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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Enabling fast charging of lithium-ion batteries through secondary- /dual- pore network: Part I - Analytical diffusion model

Battery performance is strongly correlated with electrode microstructural properties. Enabling fast charging of lithium-ion batteries requires improved through-plane ionic diffusion that can be achieved through, among other strategies, structured electrodes with a secondary- or dual-pore network (SPN). In this work, an analytical model investigates the impact of such an SPN on ionic diffusion with a composite electrode, considering various pore-channel geometries and comparing to standard electrodes with identical gravimetric- and volumetric-specific theoretical capacities. Relevant SPN design parameters and tortuosity coefficients are identified according to three optimization objectives that aim to balance the improved overall through-plane diffusion, thanks to the coarse aligned channels, and degraded in-plane diffusion because of the porous matrix densification required to maintain gravimetric- and volumetric-specific theoretical capacities. The model indicates that a relatively low amount of SPN is required and that electrodes with high through-plane tortuosity and low in-plane tortuosity benefit most from such architecture.

25 ENERGY STORAGE↗
DOE OSTI2024

At Risk Population Estimates for Belarus, Poland and Slovakia with Machine Learning

High-resolution gridded population modeling is crucial for various applications, including disaster response planning, infectious disease spread modeling, climate change impact estimation, policy development, and more. Multiple gridded population datasets have been developed, each tailored to meet specific objectives. Among them, LandScan Global dataset is designed to represent ambient and unwarned population distributions. However, this dataset relies on a statistical approach that requires manual adjustments, making it time consuming and labour intensive. Existing machine learning (ML) methods often train and test at different spatial resolutions, potentially leading to inflated results, and they rely on Census population totals for disaggregation. To address these limitations, in this study we developed population estimates using ML models trained and tested at a consistent 30 arc-second resolution (≈1 square kilometer), specifically using Random Forest (RF) and XGBoost. These models were trained on 2020 datum to predict for 2021 for three countries: Belarus, Poland, and Slovakia. Our findings show that both RF (MAE varies from 5.75 to 13.25) and XGBoost (MAE varies from 8.15 to 23.44) model performance is close to LandScan Global estimates. Furthermore, neither of the models performed the best across all grid cells: the RF model was more effective in areas with lower populations, while XGBoost excelled in more densely populated regions. The proposed approach can be used for countries where the Census data is not available.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2022

Identifying mitigation strategies for $\mathrm{COVID}$-$\mathrm{19}$ superspreading on flights using models that account for passenger movement

Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. We modeled infection risk from seating and fine-grained movement patterns – boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. Here our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95–100%, while cloth masks would have reduced it by only 40–80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Integrated Security-Safety Consequence Study of a Heat Pipe Reactor

This report presents a preliminary demonstration of integrated security/safety consequence modeling to support potential physical security exemption justifications under the new Title 10 Code of Federal Regulations Part 53 licensing pathway, which allows exemptions if sabotage-induced radiological consequences do not exceed 25 rem at the site boundary two hours after release without mitigative actions. A hypothetical heat pipe microreactor subjected to direct sabotage by a 150 lbs. TNT-equivalent high-explosive device was analyzed using a simplified radionuclide release estimate based on DOE-HDBK-3010-94, implemented in the MELCOR severe accident analysis code to model release transport and attenuation through reactor/building compartments, with MELCOR outputs coupled to MACCS to compute offsite doses and the distance-to-threshold metric D 25rem . Six scenarios were evaluated to examine sensitivities to release pathway/building configuration and radionuclide form (vapor versus aerosols with different size distributions). Results show a positive correlation between leak path factor and D 25rem and indicate that reactor building compartmentalization can substantially reduce consequences (a single-story configuration reduced D 25rem by roughly an order of magnitude relative to a cavity-only configuration), while vapor versus aerosol assumptions had limited impact for the modeled two-story case on the spatial grid used.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

Abstract This work presents the PORTALS framework (Rodriguez-Fernandez et al 2022 Nucl. Fusion 62 076036), which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO (Candy et al 2016 J. Comput. Phys. 324 73–93). This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.

