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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 OSTI2024

CORSAIR: A Framework for Human Mobility Prediction through Visit Characterization and Spatial Behavior Modeling

The rapid advancement of location acquisition technologies has led to the daily collection of vast amounts of mobile trajectory data, facilitating in-depth research on human mobility and enabling more accurate mobility prediction models. However, existing methodologies often fall short in capturing the intricate dynamics of human navigation and spatial behavior. This paper addresses this gap by exploring the multifaceted relationships between individuals and their environments, considering the diverse influences of personal preferences and experiences. Some places hold sentimental value and are visited frequently, while others serve as transient points of passage. To model these differences, we introduce CORSAIR, a novel visit characterization framework that leverages visitation patterns and dwell times to delineate an individual’s relationship with specific places. CORSAIR classifies visits into seven distinct types: casual, occasional, routine, special, anchor, important, and resettling. Also, we show that explicitly recognizing these distinct visit types and incorporating nuanced visit intents into mobility prediction models leads to a substantial improvement in prediction accuracy. This distinction allows for more precise modeling of the individual’s transitions, enhancing the personalization and relevance of location-based services. Our findings suggest that a deeper understanding of the complexities of individual-environment interactions is crucial for developing effective predictive tools in mobility research.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗
DOE OSTI2024

Immersive Visualization for Scientific Data Analysis

We will present the use of immersive visualization at the National Renewable Energy Laboratory (NREL), showcasing how immersive visualization is advancing scientific research and engineering practices and transforming our day-to-day operations. We are leveraging immersive visualization to support scientific discovery and engineering in various domains, including material design, computational fluid dynamics, immersive analytics, grid modernization, digital twins, and situated visualization. We have observed several benefits across four key areas: enhanced spatial judgments, improved understanding through interaction, increased capacity to embed high-dimensional data, and improved collaboration.

immersive analytics↗
DOE OSTI2025

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2025

Data-driven emulation of modal aerosol microphysics via neural operator-based modeling

The complexity and the small characteristic scales of aerosol microphysical processes pose a big challenge for accurate and efficient Earth system simulations at regional and global scales. In this work, we construct and evaluate a surrogate model: the aerosol deep operator network (ADON), a physics-inspired dual-net architecture for emulating the aerosol microphysics parameterization suite in the version 2 of the Energy Earth System Model (E3SMv2). The current version of the surrogate model is trained on a dataset comprising 9.8 million samples obtained from a global E3SMv2 simulation with the horizontal resolution of about one degree under cloud-free conditions. Incorporating domain spatial and temporal coordinates, as well as principle components extracted from training data, the dual-net surrogate model effectively captures the intricate representations of aerosol and the relationship with atmospheric state variables, achieving an R-squared score over $$95.7\%$$ for all the lognormal aerosol modes in the extrapolated regime. The validated model provides feature importance of input variables and their impact on the predictive capacity of the surrogate model in relation to the E3SM. The computational cost of online inference time deployed on CPUs and GPUs with lower precisions highlights ADON’s efficiency and potential in robust predictive modeling for large-scale Earth system computations.

Bai, Zhe↗
DOE OSTI2025

Illuminating the Night: A Survey of Super-Resolution Methods for Nighttime Light Images

Nighttime Light (NTL) images provide critical insights into urbanization, disaster response, and energy consumption. The VIIRS Day/Night Band (DNB) sensor offers high-quality NTL imagery with daily revisit rates, but the available spatial resolution hinders fine-grained accurate analysis. Super-resolution techniques aim to increase the resolution of NTL images, enabling more detailed assessments of infrastructure, light pollution, economic activity, and power outages. However, existing state-of-the-art super-resolution methods designed for natural images struggle with the unique characteristics of NTL data. This work provides a comprehensive review of super-resolution methods across multiple image modalities, evaluates their effectiveness on VI-IRS DNB data, and proposes a multi-modal super-resolution approach tailored to NTL imagery. The proposed approach integrates VIIRS DNB data with road networks and land use information to improve reconstruction accuracy and spatial detail. Code is available for this project athttps://code.ornl.gov/viirs-sr/sr-demos.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Illuminating the Night: A Survey of Super-Resolution Methods for Nighttime Light Images

