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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022

Abstract Oak Ridge National Laboratory (ORNL) annually develops the LandScan Global (LSG) dataset, a 30 arcsecond global gridded population dataset representing global ambient human population distribution. This multivariable dasymetric model disaggregates census counts within administrative boundaries using ancillary data. Each country’s distribution reflects cultural and socioeconomic patterns; manual validations yield a unique global dataset for assessing populations at risk. For over two decades, LSG has been a standard for estimating populations at risk, aiding U.S. federal government, academia and humanitarian organizations. During disasters such as the 2004 Indian Ocean tsunami and the 2010 Haiti earthquake and geopolitical crises such as the Syrian civil war and the 2022 Russian invasion of Ukraine, LSG supported scientific and operational communities in emergency response and recovery. In 2022, LSG datasets from 2000 onward were made publicly available through ORNL’s LandScan Portal. This data descriptor details our methodology and the application of geospatial science and machine learning to geographic and demographic data, highlighting uses in urban resiliency, emergency management, disaster response, and human health and security.

Science & Technology - Other Topics↗
DOE OSTI2023

Characterization and molecular simulation of lignin in Cyrene pretreatment of switchgrass

Biomass-derived solvents have been proposed as a novel pathway in biorefining for the realization of biofuels and bioproducts derived from lignocellulosic biomass. Cyrene derived from cellulose has recently been shown to have a high potential as a green organic solvent for pretreating poplar biomass. However, due to its high dynamic viscosity nature, high Cyrene concentration could cause negative effects on the sugar release of the pretreated biomass as well as driving up the operational cost of the lignin recovery. In this study, we combine experimental and computational approaches to examine the impact of Cyrene pretreatment with reduced Cyrene concentration under mild conditions on switchgrass lignin. Our experimental studies indicated correlation between pretreatment condition and recovery and structure modification of lignin. Switchgrass lignin extracted by Cyrene pretreatment possessed high preservation of β-O-4 ether inter-unit linkage, which could provide versatility in the integration of downstream lignin valorization into the modern biorefinery industries. Molecular modeling examining the solvation of switchgrass lignin polymer and the disaggregation of low-molecular weight lignin aggregates under pretreatment conditions indicated that a preferential interaction exists between Cyrene and lignin, which likely drives lignin release, and that the disruption of inter-lignin contacts can be modulated as a non-monotonic function of Cyrene : water ratio. Further, while Cyrene–lignin interactions permit the solubilization of lignin, simulations with proxy reactive-species reveal that changes to the diffusion of these reactive proxies and their localization near linkage sites under Cyrene conditions may inhibit chemical processes. In conclusion, the results indicated that loss of pretreatment efficacy caused by low Cyrene concentration could be compensated by prolonged pretreatment time and high catalyst dosage.

09 BIOMASS FUELS↗
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

A Data-Driven Democratized Control Architecture for Regional Transmission Operators

As probably the most complicated and critical infrastructure system, U.S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users. This paper proposes a data-driven democratized control architecture considering two democratization pathways to assist transmission system operators, with a targeted use case of developing online proactive islanding strategies. Detailed discussions on load capability profiling at transmission buses and disaggregation of DER generations are provided and illustrated with real-world utility data. By Combining network and operational constraints, transmission system operators can be equipped with new tools built on top of this architecture, to derive accurate, proactive, and strategic islanding decisions to incorporate the wide range of dynamic portfolios and needs when facing extreme events or unseen grid contingencies.

Power System Reliability, Islanding, Democratized ↗
DOE OSTI2022

Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. Here we review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

DoCeph: DPU-Offloaded Messaging in Ceph for Reduced Host CPU Utilization

Ceph is a widely used distributed object store, but its messenger layer imposes substantial CPU overhead on the host. To address this limitation, we propose DoCeph, a DPU-offloaded storage architecture for Ceph that disaggregates the system by offloading the communication-intensive messaging component to the DPU while retaining the storage backend on the host. The DPU efficiently manages communication, using lightweight RPC for metadata operations and DMA for data transfer. Moreover, DoCeph introduces a pipelining technique that overlaps data transmission with buffer preparation, mitigating hardware-imposed transfer size limitations. We implemented DoCeph on a Ceph cluster with NVIDIA BlueField-3 DPUs. Evaluation results indicate that DoCeph cuts host CPU usage by up to 92% while sustaining stable throughput and providing larger performance benefits for object writes over 1 MB.

