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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Power Optimal Control Allocation for Distributed Electric Propulsion in a Series/Parallel Hybrid Powertrain

This paper describes a thrust allocation scheme that minimizes power consumption in an electrified powertrain with Distributed Electric Propulsion. It takes advantage of an observation about component efficiency maps, that efficiency is often highest at high torque/high speed conditions. This optimal approach is demonstrated to satisfy total thrust and net yaw axis torque requirements, making it suitable for utilizing differential thrust for maneuvering.

electrified aircraft propulsion↗
NASA NTRSDate not supplied

Power Optimal Control Allocation for Distributed Electric Propulsion in a Series/Parallel Hybrid Powertrain

This paper describes a thrust allocation scheme that minimizes power consumption in an electrified powertrain with Distributed Electric Propulsion. It takes advantage of an observation about component efficiency maps, that efficiency is often highest at high torque/high speed conditions. This optimal approach is demonstrated to satisfy total thrust and net yaw axis torque requirements, making it suitable for utilizing differential thrust for maneuvering.

electrified aircraft propulsion↗
NASA NTRS2026

Power Optimal Control Allocation for Distributed Electric Propulsion in a Series/Parallel Hybrid Powertrain

This paper describes a thrust allocation scheme that minimizes power consumption in an electrified powertrain with Distributed Electric Propulsion. It takes advantage of an observation about component efficiency maps, that efficiency is often highest at high torque/high speed conditions. This optimal approach is demonstrated to satisfy total thrust and net yaw axis torque requirements, making it suitable for utilizing differential thrust for maneuvering.

electrified aircraft propulsion↗
DOE OSTI2025

High-Power Targetry for Muon Production

The production of high-intensity muon beams is crucial for advancing particle and accelerator physics, both now and in the future. Achieving these high-intensity goals requires overcoming significant challenges in high-power targetry. Here, we will outline the key challenges and explore the selection of target materials, focusing on muon production and susceptibility to radiation damage -- key factors for optimal target designs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Airships as Transformative Tools for Power Systems Logistics: Addressing Infrastructure Challenges and Enhancing Disaster Resilience

Airships represent a transformative advancement in logistics, particularly for the power systems industry. Their unique ability to transport heavy equipment and personnel efficiently over remote or disaster-stricken areas, while requiring minimal infrastructure, addresses critical gaps left by traditional methods. Combining adaptability with environmental sustainability, airships offer a low-carbon alternative for routine operations and emergency responses. This review presents current developments in airship technology, such as rigid airships designed for large payloads, demonstrate their potential to revolutionize supply chains.

Fuhr, Peter L. [ORNL] (ORCID:0000000211627445)↗
DOE OSTI2025

Leading Energy Analysis: Data-Driven Insights That Power Our Energy Future

Energy analysts at the National Renewable Energy Laboratory (NREL) use a broad set of expertise and cutting-edge tools to capture the complexities of our interconnected energy system. Decision-makers rely on these insights to drive cost savings, enhance grid reliability, support long-term innovation, and bolster America's energy workforce and global competitiveness. This fact sheet highlights some of NREL's high-impact analyses, data, and tools, largely focusing on power grid analysis.

data↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: Siva Powers America Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Siva Powers America Inc. for a Technology Commercialization Award. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2025

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Prototype Installation and Testing Awardee: Pecos Wind Power

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Pecos Wind Power for Prototype Installation and Testing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗
DOE OSTI2024

Why PV Inverter Failures May Lead to AC Power Quality Issues

Case study examines a common PV inverter failure that can drive uncommon issues in the field - namely, damage to the AC protection equipment from DC fault currents for short periods during transformer-less inverter power electronic failures.

14 SOLAR ENERGY↗
DOE OSTI2025

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

Linne, Karl C. [Chicago U.] (ORCID:000900091870358↗
DOE OSTI2025

A DATA EFFICIENT SPARSE MODELING FRAMEWORK FOR POWER ESTIMATION IN WATER TREATMENT SENSING OPERATIONS

With increasing freshwater scarcity, advanced process design mechanisms such as Closed-Circuit Reverse Osmosis (CCRO) and Digital/Physical Twin systems are gaining traction in water treatment and reuse operations. While digital and physical twin models enable improved system insight and control, their development is often expensive and computationally intensive, requiring large volumes of synthetic or experimental data to characterize underlying process dynamics. This work introduces a sparse surrogate modeling framework to estimate power consumption from measured flow and pressure variables, along with their nonlinear polynomial and interaction expansions. To ensure model reliability and reduce overfitting, a two-stage pipeline is proposed. First, a dynamic data filtering algorithm is employed to remove uninformative observations and transient operational states. Second, a sparse penalized regression technique is applied to select a minimal set of parsimonious features. The proposed model achieves high sparsity, retaining only 7 out of 34 candidate features (≈79.41% sparsity) while delivering a root mean square error (RMSE) of 0.072 on the test dataset.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Novel technology of non-contact real-time radiation damage sensors for high power targets.

