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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 OSTI2022

Geothermal Fault Zone and Fluid Imaging through Joint Airborne ZTEM and Ground MT Data Inversion Analysis

This project has aimed to achieve detailed electrical resistivity resolution at geothermal reservoir scales by combining airborne natural electromagnetic (EM) field surveying (ZTEM) with ground magnetotelluric (MT) measurements to approximate an airborne MT geophysical method. MT alone is relatively expensive and may have permitting challenges in sensitive areas. Airborne ZTEM field data contains only the magnetic field, requires a background assumption, and has been limited to relatively high frequencies, thus suffering uniqueness problems. Based on proto-type 2D simulations, ZTEM ambiguities may be reduced through formal incorporation with possibly sparse ground MT soundings, which we pursued in full 3D for this project. The methodology was tested at the high-temperature Roosevelt Hot Springs geothermal system, Utah, which was considered advantageous given the near total exposure of crystalline reservoir rocks across the project area. ZTEM and ground MT survey data were acquired in 2017, subcontracted to outside parties with which we have worked in the past. These included 80 remote-referenced tensor MT soundings over the Mineral Mountains and adjacent Roosevelt Hot Spring producing geothermal system. These MT stations abut later coverage of a similar number of MT stations taken for the Utah FORGE project providing excellent total data aperture to re-solve structure beneath both project areas better than either set alone. The airborne ZTEM survey covered 704 line kilometers in E-W flight lines with a 250 m line spacing. Although this survey was timed during a maintenance-related shutdown of power production at the Roosevelt Hot Springs, other noise sources difficult to identify but including two high-voltage state-scale transmission lines compromised the ZTEM survey badly leading to unusable responses. Thus, with DOE management concurrence, the project proceeded to emphasize inversion and interpretation of the joint SubTER-FORGE MT data sets with regard to the Roosevelt Hot Springs reservoir recharge and to deep heat sources for both it and the Utah FORGE EGS project area. We also investigated the joint ZTEM-MT sampling concept with data sets from the Eleven Mile Canyon prospect area donated by the U.S. Navy (A. Sabin, PoC). Inversion of the SubTER-FORGE MT data using the HexMT 3D finite element algorithm reveals a large, low-resistivity anomaly extending sub-vertically through the depth range of the crust beneath the western Mineral Mountains. The steep conductive zone connects in the lower crust to a more tabular conductor characteristic of much of the Great Basin that generally is ascribed to current mafic magmatic underplating, hybridization and fluid release. The location of the resolved anomaly relative to the recent (0.5-0.8 Ma) eruptive centers of the Mineral Mountains implicates it as remnants of the magma body which fed these centers. This structure appears to be currently feeding heat and fluids upward into the Roosevelt Hot Springs hydrothermal system, as well as heat laterally to the FORGE project area. Separate and joint inversion models were carried out for the donated Eleven Mile Canyon MT-ZTEM data set to demonstrate concept. ZTEM only inversion showed two main alteration zones in the western portion of the project area known from geological mapping. Joint inversion including an E-W profile of MT soundings sharpened these features considerably. It also resolved in much greater detail the graben related normal faulting structure of the central project area which lies at depths exceeding the sensitivity of ZTEM alone. The sparse number of MT da-ta relative to the ZTEM required upweighting the former by a factor of several, but an exact procedure awaits future research. Our final impression is that sparse MT data can improve resolution of the subsurface over that of ZTEM alone. However, well sampled MT data are to be preferred and offer the simplicity of interpreting just one data type, and possess the superior resolution capability coming with the electric field everywhere, and from their high bandwidth.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Effects of vessel traffic and ocean noise on gray whale stress hormones

