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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types

This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Mapping Hsp104 interactions using cross‐linking mass spectrometry

Molecular machines from the AAA+ (ATPases Associated with diverse cellular Activity) superfamily of protein disaggregases play important roles in protein folding, disaggregation and DNA processing. Recent cryo-EM structures of AAA+ molecular machines have uncovered nuanced changes in their conformation that underlie their specialized functions. Structural knowledge of these molecular machines in complex with substrates begins to explain their mechanism of activity. Here, we explore how cross-linking mass spectrometry (XL-MS) can be used to interpret changes in conformation induced by ATP in Hsp104 and how a substrate may interact with Hsp104. We applied a panel of cross-linking reagents to produce cross-linking maps of Hsp104 and interpret our data on previously determined X-ray and cryo-EM structures of Hsp104 from a thermophilic yeast, Calcarisporiella thermophila. We developed an analysis pipeline to differentiate between intra-subunit and inter-subunit contacts within the hexameric homo-oligomer. We identify cross-links that break the asymmetry that is present in Hsp104 in an ATP-hydrolysis competent conformation but is absent in an ATP-hydrolysis-defective mutant. Finally, we identify contacts between Hsp104 and a selected protein (proprotein convertase subtilisin/kexin type 9 PCSK9) to reveal contacts on the central channel of Hsp104 across the length of this protein indicating that we might have trapped interactions consistent with its translocation. Our simple and robust XL-MS-based experiments and methods help interpret how these molecular machines change conformation and bind to other proteins even in the context of homo-oligomeric assemblies enabling coupling state-of-the-art modeling approaches with XL-MS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Effect of Image Classification Accuracy on Dasymetric Population Estimation

Dasymetric mapping involves the disaggregation of count data, usually relating to population/demographics, from census enumeration areas to smaller target zones with the aid of an ancillary layer related to population density. The ancillary layer is often a binary classification such as developed versus undeveloped, building versus non-building, and residential versus non-residential, in which one class is treated as populated and the other as unpopulated. While dasymetric mapping relies heavily on ancillary data, little research has been done to address the error in ancillary data and its effects on dasymetric mapping accuracy. This chapter reports our research effort to investigate the effect of image classification accuracy on dasymetric population estimates by developing a binomial classification of buildings from high-resolution remote sensor imagery. The classifier was systematically and iteratively manipulated to generate a series of outputs with variegated accuracy characteristics. Lastly, we generated a corresponding series of population estimates based on the mapped building area from each iteration to investigate the relationship between the accuracy of classification and population estimation.

McKee, Jacob↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2025

The Two Arctic Wintertime Boundary Layer States: Disentangling the Role of Cloud and Wind Regimes in Reanalysis and Observations During MOSAiC

The wintertime central Arctic atmosphere comprises a radiatively clear and a radiatively opaque state, which are linked to synoptic forcing and mixed-phase clouds. Weather and climate models often lack process representations surrounding these states, but prior work mostly treated the problem as an aggregate of synoptic conditions, resulting in partially overlapping biases. Here, we disaggregate the Arctic states and confront ERA5 reanalysis with observations from the MOSAiC campaign over the central Arctic sea ice during winter 2019/2020. Low-level winds and liquid water path (LWP) are combined to derive different synoptic classes. Results show that the clear state is primarily formed by weak/moderate winds and the absence of liquid-bearing clouds, while strong winds and enhanced LWP primarily form the radiatively opaque state. ERA5 struggles to reproduce these basic statistics, shows too weak sensitivity of thermal radiation to synoptic forcing, and overestimates thermal radiation for similar LWP amounts. The latter is caused by a warm bias, which has a pronounced inversion structure and is largest in clear and calm conditions. Under strong synoptic forcing, the warm bias is constant with height and discrepancies in mixed-phase cloud altitude appear. Separating synoptic conditions is regarded as useful for process-oriented evaluation of the Arctic troposphere in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Gender gap in perspectives of the impacts of degradation and restoration on ecosystem services in Ethiopia

Abstract The importance of land restoration has garnered increasing attention on the global stage through large‐scale initiatives such as the Bonn Challenge. However, policies and strategies are often gender‐blind and designed in compliance with entrenched social hierarchies, exacerbating pre‐existing social inequalities that affect restoration initiatives. The challenge of developing gender‐responsive policies and initiatives is accentuated by a persistent lack of sex‐disaggregated data concerning men's and women's differing perceptions and experiences. This study aims to help fill this gap by capturing the differences in men and women's perceptions of ecosystem services before and after restoration interventions in Ethiopia. Towards that end, in October 2021, we collected data from fifty‐nine (59) paired husband‐wife households and six gender‐segregated focus group discussions in two regions of Ethiopia: Amhara and Southern Nations, and Nationalities and Peoples (SNNP). Kendall's concordance analysis established a strong degree of disagreement between men's and women's ratings of restoration outcomes for most indicators. Men attributed degradation to landscape conditions and natural forces, while women considered the lack of appropriate restoration strategies as a precursor for accelerated degradation. The study also reveals that men tended to benefit more than women from enhanced ecosystem services post‐restoration, with increased labour and land management burdens often falling on the shoulders of women. Based on these findings, we argue that including men's and women's perspectives from the earliest planning phases of restoration initiatives is essential to ensure greater equity in benefit‐sharing, mitigate trade‐offs for women, and build more nuanced, just and successful approaches to restoration.

