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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 271 records · Page 15

TRANSP-TGLF core predictive modeling of the JET DT baseline scenario

In recent years, an intense modeling activity has been focused on preparing and analyzing the second JET Deuterium–Tritium (D–T) experimental campaign DTE2. Among the numerous scientific outcomes of this campaign was the unique opportunity to test and validate the state of the art of modeling tools with fusion-relevant DT plasmas using the full metallic ITER-like wall in different scenarios. This work reports on the core predictive modeling of plasma density, electron and ion temperatures (n e , T e , T i ) performed using TRANSP (Pankin et al 2025 Comput. Phys. Commun. 312 109611) coupled with Trapped Gyro-Landau Fluid (TGLF)-SAT2 (Kinsey et al 2008 Phys. Plasmas 15 055908), (Staebler et al 2021 Nucl. Fusion 61 116007) for the JET D–T baseline scenario (I p = 3.5 MA, q 95 = 3, β N < 2, with pellet pacing) (Garzotti et al 2025 Plasma Phys. Control. Fusion 67 075011). The sensitivity to different input parameters, as the $\vec{E}$ x $\vec{B}$ shear parameterization and the values of the kinetic quantities at the boundary of the prediction domain (ρ = 0.85) has been assessed, identifying the confidence interval of the prediction results. In particular, the dependence of the electron density profile on the particle source parameters has been studied, identifying the ionization source as the main cause for the density gradient under-prediction obtained by TRANSP-TGLF, reported in (Hyun-Tae et al 2023 Nucl. Fusion 63 112004).

JET↗

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages ( Dozor and DIALS ), demonstrating that this is an appropriate framework to support future extensions and improvements.

36 MATERIALS SCIENCE↗

Ecosystems for Scientific Computing in the Age of AI

Scientific computing is at an inflection point. Artificial intelligence (AI) is reshaping how scientific software is developed, how teams collaborate, how projects are governed, and how the next generation is trained. Drawing on insights from a 2025 workshop report, this article argues that the future of discovery will depend on agile, robust ecosystems built through socio-technical co-design—the intentional integration of technical and human systems. This perspective is essential for ensuring that future scientific computing remains trustworthy, sustainable, and scalable. It combines advances in AI, high-performance computing, and software with new models for cross-disciplinary collaboration, education, and workforce development. Key recommendations include building modular, trustworthy AI-enabled software ecosystems; enabling teams to integrate AI into scientific workflows while preserving human creativity, integrity, and rigor; and developing adaptive training pathways that keep pace with rapid technological change. By sharing these perspectives, we hope to stimulate broader community dialogue and encourage coordinated action.

AI↗

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Guest Editorial for Nondestructive Testing and Evaluation (NDT&E) Special Section

Nondestructive testing and evaluation (NDT&E) are interdisciplinary fields that require a significant amount of interconnection between fundamental physics, measurement techniques, data processing, decision making, and reporting to have a significant impact on industry. NDT&E practitioners are challenged to keep up with the fast-paced evolution of materials, structures, processing, and manufacturing technologies. The development of engineered materials, complex structures and composites, and novel forming techniques require NDT&E to rapidly evolve to meet the needs of industry.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Overview of High-Power Wireless Charging Systems and Analysis of Polyphase Wireless Charging System Phase Winding and Resonant Tuning Network Connection Configurations

In spite of the high energy efficiency and environmental benefits of electric vehicles (EVs), adoption rates are increasing at a relatively slow pace, primarily because of EVs’ limited range and long charging durations. To reduce EV charging times to be comparable to refueling times of conventional vehicles, extreme fast charging systems are required. Such charging systems would reduce drivers’ range anxiety and enable long-distance interstate travel with EVs. Here, this substantial target in charging rates with 15–20 minutes of recharging times, requires research and development from grid to batteries with advanced charging systems. Wireless power transfer systems for EV charging applications are flexible, convenient, and highly efficient, and they allow automated charging. This paper reviews the power electronics and winding and resonant tuning network configurations for polyphase wireless power transfer systems for high-power wireless charging applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Ecological acclimation: A framework to integrate fast and slow responses to climate change

Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.