Nighttime Light (NTL) images provide critical insights into urbanization, disaster response, and energy consumption. The VIIRS Day/Night Band (DNB) sensor offers high-quality NTL imagery with daily revisit rates, but the available spatial resolution hinders fine-grained accurate analysis. Super-resolution techniques aim to increase the resolution of NTL images, enabling more detailed assessments of infrastructure, light pollution, economic activity, and power outages. However, existing state-of-the-art super-resolution methods designed for natural images struggle with the unique characteristics of NTL data. This work provides a comprehensive review of super-resolution methods across multiple image modalities, evaluates their effectiveness on VIIRS DNB data, and proposes a multi-modal super-resolution approach tailored to NTL imagery. The proposed approach integrates VIIRS DNB data with road networks and land use information to improve reconstruction accuracy and spatial detail. Code is available for this project at https://code.ornl.gov/viirs-sr/sr-demos.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Energy-resolved neutron imaging and diffraction including grain orientation mapping using event camera technology

Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane “super-emitters,” while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories. Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

03 NATURAL GAS↗
DOE OSTI2025

Diagnostics: Chapter 8 of the special issue: on the path to tokamak burning plasma operation

This chapter presents the activity conducted by the ITPA topical group (TG) on Diagnostics over about the last 15 years. Following a general introduction of the ITER Diagnostics led by their measurement roles, the document is organized in several subchapters detailing the design support, research and development activity conducted by each of the specialist working groups (WGs) of the TG. Please note that the magnetic diagnostics were supported at the TG without a specific WG. Their status is included in the general introduction. In the following some highlights of the subchapter’s contents are provided. Recent advances in ITER first wall (FW) diagnostics for the measurements of plasma-metallic wall interaction in support of the ITER research plan are reported. An InfraRed imaging Video Bolometer for ITER has been developed and tested on several tokamaks to measure the radiated power loss. A laser-induced breakdown spectroscopy (LIBS) technique which utilizes a pulsed laser beam to ablate locally by forming a crater, will measure local tritium inventory in the FW material. Real-time Residual Gas Analyzers will measure the neutral gas composition in a divertor port and an equatorial port during plasma operation. Due to the full metallic FW environment, the plasma-wall interaction in ITER will face several challenges such as the compromised radiated power and divertor heat flux measurements by reflection. Ray tracing and analysis codes have been developed to eliminate and correct the effects of reflection in the measurements. The characteristics of the reflecting surfaces depending on the roughness and angle of the incidence have been measured by dedicated experiments, and the results were applied to the reflection elimination. For the measurement of the metallic impurity radiation induced by eroded metallic atoms, a vacuum ultraviolet spectrometer has been developed and tested. An extensive thermonuclear diagnostic suite will be required to support the operation of ITER and the planned experimental program for future burning plasma experiments. Due to the harsh environmental conditions, the implementation of diagnostic systems in ITER is a major challenge. These conditions include high levels of neutron and gamma fluxes, neutron heating, particle bombardment. Therefore, the selection and design of diagnostic systems must take into account a number of phenomena previously unseen in diagnostic design. For this reason, the measurement of neutrons and confined or lost fast ions, with particular emphasis on alpha particles, is critical to ITER. The diagnostics associated with these measurements will be important for future plasma-burning experiments at ITER. The high neutron emission and very large plasma size in ITER make neutron diagnostics the main diagnostic method used to measure plasma parameters such as fusion power, fusion power density, ion temperature, energy of fast ions and their spatial distributions in the plasma core. Active spectroscopy techniques are methods where a neutral particle beam is injected into the plasma and information on plasma parameters is extracted from the measurement of line emission resulting from the beam-plasma interaction, either by plasma ions or by beam atoms. Spatial localization is achieved by crossing the beamline and multiple observation lines. The ITER plasma will be a high temperature, moderately dense, fully ionized collisional plasma. The plasma facing surfaces are principally metallic being fashioned from beryllium or tungsten but many other elements, arising from either structural or from operational needs, may enter this plasma. The energy range of the emitted photons range from meV (infra-red) to multi keV (x-rays) and originate from all areas of the plasma volume. The primary role of passive emission diagnostics is to identify what is in the plasma from spectral signatures. Extracting quantitative information from these measurements such as impurity content, ion temperature, rotation, degree of detachment and radiated power depends on calibrated instruments, a physics model of the atomic and molecular processes and plasma transport and an analysis workflow that takes into account environmental effects such as reflections. The particular needs for ITER have prompted a multi-machine, many-year effort to address all these aspects and this chapter reviews the work on diagnostic design, experiments and new analysis techniques. An overview of the laser diagnostics to be implemented on ITER is also provided in this paper. This includes descriptions of the Thomson scattering in the core, edge and divertor regions, polarimetry and interferometry diagnostics used for measuring plasma density and also measurements of helium density in the divertor using Laser Induced Flourescence. Techniques which can allow improvements on current measurements are also addressed in