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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 OSTI2022

The Electron-Ion Collider - A machine that will unlock the secrets of the strongest force in nature!

The computers and smartphones we use every day depend on what we learned about the atom in the last century. All information technology – and much of our economy today – relies on understanding the electromagnetic force between the atomic nucleus and the electrons that orbit it. The science of that force is well understood, but we still know little about the microcosm within the protons and neutrons that make up the atomic nucleus. That’s where Brookhaven National Laboratory (BNL) comes in. Brookhaven National Laboratory (located in Suffolk County, NY, about 60 miles east of midtown Manhattan) was recently chosen as the building site for an Electron-Ion Collider (EIC), a one-of-a-kind nuclear physics research facility. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design will take advantage of the existing and highly optimized Relativistic Heavy Ion Collider (RHIC) that’s been operating at Brookhaven Lab since 2000. Beyond sparking scientific discoveries in a new frontier of fundamental physics, the Electron-Ion Collider will trigger technological breakthroughs that have broad-ranging impact on human health and national challenges.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2024

Developing a predictive science of the biosphere requires the integration of scientific cultures

Increasing the speed of scientific progress is urgently needed to address the many challenges associated with the biosphere in the Anthropocene. Consequently, the critical question becomes: How can science most rapidly progress to address large, complex global problems? We suggest that the lag in the development of a more predictive science of the biosphere is not only because the biosphere is so much more complex, or because we do not have enough data, or are not doing enough experiments, but, in large part, because of unresolved tension between the three dominant scientific cultures that pervade the research community. We introduce and explain the concept of the three scientific cultures and present a novel analysis of their characteristics, supported by examples and a formal mathematical definition/representation of what this means and implies. The three cultures operate, to varying degrees, across all of science. However, within the biosciences, and in contrast to some of the other sciences, they remain relatively more separated, and their lack of integration has hindered their potential power and insight. Our solution to accelerating a broader, predictive science of the biosphere is to enhance integration of scientific cultures. The process of integration—Scientific Transculturalism—recognizes that the push for interdisciplinary research, in general, is just not enough. Unless these cultures of science are formally appreciated and their thinking iteratively integrated into scientific discovery and advancement, there will continue to be numerous significant challenges that will increasingly limit forecasting and prediction efforts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

The flare forecasting fiasco: Is everybody looking at the same sun?

The passionate dream of the oppressed solar flare forecaster is the scientific discovery of the cause, the energy source, and the observable antecedent conditions for the solar flare. This tunnel vision has had a profound and deleterious effect on the state-of-the-art. It has diverted attention from many promising approaches and relegated them to limbo. More importantly, it has actually inhibited the scientific search for the underlying physical mechanisms. Examples of neglected aspects of the problem cover the entire range from observing techniques and equipment to the availability and accuracy of the archived scientific data. A cursory inspection of this data leads to an obvious conclusion: there are at least two suns up there.

Ward, F. W., Jr.↗
NASA NTRS2008

Interferometry on a Balloon; Paving the Way for Space-based Interferometers

Astronomical studies at infrared wavelengths have dramatically improved our understanding of the universe, and observations with Spitzer, the upcoming Herschel mission, and SOFIA will continue to provide exciting new discoveries. The relatively low angular resolution of these missions, however, is insufficient to resolve the physical scale on which mid-to-far-infrared emission arises, resulting in source and structure ambiguities that limit our ability to answer key science questions. Interferometry enables high angular resolution at these wavelengths- a powerful tool for scientific discovery. We will build the Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII), an eight-meter baseline Michelson stellar interferometer to fly on a high-altitude balloon. BETTII's spectral-spatial capability, provided by an instrument using double-Fourier techniques, will address key questions about the nature of disks in young star clusters and active galactic nuclei and the envelopes of evolved stars. BETTII will also lay the technological groundwork for future space interferometers.

