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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bolide Infrasound Signal Morphology and Yield Estimates: A Case Study of Two Events Detected by a Dense Acoustic Sensor Network

Two bolides (2016 June 2 and 2019 April 4) were detected at multiple regional infrasound stations, with many of the locations receiving multiple detections. Analysis of the received signals was used to estimate the yield, location, and trajectory, as well as the type of shock that produced the received signal. The results from the infrasound analysis were compared with ground-truth information that was collected through other sensing modalities. This multimodal framework offers an expanded perspective on the processes governing bolide shock generation and propagation. The majority of signal features showed reasonable agreement between the infrasound-based interpretation and the other observational modalities, though the yield estimate from the 2019 bolide was significantly lower using the infrasound detections. There was also evidence suggesting that one of the detections was from a cylindrical shock that was initially propagating upward, which is unusual though not impossible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Size-resolved process understanding of stratospheric sulfate aerosol following the Pinatubo eruption

Stratospheric sulfate aerosol produced by volcanic eruptions plays important roles in atmospheric chemistry and the global radiative balance of the atmosphere. The simulation of stratospheric sulfate concentrations and optical properties is highly dependent on the chemistry scheme and microphysical treatment. In this work, we implemented a sophisticated gas-phase chemistry scheme (full chemistry, FC) and a 5-mode version of the Modal Aerosol Module with Prognostic Stratospheric Aerosol (MAM5-PSA) for the interactive treatment of stratospheric sulfate aerosol in the Department of Energy's Energy Exascale Earth System Model version 2 (E3SMv2) model to better simulate the chemistry-aerosol feedback following the Pinatubo eruption, and to compare it against a simulation using simplified chemistry (SC) and the default 4-mode version of the Modal Aerosol Module (MAM4). MAM5-PSA experiments were found to better capture the stratospheric sulfate burden from the eruption of the volcano to the end of 1992 as compared to the High-resolution Infrared Sounder (HIRS) observations, and the formation of sulfate in MAM5-PSA with FC (with an additional OH replenishment reaction) was significantly faster than in MAM4 with FC. Analyses of microphysical processes indicate that more sulfate aerosol mass was generated in total in FC experiments than in SC experiments. MAM5-PSA performs better than MAM4 in simulation of aerosol optical depth (AOD); AOD anomalies from the MAM5-PSA experiment have better agreement with observations. The simulated largest changes in global mean net radiative flux at the top of the atmosphere following the eruption were about −3 W m −2 in MAM5-PSA experiments and roughly −1.5 W m −2 in MAM4 experiments.

AEROSOL↗
DOE OSTI2026

ACDC (Automated Campbell Diagram Code) [SWR-26-042]

This application provides a web-based graphical user interface to generating Campbell Diagrams and visualizing mode shapes for OpenFAST turbine models. Determining the aeroelastic stability and dynamic characteristics of wind turbines is a critical step in turbine design and analysis. Historically, extracting natural frequencies and mode shapes from OpenFAST—the industry-standard whole-turbine simulation code—has been a fragmented and tedious process. It required manual model configuration, command-line linearization execution, and complex post-processing via proprietary scripts to handle rotating-frame dynamics. To address these workflow bottlenecks, we present the Automated Campbell Diagram Code (ACDC), an open-source graphical software tool developed by the National Laboratory of the Rockies (NLR) under the DOE-funded Distributed Wind Aeroelastic Modeling (dWAM) project. ACDC streamlines the end-to-end linearization and stability analysis workflow into a single, intuitive cross-platform application. The software guides users through OpenFAST model configuration, definition of operating points, and the automated execution of steady-state trim and linearization simulations. Under the hood, ACDC automates the complex mathematical post-processing steps required for rotating systems, including Multi-Blade Coordinate (MBC) transformations, eigenanalysis, and advanced modal tracking utilizing the Modal Assurance Criterion (MAC) and spectral clustering. Finally, ACDC processes these results to automatically generate Campbell diagrams and features a robust 3D visualization engine to animate full-system mode shapes. By eliminating the reliance on external post-processing environments and manual data manipulation, ACDC significantly accelerates dynamic analysis and lowers the barrier to entry for wind energy researchers and engineers.

Summerville, Brent [National Laboratory of the Roc↗
DOE OSTI2024

Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images

Machine learning models are known to be vulnerable to adversarial attacks, but prior works have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, these multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions.

Daw, Arka [ORNL] (ORCID:0009000633191271)↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention

In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.