particular expanding poloidal polarimetry measurements to measure field fluctuations and proposed use of dispersion interferometery which has a number of advantages over existing methods. This paper identifies particular areas where further research and testing on existing tokamaks is useful even at this advanced stage to inform the design of diagnostics for ITER. Outstanding areas of concern for the implementation of laser diagnostics, in particular with a view to reliable operation are identified. An overview of the latest developments of microwave diagnostic systems and techniques is given. The primary focus is the contributions for ITER—the next step burning plasma experiment—which is supplemented by describing recent progress of techniques applicable for fusion experiments beyond ITER. The contributions are intentionally kept concise, and are being supplemented by a rich list of references for further studies. Radiation induced effects are receiving continuous and well-deserved attention of the ITER diagnostic community and they are in many cases one of the primary design drivers of the ITER diagnostic systems. The paper summarizes recent progress in this area focusing primarily on the ITER diagnostics but in some cases provides also outlook for the possible solutions for even more demanding radiation environment of fusion reactors beyond ITER. Despite advancements in the area of modeling and simulation of various radiation induced effects, experimental testing in a nuclear environment as close as possible to the target one is still seen as unavoidable for proper qualification of particular diagnostic functional elements. Recent advancement within three diagnostic areas: optical diagnostics, magnetics and bolometers is covered. Encouraging results on qualification of silica glass vacuum window assemblies are presented. In the area of magnetic sensors, progress of irradiation tests performed on ITER in-vessel LTCC inductive sensors is presented with outlook for novel technological approaches to inductive sensors utilizing thick printing and photolithography technologies being highlighted. Summary of advancements in the area of steady state magnetic field sensors based on Hall effect is given. New results of neutron irradiation test of the ITER borosilicate glass inserts for vacuum electrical feedthroughs are summarized finding negligible swelling at target level of neutron fluence. Off-line irradiation tests of fiber optic current sensors for plasma current measurement demonstrated that both for gamma doses up to 5 MGy and a total neutron fluence up to 10 15 cm −2 , radiation induced changes are still compatible with required measurement accuracy on ITER. The ITER bolometers are given as an example how considering radiation effects may influence the diagnostic design. Finally, outlook for future main R&D directions is outlined. All optical and laser-based diagnostics in ITER will be using mirrors to guide plasma radiation toward detectors, cameras and sensors. In the hostile plasma, radiation and particle environment the optical characteristics of diagnostic mirrors will degrade directly affecting the entire performance of involved diagnostic systems. An assessment of factors affecting mirror performance is provided. Among the prime adverse factors are deposition of plasma impurities, sputtering of mirror surface and steam ingress in the vicinity of mirrors. Within the International Tokamak Physics Activity with active support by ITER central team and domestic agencies, the structured research and development (R&D) program on mitigation of risks for diagnostic mirrors is underway. Within this program the mirror material development, the passive mitigation of mirror degradation by using diagnostic ducts and shutters along with an active mirror recovery program comprising the in-situ mirror cleaning and calibration is underway. Recent developments in diagnostic mirror R&D are described in this Chapter along with an example of their implementation of R&D solutions in ITER Infrared Thermography diagnostic. An assessment of still open engineering and physics questions, considerations on mirror risks during an early phase of ITER operation are given along with an overview of diagnostic mirror evolution in the late ITER operation stage toward the demonstration fusion power plant. Several crucial areas of diagnostic R&D outlined in ITER Research Plan are addressed. The basic control groups in a fusion reactor can be broken-down in five categories: (1) plasma position, magnetic configuration, and plasma current control, (2) profile control and confinement optimization, (3) MHD control and suppression, (4) edge dissipation control, radiation and plasma exhaust control and (5) break-down optimization. These categories are coupled via the physics (a control action in one domain will affect the other domains) and via shared actuators (e.g. ECRH for impurity accumulation avoidance, current density distribution control and MHD suppression). Consequently, a supervisory control system should determine the priority of the various control tasks, their couplings, and the interfaces with the safety and interlock system. For the systematic development of the various controllers taking the complexity of the plasma and the control system into account, a model-based approach is required. A short historical overview is given of the developments in systems and control theory and control engineering with special emphasis on those developments that are most relevant for Nuclear Fusion research and operation. An overview is given of the state of the field of fusion plasma control for the control categories. It will be shown how synthetic diagnostics are being developed in ITER and how they are used in diagnostic design and design validation and how they can be in model-based controller synthesis using relatively simple models. In modern control methods, multiple diagnostics are used to constrain relatively simple models. The constrained models provide an estimate for the state. This opens the route to state controllers, such as model predictive control. A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modeling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g. to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modeling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of integrated data analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.