Rinehart, Stephen A.↗
NASA NTRS2008

The Balloon Experimental Twin Telescope for Infrared Interferometry

Astronomical studies at infrared wavelengths have dramatically improved our understanding of the universe, and observations with Spitzer, the upcoming Herschel mission, and SOFIA will continue to provide exciting new discoveries. The relatively low angular resolution of these missions, however, is insufficient to resolve the physical scales on which mid- to far-infrared emission arises, resulting in source and structure ambiguities that limit our ability to answer key science questions. Interferometry enables high angular resolution at these wavelengths, a powerful tool for scientific discovery, We will build the Balloon Experimental Twin Telescope for Infrared Interferometry (BETII), an eight-meter baseline Michelson stellar interferometer to fly on a high-altitude balloon. BETTII's spectral-spatial capability, provided by an instrument using double-Fourier techniques, will address key questions about the nature of disks in young star clusters and active galactic nuclei and the envelopes of evolved stars. BETTII will also lay the technological groundwork for future space interferometers,

Rinehart, Stephen A.↗
NASA NTRS2009

The Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII): Spatially Resolved Spectroscopy in the Far-Infrared

Astronomical studies at infrared wavelengths have dramatically improved our understanding of the universe, and observations with Spitzer, the upcoming Herschel mission, and SOFIA will continue to provide exciting new discoveries. The relatively low angular resolution of these missions, however, is insufficient to resolve the physical scale on which mid-to far-infrared emission arises, resulting in source and structure ambiguities that limit our ability to answer key science questions. Interferometry enables high angular resolution at these wavelengths - a powerful tool for scientific discovery. We will build the Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII), an eight-meter baseline Michelson stellar interferometer to fly on a high-altitude balloon. BETTII's spectral-spatial capability, provided by an instrument using double-Fourier techniques, will address key questions about the nature of disks in young star clusters and active galactic nuclei and the envelopes of evolved stars. BETTII will also lay the technological groundwork for future space interferometers and for suborbital programs optimized for studying extrasolar planets.

Rinehart, Stephen↗
NASA NTRS2009

Astronomical Studies at Infrared Wavelengths

Astronomical studies at infrared wavelengths have dramatically improved our understanding of the universe, and observations with Spitzer, Herschel, and SOFIA will continue to provide exciting new discoveries. The relatively low angular resolution of these missions, however, is insufficient to resolve the physical scale on which mid-to far-infrared emission arises, resulting in source and structure ambiguities that limit our ability to answer key science questions. Interferometry enables high angular resolution at these wavelengths - a powerful tool for scientific discovery. We will build the Balloon Experimental Twin Telescope for Infrared Interferometry (BETTII), an eight-meter baseline Michelson stellar interferometer to fly on a high-altitude balloon. BETTII's spectral-spatial capability, provided by an instrument using double-Fourier techniques, will address key questions about the nature of disks in young star clusters and active galactic nuclei and the envelopes of evolved stars. BETTII will also lay the technological groundwork for future balloon programs, paving the way for interferometric observations of exoplanets.

Rinehart, Stephen A.↗
DOE OSTI2020

Reinforcement learning for bluff body active flow control in experiments and simulations

Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by 30 % or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems.