Human use of marinescapes is rapidly increasing, especially in populated nearshore regions where recreational vessel traffic can be dense. Marine animals can have a physiological response to such elevated human activity that can impact individual health and population dynamics. To understand the physiological impacts of vessel traffic on baleen whales, we investigated the adrenal stress response of gray whales (Eschrichtius robustus) to variable vessel traffic levels through an assessment of fecal glucocorticoid metabolite (fGC) concentrations. This analysis was conducted at the individual level, at multiple temporal scales (1–7 days), and accounted for factors that may confound fGC: sex, age, nutritional status, and reproductive state. Data were collected in Oregon, USA, from June to October of 2016–2018. Results indicate significant correlations between fGC, month, and vessel counts from the day prior to fecal sample collection. Furthermore, we show a significant positive correlation between vessel traffic and underwater ambient noise levels, which indicates that noise produced by vessel traffic may be a causal factor for the increased fGC. This study increases knowledge of gray whale physiological response to vessel traffic and may inform management decisions regarding regulations of vessel traffic activities and thresholds near critical whale habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Privacy First Path Analysis using Clickstream Data

In the modern digital economy, data-driven decision making is crucial for effectively meeting the ever-evolving demands of consumer engagement and satisfaction. Clickstream data has become invaluable for understanding customer behavior, yet concerns over privacy and security persist, especially with some internet service providers profiting from its sale. This article introduces an innovative methodology that blends experiential learning with advanced cryptographic techniques, including differential privacy and graph analytics. The core objective of this methodology is to estimate Customer Lifetime Value (CLV) by analyzing clickstream data, achieving an average prediction accuracy of 92.4% in user engagement levels while ensuring user anonymity through Recency, Frequency, and Monetary (RFM) analysis. Our study introduces the concept of a “data depositor” and a privacy manager, employing the composition theorem to merge non-adaptive queries effectively. Privacy budgets (? = 1.0, d = 10-5), sensitivity-specific techniques, and data partitioning were applied. Randomization and noise addition protect data integrity, with special handling for categorical values. This approach, differing from prior studies, offers a 12.6% improvement in privacy-preserving targeting accuracy while maintaining strict confidentiality, presenting a novel path forward in data-driven decision-making.

Frequency and Monetary (RFM) analysis↗
DOE OSTI2023

PMEL Passive Acoustics Research: Quantifying the Ocean Soundscape from Whales to Wave Energy

Passive acoustic monitoring of the global ocean has increased dramatically over the last decade, providing insights into seasonal sea ice and wind/wave variability, biodiversity, geophysical hazards, and anthropogenic noise impacts. All of these phenomena are sentinels of marine ecosystem health and ocean climate change. Recognizing the utility of underwater sound, the Pacific Marine Environmental Laboratory (PMEL) formed a passive acoustic research program with the goal of quantifying deep-ocean and coastal soundscapes in support of NOAA’s mission to conserve and manage marine ecosystems. PMEL Acoustics Program researchers have built a stable of novel ocean technologies, including autonomous stationary hydrophones, mobile platforms, and near-real-time surface buoys with satellite communication capability. These passive acoustic monitoring systems have been deployed in every major ocean basin on Earth, enabling significant advancements in understanding of natural and anthropogenic sounds. This progress includes evaluation of human-made sound levels across US waters, observations of ship noise fluctuations during the COVID-19 pandemic, and evaluation of noise levels from offshore wave-energy devices. Progress in natural sound research includes assessment of seasonal variability in the presence of endangered cetacean species due to population recovery and/or changing ocean temperatures as well as early detection of the collapse of an Antarctic ice shelf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput

Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.

Baheri, Betis↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2022

Global decline in ocean memory over the 21st century

Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant

This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Deep Learning Denoiser

SAND2022-14744 O The Deep Learning Denoiser uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal and noise waveform. In the approach, the CNN provides a signal mask and a noise mask for an input signal. The Short-Time Fourier Transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Koch, Clinton↗
DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2020

Detecting the undetected: Dealing with non-routine events using advanced M&V meter-based savings approaches