ITER↗
DOE OSTI2025

Spatially Resolved Raman Spectroscopy of Thin Carbon Interphase in SiC Ceramic Matrix Composites

A dedicated analysis method is presented to extract the Raman spectrum of an interphase layer thinner than the laser spot size. We focused on spatial correlations between the contrast of optical micrographs and Raman hyperspectral data to predict the constituents of the mixed spectra measured near the interphase. By employing a mapping step size of 0.1 μm, the Raman spectrum of approximately 0.3-μm-thick carbon interphase in a SiC fiber-reinforced SiC matrix composite was extracted from data acquired with a theoretical spot size of about 0.7 μm. Notably, conventional chemometrics procedures were unable to isolate the interphase signal, instead producing a spectrum representing a mixture of interphase and matrix. This study used another composite with approximately 0.9-μm-thick interphase to validate the analysis method, enabling direct measurement of the interphase spectrum. The proposed Raman analysis method has advantages in specimen volume and turnaround time compared to traditional characterization methods, such as transmission electron microscopy. In conclusion, this study also evaluates the applicability of the analysis method to different composite materials and identifies key requirements of the measurements, including the ratio of interphase thickness to spot size and the homogeneity of the surrounding matrix.

ceramic matrix composite↗
DOE OSTI2025

Statistical evaluation of microscale stress conditions leading to void nucleation in the weak shock regime

