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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Effects of land-based wind turbine upsizing on community sound levels and power and energy density

Multiple technological, social, and market factors of wind power are evolving rapidly. Most notably, significant wind turbine scaling is occurring and is forecasted to continue. While the larger turbines expected to be deployed in the future are more powerful and efficient, they are also expected to operate at higher sound levels and require larger setbacks than those installed in the last decade. These sometimes-competing deployment trends and impacts cannot be understood via simple extrapolations of past trends. This study analyzes the effect of these future larger turbines on wind turbine micro-siting, project-level power and energy density, and community noise impacts. Due to their taller heights, larger rotors, and higher sound power levels, future wind turbines will require larger setbacks from homes and greater inter-turbine spacing, resulting in fewer turbines deployed for a given land area. This research finds these changes more than offset the effect of the higher turbine sound emissions, significantly decreasing the average sound levels that wind plant hosting communities experience. Yet, simultaneously, plant layouts using future turbine designs also result in projects with higher installed capacities and annual energy output for a given land area. These increases will likely lead to increased tax benefits and local income in the community. The deployment of fewer turbines on a smaller number of parcels could have implications on siting flexibility and landowner payments.

17 WIND ENERGY↗
DOE OSTI2023

U.S. Offshore Wind Energy Noise Reduction Associated with Installation of Fixed-Bottom Foundations: Workshop Report

As offshore wind energy development grows in the United States, solutions are needed to reduce the underwater noise and substrate vibration generated during fixed-bottom turbine installation to help protect marine life. Monopiles are currently the main turbine foundation type installed globally and impact pile driving, the primary installation method, can be a major source of noise and vibration generation during wind farm construction. Opportunities exist to reduce the amount of noise and vibration produced during future offshore wind farm development. The use of alternative foundation types and installation methods, as well as innovative noise abatement technologies, would help reduce the potential detrimental effects on sensitive marine species. To explore these opportunities, the Department of Energy’s Wind Energy Technologies Office, in collaboration with the Bureau of Ocean Energy Management and the National Oceanic and Atmospheric Administration, funded the National Renewable Energy Laboratory and the Pacific Northwest National Laboratory to organize, host, and facilitate a virtual workshop in December 2022. The goal of the workshop was to gather input from the offshore wind energy community on noise reduction strategies for the installation of fixed-bottom offshore wind turbines in U.S. waters across multiple regions, including the U.S. Atlantic Coast, Gulf of Mexico, and Great Lakes to inform recommendations on future research. The joint lab team convened industry representatives, subject matter experts, and regulators to discuss potential pathways to reduce noise and vibration associated with fixed-bottom turbine installation, including the practicality of using alternative foundations and installation methods, the effectiveness of noise abatement technologies, as well as research and monitoring needs. In preparation for the workshop, the team invited experts to be part of a steering committee and worked with its members to help focus activities to meet the workshop goals. Additional pre-workshop activities included developing the invitee list and workshop agenda, reviewing existing Construction and Operations Plans (COP) for proposed offshore wind projects, distributing a brief questionnaire to industry representatives, and drafting the workshop discussion questions and Mural boards. In total, 128 workshop participants provided over 600 comments in response to the 17 group discussion questions used over two workshop sessions. Overarching recommendations highlighted across themes included future investments to provide data sharing, consistency, transparency, and opportunities for sharing best available science, knowledge, and expertise. The joint lab team synthesized the comments and identified four key research and development themes that could help advance the successful implementation of noise and vibration reduction strategies (see report). This report provides an overview of the workshop goals and scope, reviews the pre-workshop activities, presents brief summaries of participant feedback, and concludes with detailed research themes and recommendations for future investments. Appendix A includes the group discussion questions, Appendix B includes the results of the COP review, and Appendix C includes the results of the industry questionnaire. Overall, there was a high level of interest and engagement in the workshop. The sessions provided an opportunity for significant exchange of information between workshop participants from across sectors. Participants identified a variety of opportunities for next steps towards the noise reduction of offshore wind turbine installation in U.S. waters, including recommendations for future investments to provide certainty in the use of new technologies associated with alternative foundation types, noise abatement systems, and efficacy monitoring.

17 WIND ENERGY↗
DOE OSTI2022

Hearing Loss and Dementia

This report is an OSH-DO Safety Action Bulletin that briefly discusses the link between dementia and hearing loss. The bulletin provides some basic statistics, hearing loss protection guidelines and resources for more information on the topic.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2026

Community detection robustness of graph neural networks

Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.

