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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying policy gaps for achieving the net-zero GHG emissions target in the U.S. light-duty vehicle market through electrification

The U.S. light-duty vehicle (LDV) industry, a major greenhouse gas (GHG) emitting sector, is embracing decarbonization. Considering only electrification pathways, this study uses publicly-available tools – MA3T and VISION on vehicle market penetration, fleet accounting and life-cycle analysis to quantify the policy gaps for LDVs to achieve the net-zero GHG emissions target in nine vehicle penetration cases under two electricity mix scenarios, including the U.S. administration's decarbonization strategy – 100% clean electricity by 2035. The MA3T model is a multinomial discrete choice model for market share projection by vehicle technology, and the VISION is a vehicle stocks and GHG emissions projection model by using vehicle and travel characteristics. This study projects the impacts of technology and policy enforcement on shaping the dynamics and decarbonization of the LDV market. Additionally, achieving the expected improvement of battery technology and charging infrastructure is critical but can only reduce the 2050 GHG emissions to 48–54% of the 2020 level under the electricity renewable mix scenario. It is almost impossible to achieve a 100% battery electric vehicle stock by 2050 and the 2050 net-zero target in the LDV industry unless ban of internal combustion engine technology is implemented starting in 2035 and under the 2035 100% clean electricity scenario. These extreme conditions also sacrifice most from the consumer welfare perspective. A greater policy forcing intensity accelerates plug-in electric vehicle penetration, while with declining marginal effect and reduced consumer welfare. Among the investigated policy scenarios, the policy forcing intensity equivalent to a fuel tax of $1–2 per gasoline gallon reduces the most GHG emissions while keeping a positive consumer welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Analytical error bars and RSD for neutron multiplicity counting

This article derives the standard deviation of the moments of neutron multiplicity distributions for both random and (shift register) triggered counting gates by propagating the multinomial distribution of errors in the counting distribution. Exact and approximate analytical formulas for the singles, doubles, and triples error bars are given along with the corresponding RSD (relative standard deviation). These analytical formulas provide a simple way to analyze and predict error bars without needing empirical data or simulations. The comparison of these analytical error bars to actual measurement errors can reveal systematic errors that are not being accounted for in the modeling of the data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2015

Allelic variation contributes to bacterial host specificity

Understanding the molecular parameters that regulate cross-species transmission and host adaptation of potential pathogens is crucial to control emerging infectious disease. Although microbial pathotype diversity is conventionally associated with gene gain or loss, the role of pathoadaptive nonsynonymous single-nucleotide polymorphisms (nsSNPs) has not been systematically evaluated. Here, our genome-wide analysis of core genes within Salmonella enterica serovar Typhimurium genomes reveals a high degree of allelic variation in surface-exposed molecules, including adhesins that promote host colonization. Subsequent multinomial logistic regression, MultiPhen and Random Forest analyses of known/suspected adhesins from 580 independent Typhimurium isolates identifies distinct host-specific nsSNP signatures. Moreover, population and functional analyses of host-associated nsSNPs for FimH, the type 1 fimbrial adhesin, highlights the role of key allelic residues in host-specific adherence in vitro. In conclusion, together, our data provide the first concrete evidence that functional differences between allelic variants of bacterial proteins likely contribute to pathoadaption to diverse hosts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Science & Technology - Other Topics↗
DOE OSTI2023

Microbial community dynamics and cycling of plutonium and iron in a seasonally stratified and radiologically contaminated pond

Plutonium (Pu) cycling and mobility in the environment can be impacted by the iron cycle and microbial community dynamics. We investigated the spatial and temporal changes of the microbiome in an iron (Fe)-rich, plutonium-contaminated, monomictic reservoir (Pond B, Savannah River Site, South Carolina, USA). The microbial community composition varied with depth during seasonal thermal stratification and was strongly correlated with redox. During stratification, Fe(II) oxidizers (e.g., Ferrovum, Rhodoferax, Chlorobium) were most abundant in the hypoxic/anoxic zones, while Fe(III) reducers (e.g., Geothrix, Geobacter) dominated the deep, anoxic zone. Sulfate reducers and methanogens were present in the anoxic layer, likely contributing to iron and plutonium cycling. Multinomial regression of predicted functions/pathways identified metabolisms highly associated with stratification (within the top 5%), including iron reduction, methanogenesis, C1 compound utilization, fermentation, and aromatic compound degradation. Two sediment cores collected at the Inlet and Outlet of the pond were dominated by putative fermenters and organic matter (OM) degraders. Overall, microbiome analyses revealed the potential for three microbial impacts on the plutonium and iron biogeochemical cycles: (1) plutonium bioaccumulation throughout the water column, (2) Pu–Fe-OM-aggregate formation by Fe(II) oxidizers under microaerophilic/aerobic conditions, and (3) Pu–Fe-OM-aggregate or sediment reductive dissolution and organic matter degradation in the deep, anoxic waters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks

