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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The new physics case for beam-dump experiments with accelerated muon beams

As the field examines a future muon collider as a possible successor to the LHC, we must consider how to fully utilize not only the high-energy particle collisions, but also any lower-energy staging facilities necessary in the R&D process. An economical and efficient possibility is to use the accelerated muon beam from either the full experiment or from cooling and acceleration tests in beam-dump experiments. Beam-dump experiments are complementary to the main collider as they achieve sensitivity to very small couplings with minimal instrumentation. We demonstrate the utility of muon beam-dump experiments for new physics searches at energies from 10 GeV to 5 TeV. We find that, even at low energies like those accessible at staging or demonstrator facilities, it is possible to probe new regions of parameter space for a variety of generic BSM models, including muonphilic, leptophilic, L μ − L τ , and dark photon scenarios. Such experiments could therefore provide opportunities for discovery of new physics well before the completion of the full multi-TeV collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Robustness: The Missing Ingredient in Generation Scheduling

This article highlights robustness as an essential factor to cope with the ever-increasing levels of uncertainty in generation scheduling under significant renewable energy penetration, as is the case in Brazil and Spain. To that end, robust generation scheduling is framed within the different optimization-based approaches that are available for uncertainty handling. In addition, the suitability of robust optimization to accommodate practical security criteria in generation scheduling is also emphasized. Interestingly, this article points out the existence of an effective algorithm allowing the discovery of critical or so-called umbrella scenarios, which paves the way for the implementation of robust generation scheduling in industry practice.

Street, Alexandre↗
NASA NTRS1998

Technology Development, Implementation, and Assessment: K-16 Pre-Service, In-Service, and Distance Learning Initiatives

This summer 22 kindergarten through 8th grade teachers attended a 3-week Teacher Enhancement Institute (TEI) at NASA Langley Research Center. TEI is funded by NASA Education Division and is a collaborative effort between NASA Langley's Office of Education and Christopher Newport University. Selected teacher teams were drawn from Langley's 5-state precollege service region, which includes Kentucky, North Carolina, South Carolina, Virginia, and West Virginia. The goal of TEI was for teachers to learn aeronautics and the broad application of science and technology through a problem-based learning (PBL) strategy. PBL is an instructional method using a real world problem, also known as an ill-structured problem, as the context for an in-depth investigation. Most real life problems are ill-structured, as are all the really important social, political and scientific problems. The teachers were immediately immersed in an ill-structured problem to design a communication strategy for the White House Commission on Aviation Safety and Security to educate and disseminate aviation information to the general public. Specifically, the communication strategy was to focus on aeronautics principles, technology and design associated with US general aviation revitalization and aviation safety programs. The presented problem addressed NASA's strategic outcome to widely communicate the content, relevancy and excitement of its missions and discoveries to the general population. Further, the PBL scenario addressed the technological challenges being taken up by NASA to revolutionize air travel and the way in which aircraft are designed, built, and operated. It also addressed getting people and freight safely and efficiently to any location in the world at a reasonable cost. With a "real" need-to-know problem facing them, the teachers set out to gather information and to better understand the problem using inquiry-based and scientific methods. The learning in this aeronautics scenario was driven by the direction taken by participants. With the support of the TEI faculty, the teachers quickly identified NASA Langley researchers that served as consultants to help solve the problem. To achieve their goal, the teacher teams developed lesson plans for elementary and middle school students, wrote a newspaper, published a brochure to educate the general public, constructed games for children of all ages, and produced a video. As a second problem, the TEI participants will design their own aeronautic lesson plan and immerse their 1997-98 school year students in the problem. The problem is for the students "to create a traveling hands-on, minds-on aeronautics museum exhibit created for children by children." As a culminating activity, the Virginia Air and Space Center in Hampton, VA, will set up a special display of the exhibits in the Summer 1998. The TEI faculty will visit each TEI teacher's classroom during the academic school year to observe the implementation of the unit. In addition to the classroom observations, electronic follow-up sessions will be conducted during the school year to support the teachers' efforts in developing their PBL units to integrate technology in math and science instruction. These sessions eill be conducted using the Internet. Teachers will be connected through a chat-line to share ideas, ask questions, and generate solutions.