Mponela, Powell↗
DOE OSTI2022

Atomic structure of the Leishmania spp. Hsp100 N-domain

We report Hsp100 is an ATP-dependent unfoldase that promotes protein disaggregation or facilitates the unfolding of aggregation-prone polypeptides marked for degradation. Recently, new Hsp100 functions are emerging. In Plasmodium, an Hsp100 drives malaria protein export, presenting a novel drug target. Whether Hsp100 has a similar function in other protists is unknown. We present the 1.06 angstrom resolution crystal structure of the Hsp100 N-domain from Leishmania spp., the causative agent of leishmaniasis in humans. Our structure reveals a network of methionines and aromatic amino acids that define the putative substrate-binding site and likely evolved to protect Hsp100 from oxidative damage in host immune cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

What is (quantitative) system dynamics modeling? Defining characteristics and the opportunities they create

A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and AI, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantifying spatiotemporal variability in occupant exposure to an indoor airborne contaminant with an uncertain source location

Well-mixed zone models are often employed to compute indoor air quality and occupant exposures. While effective, a potential downside to assuming instantaneous, perfect mixing is underpredicting exposures to high intermittent concentrations within a room. When such cases are of concern, more spatially resolved models, like computational-fluid dynamics methods, are used for some or all of the zones. But, these models have higher computational costs and require more input information. A preferred compromise would be to continue with a multi-zone modeling approach for all rooms, but with a better assessment of the spatial variability within a room. Here to do so, we present a quantitative method for estimating a room's spatiotemporal variability, based on influential room parameters. Our proposed method disaggregates variability into the variability in a room's average concentration, and the spatial variability within the room relative to that average. This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures. To demonstrate the utility of this method, we simulate contaminant dispersion for a variety of possible source locations. We compute breathing-zone exposure during the releasing (source is active) and decaying (source is removed) periods. Using CFD methods, we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28% of the source average exposure, whereas variability in the different average exposures was lower, only 10% of the total average. We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure, it does not have a particularly large influence on the spatial distribution during the decaying period, or on the average contaminant removal rate. By systematically characterizing a room's average concentration, its variability, and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration. We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Using energy storage systems to extend the life of hydropower plants

Despite their advantages, distributed energy resources (DERs) bring inherent uncertainty and variability into the landscape of modern power systems. As DER penetration grows, conventional generators like hydropower plants have to respond more often to arrest the imbalance in the net load. Hydropower turbines provide their best operational performance with minimal wear and tear when operating at regions of maximum efficiency. However, the current needs for hydropower plants require them to operate under varying load conditions and thus sub-optimal operating points leading to additional stress. To relieve the hydropower plants, this paper proposes a hybridization strategy where a hydropower unit is paired with an energy storage system (ESS) to increase operational flexibility and mitigate damage to the hydro plant. Models are developed to represent the operation of the hybrid system, quantify degradation, and assess economic benefits. Moreover, an innovative controller disaggregates the market dispatch signal into separate control setpoints for the ESS and hydropower unit. In case studies performed on a real-world hydropower facility, it was found that the ESS-based hybridization can extend the life of the hydropower plant by 5% on average. Notably, the economic benefits from reduced maintenance and deferred investment are estimated to be around $3.6 million.

13 HYDRO ENERGY↗
DOE OSTI2023

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

14 SOLAR ENERGY↗
DOE OSTI2024

Developing Benzodithiophene-Free donor polymer for 19.36% efficiency Green-Solvent-Processable organic solar cells

Here, in this work, a newly benzodithiophene-free D-A polymer donor, named PDTP-BDD, was developed for realizing green-solvent processed high-performance OSCs. By bridging two electron-rich unit of dithieno[3,2-b:2',3'-d]pyridin-5(4H)-one (DTP) and electron-deficient benzo[1,2-c:4,5c']dithiophene-4,8-dione (BDD) with thiophene units, PDTP-BDD possesses high absorption in the short wavelength range and a deep HOMO energy level. The rigid building blocks also make PDTP-BDD has strong aggregation and poor solubility in common halogen-free solvents (such as o-xylene) at room temperature, but it is readily dissolved and disaggregated at high temperature (120 °C). After cooling down to a lower temperature (60 °C), PDTP-BDD self-assembled and pre-aggregated slowly in the solution at a long time. By employing a delayed processing strategy in the layer-by-layer processed OSCs (LbL-OSCs), an optimized fibril network of the underling layer was realized, enabling the permeation of acceptor into the donor network. The optimized PDTP-BDD/L8-BO-based LbL-OSCs realized a high PCE of 18.42 %. By adding a small amount of D18 to further optimize the PDTP-BDD fibril network, an impressive PCE of 19.36 % was achieved finally in the resulting ternary LbL-OSCs, which is the highest value for OSCs processed by halogen-free solvents.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Energy System Transitions and Low-Emission Pathways in Australia, Brazil, Canada, China, EU-28, India, Indonesia, Japan, Republic of Korea, Russia, and United States