Gupta, Nidhi↗
DOE OSTI2026

SpaceNet 9—Cross-Sensor Alignment of Optical and SAR Imagery

Precise registration of high-resolution synthetic aperture radar (SAR) and optical imagery is necessary for realizing the full potential and benefits of multimodal image analysis. However, two significant challenges presently exist. First, there is a lack of annotated datasets and benchmarks available for high-resolution SAR–optical image registration. Second, an assessment of efficient and reliable image registration methods that can precisely align these modalities is lacking. Here, we present a holistic description of the SpaceNet 9 Challenge and its results. We present a description of the dataset and baseline algorithm along with the results of the challenge, including a description of the winning algorithms. We release the SpaceNet 9 dataset along with open-sourcing the winning algorithms and baseline. The objective of SpaceNet 9 was to compute a dense displacement map that indicates the shift needed to align pixels in an optical image to the pixels in a SAR image. The challenge launched in April 2025 and was active for approximately two months. The top five solutions reduced image alignment error from approximately 34 m to under 13 m for public and private test data, with the best results obtaining a registration error of only 8.5 and 6.7 m on the public testing and private testing dataset, respectively. Usage of pretrained image matching models, robust outlier rejection with RANSAC, and estimating local displacement were common among the top solutions. The results of this challenge provide insight into high-resolution SAR–optical image registration and offer opportunities for future benchmarking in this domain. The baseline algorithm, winning solutions, and datasets are available at https://spacenet.ai/sn9-challenge/.

benchmark datasets↗
DOE OSTI2025

Binder Jet Additive Manufacturing of Silicon Carbide Solar Reactor (CRADA Report)

Achieving high powder packing density is critical in binder jet additive manufacturing (BJAM), as it directly influences the final part density, mechanical properties, and sintering behavior. Multi-modal powder blends, which combine particles of different sizes, have been explored as a strategy to optimize packing efficiency and minimize defects. In this study, bimodal and trimodal powder blends were obtained by mixing silicon carbide powder in three different sizes. These results show that increased powder density is achievable with bimodal powder blends but is reduced in trimodal blends, and it was found that a 13% increase in the powder tap density was achieved using a bimodal blend of powder. The powder size distribution of the bimodal blend was measured at various stages during binder jet additive manufacturing and, no measurable powder separation occurred even after eight prints. Overall, this study shows limited advantage to trimodal powder blends but good promise for trimodal blends for increasing printed density while maintaining reusability in the binder jet process.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Spectro-Microscopic Investigation of the Aerosol Impact on Hydrological Processes

This project investigated physico-chemical characteristics of atmospheric particles collected during DOE ARM field campaigns and how they are involved in the hydrological cycle. Our efforts were focused on multi-modal chemical imaging and molecular characterization of atmospheric particles and snow samples collected during the Surface-Atmosphere Integrated Field Laboratory (SAIL) campaign and particle samples from the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) campaign.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

NREL OpenPATH: An Open-Source, Extensible Platform for Instrumenting Travel Behavior Data

NREL OpenPATH is an open-source, extensible platform that allows communities to instrument their own travel behavior data. The platform consists of a smartphone app, server and analysis pipeline, and enables collection of opt-in, multi-modal, end-to-end travel diaries. It makes the aggregate statistics available via a public dashboard, and allows deployers to download and visualize trip and trajectory data through the admin dashboard. It also allows for customization of the initial demographic survey and the trip-level qualitative information collected. Our goal is to provide an easy-to-use tool that can democratize travel behavior data collection by empower communities of all sizes to recruit participants and obtain a holistic picture of their travel patterns. The platform has been used by close to 40 partners, to collect data from thousands of participants. Upon signing a simple MOU, it is currently available for free to universities, non-profits and public agencies in the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Optimal Stopping Ages for Colorectal Cancer Screening