This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Prevalence of Self-Reported Bothersome Tinnitus in U.S. Army Soldiers From January 1, 2015, Through September 30, 2019

Purpose: Tinnitus is a symptom of auditory dysfunction or injury and can be a precursor to permanent hearing loss. Tinnitus can interfere with communication, sleep, concentration, and mood; when this occurs, it is referred to as bothersome tinnitus. Annual hearing surveillance in the U.S. Army includes screening for bothersome tinnitus. Estimating the prevalence of self-reported bothersome tinnitus can facilitate prioritization of prevention and education efforts. The objective of this study was to examine Army hearing conservation data to estimate the prevalence of self-reported bothersome tinnitus as a function of age, hearing, sex, service component, and pay grade. Method: The study employed a cross-sectional, retrospective design. Records from 1,485,059 U.S. Army Soldiers retrieved from the Defense Occupational and Environmental Health Readiness System–Hearing Conservation were analyzed. Descriptive statistics and multinomial logistic regression analysis were used to estimate the prevalence of bothersome tinnitus and its associations with Soldiers' demographic characteristics. Results: The estimated prevalence of self-reported bothersome tinnitus was 17.1% between January 1, 2015, and September 30, 2019; 13.6% of the Soldiers reported “bothered a little” and 3.5% reported “bothered a lot.” Proportionally, the prevalence of self-reported bothersome tinnitus was higher for males, older Soldiers, and Reserve Component Soldiers. For every 1-year increase in age, the odds of self-reporting “bothered a little” tinnitus relative to “bothered not at all tinnitus” would be expected to increase by 2.2% (2.1%, 2.3%), and the odds of self-reporting “bothered a lot” tinnitus relative to “bothered not at all” tinnitus would be expected to increase by 3.6% (3.5%, 3.7%). Conclusions: The self-reported prevalence of bothersome tinnitus in the U.S. Army (17.1%) is substantially higher than that of the general population, where prevalence of bothersome tinnitus is estimated at 6.6%. Examination of bothersome tinnitus among Soldiers is an important step toward optimizing prevention, education, and intervention efforts.

Audiology & Speech-Language Pathology↗
DOE OSTI2024

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

42 ENGINEERING↗
DOE OSTI2022

Analysis of overlapping count data

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Additionally, proofs of the paper’s results are also included in the supplemental material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

How uncertain is the future of electric vehicle market: Results from Monte Carlo simulations using a nested logit model

Plug-in electric vehicles (PEVs) are widely regarded as an important component of the technology portfolio designed to accomplish policy goals in sustainability and energy security. However, the market acceptance of PEVs in the future remains largely uncertain from today's perspective. By integrating a consumer choice model based on nested multinomial logit and Monte Carlo simulation, this study analyzes the uncertainty of PEV market penetration using Monte Carlo simulation. Results suggest that the future market for PEVs is highly uncertain and there is a substantial risk of low penetration in the early and midterm market. Top factors contributing to market share variability are price sensitivities, energy cost, range limitation, and charging availability. The results also illustrate the potential effect of public policies in promoting PEVs through investment in battery technology and infrastructure deployment. Here, continued improvement of battery technologies and deployment of charging infrastructure alone do not necessarily reduce the spread of market share distributions, but may shift distributions toward right, i.e., increase the probability of having great market success.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Social-ecological interactions in a disaster context: Puerto Rican farmer households’ food security after Hurricane Maria

Islands are uniquely vulnerable to extreme weather events and food insecurity, and have additional response challenges due to their limited landmasses and economies, isolation, colonial legacies, and high dependence of food imports. Domestic farmers have a key role in producing food for island communities like Puerto Rico, which can safeguard food security when food importation may be challenging. Nevertheless, in the context of disaster, farmers themselves may be vulnerable to food insecurity and unable to contribute to domestic markets. This paper examines Puerto Rican farmers households’ food security in the aftermath of 2017’s Hurricane Maria using a social-ecological lens. Survey data from 405 farmers gathered eight months after Maria, coupled with biophysical data from the hurricane’s impacts (winds, rains, and landslides), were analyzed. Overall, 69% of farmers experienced at least one month of food insecurity in the aftermath of Hurricane Maria, and 38% reported persistent food insecurity (three months or more). A multinomial logistic regression suggests that biophysical impacts, but especially social factors, such as age and constraint access to external sources of support, are linked with persistent food insecurity. This suggests that the biophysical impacts of the hurricane interact with existing infrastructure and social resources to affect farmer vulnerability and the food environment in different ways. Thus, strengthening adaptive capacity in multiple domains can help farmers and vulnerable populations better navigate the disruptions faced during disasters to alleviate food insecurity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluating Forest Biomass Recovery in South Central Alabama Pine Plantations

