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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

First measurement in the Gamow window of a reaction for the γ-process in inverse kinematics: 76 Se(α,γ) 80 Kr

The p-nuclei are the few stable nuclei heavier than iron on the neutron-deficient side of the valley of stability that cannot be produced through astrophysical neutron-capture reactions. The limited experimental data on reactions through which the p-nuclei might be produced leaves the origin of their production largely unknown. This work presents the first cross section measurements of the 76 Se(α,γ) 80 Kr reaction. The rate of the time reversed reaction, 80 Kr(γ,α) 76 Se, is one of the most uncertain of possible reactions which can occur at the 80 Kr branching point on the ,-process photo-disintegration pathway. The reaction flow through 80 Kr will directly affect the final abundance of the p nuclide 78 Kr. Experimental cross sections at two astrophysically relevant energies are reported and compared to cross sections calculated using Hauser-Feshbach codes TALYS, NON-SMOKER, and SMARAGD. The success of these first (α,γ) cross section measurements performed in inverse kinematics in the energy region of the γ-process opens the door for future studies of reactions on radioactive γ-process nuclides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

57 Zn β-delayed proton emission establishes the 56 Ni rp-process waiting point bypass

We measured the 57 Zn β-delayed proton (βp) and γemission at the National Superconducting Cyclotron Laboratory. We find a 57 Zn half-life of 43.6 ± 0.2ms, βp branching ratio of (84.7 ±1.4)%, and identify four transitions corresponding to the exotic β-γ-pdecay mode, the second such identification in the fp-shell. The p/γratio was used to correct for isospin mixing while determining the 57 Zn mass via the isobaric multiplet mass equation. Previously, it was uncertain as to whether the rp-process flow could bypass the textbook waiting point 56 Ni for astrophysical conditions relevant to Type-I X-ray bursts. Our results definitively establish the existence of the 56 Ni bypass, with 14-17% of the rp-process flow taking this route.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Probing the limits of statistical neutron capture for the r process: Experimental constraints on 141 Cs nuclear level densities

The r-process abundance peaks, particularly near mass number A ∼ 130, reflect underlying nuclear structure effects such as closed neutron shells, yet modeling the nucleosynthesis in this region remains hindered by uncertain neutron-capture rates. These rates are especially sensitive to nuclear level densities (NLDs) and γ-ray strength functions of neutron-rich nuclei, where experimental data are scarce. We present the first experimental constraint on the NLD of 141 Cs using the β-Oslo method, extending sensitivity to the neutron-rich regime near the N = 82 closed shell. Our data allow for critical calibration of microscopic NLD models and reveal that 141 Cs lies near the limit of statistical model applicability. Using this experimental input, we evaluate radiative neutron-capture rates across neighboring isotones using both Hauser–Feshbach (HF) and High Fidelity Resonance (HFR) models. Our results show order-of-magnitude rate increases for nuclei along the N = 86 line, signaling a transition to resonance-dominated capture in this region. These findings underscore the importance of constraining NLDs to improve r-process reaction network predictions, particularly in environments where the validity of statistical models breaks down.

Nuclear level density↗
DOE OSTI2023

Quantification of the 35 Cl($\textit{n, p}$) reaction channel

The evaluated 35 Cl($\textit{n, p}$) cross sections in the ENDF/B-VIII.0 nuclear data library remain uncertain because of the lack of measured data in the neutron energy range between 100-600 keV. In this energy region, the R-matrix analysis relies heavily on estimated ($\textit{n, p}$) partial widths and level density information. This resulted in significant differences of the evaluated cross sections and predicted reactivity in nuclear reactor applications with respect to previous nuclear data libraries. Here, we present a methodology to quantify the cross sections for the 35 Cl($\textit{n, p}$) reaction channel from the combination of measured total cross sections and model calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Toward reducing uncertainty quantification costs in DEM models of particulate flow: Testing simple, sensitivity-based, forward uncertainty propagation techniques

The performance of two conceptually-simple uncertainty quantification techniques are tested against the rigorous nested-loop sampling technique of Roy and Oberkampf (Comput Methods Appl Mech Eng, 200: 2131–2144, 2011) (herein called full-sampling) using two very small-scale DEM-based models of particulate flow (one gas-solid flow and one granular flow). The first simplified forward uncertainty propagation technique, reduced-sampling, uses a sensitivity analysis to eliminate uncertain inputs that have little impact on the model output prior to nested-loop sampling. The second technique, boundary-sampling, uses a sensitivity analysis to inform the selection of two bounding cases for each key model output. In conclusion, the uncertainties in the model outputs obtained via the reduced- and boundary-sampling methods agree well with those from full-sampling for both the gas-solid and granular flow models while yielding computational savings of 65–75% (reduced sampling) and 94–97% (boundary sampling).