Here, we investigate the heterogeneity of the stress state driven by anisotropic deformation response at the single crystal level through five statistical volume element (SVE) calculations of polycrystalline BCC tantalum. This work focuses on grain boundaries as a prominent material defect type prone to void nucleation based upon experimental observations of predominantly intergranular void nucleation in this material. The SVEs are constructed to be statistically representative of larger volumes of material and are meshed such that mean and standard deviation of grain size and orientation information is reconstructed. The computational meshes feature hexahedral (brick) elements and smooth conformal grain boundaries where significant stress concentration is known to occur, a tail effect of interest in the extreme events process of dynamic ductile damage. An existing micromechanical crystallographic plasticity model shown to capture the single crystal behavior of BCC tantalum well is used to perform the polycrystal calculations. The model includes representation of the non-Schmid effect of non-planar screw dislocation kinetics in tantalum. A three-dimensional stress state time profile predicted by damage modeling of a flyer plate impact experiment is applied as boundary conditions to each SVE. Resulting grain boundary stress state statistics are strongly non-Gaussian. Significant structural evolution is observed within the compressive hold before unloading into tension in the stress profile. Strong angular dependence of grain boundary traction magnitude with shock direction is observed. Non-Schmid effects continue to suggest their influence on propensity of microstructural defect types to nucleate voids. A general void nucleation criterion is proposed using probability theory. The general framework is specified to polycrystalline BCC tantalum in the weak shock regime to include the SVE calculations and literature molecular dynamics calculations of grain boundary void nucleation strength. Probability density functions (PDFs) are used to describe the interaction between the local stress state heterogeneity and the distributed grain boundary void nucleation strength state. A causation entropy maximization procedure removes the requirement for ad hoc selection of a PDF functional form and provides a rigorous procedure for data-based PDF determination. The resulting physically informed PDF describes the spatial appearance frequency of nucleated voids as a function of applied macroscale pressure. Lower length scale physics are thus packaged in a precise and computationally efficient way to provide computational plasticity insight to macroscale dynamic ductile damage models.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FTIR imaging identifies alterations in lung tissue structure and biochemical composition in human idiopathic pulmonary fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and fatal lung disease characterized by damage to the epithelial tissue and a reduced ability of the alveoli to repair themselves. This impaired repair process leads to abnormal accumulation of extracellular matrix (ECM), resulting in scarring and stiffening of lung tissue. Fourier transform infrared imaging (FTIRI) is a promising technique for imaging the biochemical changes related to fibrotic changes in a label-free and non-destructive manner, which can be analyzed to mark the progression of IPF. In this study, FTIRI was used to image human lung tissue biopsies with IPF and control biopsies without disease. In-depth spectral analyses were performed to observe the biochemical changes in the tissue composition using FTIRI. The parameters that were analyzed included collagen structure, total lipid content, lipid chain length, and phospholipids. Results showed a significant increase in lipid content in IPF compared to control, where long chain lipids dominated and phospholipids were reduced. Minor changes in collagen structure were also observed in IPF, likely attributed to the excess formation of extracellular matrix in the disease. These findings indicate that FTIRI has the potential to be a promising diagnostic technique to understand the molecular changes during IPF, as analysis of infrared data can reveal detailed biochemical information regarding disease progression and provide spatial insights on the molecular changes across the IPF lung tissue.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Analysis of static Wilson line correlators from lattice QCD at finite temperature with T -matrix approach

The thermodynamic T-matrix approach is used to study Wilson line correlators (WLCs) for a static quark-antiquark pair in the quark-gluon plasma (QGP). Selfconsistent results that incorporate constraints from the QGP equation of state can approximately reproduce WLCs computed in 2+1-flavor lattice-QCD (lQCD), provided the input potential exhibits less screening than in previous studies. Utilizing the updated potential to calculate pertinent heavylight T-matrices we evaluate thermal relaxation rates of heavy quarks in the QGP. We find a more pronounced temperature dependence for low-momentum quarks than in our previous results (with larger screening), which turns into a weaker temperature dependence of the (temperature-scaled) spatial diffusion coefficient, in fair agreement with the most recent lQCD data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Tropical Cyclone Super Resolution using conditional diffusion denoising probabilistic model from mesoscale simulation to LES