The Paris Agreement invited Parties to submit long-term, low-emission development strategies. This study presents national low-emission scenarios to inform such strategies for Australia, Brazil, Canada, China, EU, India, Indonesia, Japan, Russia, Republic of Korea and the USA. The study uses country-level energy–economy and integrated assessment models (IAMs) that incorporate detailed and disaggregated representations of the energy demand and supply systems that are difficult to capture in global IAMs. Models were used to explore low-carbon pathways up to 2050, providing insights in their implications for country emissions, energy system restructuring, energy–economy indicators and system costs. The analysis shows that the low-carbon scenarios of most economies studied here are consistent with pathways limiting global warming to below 2 °C.

Fragkos, Panagiotis↗
DOE OSTI2020

Probabilistic analysis of masked loads with aggregated photovoltaic production

In this paper we present a probabilistic analysis framework to estimate behind-the-meter photovoltaic generation in real time. We develop a forward model consisting of a spatiotemporal stochastic process that represents the photovoltaic generation and a stochastic differential equation with jumps that represents the demand. Here, we employ this model to disaggregate the behind-the-meter photovoltaic generation using net load and irradiance measurements.

14 SOLAR ENERGY↗
DOE OSTI2022

A method for determining optimal electric range by considering electric vehicle lightweighting on perceived ownership cost

The limited driving range due to high costs and low energy densities of batteries constraints the battery electric vehicle (BEV) market growth. Lightweighting in theory can reduce energy consumption rate and extend the driving range. The knowledge gap is to quantitatively link the cost-effectiveness of light-weight technologies with range extension and consumer acceptance of BEVs. In this study, a physics-based energy consumption model of BEVs is constructed and associated with a statistics-based model on the basis of travel surveys. A perceived cost of ownership (PCO) is then developed by adding intangible costs to traditional total cost of ownership models. We estimate, at the disaggregate vehicle model and driver level and the aggregate market level, 1) the extended range due to lightweighting for a given battery size; and 2) the optimal electric range based on lightweighting decisions. The cost-effectiveness of lightweighting for BEV range extension is found to vary with income-dependent daily range limitation value, driving patterns and lightweighting technology costs. In general, adopting lightweighting in BEVs is more cost-effective for consumers with higher daily limitation value, as well as for those with higher driving intensity or suitable daily driving patterns. When the lightweighting involves a higher vehicle production cost, less lightweighting could reduce the overall PCO for BEV owners. 4 of the selected top ten BEV models are found to benefit from additional 2.09%–4.45% lightweighting. Finally, the method built in this study can guide automakers in planning R&D investments in battery and lightweighting technologies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Third international challenge to model the medium- to long-range transport of radioxenon to four Comprehensive Nuclear-Test-Ban Treaty monitoring stations

In 2015 and 2016, atmospheric transport modeling challenges were conducted in the context of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) verification, however, with a more limited scope with respect to emission inventories, simulation period and number of relevant samples (i.e., those above the Minimum Detectable Concentration (MDC)) involved. Therefore, a more comprehensive atmospheric transport modeling challenge was organized in 2019. Stack release data of Xe-133 were provided by the Institut National des Radioéléments/IRE (Belgium) and the Canadian Nuclear Laboratories/CNL (Canada) and accounted for in the simulations over a three (mandatory) or six (optional) months period. Best estimate emissions of additional facilities (radiopharmaceutical production and nuclear research facilities, commercial reactors or relevant research reactors) of the Northern Hemisphere were included as well. Model results were compared with observed atmospheric activity concentrations at four International Monitoring System (IMS) stations located in Europe and North America with overall considerable influence of IRE and/or CNL emissions for evaluation of the participants’ runs. Participants were prompted to work with controlled and harmonized model set-ups to make runs more comparable, but also to increase diversity. It was found that using the stack emissions of IRE and CNL with daily resolution does not lead to better results than disaggregating annual emissions of these two facilities taken from the literature if an overall score for all stations covering all valid observed samples is considered. A moderate benefit of roughly 10% is visible in statistical scores for samples influenced by IRE and/or CNL to at least 50% and there can be considerable benefit for individual samples. Effects of transport errors, not properly characterized remaining emitters and long IMS sampling times (12–24 h) undoubtedly are in contrast to and reduce the benefit of high-quality IRE and CNL stack data. Complementary best estimates for remaining emitters push the scores up by 18% compared to just considering IRE and CNL emissions alone. Despite the efforts undertaken the full multi-model ensemble built is highly redundant. An ensemble based on a few arbitrary runs is sufficient to model the Xe-133 background at the stations investigated. The effective ensemble size is below five. An optimized ensemble at each station has on average slightly higher skill compared to the full ensemble. However, the improvement (maximum of 20% and minimum of 3% in RMSE) in skill is likely being too small for being exploited for an independent period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