42 ENGINEERING↗
DOE OSTI2020

Glacial fluctuations in tropical Africa during the last glacial termination and implications for tropical climate following the Last Glacial Maximum

The tropics are a dynamic component of the global climate system and exert a profound influence on modern climate variability. Yet the role of the tropics in past climate changes, for example during the last glacial termination, is uncertain. This uncertainty is due in part to the relative paucity of terrestrial temperature records from the tropics, which hinders efforts to understand the mechanisms that influenced deglacial warming and abrupt climate events. Tropical glaciers are sensitive to changes in climate, and mapping and dating past tropical glacial fluctuations provides a valuable record of terrestrial climate changes in the low latitudes. We report a chronology of past glacial extents in the Rwenzori Mountains of Uganda (0.3°N, 29.9°E) based on 51 beryllium-10 ( 10 Be) surface-exposure ages from three separate glacial catchments. Results indicate that Rwenzori glaciers retreated considerably during Heinrich Stadial 1. The rate of net glacial recession slowed between ~15 and 11 ka, although glaciers continued to retreat during this period. Rwenzori glaciers then retreated more rapidly during the early Holocene. The Rwenzori glacial chronology is broadly similar to glacial chronologies reconstructed elsewhere in East Africa and in tropical South America. We suggest that this similarity may reflect coherent, tropics-wide temperature fluctuations during the last deglaciation. We report that the Rwenzori glacial chronology provides crucial information on the global footprint of deglacial warming and abrupt climate events and, thus, the potential mechanisms that influenced tropical climate during the last glacial termination.

58 GEOSCIENCES↗
DOE OSTI2021

Model correction and updating of a stochastic degradation model for failure prognostics of miter gates

Understanding the degradation of the quoin block is vital for failure prognostics in miter gates. Due to the complicated degradation mechanism, degradation models based on simplifications and assumptions cannot accurately describe the damage evolution. It is observed that small errors in a simplified degradation model can lead to a large discrepancy in the remaining useful life estimation attributed to error accumulation over time. Aiming to address this issue in failure prognostics, this paper presents a dynamic model correction framework for a simplified degradation model using strain measurements. In the proposed framework, a polynomial chaos expansion (PCE) model is employed to compensate the missing physics in a simplified stochastic degradation model. Here, a maximum likelihood estimation method is developed to estimate the uncertain parameters of the simplified physics-based degradation model along with the unknown PCE model parameters using strain measurements as the observables. The updated damage degradation model is then applied to failure prognostics of a miter gate. Results of a case study show that the proposed approach can effectively improve the accuracy of failure prognostics in miter gates.

42 ENGINEERING↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗
DOE OSTI2022

Charging needs for electric semi-trailer trucks

Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for heavy-duty battery-electric semi-trailer trucks is nascent, and specific charging requirements remain uncertain. We leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging - mid-shift (fast) and off-shift (slow) - and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., =350 kW) can supply 35% to 77% of total energy demand for local and regional trucks with =300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44% to 57% of energy demand for long-haul trucks with =500-mile range. However, demand shifts from mid-shift to off-shift charging as the range for battery-electric trucks increases and when off-shift charging is widely available. Finally, we observe geographic trends in charging demand, finding that local trucks have greater demand within urban areas, whereas long-haul trucks have more demand along rural interstate corridors. As the range for battery-electric trucks increases, we show that charging demand shifts from rural to urban locations due to observed vehicle dwell tendencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Role of emission controls in reducing the 2050 climate change penalty for PM2.5 in China