42 ENGINEERING↗
DOE OSTI2023

FAIR AI models in high energy physics

Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗
NASA NTRS2019

Enabling Real-time Multi-messenger Astrophysics Discoveries with Deep Learning

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

E A Huerta↗
NASA NTRS2002

Transit Detection with a Distributed Network of Telescopes

The discovery since 1995 of more than 80 planets around nearby solar-like stars and the photometric detection of a transit of the planet orbiting HD 209458 (producing a more than 1% drop in brightness that lasts 3 hours) has heralded a new era in astronomy. It has now been demonstrated that small telescopes equipped with sensitive and stable electronic detectors can produce fundamental scientific discoveries regarding the frequency and nature of planets outside the solar system. The modest equipment requirements for the measurement of extrasolar planetary transits are achieved by commercial small aperture telescopes and CCD imagers common among amateur astronomers. With equipment already in hand and armed with target lists, observing techniques and software procedures developed b NASA's Ames Research Center and the University of California at Santa Cruz, non-professional astronomers can contribute significantly to the study of planets around others stars. Statistical analyses of the population of parent stars of the known extrasolar planets indicate that approximately one in ten metal-rich stars should harbor a short-period planet. Given the ten percent chance that a given short-period planet displays transits, we therefore expect that approximately 1% of the most metal rich stars will have a planetary companion detectable by this project. A catalog of 206 highly metal rich nearby F, G and K stars has been compiled, and this catalog will provide a rich source of targets. In addition, main sequence F, G, K and M stars identified to have "transit-like" features in the Hipparcos satellite photometry archive will also be monitored. A commercially available "amateur grade" telescope/CCD/software system acquired late during the 2001 "transit season" for HID 209458 has achieved 0.47% RMS precision for 13 minute time sampling from a suburban backyard under less than ideal observing conditions and a realistic range of airmass values.

Castellano, T.↗
DOE OSTI2023

CORE: A Global Aggregation Service for Open Access Papers

This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Community Requirements Meta-Analysis: Characterizing Needs and Opportunities for HPDF

This High Performance Data Facility (HPDF) Project is creating a new scientific user facility to provide advanced infrastructure for data-intensive science, supporting the DOE’s Office of Science (SC) community. HPDF’s mission is to enable and accelerate scientific discovery by delivering state-of-the-art data management infrastructure, capabilities, and tools. This meta-analysis examines the needs of the breadth of the SC community, captured in publicly available community reports or mission documents. The meta-analysis identifies and provides initial characterization of fifteen core requirements for the HPDF Project team to consider during the conceptual design phase. The fifteen requirements illustrate how scientific work among SC communities requires modern, seamless user experiences across the ASCR Ecosystem to advance the use of large volumes of heterogeneous data. The scientific community requires support for the missing middle of compute between local and HPC to interactively and collaboratively use growing datasets. Data producers and end users will benefit from enhanced data catalogs and portals that improve data access through advanced search of well curated data. The fifteen requirements are examined here organized across five themes for discussion. Examples in each theme illustrate the array of scientific needs that convey the important role that the fully realized and operational High Performance Data Facility will be able to play as an integral part of the evolving ASCR Ecosystem. Our amalgamated data tables from ESnet reports demonstrate ranges to the volumes of data HPDF must be concerned with, but limitations are inherent to this meta-analysis (see Key Challenges & Limitations). Feedback and validation of these requirements along with additional details and emergent community requirements will be gathered through user research and design activities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Shadow of the Future: Developing Trust and Software within the Exascale Computing Project

Collaboration and team science are emerging areas of interest in software production. Historically, multi-institutional research collaborations are difficult to initiate and maintain, negatively impacting communication, negotiation, and dialogue between industry, government, and academic researchers. The Exascale Computing Project (ECP), a massive, multi-team, high-stakes initiative, facilitated broader research collaboration under a shared funding structure and extended timeline to support scientific discovery. Here, we conducted interviews with ECP teams, representing a variety of domain specialties, research institutions, and programming backgrounds. Using thematic analysis, we assessed how ECP’s structure created an environment of increased trust among projects and how software shared between teams facilitated sustained collaboration. We found that the expectation of future collaboration, i.e., the shadow of the future, greatly enhanced trust among teams and the quality of scientific software produced. Based on our findings within ECP projects, we connect to the existing literature on trust in software engineering and share recommendations for sustainable multi-institutional collaboration and shared best software practices.

Exascale computing project↗
DOE OSTI2022

Transforming Energy Through Computational Excellence: Advanced Scientific Visualization Reveals Energy Insights

The National Renewable Energy Laboratory's world-class researchers and analysts, along with the Insight Center (our state-of-the-art scientific visualization facility) make data immersion a reality, allowing users to step into and explore their data. With the rise of large, diverse, and distributed data sets, scientific visualization is now critical to the process of scientific discovery and to managing and analyzing data and extracting insights. NREL provides visualization capabilities and facilities that are supported by state-of-the-art equipment, leading-edge techniques, and expert staff.

data science↗