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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data-driven design of electrolyte additives supporting high-performance 5 V LiNi 0.5 Mn 1.5 O 4 positive electrodes

LiNi 0.5 Mn 1.5 O 4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li + /Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.

batteries↗
DOE OSTI2026

Solvent-mediated contaminant removal from plastic waste using thermodynamic modeling

Plastics recycling is hindered by the compositional complexity of plastic waste, which can include numerous polymer components as well as low concentrations of additives and non-intentionally added substances. These latter small-molecule species, which we collectively refer to as contaminants, can harm human health and will build up in recycled plastic causing environmental and downstream processing challenges if not removed. In this work, we present molecular modeling approaches using the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) to guide the selection of solvents that are capable of removing targeted contaminants from plastic waste. By considering the thermodynamic partitioning of contaminant species between a solvent phase and polymer phase, we identify guidelines for solvent selection to promote either the low-temperature extraction of contaminants from plastic waste or the removal of contaminants as part of a dissolution-based plastics recycling process. We present four case studies to illustrate the application of the computational approach to the removal of brominated flame retardants, phthalates, and selected perfluoroalkyl substances, and compare to both literature and newly collected experimental data to illustrate model prediction accuracy. Furthermore, the case studies highlight the capability of the modeling approach to help design recycling processes that explicitly account for contaminant removal, thereby increasing product purity during dissolution-based recycling or facilitating chemical recycling of contaminant-free plastics.

Zhou, Panzheng [University of, Wisconsin, Madison,↗
DOE OSTI2024

Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses

Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Elemental and Isotopic Yields from T Coronae Borealis: Predictions and Uncertainties

T Coronae Borealis (T CrB) is a symbiotic recurrent nova system expected to undergo its next outburst within the next 2 yr. Recent hydrodynamic simulations have predicted the nucleosynthetic yields for both carbon–oxygen (CO) and oxygen–neon (ONe) white-dwarf models, but without accounting for thermonuclear reaction rate uncertainties. We perform detailed Monte Carlo postprocessing nucleosynthesis calculations based on updated thermonuclear reaction rates and uncertainties from the 2025 evaluation. We quantify the resulting abundance uncertainties and identify the key nuclear reactions that dominate them. Our results show that both the CO and ONe nova models robustly produce characteristic CNO isotopes. More pronounced abundance differences emerge for elements with A ≥ 20. Sulfur is the most robust observational discriminator between the CO and ONe nova models, with a model-to-model difference of a factor of ≈30 and minimal sensitivity to reaction rate uncertainties. Neon, silicon, and phosphorus exhibit even larger abundance differences (factors of ≈150–250), providing strong diagnostic potential. While their predicted yields are subject to larger uncertainties, these remain smaller than the model-to-model differences, allowing these elements to serve as useful, though less precise, tracers of white-dwarf composition. Chlorine, argon, and potassium also differ between models, but the 1σ-abundance ranges for the CO and ONe models overlap, reducing their present usefulness as composition tracers. We find that only nine nuclear reactions dominate the abundance uncertainties of the most diagnostically important isotopes, and their influence is largely independent of the underlying white-dwarf composition. These results provide guidance for future experimental efforts and for interpreting ejecta compositions in the next eruption of T CrB.

Chemical Abundances↗
DOE OSTI2024

CIGS Technology Advancement via Fundamental Modeling of Defect/Impurity Interactions (Final Technical Report)

The primary goals of the proposed work were to provide modeling tools (and the associated insight which comes along with model development) for design and optimization of CuIn x Ga 1-x Se 2 (CIGS) and CdSeTe (CST) solar cell manufacturing processes and to establish the foundation for comprehensive end-to-end predictive modeling tools to enable optimization of thin film photovoltaic technology for performance, cost, yield, and reliability. The initial focus of efforts within this project was to develop coupled process/optical/device models for CIGS PV technology and to work with Siva Power to apply that TCAD (technology computer-aided design) system to improve the efficiency and reduce manufacturing costs for CIGS solar cells. Our approach to that end was to generate an extensive database of DFT calculations and to use those calculations via statistical thermodynamics methods and Monte Carlo simulation to develop and characterize models for the behavior of native defects as well as intentional and unintentional impurities, including the redistribution of the primary components of CIGS films. Increased effort went toward coupling those models for defect behavior and composition evolution to the performance of multicrystalline CIGS solar cells via prediction of doping level and recombination lifetime as function of manufacturing process. In the second budget period, the project pivoted to developing a similar system for the CdSeTe system, focused especially on understanding the role of Se/Te alloy concentration. Execution of the project resulted in the successful development of TCAD systems for both CIGS and CdSeTe thin film PV within the Synopsys Sentaurus framework by utilizing the Alagator interface. In the first budget period of the project, we developed quantitative models for the major components of CIGS PV and implemented them within a framework that couples process, optical, and device simulation. From the insights we have gained, we identified novel opportunities for enhancing CIGS solar cell performance and have laid the groundwork to further optimize the layer structure, composition profile, and thermal cycles for substantially improved efficiency and lower manufacturing costs. For the CIGS system, process changes to achieve greater than 1% absolute enhancement in efficiency were identified, but testing of those approaches was stymied by lack of a domestic CIGS manufacturing partner after the closure of Siva Power as well as Miasole. For CdSeTe, a fully capable TCAD system only became ready to apply near the end of the project period, so substantial opportunities remain to apply those models to enhance the leading thin film PV technology.