climate adaptation↗

Linking Community‐Climate Disequilibrium to Ecosystem Function

Turnover in species composition often lags behind the pace of climate change, resulting in mismatches between climate and communities. However, the impact of these community‐climate disequilibria on ecosystem functions is rarely considered, and current methods for measuring disequilibria assume that species ranges were, until recently, in equilibrium with climate. Here, in this work, we develop a simple theoretical model to address both of these problems by linking community‐climate disequilibrium with ecosystem functioning. We show how disequilibrium can impair functioning in the near‐term even when climate change is expected to enhance functioning in the long‐term. Responses are most likely to change over time in communities where turnover is slow, the impact of disequilibrium counteracts the direct effects of climate on ecosystem function, and pre‐existing disequilibrium is large. These findings emphasise the importance of precise and unbiased estimates of community‐climate disequilibria for improving ecological forecasts. By fitting our model to time series of both climate and ecosystem function from a metacommunity simulation, we show the potential for community‐climate disequilibrium to be inferred without direct knowledge about species' distributions or climatic tolerances. We end by outlining a research agenda to apply dynamic disequilibrium concepts and test novel hypotheses across diverse ecosystems.

climate change↗

Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era

Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era. This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. Further, it first reviews the basic concepts of data, information, knowledge, database, and database system as well as the pros and cons in different types of data management and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and, furthermore, provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.

36 MATERIALS SCIENCE↗

Numerical simulation projects in micromagnetics with Jupyter

We report a case study where an existing materials science course was modified to include numerical simulation projects on the micromagnetic behavior of materials. The Ubermag micromagnetic simulation software package is used in order to solve problems computationally. The simulation software is controlled through the Python code in Jupyter notebooks. Our experience is that the self-paced problem-solving nature of the project work can facilitate a better in-depth exploration of the course contents. We discuss which aspects of the Ubermag and the project Jupyter ecosystem have been beneficial for the students' learning experience and which could be transferred to similar teaching activities in other subject areas.

97 MATHEMATICS AND COMPUTING↗

Quantifying Uncertainty in HPC Job Queue Time Predictions

High Performance Computing (HPC) has developed at an unprecedented pace in recent decades. This growth has demanded corresponding development in the area of HPC Operational Data Analytics (ODA), which encompasses a wide range of data analysis techniques, ML/AI efforts, tools, and visualizations. Published studies in ODA offer a variety of practical ways to inform HPC users, administrators, procurement managers, and other stakeholders. Uncertainty analysis, however, is rare in the related published literature. For instance, we identify only 1 out of 14 existing studies focused on job queue time prediction that investigates the uncertainty aspect of their proposed predictions. We recognize the utmost importance uncertainty quantification can have in such predictive analytics solutions, with consequences in how users interpret information they receive, and attempt to bridge this gap. With the goal of improving access to such insights, we develop a process for determining upper and lower bounds of the predicted queue times of a regression model at a specified confidence level. Our current research is focused on the uncertainty in predicting job queue times, yet our approach may be employed in predicting other metrics.

HPC↗

Miniaturize the Redox Flow Battery for Accelerated Materials Discovery and Development

Redox flow batteries are a promising technology for grid-scale energy storage. The aqueous organic redox flow battery is of particular interest for its potentially low material cost and sustainability. Developing novel organic active material for flow battery electrolytes typically entails molecular engineering toward desired properties, necessitating organic synthesis. In a research laboratory setting, the synthesis of specifically designed organic molecules featuring targeted functional groups is time and resources intensive. In the past, synthesizing materials required for battery testing has often required gram-scale production, presenting considerable constraints on the pace of novel organic material discovery. In this report, we introduce a miniaturized cell design that mandates only milligram-scale material synthesis while yielding testing outcomes equivalent or superior to those reported with other commercially available or homemade flow cells in the literature. The test results under various pH conditions validate the scale-down strategy to accelerate the flow battery material discovery and development using the newly designed mini cell. This approach offers researchers an efficient means to notably reduce the time and resources required to develop novel materials for flow batteries.

25 ENERGY STORAGE↗

ECP libraries and tools: An overview

The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.