Previous studies demonstrated that global warming can lead to deteriorated air quality even when anthropogenic emissions were kept constant, which has been called a climate change penalty on air quality. It is expected that anthropogenic emissions will decrease significantly in the future considering the aggressive emission control actions in China. However, the dependence of climate change penalty on the choice of emission scenario is still uncertain. To fill this gap, we conducted multiple independent model simulations to investigate the response of PM2.5 to future (2050) climate warming (RCP8.5) in China but with different emission scenarios, including the constant 2015 emissions, the 2050 CLE emissions (based on Current Legislation), and the 2050 MTFR emissions (based on Maximum Technically Feasible Reduction). For each set of emissions, we estimate climate change penalty as the difference in PM2.5 between a pair of simulations with either 2015 or 2050 meteorology. Under 2015 emissions, we find a PM2.5 climate change penalty of 1.43 µg m-3 in Eastern China, leading to an additional 35,000 PM2.5-related premature deaths [95% confidence interval (CI), 21,000-40,000] by 2050. However, the PM2.5 climate change penalty weakens to 0.24 µg m-3 with strict anthropogenic emission controls under the 2050 MTFR emissions, which decreases the associated PM2.5-related deaths to 17,000. The smaller MTFR climate change penalty contributes 14% of the total PM2.5 decrease when both emissions and meteorology are changed from 2015 to 2050, and 24% of total health benefits associated with this PM2.5 decrease in Eastern China. This finding suggests that controlling anthropogenic emissions can effectively reduce the climate change penalty on PM2.5 and its associated premature deaths, even though a climate change penalty still occurs even under MTFR. Strengthened controls on anthropogenic emissions is key to attaining air quality targets and protecting human health in the context of future global climate change.

Liu, Song↗
DOE OSTI2021

Identifying schools at high-risk for elevated lead in drinking water using only publicly available data

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then used to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Nexus of electrification and energy efficiency retrofit of commercial buildings at the district scale

Rapid electrification of buildings at the district scale is needed for cities to achieve climate change mitigation goals. However, most electrification studies focus on either the single building level or the city/region building stock level, and depend on the slow and uncertain process of requesting personally identifiable customer energy usage data from utilities. To answer a key question facing local policymakers: “Where can electrification proceed at scale without first upgrading the grid?” this study aims to quantify and inform building electrification impacts at the district scale using detailed building energy modeling and based on public records datasets. We explore how energy efficiency retrofits can help mitigate increased peak electric demand, and quantify impacts to energy use and carbon emissions. Building energy models of a baseline, and scenarios of simple electrification, energy retrofits, and electrification in combination with retrofits were created and simulated for 54 commercial buildings in two contiguous districts of San Francisco. A simple electrification scenario increased annual electricity consumption but reduced annual site energy usage by 15% to 17%, mainly due to replacing inefficient gas furnaces and boilers with more efficient heat pumps. Peak demand increased 7.4% for Fisherman's Wharf (e.g. within the capacity of the existing power grid), while the Design District showed a marginal decrease. Annual carbon emissions were reduced by 46% and 37%. Combining electrification with efficiency upgrades reduced peak demand by 26% and 40%, and annual carbon emissions by 63% and 64% for the two districts. Furthermore, these results indicate that impacts of electrification depend on the mix of building uses within a district, and coupling electrification with energy efficiency upgrades is an effective strategy to decarbonize buildings while maintaining or reducing the peak electric demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Marine energy supported multi-energy system planning and operation optimization for sustainable coastal community

The growing need for sustainable energy solutions in coastal areas necessitates the development of integrated systems that leverage abundant marine resources. In this study, a standalone Marine Energy Supported Multi-Energy System (MRE-MES) is designed for sustainable coastal community development, utilizing renewable marine resources, including offshore wind, wave, and solar energy, to address the energy needs of electricity, heat, freshwater, and hydrogen. The proposed MRE-MES incorporates a co-optimization model that simultaneously balances capacity planning and operational efficiency to minimize costs and environmental impacts. The system is tested under different renewable energy penetration levels and demand uncertainties, using a two-stage stochastic programming to account for variability in renewable resources and consumption needs. The experimental results indicate that in the optimal system capacity configuration, the percentage of total renewable energy generation is around 80 %, with or without capacity limitation constraints on PV, water tank, and hydrogen storage. Compared to the worst-case scenario in Monte Carlo experiments, two-stage stochastic optimization results in a more robust decision that effectively mitigates the risks posed by future uncertain demand conditions. In conclusion, the findings highlight the viability of marine energy for providing a resilient, comprehensive energy solution to coastal communities.