Importance Prior studies have shown that the benefits, harms, and costs of colorectal cancer (CRC) screening at older ages are associated with a patient’s sex, health, and screening history. However, these studies were hypothetical exercises and not directly informed by data on CRC risk. Objective To identify the optimal stopping ages for CRC screening by sex, comorbidity, and screening history from a cost-effectiveness perspective. Design, Setting, and Participants This economic evaluation first validated the MISCAN-Colon (Microsimulation Screening Analysis–Colon) model against community-based CRC incidence and mortality rates for 2 subcohorts of the PRECISE (Optimizing Colorectal Cancer Screening Precision and Outcomes in Community-Based Populations) cohort. Subsequently, different CRC screening scenarios were simulated in older individuals. Cohorts of US adults aged 76 to 90 years varied by sex and comorbidity status (none, low, moderate, or severe). Statistical and sensitivity analyses were performed from March 2023 to May 2024. Exposures CRC screening histories including fecal immunochemical test (FIT) or colonoscopy, such as a negative colonoscopy result from 10, 15, 20, 25, or 30 years before the index age; 1 to 5 negative FIT results within 5 years of the index age, with different patterns of recency; or a combination of negative colonoscopy and negative FIT results. Main Outcomes and Measures The main outcomes included estimated lifetime clinical outcomes, incremental costs, and quality-adjusted life-years gained (QALYG) associated with 1 additional FIT or colonoscopy. Optimal stopping age for screening, defined as the oldest age for which the incremental cost-effectiveness ratio was still below the willingness-to-pay threshold of $\$$100 000 per QALYG, was evaluated. Results The first of the 2 PRECISE subcohorts used in validating the simulation model included 25 974 adults (15 060 females [58.0%]; 54.7% aged 76 to 80 years) with a negative colonoscopy result 10 years before the index date. The second subcohort consisted of 118 269 adults (67 058 females [56.7%]; 90.5% aged 76 to 80 years) with a negative FIT result 1 year before the index date. Older age, male sex, higher comorbidity levels, and recent CRC screenings were associated with reduced incremental benefit and cost-effectiveness of additional screening. For the reference cohort of 76-year-old females without comorbidities and a negative colonoscopy result 10 years before the index age, 1 additional colonoscopy cost $\$$38 226 per QALYG. For cohorts with otherwise equivalent characteristics, associated costs increased to $\$$1 689 945 per QALYG for females at age 90 years without comorbidities and a negative colonoscopy results 10 years before the index age, $\$$51 604 per QALYG for males at age 76 years without comorbidities and a negative colonoscopy result 10 years before the index age, and $\$$108 480 per QALYG for females at age 76 years with severe comorbidities and a negative colonoscopy result 10 years before the index age and decreased to $\$$16 870 per QALYG for females without comorbidities and a negative colonoscopy result 30 years before the index age. The optimal stopping ages across different cohorts ranged from younger than 76 to 86 years for colonoscopy and younger than 76 to 88 years for FIT. Conclusions and Relevance In this economic evaluation, age, sex, screening history, comorbidity, and future screening modality were associated with the clinical outcomes, cost-effectiveness, and optimal stopping age for CRC screening. These results can inform guideline development and patient-directed informed decision-making.

Harlass, Matthias [Erasmus Erasmus University Medi↗
DOE OSTI2026

Shapeshifting Nanocatalyst for CO2 Conversion

The conversion of CO2 into high-value chemicals through a photoreduction reaction in water is a promising route to reduce the dependence on fossil fuels. Enhancing selectivity toward hydrocarbons or alcohols can be achieved by Ag-Cu alloys. However, the stabilized surface state created by Ag-Cu interactions is still poorly understood. In this work, multi-modal in situ X-ray experiments reveals underlying mechanisms and the evolution of Ag-Cu nanoparticles under CO2 reduction reaction (CO2RR) conditions. Both morphological and chemical changes of Ag and Cu species induced by diffusion mechanics are tracked during nanocatalyst operation. The initial spheroid Ag-Cu nanoparticles are composed of a Cu-rich shell and Ag-rich core. The reduction treatment promotes Ag migration toward the surface. During photocatalytic CO2 reduction reaction, Cu atoms migrate back to the surface, forming Ag-Cu-O species. The study observes the surface oxidation of Cu(0) to Cu+ and the presence of Ag at the sub-surface region. Furthermore, nanoparticles change their shape, decreasing their specific surface area, driven by Cu diffusion during the CO2 photoreduction reaction. The results provide invaluable insights into the dynamic restructuring of the catalyst under reaction conditions and into the active species responsible for CO2 conversion.

CO2 reduction reaction↗
DOE OSTI2026

Decoding THz‐Driven Dynamic Fingerprints of Ferroelectric Nanotwin Networks

Ultrafast polarization dynamics in ferroelectrics are of considerable interest for high-speed tunable dielectrics and electro-optics. Extended domain wall networks formed in ferroelectric twin nanodomains can support collective dynamics in the terahertz regime but require techniques that track polarization and strain evolution driven by ultrafast stimulus. Here, we use multi-modal probing of THz-pulse-driven excitations in PbTiO 3 /SrTiO 3 superlattices by combining X-ray free electron laser measurements that directly tracks lattice changes, with optical second harmonic generation that tracks the electronic potential coupled with the lattice potential. Dynamical phase-field modeling enables fingerprinting of these collective modes as superpositions of domain “breathing” through wall oscillations and polarization “rotations” with still walls. Ultrafast domain wall motion at 0.1–0.5 THz is observed at practical fields of 100 kV/cm with wall velocities of >4000 m/s, approaching typical speed of sound in PbTiO 3 . A unique “charging” mode is discovered that can electrically charge and discharge domain walls on ∼4 ps time scale thus dynamically tuning wall conductivity. Integrated experimental and theoretical fingerprinting of the dynamical landscape presented here enables ultrafast control of ferroics for high-speed microelectronics and optical applications.

