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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Inference of the Mass Composition of Cosmic Rays with Energies from 10 18.5 to 10 20 eV Using the Pierre Auger Observatory and Deep Learning

We present measurements of the atmospheric depth of the shower maximum X max , inferred for the first time on an event-by-event level using the surface detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the X max distributions up to energies of 100 EeV ( 10 20 eV ), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the fluorescence detector data, we find evidence that the rate of change of the average X max with the logarithm of energy features three breaks at 6.5 ± 0.6 ( stat ) ± 1 ( syst ) EeV , 11 ± 2 ( stat ) ± 1 ( syst ) EeV , and 31 ± 5 ( stat ) ± 3 ( syst ) EeV , in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured X max distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 and 100 EeV. Published by the American Physical Society 2025

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Deep Learning without Global Optimization by Random Fourier Neural Networks

Here we introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

mphys-surrogate-model

This repository contains python scripts for building and studying reduced-order-modeling representations of droplet coalescence for eventual use in atmospheric models. The included data are generated from high-fidelity superdroplet methods and are utilized by machine learning pipelines to build data-driven models of droplet size distributions that evolve under coalescence. This repository further includes scripts to determine prediction (uncertainty) intervals on the data-driven model products based on conformal prediction.

Katona, JonasE [Lawrence Livermore National Labora↗
DOE OSTI2025

Optimal Transport as a Tool for Scientific Discovery in Radiation Biology

This report summarizes findings from research conducted for the “Exploration of the Poten tial for Artificial Intelligence and Machine Learning to Advance Low-Dose Radiation Biology Re search” (RadBio-AI) program, supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Awards KP1601011/FWP CC121 and KP1601017/FWP CC121. The research reported here was undertaken in an effort to assess the potential of optimal measure transport methods as components within the larger scope of a com putational framework envisioned to support research in the radiation biology domain. Within this effort, our interest centered on enabling a unified generic framework where probabilistic modeling, inference, and statistical learning can be carried out for a wide range of data distributions. As described next in Section 1 (and in more detail in our original publication), optimal measure transport offers the possibility of such unified approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Engaging with Utilities and Key Stakeholders [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series. This presentation overviews the interconnection process for distributed energy resources and how to engage well with utilities. The workshop will be held on March 25, 2026.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

Improving Search Algorithms by Using Intelligent Coordinates

We consider algorithms that maximize a global function G in a distributed manner, using a different adaptive computational agent to set each variable of the underlying space. Each agent eta is self-interested; it sets its variable to maximize its own function g (sub eta). Three factors govern such a distributed algorithm's performance, related to exploration/exploitation, game theory, and machine learning. We demonstrate how to exploit alI three factors by modifying a search algorithm's exploration stage: rather than random exploration, each coordinate of the search space is now controlled by a separate machine-learning-based player engaged in a noncooperative game. Experiments demonstrate that this modification improves simulated annealing (SA) by up to an order of magnitude for bin packing and for a model of an economic process run over an underlying network. These experiments also reveal interesting small-world phenomena.

Wolpert, David H.↗
NASA NTRS2008

MODIS Land Data Products: Generation, Quality Assurance and Validation

The Moderate Resolution Imaging Spectrometer (MODIS) on-board NASA's Earth Observing System (EOS) Terra and Aqua Satellites are key instruments for providing data on global land, atmosphere, and ocean dynamics. Derived MODIS land, atmosphere and ocean products are central to NASA's mission to monitor and understand the Earth system. NASA has developed and generated on a systematic basis a suite of MODIS products starting with the first Terra MODIS data sensed February 22, 2000 and continuing with the first MODIS-Aqua data sensed July 2, 2002. The MODIS Land products are divided into three product suites: radiation budget products, ecosystem products, and land cover characterization products. The production and distribution of the MODIS Land products are described, from initial software delivery by the MODIS Land Science Team, to operational product generation and quality assurance, delivery to EOS archival and distribution centers, and product accuracy assessment and validation. Progress and lessons learned since the first MODIS data were in early 2000 are described.

Masuoka, Edward↗
NASA NTRS2014

Interdisciplinary Interactions During R&D and Early Design of Large Engineered Systems

Designing Large-Scale Complex Engineered Systems (LaCES) such as aircraft and submarines requires the input of thousands of engineers and scientists whose work is proximate in neither time nor space. Comprehensive knowledge of the system is dispersed among specialists whose expertise is in typically one system component or discipline. This study examined the interactive work practices among such specialists seeking to improve engineering practice through a rigorous and theoretical understanding of current practice. This research explored current interdisciplinary practices and perspectives during R&D and early LaCES design and identified why these practices and perspectives prevail and persist. The research design consisted of a three-fold, integrative approach that combined an open-ended survey, semi-structured interviews, and ethnography. Significant empirical data from experienced engineers and scientists in a large engineering organization were obtained and integrated with theories from organization science and engineering. Qualitative analysis was used to obtain a holistic, contextualized understanding. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. For example, organization culture is an important underlying factor that guided researchers more toward individual sovereignty over cross-disciplinarity. The organization structure and the engineered system architecture also serve as constraints to the engineering work. Many differences in work practices were observed, including frequency and depth of interactions, definition or co-construction of requirements, clarity or creation of the system architecture, work group proximity, and cognitive challenges. Practitioners are often unaware of these differences resulting in confusion and incorrect assumptions regarding work expectations. Cognitively, the enactment and coconstruction of knowledge are the fundamental tasks of the interdisciplinary interactions. Distributed and collective cognition represent most of the efforts. Argument, ignorance, learning, and creativity are interrelated aspects of the interactions that cause discomfort but yield benefits such as problem mitigation, broader understanding, and improved system design and performance. The quality and quantity of social interrelations are central to all work across disciplines with reciprocity, respectful engagement, and heedful interrelations being significant to the effectiveness of the engineering and scientific work.