14 SOLAR ENERGY↗
DOE OSTI2024

Uncertainty in determining carbon dioxide removal potential of biochar

A quantitative and systematic assessment of uncertainty in life-cycle assessment is critical to informing sustainable development of carbon dioxide removal (CDR) technologies. Biochar is the most commonly sold form of CDR to date and it can be used in applications ranging from concrete to agricultural soil amendments. Previous analyses of biochar rely on modeled or estimated life-cycle data and suggest a cradle-to-gate range of 0.20–1.3 kg CO 2 net removal per kg of biomass feedstock, with the range reported driven by differences in energy consumption, pyrolysis temperature, and feedstock sourcing. Herein, we quantify the distribution of CDR possible for biochar production with a compositional life-cycle inventory model paired with scenario-aware Monte Carlo simulation in a 'best practice' (incorporating lower transportation distances, high pyrolysis temperatures, high energy efficiency, recapture of energy for drying and pyrolysis energy requirements, and co-generation of heat and electricity) and 'poor practice' (higher transportation distances, lower pyrolysis temperatures, low energy efficiency, natural gas for energy requirements, and no energy recovery) scenarios. In the best-practice scenario, cradle-to-gate CDR (which is representative of the upper limit of removal across the entire life cycle) is highly certain, with a median removal of 1.4 kg of CO 2 e/kg biomass and results in net removal across the entire distribution. In contrast, the poor-practice scenario results in median net emissions of 0.090 kg CO 2 e/kg biomass. Whether this scenario emits (66% likelihood) or removes (34% likelihood) carbon dioxide is highly uncertain. The emission intensity of energy inputs to the pyrolysis process and whether the bio-oil co-product is used as a chemical feedstock or combusted are critical factors impacting the net carbon dioxide emissions of biochar production, together responsible for 98% of the difference between the best- and poor-practice scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Discovering tungsten-based composites as plasma facing materials for future high-duty cycle nuclear fusion reactors

Abstract Despite of excellent thermal properties and high sputtering resistance, pure tungsten cannot fully satisfy the requirements for plasma facing materials in future high-duty cycle nuclear fusion reactions due to the coupled extreme environments, including the high thermal loads, plasma exposure, and radiation damage. Here, we demonstrated that tungsten-based composite materials fabricated using spark-plasma sintering (SPS) present promising solutions to these challenges. Through the examination of two model systems, i.e., tungsten-zirconium composite for producing porous tungsten near the surface and dispersoid-strengthened tungsten, we discussed both the strengths and limitations of the SPS-fabricated materials. Our findings point towards the need for future studies aimed at optimizing the SPS process to achieve desired microstructures and effective control of oxygen impurities in the tungsten-based composite materials.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Integrated modeling of RF-induced tungsten erosion at ICRH antenna structures in the WEST tokamak *