Capacity planning↗
DOE OSTI2023

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework - a discrete event simulation and life cycle assessment framework - is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model's output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

17 WIND ENERGY↗
DOE OSTI2022

Carbon acquisition ecological strategies to connect soil microbial biodiversity and carbon cycling

Soil carbon feedbacks to global change are uncertain, and the biological processes that govern soil organic matter decomposition are not resolved in current ecosystem models. Though it is recognized that microbial biodiversity influences decomposition rates, incorporating this relationship into ecosystem models is challenging because microbial communities are prohibitively diverse. It is likely necessary to distill microbial biodiversity by focusing on functional groups or ecological strategies. The ecological strategies that currently dominate the microbial ecology literature derive from macroecological theory, have clear weaknesses, and have had limited success when applied to predict soil carbon dynamics. Here, we present a new framework for soil microorganisms: Carbon Acquisition Ecological Strategies (CAES), and we outline a path toward incorporating microbial biodiversity into ecosystem models using this framework to enhance predictions of soil carbon feedbacks to global change. Because a microorganism's diet is central to its ecological niche and likely to covary with other ecologically significant traits, we posit that carbon acquisition may serve as a tractable foundation for developing ecological strategies. Further, we describe four candidate ecological strategies for soil microorganisms: 1° decomposers that assimilate complex plant polymers, 2° decomposers that assimilate microbial necromass, passive consumers that assimilate dissolved organic carbon, and predatory microbes that assimilate live microbial biomass. These strategies are directly linked to soil carbon pools currently represented in ecosystem models and may provide a foundation for greater integration of microbial community dynamics into ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Methane flux from transplanted soil monoliths depends on moisture, but not origin

Soils both produce and consume methane (CH 4 ), a potent greenhouse gas that contributes to climate change. In coastal forests, upland soils are shifting from being CH 4 sinks to sources as sea levels rise, increasingly flooding soils with little prior inundation history. Ecosystem CH 4 budgets are highly uncertain due in part to the difficulty in separating fluxes measured at the soil surface into individual production and consumption processes which are likely to have different responses to future environmental conditions. Here, we measured growing season CH 4 fluxes from soil monoliths transplanted four years prior along an inundation and salinity gradient to determine how changes in abiotic conditions control CH 4 flux rates. To parse net fluxes measured at the soil surface into their component gross rates, we paired field measurements with a stable isotope pool dilution incubation of surface soils. Throughout the growing season, net soil surface CH 4 flux was positively correlated with soil moisture (p < 0.01), with lowland-located soils tending towards CH 4 sources (mean 0.349 ± 1.11 mg CH 4 -C m -2 hr -1 , error is standard deviation) and upland-located soils tending towards CH 4 sinks (-0.003 ± 0.003 mg CH 4 -C m -2 hr -1 ). Transplanted soils’ fluxes were statistically identical to their native neighbors once microtopography-driven differences in soil moisture were controlled for. The pool dilution experiment revealed that production and consumption rates were similar in upland and lowland surface soils (2.82 ± 3.29 µmol CH 4 g -1 dry soil d -1 production, 3.47 ± 2.03 µmol CH 4 g -1 dry soil d -1 consumption), indicating the majority of production likely occurs at depth in lowland soils. Both gross and net fluxes from transplanted soils showed no effect of soil origin after four years, suggesting low resistance of CH 4 cycling to global change drivers. Our results indicate the strength of the coastal forest CH 4 sink is likely to decrease in proportion to sea-level rise.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A review on the integration of probabilistic solar forecasting in power systems

As one of the fastest growing renewable energy sources, the integration of solar power poses great challenges to power systems due to its variable and uncertain nature. As an effective approach to promote the integration of solar power in power systems, the value of probabilistic forecasts is being increasingly recognized in the recent decade. While the current use of probabilistic forecasts in power systems is limited, enormous amount of research has been conducted to promote the adoption of probabilistic forecasts and many methods have been proposed. This paper gives a comprehensive review on how probabilistic solar forecasts are utilized in power systems to address the challenges. Potential methods to deal with uncertainties in power systems are summarized, such as probabilistic load flow models, stochastic optimization, robust optimization, and chance constraints. Additionally, specific areas where these methods can be applied are discussed and state-of-the-art studies are summarized.

14 SOLAR ENERGY↗