McGowan, Anna-Maria Rivas↗
NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off- the-Shelf (COTS) fan and motor, an Electronic Speed Controller, a custom-designed inlet bellmouth, and several experimental inlet duct acoustic liners. Fan speed, thrust, and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large regional single-aisle aircraft. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller measured during this test are presented in a separate report.

L Danielle Koch↗
NASA NTRS2024

SupportU: Smart UAS Program for the Population by Offering Resources and Tools to the Unhoused

Global warming, challenging economic conditions, the opioid epidemic, and other widespread problems, have impacted people globally, particularly over the past several years. Preventable diseases like the common cold and the effects of heat stroke have become increasingly prevalent due to these issues. It is estimated that 150 million people of the world’s population are unhoused globally, with many dwelling in unsafe and unsanitary conditions while lacking access to basic hygienic items and other essentials. Unsanitary conditions coupled with this lack of access exacerbates and prolongs health problems and harms quality of life. Traditional methods of aid, such as homeless shelters and meal programs, face numerous challenges such as having limited reach and resources. To address these problems, the Smart UAS Program for the Population by Offering Resources and Tools to the Unhoused (SUPPORT U) utilizes Uncrewed Aircraft Systems (UAS) to deliver resources to the unhoused and those in need of basic aid, prior to and during extreme temperature conditions, and after natural disasters in rural, suburban, and urban areas. The UA is envisioned to be an autonomous aircraft capable of efficiently distributing essential supplies, including blankets, water, food, and medicine. The UAS fleet relies on advanced navigation and communication technologies to accurately identify unhoused people and efficiently and safely distribute materials to them. This will be done through a machine learning algorithm. By focusing on identified “homeless clusters”, places where unhoused individuals are concentrated, the UAS network increases access to critical resources, thereby helping to mitigate some of the external risks to the health of unhoused individuals. The UA can also be used during crises, such as by transporting supplies to medical tents that are stationed in difficult-to-reach areas suffering from natural disasters.

Samuel Beard↗
NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Thermal Analysis and Testing of the Electronic Speed Controller (ESC)

An electric ducted fan system was tested at the NASA Glenn Research Center. Main components of the system include a commercial-off-the-Shelf (COTS) fan and motor, electronic speed controller, and cooling system. Motor speed, voltage, current, and temperatures were measured in this static ground test of the propulsor. The thermal performance of the Electronic Speed Controller (ESC) was also measured using two different methods to cool the ESC: air cooling and cold plate cooling. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Single Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large regional single-aisle aircraft. Lessons learned will be used to guide development of future QUEEN prototypes and thermal management systems.

Firas G Asfoor↗
NASA NTRSDate not supplied

Comparison of Entry Descent and Landing Aerodynamic Databases with Uncertainty Quantification Developed Using Machine Learning Techniques

When developing the aerodynamic databases for use in trajectory simulations, it is important to develop a system of metrics to qualify which aerodynamic models are best to use. Since aerodynamics are just one input into trajectory simulations, the results of these simulations do not reflect on the quality of the aerodynamic database used. This means that aerodynamic database comparisons must be done offline. While traditional metrics that focus on mean/nominal predictions are a good first step, more robust estimates of the prediction interval become important as more focused uncertainty models are developed. We explore the limitations of evaluating aerodynamic models based purely on nominal-centered response surfaces. Before elaborating and evaluating metrics based on distributed models, the value of evaluating prediction interval and confidence interval are discussed to conclude that prediction intervals are more relevant to the use of trajectory analysis. Several metrics to evaluate the prediction interval are introduced with a focus on the standard calibration metric. Finally, we compare candidate models using both mean and distributed metrics. A finalized candidate model developed using state of the art machine learning methods is compared to a baseline model developed using traditional aerodynamic database modeling techniques.

Aerodynamic Database↗
NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗
NASA NTRS2019

Cumulus Lessons Learned: Building, Testing, and Sharing a Cloud Archive

Cumulus is a scalable, extensible cloud-based archive system which is capable of ingesting, archiving, and distributing data from both existing on-prem sources and new cloud-native missions. As we have built and evolved the system with contributions from seven NASA EOSDIS organizations, we have learned several lessons about how to build a robust, broadly-applicable, microservices-based cloud system for geospatial data which we will share in this talk.

Quinn, Patrick↗
DOE OSTI2025

Considerations for Distributed Edge Data Centers and Use of Building Loads to Support Large Interconnections

The rapid expansion of artificial intelligence (AI) and machine learning is driving unprecedented electricity demand from data centers. It is predicted that by 2030, 90% of AI workloads will be inference-based, requiring interconnection of multiple low-latency edge data centers (<20 MW) sited closer to end users - often on already constrained distribution feeders. Although individually small, these loads can aggregate to large loads per feeder, straining infrastructure, creating multi-year interconnection delays, and driving up customer costs. This paper proposes a data center-focused grid-integration framework that combines feeder hosting capacity analysis with building energy efficiency, building load flexibility, and waste heat reuse to expand effective feeder and substation headroom. Such approaches can reduce interconnection delays, lower costs for ratepayers, and accelerate AI-ready infrastructure deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems

Accurate and quick identification of high-impedance faults (HIFs) is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage-current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage-current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification.

explainable artificial intelligence↗
DOE OSTI2024

A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems: Preprint

Accurate and quick identification of high-impedance faults (HIFs) is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage-current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage-current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification.

explainable artificial intelligence↗