This paper introduces STRIPE (Simulated Transport of RF Impurity Production and Emission), an advanced modeling framework developed to analyze material erosion and the global transport of eroded impurities originating from radio-frequency (RF) antenna structures in magnetic confinement fusion devices. STRIPE integrates multiple physics modules: SolEdge3x for scrape-off-layer plasma profiles, COMSOL for 3D RF rectified sheath potentials, RustBCA for erosion yields and surface interactions, and global impurity transport for 3D ion energy-angle distributions and impurity transport. The framework is applied to an ion cyclotron RF-heated L-mode discharge (#57877) in the WEST tokamak, where it predicts a thirty-fold increase in gross tungsten erosion at antenna limiters during the transition from ohmic to ICRH operation. Additionally, under ICRH conditions, a tenfold enhancement in erosion is observed when comparing RF sheath effects to purely thermal sheath conditions. High-charge-state oxygen ions ($\mathrm{O}$ 6+ and above) are identified as the dominant contributors to tungsten sputtering. To validate the model, a synthetic diagnostic tool based on inverse photon efficiency (S/XB coefficients) from the ColRadPy collisional-radiative model enables direct comparison with spectroscopic measurements. Model predictions using a plasma composition of 1% oxygen and 99% deuterium show good agreement with observed W − I (400.9 nm) emission for discharge #57877, supporting the accuracy of the STRIPE framework. This study focuses specifically on gross erosion calculations to demonstrate STRIPE’s capabilities. Future extensions of this work will incorporate net erosion, re-deposition, self-sputtering effects, and whole-device modeling of sputtered tungsten impurity transport. STRIPE is also being applied to other RF-heated linear and toroidal devices, offering valuable insights for antenna design, impurity control, and performance optimization in next-generation fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Modelling the ejection of primary aerosols during the fast pyrolysis of biomass anisotropic particles

A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.

09 BIOMASS FUELS↗
DOE OSTI2024

Unleashing the Potential of Fast Charging Batteries: Leveraging Anion Redox Chemistry in Ni- and Co-Free Cathodes

Designing Li-ion battery cathodes free from critical raw materials such as Co and Ni has a huge technological and societal impact. Though anion redox-based Li-rich oxide cathodes allow designing Co and Ni free cathode compositions, the Li-rich oxides witnessed voltage fade, voltage hysteresis, and irreversible oxygen release despite their high capacity. Conversely, anion redox through highly covalent chalcogenides (S/Se) is emerging due to the improved covalency between metal d and ligand p bands. Here, we investigate the tuning of multi-chalcogen (S/Se) p-band and redox-active metal d-band in a model Li-rich chalcogen composition Li 1.13 Ti 0.57 Fe 0.3 S 2-y Se y (y = 0 - 1) through in-depth electrochemical, X-ray spectroscopy, and DFT-based electronic structure investigations. Introducing the appropriate amount of Se p band character in anion redox sulfides increases interlayer distance and metal - ligand covalency without modifying the original crystal structure, promoting significant electrochemical reversibility through mixed anionic (Se 2- /Se n- , S 2- /S n- , wherein n<2) and cationic (Fe 2+ /Fe 3+ ) redox reactions. Here we show the detailed Fe, S, and Se redox contributions during Li insertion/extraction through X-ray Absorption (XAS) and Hard X-ray Photoemission Spectroscopy (HAXPES) measurements. The orbital tuning approach improves rate capability for more than 10 C charge-discharge rate, exhibiting more than 50% of its original capacity obtained at C/20 rate. The buffer cation in the lattice (Ti 4+ ) remains electrochemically inactive even after significant Se p-band introduction in the sulfide framework. Overall, this work takes advantage of multi-anion redox chemistry to uncover practically demanding fast charging-discharging characteristics in intercalation cathodes. The obtained knowledge of this design can be extended to other oxide and chalcogen cathodes for high performance Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Parameter Study of the Running-In Process for the Generic Pebble Bed Reactor (GPBR200)

The run-in period of a pebble bed reactor is complex and difficult to model given significant heterogeneity in core composition, power, and temperature. While it is understood that the initial composition of the core should eventually result in the same equilibrium core composition, the approach to equilibrium can vary significantly depending on factors such as start-up fuel enrichment and power ramp rate. To explore this, a high-fidelity model of the General Pebble Bed Reactor was used to vary power ramp schemes and start-up core compositions. It was found that both the initial core composition and power ramp rate had a significant impact on the flow rate of pebbles during early time steps, with higher ramp rates and low enrichment resulting in non-physical flow rates. Power ramp rate alone was found to dictated maximum pebble power peaking observed during the run-in process, with higher ramp rates resulting in greater peak pebble powers. Start-up fuel enrichment and power ramp were found to both impact total fuel consumption, although the impact of start-up fuel enrichment was generally secondary to ramp rate.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Fused Deposition Modeling Additive Manufacturing of Carbonized Structures via Waste-Enhanced Filaments

This report details the development, characterization, and use of coal-enhanced composite materials in additive manufacturing applications. High coal loading formulations—containing up to 70 wt.% coal—were successfully extruded and processed using commercially available 3D printers. Extensive experimental testing was conducted to assess the mechanical, thermal, and microstructural properties of the composites. In parallel, multi-scale computational modeling was employed to elucidate atomistic interactions and evaluate the effects of printing-induced defects on structural performance. Large-scale printability trials demonstrated the feasibility of fabricating complex components for tooling and construction applications, including wind turbine blade molds and modular wall sections. Techno-economic analyses demonstrated the cost-effectiveness and scalability of coal-enhanced composites for large-scale additive manufacturing applications such as wind turbine blade tooling.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Overcoming time and complexity limitations in molecular dynamics investigations of equilibrium melting