97 MATHEMATICS AND COMPUTING↗

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗

Nitrogen starvation causes lipid remodeling in Rhodotorula toruloides

Abstract Background The oleaginous yeast Rhodotorula toruloides is a promising chassis organism for the biomanufacturing of value-added bioproducts. It can accumulate lipids at a high fraction of biomass. However, metabolic engineering efforts in this organism have progressed at a slower pace than those in more extensively studied yeasts. Few studies have investigated the lipid accumulation phenotype exhibited by R. toruloides under nitrogen limitation conditions. Consequently, there have been only a few studies exploiting the lipid metabolism for higher product titers. Results We performed a multi-omic investigation of the lipid accumulation phenotype under nitrogen limitation. Specifically, we performed comparative transcriptomic and lipidomic analysis of the oleaginous yeast under nitrogen-sufficient and nitrogen deficient conditions. Clustering analysis of transcriptomic data was used to identify the growth phase where nitrogen-deficient cultures diverged from the baseline conditions. Independently, lipidomic data was used to identify that lipid fractions shifted from mostly phospholipids to mostly storage lipids under the nitrogen-deficient phenotype. Through an integrative lens of transcriptomic and lipidomic analysis, we discovered that R. toruloides undergoes lipid remodeling during nitrogen limitation, wherein the pool of phospholipids gets remodeled to mostly storage lipids. We identify specific mRNAs and pathways that are strongly correlated with an increase in lipid levels, thus identifying putative targets for engineering greater lipid accumulation in R. toruloides . One surprising pathway identified was related to inositol phosphate metabolism, suggesting further inquiry into its role in lipid accumulation. Conclusions Integrative analysis identified the specific biosynthetic pathways that are differentially regulated during lipid remodeling. This insight into the mechanisms of lipid accumulation can lead to the success of future metabolic engineering strategies for overproduction of oleochemicals.

59 BASIC BIOLOGICAL SCIENCES↗

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25 μ s at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10 cm −1 , 150 cm −1 ] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Metais, Samuel↗

Cross-family and phage-specific gene requirements for Klebsiella infection revealed by scalable RB-TnSeq genetic screens.

Bacteriophages are being cataloged at an accelerating pace and are recognized as key players in nutrient and energy cycling across ecosystems. Yet the bacterial genetic determinants that govern phage-host specificity and infection success remain poorly understood, particularly in clinically and ecologically important genera such as Klebsiella where prior receptor characterization has been almost entirely limited to capsulated strains. Here we used a randomly barcoded, genome-wide, loss-of-function transposon mutant library (RB-TnSeq) of Klebsiella sp. M5al, a naturally acapsular, nitrogen-fixing rhizobacterium, to generate the first systematic, cross-family map of phage receptor gene dependencies in Klebsiella. Challenging the library against 25 double-stranded DNA phages spanning five families in 213 parallel assays, we identified 42 bacterial genes associated with phage infection, of which 15 had no prior association with phage infection in any bacterial system. Disruption of surface receptor biosynthesis genes conferred cross-resistance across multiple phage families, while intracellular gene disruptions had predominantly phage-specific effects. Clonal validation of eight genes confirmed LPS outer core biosynthesis genes as primary receptor determinants alongside additional host factors spanning outer membrane transport, cofactor biosynthesis, and two-component signaling. Comparative analysis across all 25 phages revealed that phage genus rather than family is the stronger predictor of host gene dependency profiles, a finding with direct implications for the functional annotation of uncharacterized phage isolates and rational phage cocktail design. Together, these findings provide a community resource for linking phage genomic diversity to functional host interaction space in this ecologically and clinically important genus.

Gittrich, Marissa R↗

Ketone monoester plus high‐dose glucose supplementation before exercise does not affect immediate post‐exercise erythropoietin concentrations versus glucose alone

Abstract The objective of this study was to examine the effect of consuming ketone monoester plus a high dose of carbohydrate from glucose (KE + CHO) on the change in erythropoietin (EPO) concentrations during load carriage exercise compared with carbohydrate (CHO) alone. Using a randomized, crossover design, 12 males consumed KE + CHO (573 mg KE/kg body mass, 110 g glucose) or CHO (110 g glucose) 30 min before 4 miles of self‐paced treadmill exercise (KE + CHO:51 ± 13%, CHO: 52 ± 12% V̇O 2peak ) wearing a weighted vest (30% body mass; 25 ± 3 kg). Blood samples for analysis were obtained under resting fasted conditions before (Baseline) consuming the KE + CHO or CHO supplement and immediately after exercise (Post). βHB increased ( p < 0.05) from Baseline to Post in KE + CHO, with no change in CHO. Glucose and glycerol increased ( p < 0.05) from Baseline to Post in CHO, with no effect of time in KE + CHO. Insulin and lactate increased ( p < 0.05) from Baseline to Post independent of treatment. EPO increased ( p < 0.05) from Baseline to Post in KE + CHO and CHO with no difference between treatments. Although KE + CHO altered βHB, glucose, and glycerol concentrations, results from this study suggest that KE + CHO supplementation before load carriage exercise does not enhance immediate post‐exercise increases in EPO compared with CHO alone.

Howard, Emily E.↗