Petersen, Richard↗
NASA NTRS2006

Application of State Analysis and Goal-Based Operations to a MER Mission Scenario

State Analysis is a model-based systems engineering methodology employing a rigorous discovery process which articulates operations concepts and operability needs as an integrated part of system design. The process produces requirements on system and software design in the form of explicit models which describe the behavior of states and the relationships among them. By applying State Analysis to an actual MER flight mission scenario, this study addresses the specific real world challenges of complex space operations and explores technologies that can be brought to bear on future missions. The paper describes the tools currently used on a daily basis for MER operations planning and provides an in-depth description of the planning process, in the context of a Martian day's worth of rover engineering activities, resource modeling, flight rules, science observations, and more. It then describes how State Analysis allows for the specification of a corresponding goal-based sequence that accomplishes the same objectives, with several important additional benefits.

mission planning↗
DOE OSTI2025

Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Advanced Computing Annual Report 2024

In fiscal year (FY) 2024, the National Renewable Energy Laboratory (NREL) took a major leap forward with the completed full buildout of Kestrel, the Office of Energy Efficiency and Renewable Energy's newest high-performance computing (HPC) system. Kestrel is already supporting science across the portfolio, bringing roughly 44 petaflops of computing power, which is more than five times the capacity of our previous supercomputer, Eagle. By delivering greater GPU capacity, Kestrel enables faster progress in artificial intelligence (AI) and opens new avenues in energy research - from defining long-term planning scenarios to accommodate a growing power system to material discovery to improving energy efficiency in photovoltaics (PV). Across the portfolio, research is being accelerated by Kestrel's impressive power. During FY24, 427 projects and more than 700 researchers used NREL's HPC, supporting the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy across 13 funding areas. Through these collaborations, researchers produced more than 450 technical outputs, including 195 articles in peer-reviewed publications, pushing the boundaries of science and engineering. This year's report features new sections spotlighting the expanding roles of Artificial Intelligence and Accelerated Computing. We also introduce an early career section to celebrate the accomplishments of our up-and-coming researchers, whose pioneering work is shaping the future of energy. We hope you enjoy the new insights and discoveries highlighted in these pages.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2008

Evaluation of Sulfur 'Concrete' for Use as a Construction Material on the Lunar Surface

Combining molten sulfur with any number of aggregate materials forms, when solid, a mixture having attributes similar, if not better, to conventional water-based concrete. As a result the use of sulfur "concrete" on Earth is well established, particularly in corrosive environments. Consequently, discovery of troilite (FeS) on the lunar surface prompted numerous scenarios about its reduction to elemental sulfur for use, in combination with lunar regolith, as a potential construction material; not requiring water, a precious resource, for its manufacture is an obvious advantage. However, little is known about the viability of sulfur concrete in an environment typified by extreme temperatures and essentially no atmosphere. The experimental work presented here evaluates the response of pure sulfur and sulfur concrete subjected to laboratory conditions that approach those expected on the lunar surface, the results suggesting a narrow window of application.

Grugel, R. N.↗
DOE OSTI2025

CUPID, the Cuore upgrade with particle identification

CUPID, the CUORE Upgrade with Particle Identification, is a next-generation experiment to search for neutrinoless double beta decay (0 νββ ) and other rare events using enriched Li 2 100 MoO 4 scintillating bolometers. It will be hosted by the CUORE cryostat located at the Laboratori Nazionali del Gran Sasso in Italy. The main physics goal of CUPID is to search for 0 νββ of 100 Mo with a discovery sensitivity covering the full neutrino mass regime in the inverted ordering scenario, as well as the portion of the normal ordering regime with lightest neutrino mass larger than 10 meV. With a conservative background index of 10 −4 cts/(keV·kg·yr), 240 kg isotope mass, 5 keV FWHM energy resolution at 3 MeV and 10 live-years of data taking, CUPID will have a 90% C.L. half-life exclusion sensitivity of 1.8 · 10 27 yr, corresponding to an effective Majorana neutrino mass ( m ββ ) sensitivity of 9–15 meV, and a 3σ discovery sensitivity of 1 · 10 27 yr, corresponding to an m ββ range of 12–21 meV.

Alfonso, K. [Virginia Polytechnic Institute and St↗
NASA NTRS1989

Millisecond radio pulsars in globular clusters

It is shown that the number of millisecond radio pulsars, in globular clusters, should be larger than 100, applying the standard scenario that all the pulsars descend from low-mass X-ray binaries. Moreover, most of the pulsars are located in a small number of clusters. The prediction that Teran 5 and Liller 1 contain at least about a dozen millisecond radio pulsars each is made. The observations of millisecond radio pulsars in globular clusters to date, in particular the discovery of two millisecond radio pulsars in 47 Tuc, are in agreement with the standard scenario, in which the neutron star is spun up during the mass transfer phase.