In a rapidly evolving energy industry, utilities are dealing with new challenges like integrating distributed energy resources and market saturation for advanced lighting retrofits. Demand-side management programs require new approaches to meet aggressive carbon reduction goals. Advanced measurement & verification (M&V) is an energy data analysis method using smart meter data in combination with analytics to quantify energy efficiency project savings. Advanced M&V shows great promise for supporting next generation commercial programs including retro commissioning, multi-measure retrofits, and behavior change programs. Advanced M&V captures real project impacts at the meter, but sometimes non-project events can also impact consumption (so-called “non-routine events” [NREs]). Accurately detecting and accounting for NREs is important for reducing uncertainty of savings estimates and helps manage investment risk for different stakeholders (e.g., utilities, building owners, ESCOs). Recent research has shown promise in establishing data-driven techniques to identify and adjust for NREs, but fundamental questions still remain, such as: how can you distinguish NREs from acceptable noise in energy consumption profiles? What is the frequency and magnitude of NREs? Can their detection and adjustment be automated and streamlined? This paper documents the state of the art in NRE quantification and analysis. The results of research to quantify the frequency, nature and direction of NREs, and methods and metrics for determining a trigger threshold for taking action on NREs are presented. The paper also documents the latest technical guidance on application of NRE detection and adjustment methods.

Fernandes, Samuel↗
DOE OSTI2024

Enhanced imaging of electronic hot spots using quantum squeezed light

Detecting electronic hot spots is important for understanding the heat dissipation and thermal management of electronic and semiconductor devices. Optical thermoreflective imaging is being used to perform precise temporal and spatial imaging of heat on wires and semiconductor materials. We apply quantum squeezed light to perform thermoreflective imaging on micro-wires, surpassing the shot-noise limit of classical approaches. We obtain a far-field temperature sensing accuracy of 42 mK after 50 ms of averaging and show that a 256×256 pixel image can be constructed with such sensitivity in 10 min. We can further obtain single-shot temperature sensing of 1.6 K after only 10 μs of averaging, enabling a dynamical study of heat dissipation. Not only do the quantum images provide accurate spatiotemporal information about heat distribution but also the measure of quantum correlation provides additional information, inaccessible by classical techniques, which can lead to a better understanding of the dynamics. We apply the technique to both aluminum and niobium microwires and discuss the applications of the technique in studying electron dynamics at low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument

Abstract The Dark Energy Spectroscopic Instrument (DESI) embarked on an ambitious 5 yr survey in 2021 May to explore the nature of dark energy with spectroscopic measurements of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the baryon acoustic oscillation method to measure distances from the nearby universe to beyond redshift z > 3.5, and employ redshift space distortions to measure the growth of structure and probe potential modifications to general relativity. We describe the significant instrumentation we developed to conduct the DESI survey. This includes: a wide-field, 3.°2 diameter prime-focus corrector; a focal plane system with 5020 fiber positioners on the 0.812 m diameter, aspheric focal surface; 10 continuous, high-efficiency fiber cable bundles that connect the focal plane to the spectrographs; and 10 identical spectrographs. Each spectrograph employs a pair of dichroics to split the light into three channels that together record the light from 360–980 nm with a spectral resolution that ranges from 2000–5000. We describe the science requirements, their connection to the technical requirements, the management of the project, and interfaces between subsystems. DESI was installed at the 4 m Mayall Telescope at Kitt Peak National Observatory and has achieved all of its performance goals. Some performance highlights include an rms positioner accuracy of better than 0.″1 and a median signal-to-noise ratio of 7 of the [O ii ] doublet at 8 × 10 −17 erg s −1 cm −2 in 1000 s for galaxies at z = 1.4–1.6. We conclude with additional highlights from the on-sky validation and commissioning, key successes, and lessons learned.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Quantification of human contribution to soil moisture-based terrestrial aridity

Abstract Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N–65°N, during 1971–2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0–10 cm SSI during August–November, and 0–100 cm during September–April. We further develop and apply an emergent constraint method on the future SSI’s signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0–10 cm than 0–100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Landscape fragmentation overturns classical metapopulation thinking

Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally (“residents”) were often more resilient to fragmentation than long-ranging “migrants.” This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