This report summarizes the contributions of an intern participating in the Community College Internship (CCI) program at Fermilab, focusing on the development of a novel, non-contact, real-time radiation damage sensor technology. The core objective is to create a reliable sensor capable of measuring radiation-induced degradation on high-power targets without physical contact. The experiment involves using a Class 3B supercontinuum laser directed toward a single material sample placed within a vacuum test chamber. The laser beam reflects off the sample's surface, with changes in reflectivity, indicative of radiation damage, measured by a spectrometer positioned at the chamber’s output port. The intern’s primary responsibilities included designing an interlock system to ensure laser operational safety, developing a camera-based monitoring system using Raspberry Pi devices, and creating structural supports using 3D modeling and printing techniques. Components for the interlock and camera systems were successfully designed and ordered, with preliminary 3D models printed and refined through iterative testing. Challenges encountered in the 3D printing process, such as fragile initial prototypes and difficult support removal, were overcome by adjusting printer settings and incorporating design enhancements like chamfered edges. Future activities, pending component delivery, involve installing and configuring the interlock and camera systems, as well as further improving the structural supports. Overall, the internship significantly enhanced the intern’s technical proficiency in hardware design, software integration, and advanced 3D printing, contributing directly to Fermilab’s operational safety standards and experimental effectiveness in high-energy physics research.

Pumarino Meza, Rafael [Unlisted; Fermilab]↗
DOE OSTI2026

IceCube's convex all-sky neutrino spectrum consistent with the magnetically powered corona scenario for active galactic nuclei

High-energy multimessenger background analyses over the past decade have provided evidence for a population of hidden neutrino sources that are opaque to GeV-TeV gamma rays, a picture bolstered by recent observations of the nearby active galaxy NGC 1068. The coronal regions in the hearts of active galactic nuclei (AGNs) have been proposed as the most promising sites for such hidden nonthermal particle production, and NGC 1068 is expected to be the most neutrino-active galaxy for IceCube. We demonstrate that the latest all-sky neutrino spectrum, exhibiting a spectral bend around 3-30 TeV, is consistent with predictions of the magnetically powered corona scenario, and the models for the all-sky neutrino flux can simultaneously explain the multimessenger data from NGC 1068 within observational and modeling uncertainties. We further show, in a largely model-independent way, that the contribution from NGC 1068-like sources does not overshoot the observed medium-energy neutrino flux. Finally, we highlight the key role of the Eddington ratio, which can drive substantial variations in the predicted neutrino fluxes of nearby AGNs, and we encourage systematic multimessenger searches for the neutrino-brightest AGNs.

Murase, Kohta [Penn State U.; Penn State U., Unive↗
DOE OSTI2025

Rancor-HUNTER: Using a Simulator Engine for Realistic Human Performance Modeling of Nuclear Power Operations

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a software system to simulate human performance in support of human reliability analysis (HRA) in nuclear power plants. This paper summarizes recent work to integrate HUNTER with a plant simulator, namely the Rancor Microworld Simulator. Rancor is an offshoot of earlier work at Idaho National Laboratory (INL) to support plant modernization. The graphical software tools used to mimic digital human-system interface upgrades at INL’s Human Systems Simulation Laboratory were linked to the Rancor Microworld Simulator, an INL-developed simplified plant model. HUNTER becomes a “virtual operator” coupled to the Rancor simulator, thereby allowing a tight coupling between a digital human twin and a digital twin of the plant. Rancor-HUNTER may be run through Monte Carlo iterations across a dynamic range of performance shaping factors, thereby producing distributions of human performance in terms of procedure paths, errors instantiations, and task durations. This paper overviews the various unique features of Rancor-HUNTER and presents an example run of Rancor-HUNTER for a startup scenario.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Using a Large Language Model for Accurate Technical Language Generation in the Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Machine learning (ML) methods for predictive maintenance (PdM) are emerging as effective proactive strategies for diagnosing equipment degradation and enabling effective decision-making. However, explainability and trustworthiness of artificial intelligence are two salient challenges that need to be addressed for wider deployment of these technologies in nuclear power plants (NPPs). Large language models (LLMs) offer a unique approach to tackle these challenges by explaining PdM, work orders, diagnosis results, and ML algorithms to users, who may not be familiar with ML and PdM in general. Moreover, by dynamically retrieving relevant information from technical documents and evaluating factuality of LLM generation, the accuracy and relevance of LLM generations can be improved. This work demonstrates using LLMs to explain the causes and consequences of circulating water system failures based on multiyear NPP work orders. This work tests the capability of multimodal LLM approaches in explaining the differences in the circulating water system from both the Salem and Hope Creek NPPs using both text and image resources. This work also demonstrates the use of multimodal LLMs in describing the diagnosis tab of a predictive maintenance software named VIsualization for PrEdictive maintenance Recommendation (VIPER) to users.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Integrate Power Conversion Unit with MAGNET to Enable Integrated Microreactor Heat Transfer System Testing

Idaho National Laboratory obtained a closed, Brayton-cycle, power conversion unit (PCU) from Sandia National Laboratories. This PCU began as a commercially available, 30 kWe, C30, gas turbine from Capstone. The C30 was modified to use heat from an electric heater in a closed-loop system pressurized with nitrogen or dry air. INL has modified the unit further to integrate it with MAGNET and use heat from a microreactor test article.

42 - ENGINEERING↗
DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