Verbunt, Frank↗
NASA NTRS1989

Millisecond radio pulsars in globular clusters

It is shown that the number of millisecond radio pulsars, in globular clusters, should be larger than 100, applying the standard scenario that all the pulsars descend from low-mass X-ray binaries. Moreover, most of the pulsars are located in a small number of clusters. The prediction that Teran 5 and Liller 1 contain at least about a dozen millisecond radio pulsars each is made. The observations of millisecond radio pulsars in globular clusters to date, in particular the discovery of two millisecond radio pulsars in 47 Tuc, are in agreement with the standard scenario, in which the neutron star is spun up during the mass transfer phase.

Verbunt, Frank↗
DOE OSTI2025

Discovering Electroweak Interacting Dark Matter at Muon Colliders Using Soft Tracks

Minimal dark matter models feature one neutral particle that serves as a thermal relic dark matter candidate, as well as quasidegenerate charged states with TeV masses. When the charged states are produced at colliders, they can decay into dark matter and a low-momentum (soft) charged particle, which is challenging to reconstruct at hadron colliders. We demonstrate that a 3 TeV muon collider is capable of detecting these soft tracks, enabling the discovery of thermal Higgsinos and similar dark matter candidates that constitute highly motivated scenarios for future collider searches. Published by the American Physical Society 2025

Capdevilla, Rodolfo (ORCID:0000000201227704)↗
DOE OSTI2025

Discovering Electroweak Interacting Dark Matter at Muon Colliders Using Soft Tracks

Minimal dark matter models feature one neutral particle that serves as a thermal relic dark matter candidate, as well as quasidegenerate charged states with TeV masses. When the charged states are produced at colliders, they can decay into dark matter and a low-momentum (soft) charged particle, which is challenging to reconstruct at hadron colliders. We demonstrate that a 3 TeV muon collider is capable of detecting these soft tracks, enabling the discovery of thermal Higgsinos and similar dark matter candidates that constitute highly motivated scenarios for future collider searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Anomaly detection in collider physics via factorized observables

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called : factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Hydrodynamic simulations of white dwarf–white dwarf mergers and the origin of R Coronae Borealis stars

We study the properties of double white dwarf (DWD) mergers by performing hydrodynamic simulations using the new and improved adaptive mesh refinement code OCTO-TIGER. We follow the orbital evolution of DWD systems of mass ratio $q=0.7$ for tens of orbits until and after the merger to investigate them as a possible origin for R Coronae Borealis (RCB) type stars. We reproduce previous results, finding that during the merger, the helium WD donor star is tidally disrupted within 20–80 min since the beginning of the simulation onto the accretor carbon–oxygen WD, creating a high temperature shell around the accretor. We investigate the possible helium burning in this shell and the merged object’s general structure. Specifically, we are interested in the amount of oxygen-16 dredged-up from the accretor to the hot shell and the amount of oxygen-18 produced. This is critical as the discovery of very low oxygen-16 to oxygen-18 ratios in RCB stars pointed out the merger scenario as a favourable explanation for their origin. A small amount of hydrogen in the donor may help keep the oxygen-16 to oxygen-18 ratios within observational bounds, even if moderate dredge-up from the accretor occurs. In addition, we perform a resolution study to reconcile the difference found in the amount of oxygen-16 dredge-up between smoothed-particle hydrodynamics and grid-based simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

2MASS J15491331-3539118: a new low-mass wide companionof the GQ Lup system

Substellar companions at wide separation around stars hosting planets or brown dwarfs (BDs) yet close enough for their formation in the circumstellar disc are of special interest. In this Letter we report the discovery of a wide (projected separation ~16".0, or 2400AU, and position angle 114.61°) companion of the GQLup A-B system, most likely gravitationally bound to it. A VLT/X-shooter spectrum shows that this star, 2MASS J15491331-3539118, is a bonafide low-mass (~0.15 M(ʘ) ) young stellar object (YSO) with stellar and accretion/ejection properties typical of Lupus YSOs of similar mass, and with kinematics consistent with that of the GQLup A-B system. A possible scenario for the formation of the triple system is that GQ Lup A and 2MASS J15491331-3539118 formed by fragmentation of a turbulent core in the Lup I filament, while GQ Lup B, the BD companion of GQ Lup A at 0".7, formed in situ by the fragmentation of the circumprimary disc. The recent discoveries that stars form along cloud filaments would favour the scenario of turbulent fragmentation for the formation of GQ Lup A and 2MASS J15491331-3539118.

J. M. Alcalá↗
DOE OSTI2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

97 MATHEMATICS AND COMPUTING↗