Physics↗
DOE OSTI2024

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Pike, Christopher (ORCID:0000000155888033)↗
DOE OSTI2024

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Chemical freeze-out parameters of net-kaons in heavy-ion collisions

In this work, we study chemical freeze-out parameters for heavy-ion collisions by performing two different thermal analyses. We analyze results from thermal fits for particle yields, as well as, net-charge fluctuations in order to characterize the chemical freeze-out. The Hadron Resonance Gas (HRG) model is employed for both methods. By separating the light hadrons from the strange hadrons in thermal fits, we study the proposed flavor hierarchy. For the net-charge fluctuations, we calculate the mean-over-variance ratio of the net-kaon fluctuations in the HRG model at the five highest energies of the RHIC Beam Energy Scan (BES) for different particle data lists. We compare these results with recent experimental data from the STAR collaboration in order to extract sets of chemical freeze-out parameters for each list. We focused on particle lists which differ largely in the number of resonant states. By doing so, our analysis determines the effect of the amount of resonances included in the HRG model on the freeze-out conditions. Our findings have potential impact on various other models in the field of relativistic heavy ion collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Implications of a weakening N = 126 shell closure away from stability for r -process astrophysical conditions

The formation of the third r-process abundance peak near A ∼ 195 is highly sensitive to both nuclear structure far from stability and the astrophysical conditions that produce the heaviest elements. In particular, the N = 126 shell closure plays a crucial role in shaping this peak. Experimental data hints that the shell weakens as proton number departs from Z = 82, a trend largely missed by global mass models. To investigate its impact on r-process nucleosynthesis, we employ both standard global models with strong closures and modified Duflo-Zuker (DZ) models that reproduce the weakening, combined with three sets of β − -decay rates. Strong shell closures generate sharply peaked abundances, whereas weakened closures consistent with the experimental trend produce broader, flatter patterns. Accurately reproducing the solar third peak under weakened shell strength requires sufficiently neutron-rich conditions that significant fission occurs, and slower decay rates. These results demonstrate that a weakening N = 126 shell closure away from stability imposes significant constraints on the astrophysical environments of the r-process and underscores the need for precise mass measurements and improved characterization of β − -decay properties in this region.

N = 126 closed shells↗
DOE OSTI2026

Quantitative stable isotope probing (qSIP)-informed metagenomics identifies viruses infecting chemoautotrophs

Aquatic environments absorb ~2.5 gigatonnes of atmospheric carbon each year1, more than the carbon stored in the atmosphere, soils, and all biomass combined. Primary producers transform this dissolved inorganic carbon into biomass that can subsequently flow into other trophic levels, or be released back into the environment through viral lysis. While there is substantial knowledge about the diversity and activity of viruses infecting photoautotrophic primary producers and the ecosystem impact, little is known about viruses infecting chemoautotrophs, representing a gap in our understanding of key processes driving microbial carbon cycling. Here, we combine metagenomics with quantitative 12/13C stable isotopic probing (qSIP) mesocosm experiments in a marine-derived meromictic pond to quantify population-specific isotopic enrichment, identify key chemoautotrophic primary producers, and virus-host dynamics. Isotopically enriched carbon is tracked from the genomes of chemoautotrophs to putative viruses, showing that active populations of hydrogen/sulfur-oxidizing chemoautotrophs (Thiomicrorhabdus, Hydrogenovibrio, Sulfurimonas, Sulfurovum) are targeted by viruses. This work provides the foundation for revealing the diversity and activity of viruses infecting globally-widespread chemoautotrophs. Our study sheds light on trophic interactions that impact microbial carbon cycling in aphotic environments and builds toward biogeochemical models that incorporate viral impacts on chemoautotrophic microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development of Explainable Data-Driven Turbulence Models with Application to Liquid Fuel Nuclear Reactors

Liquid fuel nuclear reactors offer innovative possibilities in terms of nuclear reactor designs and passive safety systems. Molten Salts Reactors (MSRs) with a fast spectrum are a particular type of these reactors using liquid fuel. MSFRs often involve large open cavities in their core in which the liquid fuel circulates at a high speed to transport the heat generated by the nuclear reactions into the heat exchangers. This high-speed flow yields a turbulent field with large Reynolds numbers in the reactor core. Since the nuclear power, the neutron precursor’s transport and the thermal exchanges are strongly coupled in the MSFR’s core cavity, having accurate turbulent models for the liquid fuel flow is necessary to avoid introducing significant errors in the numerical simulations of these reactors. Nonetheless, high-accuracy simulations of the turbulent flow field in the reactor cavity of these reactors are usually prohibitively expensive in terms of computational resources, especially when performing multiphysics numerical calculations. Therefore, in this work, we propose a novel method using a modified genetic algorithm to optimize the calculation of the Reynolds Shear Stress Tensor (RST) used for turbulence modeling. The proposed optimization methodology is particularly suitable for advanced liquid fuel reactors such as the MSFRs since it allows the development of high-accuracy but still low-computational-cost turbulence models for the liquid fuel. We demonstrate the applicability of this approach by developing high accuracy Reynolds-Averaged Navier–Stokes (RANS) models (averaged flow error less than 5%) for a low and a large aspect ratio in a Backward-Facing Step (BFS) section particularly challenging for RANS models. The newly developed turbulence models better capture the flow field after the boundary layer tipping, over the extent of the recirculation bubble, and near the boundary layer reattachment region in both BFS configurations. The main reason for these improvements is that the developed models better capture the flow field turbulent anisotropy in the bulk region of the BFS. Then, we illustrate the interest in using this turbulence modeling approach for the case of an MSFR by quantifying the impact of the turbulence modeling on the reactor key parameters.