Goel, Jaidev [Virginia Polytechnic Inst. and State↗
DOE OSTI2025

Listening for Neutrinos with Quantum Acoustics

Many low-threshold superconducting detectors rely on athermal phonon sensing, in which meV-scale phonons from a DM scatter are sensed via their ability to break Cooper pairs in superconducting films. While detectors based on such pairbreaking sensors are highly advantageous and scalable, their sensor threshold is ultimately limited by the energy needed to break a Cooper pair, 2Δ. We present a novel detector architecture for single phonon detection at the O(10μeV) scale, the qubit-coupled hBAR, that does not share this limitation. This architecture, composed of a superconducting qubit piezoelectrically coupled to a high-overtone bulk acoustic resonator, was originally developed in the context of the quantum acoustics community and has a sensor threshold ultimately limited by thermal noise. Such ultra-low-threshold detectors have a wide range of fundamental science applications, including reactor monitoring via neutrinos.

Baxter, Daniel Stephen [Fermilab] (ORCID:000000030↗
DOE OSTI2024

The influence of exploration activities of a potential lithium mine to the environment in Western Serbia

The proposed exploitation of the Jadar Valley lithium/borate deposit in Serbia, by the Rio Tinto Corporation, indicates that it would become large-scale processing of boron- and lithium-containing ore. It would be one of the world’s very first lithium mines in populated and agricultural area. The company claims that the envisioned mining will be in accordance with environmental protection requirements. The Jadar Valley deposits have been claimed to cover 90% of Europe’s current lithium needs. Yet, local opposition to the mining has arisen due to potential devastating impacts on groundwater, soil, water usage, biodiversity loss, and waste accumulation. Research drilling by the mining company has already produced environmental damage, with mine water containing high levels of boron leaking from exploratory wells and causing crops to dry out. Furthermore, our investigations reveal substantially elevated downstream concentrations of boron, arsenic, and lithium in nearby rivers as compared to upstream regions. Additionally, here we show that soil samples exhibit repeated breaches of remediation limit values with environmental consequences on both surface and underground waters. With the opening of the mine, problems will be multiplied by the tailings pond, mine wastewater, noise, air pollution, and light pollution, endangering the lives of numerous local communities and destroying their freshwater sources, agricultural land, livestock, and assets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Geochemistry & Geophysics↗
DOE OSTI2025

Estimating Eigenenergies from Quantum Dynamics: A Unified Noise-Resilient Measurement-Driven Approach

Ground state energy estimation in physical, chemical, and materials sciences is one of the most promising applications of quantum computing. In this work, we introduce a new hybrid approach that finds the eigenenergies by collecting real-time measurements and post-processing them using the machinery of dynamic mode decomposition (DMD). From the perspective of quantum dynamics, we establish that our approach can be formally understood as a stable variational method on the function space of observables available from a quantum many-body system. We also provide strong theoretical and numerical evidence that our method converges rapidly even in the presence of a large degree of perturbative noise, and show that the method bears an isomorphism to robust matrix factorization methods developed independently across various scientific communities. Our numerical benchmarks on spin and molecular systems demonstrate an accelerated convergence and a favorable resource reduction over state-of-the-art algorithms. The DMD-centric strategy can systematically mitigate noise and stands out as a leading hybrid quantum-classical eigensolver.

Shen, Yizhi↗
DOE OSTI2026

Dataset of U.S. School Bus Depots

A large body of public health literature describes how undesirable or dangerous facilities, such as truck depots and industrial plants, located in or near communities can lead to health harms. Research also describes the high levels of traffic-related air and noise pollution that is linked to health harms and may be disproportionately distributed near many schools. Therefore, a primary use case for this dataset is to analyze the location of school bus depots and to create an evidence base that would better enable the work of community members, advocates, and other stakeholders toward improving air quality and public health. Other possible uses for this school bus depot dataset include electricity grid planning and reliability, given recent momentum toward school bus electrification. This dataset was created using an object-based approach with remote sensing data. The primary source of aerial imagery was the National Agriculture Imagery Program (NAIP) dataset. NAIP imagery was analyzed to locate individual school buses based on their color and size, and then classified clusters of school buses as potential depots, which were then verified visually. The resulting dataset contains 11,309 depots across the 48 contiguous U.S. states and Washington, D.C. Fifty-one percent (5,730 depots) are at schools, defined as being 350 meters or less from the nearest school. The accuracy of the dataset was assessed by comparing it with independent reference datasets containing 506 depots from the records of two school transportation companies. We found good agreement, with an omission error rate of 15.2% (77 depots). This dataset represents one of the only remote sensing projects to conduct object detection using data at the sub-meter to 1-meter resolution for a continental-scale application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Vortical interactions in turbulent thermoacoustic systems