Accurate modeling of tropical cyclone wind fields is essential for the design, risk assessment, and operational planning of offshore energy infrastructure. While mesoscale simulations are widely used thanks to their computational efficiency, they lack the necessary resolution to capture key features such as wind shear and veer profiles as well as the distribution turbulent kinetic energy (TKE). High-fidelity large-eddy simulation (LES) models on the other hand, can resolve turbulent structures and provide a more accurate representation of the complex wind field, albeit at a higher computational cost. To address this modeling gap, we introduce a two-part generative framework to enhance the resolution and physics-capturing ability of mesoscale simulations. First, a reduced-order model based on Karhunen–Loève (KL) decomposition is used to extract dominant spatial modes from one-dimensional mean wind profiles. A multilayer perceptron (MLP) is trained to map mesoscale mode weights to their LES counterparts, enabling accurate reconstruction of vertical velocity profiles. Second, a conditional Diffusion Denoising Probabilistic Model (DDPM) is developed to super-resolve coarse and low-fidelity mesoscale velocity fields, recovering fine-scale turbulence structures and stress distributions. The framework is evaluated across different tropical cyclone intensity categories defined by the Saffir–Simpson scale and demonstrates strong performance in both interpolation and extrapolation tasks. The generated fields accurately reproduce spatial coherence, stress distributions, and spectral energy characteristics observed in LES data. By bridging the fidelity gap between mesoscale and LES outputs, this approach offers a scalable, data-driven solution for enhancing the representation of tropical cyclone wind fields, enabling more robust offshore energy infrastructure systems design in tropical-cyclone-prone areas.

17 WIND ENERGY↗
DOE OSTI2024

ORION Code Infrastructure Upgrades

Prior to this update, ORION was capable of processing cases and data that spanned a single UTM zone and assumed that all spatial inputs were given in units of meters. During the 2024 SMART bridge funding period, we completed upgrades to the code that permit general spatial projections, basin-scale datasets, and units.

58 GEOSCIENCES↗
DOE OSTI2024

Offshore Geologic Carbon Storage Data Collection and International Project Inventory

We present an interactive data collection to aggregate, understand, and disseminate the data that are publicly available to support offshore GCS which can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments. The Offshore Geologic Carbon Storage Data Collection is an Experience Builder web application of multiple web mapping applications, aggregated into a single tool for each data type for access, visualization, and exploration. We also present a spatial inventory of global offshore GCS efforts to visualize the scale and locations of actualized and potential offshore GCS. It includes project location, project type and stage, CO2 storage resource potential, injection rate, reservoir and seal geology, and key literature references. Quantitative and qualitative comparisons of the distribution and magnitude of projects by their attributes lends spatial insight into the status of global GCS operations and storage resource potential, thereby enabling comparative assessments and cross-cutting knowledge transfer for projects in development. These datasets illuminate trends in ongoing offshore projects and can be leveraged by stakeholders to estimate storage resources, identify subsurface analogs, review regulations, and address challenges to offshore GCS. Additionally, opportunities for concurrent decarbonization strategies can be identified.

Mulhern, Julia↗
DOE OSTI2024

International Offshore Geologic Carbon Storage Project Inventory and Data Collection

We present an interactive data collection to aggregate, understand, and disseminate the data that are publicly available to support offshore GCS which can be leveraged by stakeholders to understand where GCS may be viable offshore, create GCS project analogs, and address challenges to GCS in offshore environments. The Offshore Geologic Carbon Storage Data Collection is an Experience Builder web application of multiple web mapping applications, aggregated into a single tool for each data type for access, visualization, and exploration. We also present a spatial inventory of global offshore GCS efforts to visualize the scale and locations of actualized and potential offshore GCS. It includes project location, project type and stage, CO2 storage resource potential, injection rate, reservoir and seal geology, and key literature references. Quantitative and qualitative comparisons of the distribution and magnitude of projects by their attributes lends spatial insight into the status of global GCS operations and storage resource potential, thereby enabling comparative assessments and cross-cutting knowledge transfer for projects in development. These datasets illuminate trends in ongoing offshore projects and can be leveraged by stakeholders to estimate storage resources, identify subsurface analogs, review regulations, and address challenges to offshore GCS. Additionally, opportunities for concurrent decarbonization strategies can be identified.

Mulhern, Julia↗