Abstract A hybrid Monte-Carlo molecular-dynamics method for determining solidus and liquidus compositions in multicomponent systems is presented that overcomes both the time limitations in conventional molecular dynamics that prevent the evolution of distinct solid and liquid compositions via diffusion and the complexity challenge that prevents use of thermodynamic assessment in systems of many components. This hybrid method is validated in the Cu–Ni system against an independent assessment of solidus and liquidus compositions based on the regular solution model. Strategies for efficient mapping of different phase diagrams, based on the thermodynamic parameter T 0 , the temperature at which two phases of the composition X 0 have equal free energies, are presented and then demonstrated for the copper-nickel fully miscible system and the gold–silicon eutectic system. A calculation of the solidus and liquidus sampled during the equilibrium melting of equiatomic CrMnFeCoNi is performed, indicating that this method has potential to be extended to the study of many component alloys.

Au-Si↗
DOE OSTI2025

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations

Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comparison of interlaminar damage modeling strategies for hybrid composite/aluminum laminates subjected to low-velocity impact

Low-velocity impact of hybrid metal-composite structures was investigated experimentally and computationally. Composite laminates consisting of 2D woven glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP) were joined with a 6061-T6 aluminum plate using an epoxy adhesive. Two variations of the structure were studied; one consisting of all plies oriented at 0° and one consisting of all plies oriented at 45°. A drop tower was used to impact structures at a range of energies, including energies above and below the threshold at which the aluminum layer was perforated. Numerical simulations were implemented using Sierra/SM, an in-house transient dynamics finite element code developed at Sandia National Laboratories. A Hosford plasticity model was used to describe the response of the aluminum layer. A newly implemented orthotropic continuum damage mechanics (CDM) constitutive model was used to represent the composite laminate. This 3D-CDM model was compared to a cohesive zone model (2D-CDM/CZM) to investigate efficacy of aluminum perforation energy prediction, delamination prediction, and computational cost. Accuracy of each model was evaluated using the experimental results. Each showed good agreement with the tests for both the force and velocity histories, as well as the observed damage mechanisms. The 2D-CDM/CZM model was marginally more accurate in capturing both the composite and aluminum behavior — this model averaged error percentages of -11.2% and 10.8% for residual velocity and peak force, respectively. Meanwhile, the 3D-CDM model predictions yielded average error percentages of -35.5% (velocity) and 22.6% (force). However, the 3D-CDM model generally resulted in a decreased computational cost; the average run time was 14% shorter than the 2D-CDM/CZM model and 3x as many timesteps per hour were computed using the same computational resources. In conclusion, new experimental data on the impact and perforation resistance of metal-composite laminates is presented in addition to numerical predictions of the impact behavior.

Carbon fiber↗
DOE OSTI2026

Lidar Measurements and High-Resolution Mesoscale Modeling of Coastally Trapped Disturbances off the Coast of California

Coastally Trapped disturbances (CTDs) are shifts in wind direction from the pre-dominant direction to equatorward to poleward for a period of time. These CTDs occur during the warm season off the California coast and impact coastal weather conditions and planned offshore wind plants. This study assesses the characteristics of CTD events as observed by lidar and other offshore buoys, then evaluates the ability of modeling systems to capture the correct characteristics, leveraging model output from the High-Resolution Rapid Refresh (HRRR) operational modeling system and the NOW-23 (National Offshore Wind) model dataset. CTDs were analyzed for October 2020 and May through to October of 2021, identifying 18 unique CTD events, confirmed by a nearby National Data Buoy Center (NDBC) buoy. The HRRR model captured most of these events, but the NOW-23 model output contained only 12 events. Composites of the wind, temperature, and pressure perturbations pre-, during, and post-event demonstrated the diminishment in wind speed, particularly for the alongshore component. Although the NOW-23 model captured the alongshore wind component and pressure perturbations well, the cross-shore wind component and temperature perturbations varied substantially. When the turbulent kinetic energy deviation and wind shear was positive across all levels pre-event, the NOW-23 modeling system was less likely to capture the CTD event. In contrast, the events that were captured by the model tended to have negative wind shear aloft pre-event.

coastal meteorology↗