This study examines the dynamics of vortical interactions and their implications for mitigating thermoacoustic instability in a turbulent combustor. The regions of intense vortical interactions are identified as vortical communities in the network space of weighted directed vortical networks constructed from two-dimensional experimental velocity data. One can expect vortical interactions in the combustor to be strongest near the moment of vortex shedding, as the shed vortices gradually weaken due to dissipation while convecting downstream. However, we show that, during the state of thermoacoustic instability, there is a non-trivial consistent phase lag of approximately 52° between the shedding of the coherent structures from the backward-facing step and the time instant when the vortical interactions attain their local maximum value. We explain this phase lag by investigating the correlation between acoustic pressure fluctuations, spatio-temporal dynamics of coherent structures and vortical interactions in the reaction field of the combustor. We also show the aperiodic variation of vortical interactions during the states of combustion noise and aperiodic epochs of intermittency. Furthermore, the spatio-temporal evolution of pairs of vortical communities with the maximum inter-community interactions provides insight into explaining the critical regions detected in the reaction field during the states of intermittency and thermoacoustic instability, also identified in previous studies. As a result, we further show that the most efficient suppression of thermoacoustic instability via air microjet injection is achieved when steady air jets are introduced to disrupt the maximum inter-community interactions present during the state of thermoacoustic instability.

Sahay, Ankit [Indian Institute of Technology Madra↗
DOE OSTI2023

Quantum Devices for Neutrino and Rare Particle Detection (Final Report)

This report summarizes the technical work completed under the DOE-QuanISED program. This grant allowed the MIT group --and associated groups on the project, including UMass Amherst and Argonne National Laboratory -- to explore the use of superconducting bolometers for the purpose of neutrino and low mass dark matter detection. These low threshold bolometric detection techniques have wide applicability in nuclear and particle physics, including searching for low mass weakly-interacting dark matter; detecting neutrinos via coherent neutrino scattering; searching for axions and nuclear non-proliferation. In addition, we explored how using techniques readily used by the quantum computing community–such as multiplexed microwave resonators and quantum amplifiers—could be used as a low noise readout scheme for an array of superconducting detectors. We briefly summarized the accomplishments under the project, and what future research directions have spawned from this research.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Verification and Performance Impact of the New Parallel MCNP6.3 Particle Track Output Capability for Subcritical Multiplication Simulations

The MCNP6® code, version 6.3, has several new features that are intended to ultimately replace legacy features that are now marked for deprecation. One of these features is the new particle track output (PTRAC) format and capability, where the legacy PTRAC capability still exists alongside the modern PTRAC capability in MCNP6.3. While the MCNP6.3 code has been extensively verified and validated for many applications, the PTRAC feature is not exercised in any of the typical verification and validation (V&V) applications studied during the course of a typical MCNP code release. The primary goal of this paper is to verify that the legacy and modern PTRAC feature produces equivalent results for subcritical multiplication benchmarks previously studied. In the process of verifying that the simulated benchmark results are equivalent, the computational performance is compared between the legacy and modern PTRAC uses. In addition to verification of the update, which is important to the community as a whole, this effort also supports advances in the simulation of recent subcritical neutron noise measurements that require higher computational effort per second of real-time measurement than that of systems typically measured.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Image fusion for neutron tomography of nuclear fuel

Image fusion, the process of combining different images together, can be useful to create a more complete picture. In this work, image fusion is applied to neutron tomography of nuclear fuel with the goal of enhancing the information obtained about the fuel. Different reconstruction methods, such as Feldkamp, Davis and Kress filtered back projection and Simultaneous Reconstruction Technique, were combined to enhance image quality. This methodology was shown to reduce noise and ring artifacts without sacrificing sharp edges, allowing for a more accurate representation of sample geometry. Technique enhancements and future applications for the neutron imaging community are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Gravitational wave measurement in the mid-band with atom interferometers

Abstract Gravitational Waves (GWs) have been detected in the ∼ 100 Hz and nHz bands, but most of the gravitational spectrum remains unobserved. A variety of detector concepts have been proposed to expand the range of observable frequencies. In this work, we study the capability of GW detectors in the “mid-band”, the ∼ 30 mHz– 10 Hz range between LISA and LIGO, to measure the signals from and constrain the properties of ∼ 1 – 100M ⊙ compact binaries. We focus on atom-interferometer-based detectors. We describe a Fisher matrix code,AIMforGW, which we created to evaluate their capabilities, and present numerical results for two benchmarks: terrestrial km-scale detectors, and satellite-borne detectors in medium Earth orbit. Mid-band GW detectors are particularly well-suited to pinpointing the location of GW sources on the sky. We demonstrate that a satellite-borne detector could achieve sub-degree sky localization for any detectable source with chirp mass ℳ c ≲ 50M ⊙ . We also compare different detector configurations, including different locations of terrestrial detectors and various choices of the orbit of a satellite-borne detector. As we show, a network of only two terrestrial single-baseline detectors or one single-baseline satellite-borne detector would each provide close-to-uniform sky-coverage, with signal-to-noise ratios varying by less than a factor of two across the entire sky. We hope that this work contributes to the efforts of the GW community to assess the merits of different detector proposals.

Astronomy & Astrophysics↗