Abstract Improved use of forest biomass has been presented as a viable option to satisfy a portion of the demand for sustainable alternative sources of energy. Yet, there are considerable gaps in our understanding related to the efficiencies of current state-of-the-art forest biomass recovery systems. Southern pine plantation biomass stands typically exhibit higher stand densities and smaller-diameter trees than conventional stands, which, in turn, may result in reduced recovery efficiencies. In this study, the impact of new harvest systems for biomass recovery was investigated in typical southern pine plantation biomass harvests. Specifically, spatial and temporal effects on residue distribution were examined following biomass harvest of 14- and 24-year-old loblolly pine plantations. Preharvest total standing biomass for the younger site at 90 t ac–1 (220 t hectare–1) was half that of the older site at 160 t ac–1 (390 t hectare–1). Although the analysis detected no significant temporal effects on residue distributions, the preharvest condition exhibited 100 percent ground cover, whereas postharvest conditions had nearly 20 percent of the area designated as bare. Two of the five residue classifications, light debris and litter-herbaceous, were found to have a significantly higher incidence of occurrence than the other residue classifications on the sites based on a multinomial regression. In general, we found recovery efficiencies for both sites of 80 percent or greater for both methods of determination, by destructive sampling and based on load tickets.

Forestry↗
DOE OSTI2024

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Effect of finite particle number sampling on baryon number fluctuations

The effects of finite particle number sampling on the net baryon number cumulants, extracted from fluid dynamical simulations, are studied. The commonly used finite particle number sampling procedure introduces an additional Poissonian (or multinomial if global baryon number conservation is enforced) contribution which increases the extracted moments of the baryon number distribution. If this procedure is applied to a fluctuating fluid dynamics framework, one severely overestimates the actual cumulants. We show that the sampling of so-called test particles suppresses the additional contribution to the moments by at least one power of the number of test particles. We demonstrate this method in a numerical fluid dynamics simulation that includes the effects of spinodal decomposition due to a first-order phase transition. Furthermore, in the limit where antibaryons can be ignored, we derive analytic formulas which capture exactly the effect of particle sampling on the baryon number cumulants. These formulas may be used to test the various numerical particle sampling algorithms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Association between algal productivity and phycosphere composition in an outdoor Chlorella sorokiniana reactor based on multiple longitudinal analyses

Summary Microalgae as a biofuel source are of great interest. Bacterial phycosphere inhabitants of algal cultures are hypothesized to contribute to productivity. In this study, the bacterial composition of the Chlorella sorokiniana phycosphere was determined over several production cycles in different growing seasons by 16S rRNA gene sequencing and identification. The diversity of the phycosphere increased with time during each individual reactor run, based on Faith’s phylogenetic diversity metric versus days post‐inoculation ( R = 0.66, P < 0.001). During summer months, Vampirovibrio chlorellavorus , an obligate predatory bacterium, was prevalent. Bacterial sequences assigned to the Rhizobiales, Betaproteobacteriales and Chitinophagales were positively associated with algal biomass productivity. Applications of the general biocide, benzalkonium chloride, to a subset of experiments intended to abate V. chlorellavorus appeared to temporarily suppress phycosphere bacterial growth, however, there was no relationship between those bacterial taxa suppressed by benzalkonium chloride and their association with algal productivity, based on multinomial model correlations. Algal health was approximated using a model‐based metric, or the ‘Health Index’ that indicated a robust, positive relationship between C. sorokiniana fitness and presence of members belonging to the Burholderiaceae and Allorhizobium–Neorhizobium–Pararhizobium–Rhizobium clade. Bacterial community composition was linked to the efficiency of microalgal biomass production and algal health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Citywide Impacts of E-Commerce: Does Parcel Delivery Travel Outweigh Household Shopping Travel Reductions?

E-commerce has facilitated online ordering of goods by households in recent years. This technological advancement has disrupted shopping related transportation. While the National Household Travel Survey (NHTS [1]) finds that household shopping frequency has declined in the last 10-20 years, deliveries by parcel delivery trucks and vans [2] have increased. However, the net effect of these phenomena on overall trip making, vehicle-miles traveled (VMT) and fuel consumption has not been quantified. From a regional planning perspective, understanding the net effect is important for informing city policies--for example, in regards to land use and transportation planning. The objective of this research is to address this gap. In this study, the net regional impact of e-commerce on transportation and fuel consumption is evaluated. The approach relies on a powerful, agent-based modeling framework (POLARIS [3]) that models decisions made by individual household and commercial agents. E-commerce demand is modeled for each household using a bilevel multinomial probit structure that evaluates e-commerce participation and ordering frequency. Last-mile delivery tours were constructed using GIS-based tools and information from a major parcel delivery company [4]. After integrating the resulting supply and demand models with all other passenger and commercial traffic within POLARIS, a traffic simulation was performed and subsequently VMT and energy consumption were analyzed. The study finds that while e-commerce has generated an increase in parcel truck delivery trips, the net effect of e-commerce is a reduction in VMT and fuel consumption due major reductions in these quantities via shopping trip reductions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