THz dynamics↗
DOE OSTI2024

Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

Juhong, Aniwat↗
DOE OSTI2025

Shot‐Noise‐Driven Macroscopic Vibrations and Displacement Transduction in Quantum Tunnel Junctions

Back‐action is an inevitable result of quantum measurement. Although the microscopic impacts of shot‐noise back‐action have been explored, macroscopic evidence is seldom documented, especially in the field of electrical transport. Tunneling shot‐noise has been shown to excite the fundamental flexural mode of the host crystal using a sub‐GHz lumped‐element radio‐frequency quantum point contact (QPC). Here, in this study, this aspect of shot‐noise back‐action is examined at much higher operational frequencies and wider bandwidths by employing a GaAs QPC integrated into a planar superconducting cavity within the circuit‐QED framework, leading to the observation of the excitation of multiple mechanical modes. The device operates in the shot‐noise‐limited regime. Constructed on a piezoelectric platform, there is positive feedback between the electrical and mechanical degrees of freedom within the QPC. Shot‐noise excites piezoelectric vibrational modes; concurrently the resulting polarization charges enhance tunneling and develop peaks in the shot‐noise spectra at the modal frequencies. The excitation of vibrational modes is a macroscopic demonstration of measurement back‐action, and the amplitudes of the noise‐peaks enable us to calibrate the displacement sensitivity of the QPC‐resonator systems, which is in the range $≈ 35 fm/√ Hz$, making it an excellent sensor for ultra‐sensitive and rapid strain/displacement detection.

Kumbhakar, Prasanta [Indian Institute of Science E↗
DOE OSTI2026

Surface Vacancy Engineering Re-Routes First-Cycle Redox for Stabilized Li-Rich Layered Cathodes

We demonstrate that atomic-scale surface disorder can control the first-cycle redox sequence of Li-rich layered oxides, eliminating the detrimental process of oxygen release and lattice collapse that degrades performance. In Li1.14Ni0.32Mn0.54O2 (LNMO), a simple chemical treatment introduces oxygen and transition metal (TM) vacancies confined to the particle surface while preserving the bulk layered framework. Multi-modal synchrotron analyses reveal that these vacancies trigger an early oxygen oxidation below 4.4 V, delay nickel oxidation to higher potential, and suppress the formation of covalent Ni4+─O states. This modified pathway prevents irreversible oxygen release, suppresses manganese dissolution, and maintains metal-oxygen coordination at high voltages. Consequently, the treated cathode delivers higher first-cycle Coulombic efficiency (CE), mitigated voltage fade, and superior capacity retention. By directly linking engineered surface disorder to redox reactions and associated structural transformations, this work establishes a general design principle for durable, high-energy-density cathodes.

Kang, Seongkoo↗
DOE OSTI2025

Tailored Frequency Conversion in Nonlinear Subwavelength Grating Metaslabs

The use of metaslab waveguides consisting of subwavelength gratings (SWGs) is proposed as a highly flexible approach to obtain phase‐matched frequency conversion in nonlinear integrated devices. Control over the phase matching (PM) condition is achieved through modification of the linear modal propagation constants by the SWG. The method is experimentally demonstrated by fabrication of 0.4 mm‐long GaAs metaslabs designed for second harmonic generation of near‐infrared light. A controlled spectral shift of up to 100 nm in the PM condition, relative to a uniform slab is observed. Excellent agreement is found between the experimental results and finite‐difference eigenmode simulations, which predict a tunable frequency conversion bandwidth of ∼300 nm for the design parameters. The different regimes of PM control supported by the structure are discussed. This work highlights the potential of SWGs as building blocks for tailored frequency conversion in nonlinear integrated devices.

Form birefringence↗
DOE OSTI2025

Modeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model

Abstract Background Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter‐reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case. Purpose This study aims to develop a deep learning‐based technique to generate multiple CTV contours for each case, simulating the inter‐reader variability in the clinical practice. Methods We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG‐PET), x‐ray computed tomography (CT), and pre‐contrast T1‐weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi‐modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model‐based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter‐reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation. Results The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state‐of‐the‐art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state‐of‐the‐art models). It also achieved the second‐highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation. Conclusions The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter‐reader variability in CTV delineation.

Dong, Yafei [Yale Biomedical Imaging Institute Yal↗