42 ENGINEERING↗
DOE OSTI2024

Implications of pond reliability on the techno-economic and life cycle environmental impacts of algal biofuels

Despite extensive research on algal bioproducts, there is limited understanding of how pond contamination affects their economics and environmental impacts. This work compared the costs and environmental impacts of algal biofuels across different pond failure scenarios. Pond failure was simulated by a reliability model based on pond mean-time-to-failure (MTTF). The reliability model was integrated with a process model to analyze the impacts of pond failure on the operations of algal farms and biorefineries. Process model outputs were used for techno-economic analysis and life cycle assessment to determine the minimum fuel selling price (MFSP), global warming potential (GWP), and freshwater consumption impacts of algal biofuels for five MTTF scenarios of 20, 54, 80,120, and 350 days, assuming an average mean-time-to-reset of 7 days. Results show that higher MTTFs reduce the cost and environmental impact of algal biofuels, but with diminishing returns. The average MFSPs for the 20-day, 54-day, and 350-day MTTF scenarios were $\$3.52$, $\$2.54$, and $\$2.10$ per liter of gasoline equivalent, respectively. The GWP for the same scenarios were 131, 96, and 83 g CO 2eq MJ –1 , respectively. This study highlights the significant impact of larger seed trains, required under low MTTFs, on the costs and greenhouse gas emissions of algal biofuels. Moreover, the work shows that algal biofuels fail to be cost-competitive with conventional fuels, even when productivities are increased from 17 to 35 g m –2 d –1 . Furthermore, this work is the first to explore the implications of pond failure on the sustainability of algal biofuels and provides valuable insights to algae farmers on how to reduce the costs and financial risks of algal cultivation through process design and pond management strategies.

09 BIOMASS FUELS↗
DOE OSTI2022

Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach

This study implements a data-intensive supervised ML approach through a quasi-experimental framework with the objective of quantifying the impact of oil and gas operator-specific proprietary data on ML-based predictive model performance relative to using oil and gas datasets that may be more commonly publicly available. The models are designed to jointly predict daily oil, gas, and water production for horizontal wells as a function of bottom-hole pressure drawdown, spatial placement across the study domain, and well completion attributes. Model performance is quantified on holdout test data to evaluate how each dataset affects resulting model variant performance.

02 PETROLEUM↗
DOE OSTI2024

Advancing earth system model calibration: a diffusion-based method

Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Liu, Yanfang↗
DOE OSTI2021

Systems Analysis Approach to Polyethylene Terephthalate and Olefin Plastics Supply Chains in the Circular Economy: A Review of Data Sets and Models

The environmental and economic impacts of implementing a circular economy in plastic waste supply chains are not well understood. The proposed systems analysis framework assesses environmental, social, and economic impacts of plastic waste supply chains in a circular economy. The first objective of this article is to identify datasets, models and knowledge gaps associated with waste plastic supply chain processes, mainly in the U.S. Our literature review indicated that the best datasets exist for virgin plastic resin production, mechanical recycling, landfilling, and incineration, with materials recovery facility being intermediate, and with chemical recycling the lowest. The second objective of this article is to develop an illustrative application of the framework by conducting a preliminary systems analysis of PET bottles with closed-loop recycling. Here, the preliminary systems analysis of PET bottles utilized a linear programming optimization method. Our optimization model indicated that both chemical and mechanical recycling processes are needed to achieve a true circular economy of PET bottles with the least greenhouse gas emissions, specifically reductions of 24% when compared with the linear economy. Good quality and standardized life cycle assessment and techno-economic analysis studies are needed to better understand the environmental, economic, and social impacts of advanced sorting and chemical recycling technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development.

16S rRNA↗