components were activated. Although the gate valve protecting the Resolve sensor with a thin beryllium X-ray entrance window was not yet opened, scientific observation started in 2024 February with the planned performance verification observation program. The nominal observation program commenced with the following guest observation program beginning in 2024 September.

Astronomy and AstroPhysics↗
DOE OSTI2024

Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models

Collisional-radiative (CR) models describe the atomic processes in a plasma by tracking the population density in the ground and excited states for each charge state of the atom/ion. These models predict important plasma properties such as charge state distributions and radiative emissivity and opacity. Accurate descriptions of the CR balance of the plasma are essential in fusion whole device modeling, especially when significant impurities are introduced into the plasmas. In an integrated fusion plasma and CR simulation, a CR model, which is a high-dimensional stiff ODE, needs to be solved on each grid point in the configuration space, and can overwhelm the overall computational cost. In this work, we propose a machine-learning-based method that discovers a latent space and learns its corresponding latent dynamics, which can capture the essential physics to make accurate predictions at much lower online computational cost. The proposed approach is physics-assisted, due to its combination of a physical latent space and a data-driven latent space. It has been demonstrated that the proposed architecture can predict both the full-order CR dynamics and a physical quantity of interest accurately.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Popnet : computer vision based deep learning model for forecasting gridded population

Here, this study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial variables and population data from 2000 to 2020, Popnet predicts South Korea’s population trends by age groups (under 14, 15-64 and over 65) up to 2040. In validation, it outperforms traditional machine learning and state-of-the-art computer vision models. The output of this model discovered significant polarisation: population growth in urban areas, especially the capital region, and severe depopulation in rural areas. Popnet is a robust tool for offering significant insights to policymakers and related stakeholders about the detailed future population, which allows them to establish detailed, localised planning and resource allocations.

computer vision↗
DOE OSTI2025

The genomic footprints of wild Saccharum species trace domestication, diversification, and modern breeding of sugarcane

Sugarcane is a major crop of unclear origins due to its complex polyploid interspecific genome. We analyzed genome ancestries using whole-genome sequence data from 390 representative accessions based on repeated k-mers and chloroplast phylogeny. The results provided evidence that Saccharum officinarum was domesticated in the New Guinea region from the S. robustum wild species and revealed that its genome is a mosaic involving different S. robustum subgroups. We discovered a wild Saccharum contributor to most modern cultivars, likely originating from East Melanesia. We highlighted two early centers of sugarcane diversification associated with human transport, one in continental Asia through hybridization with different S. spontaneum subgroups and one in the Melanesian and Polynesian islands via hybridization with the discovered ancestor and Miscanthus. Finally, we revealed the genome ancestry of modern cultivars, highlighting untapped wild Saccharum diversity as a source of alleles for breeding programs.

Garsmeur, Olivier [CIRAD, Montpellier (France). Ag↗