Park, Kuri [Sogang University]↗
DOE OSTI2024

Automobile and Technology Lifecycle-Based Assignment (ATLAS) v2.0.12

ATLAS is a comprehensive vehicle transaction and technology adoption microsimulator. ATLAS evolves the fleet mix of individual households by simulating the transaction (vehicle addition, disposal, and replacement) and choice (vehicle type, vintage, powertrain, and tenure) decisions in response to the co-evolving demographics, land use, and vehicle technology simulations. Different from the existing vehicle models that are either static or aggregated (e.g. stock model), ATLAS is fully disaggregated and dynamic following a sequential and circumstantial decision-making trajectory. This fine-grained approach not only enhances the realism of the simulation but also provides a nuanced understanding of the dynamics inherent in vehicle fleet evolution. ATLAS outputs are fully compatible with subsequent agent-based transportation modeling system and can enable distributional effect analysis regarding the fleet turnover among heterogeneous populations. ATLAS expands the typical new sale focused vehicle choice modeling to including used vehicle transactions that are of increasing interests to understanding the vehicle adoption behavior among lower income households.

Jin, Ling↗
DOE OSTI2024

Firm Synthesizer and Supply-chain Simulator (SynthFirm) v1.0

SynthFirm is a large-scale agent-based freight demand model which generates a complete synthetic population of firms in the U.S. and the business-to-business commodity flows between them. Using publicly available data sources as inputs, SynthFirm simulates detailed firm and fleet characteristics, commodity production and consumption, formation of supply chains, and selection of shipping modes, all of which are essential drivers of commodity flow at a disaggregate level.

Xu, Xiaodan↗
DOE OSTI2025

Firm Synthesizer and Supply-chain Simulator (SynthFirm) v2.0

SynthFirm is a national-scale agent-based freight demand model which generates a complete synthetic population of firms in the U.S. and the business-to-business commodity flows between them. Using publicly available data sources as inputs, SynthFirm simulates detailed firm and fleet characteristics, commodity production and consumption, formation of supply chains, and selection of shipping modes, all of which are essential drivers of commodity flow at a disaggregate level. The SynthFirm 2.0 version includes national commercial vehicle fleet generation, international trade simulation and automized model validation pipeline, which allows seemless deployment across the nation and build a comprehensive freight inventories at national scale or for selected region.

Yang, Hung-Chia [Lawrence Berkeley National Labora↗
DOE OSTI2025

An Alternative Ensemble Streamflow Prediction Approach Using Improved Subseasonal Precipitation Forecasts from the North America Multi-Model Ensemble Phase II

In this article, streamflow forecasting at a subseasonal time scale (10–30 days into the future) is important for various human activities. The ensemble streamflow prediction (ESP) is a widely applied technique for subseasonal streamflow forecasting. However, ESP’s reliance on the randomly resampled historical precipitation limits its predictive capability. Available dynamical subseasonal precipitation forecasts provide an alternative to the randomly resampled precipitation in ESP. Prior studies found the predictive performance of raw subseasonal precipitation forecast is limited in many regions such as the central south of the United States, which raises questions about its effectiveness in assisting streamflow forecasting. To further assess the hydrologic applicability of dynamical subseasonal precipitation forecasts, we test the subseasonal precipitation forecast from North America Multi-Model Ensemble Phase II (NMME-2) at four watersheds in the central south region of the United States. The subseasonal precipitation forecasts are postprocessed with bias correction and spatial disaggregation (BCSD) to correct bias and improve spatial resolution before replacing the randomly resampled precipitation in ESP for streamflow predictions. The performance of the resulting streamflow predictions is benchmarked with ESP. Evaluation is conducted using Kling–Gupta Efficiency (KGE), continuous ranked probability score (CRPS), probability of detection (POD), false alarm ratios (FARs), as well as reliability diagrams. Our results suggest that BCSD-corrected subseasonal precipitation forecasts lead to overall improved streamflow predictions due to added skills in winter and spring. Our results also suggest that BCSD-corrected subseasonal precipitation forecasts lead to improved predictions on the occurrence of high-percentile streamflow values above 75%. Overall, BCSD-corrected subseasonal precipitation has shown promising performance, highlighting its potential broader applications for river and flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Macro Analysis to Estimate Electric Vehicles Fast-Charging Infrastructure Requirements in Small Urban Areas

Electric vehicles (EVs) are known to reduce emissions and fossil fuel dependency. However, the limited range, long charging time, and inadequate charging infrastructure have hampered the adoption of EVs. The current EV charging infrastructure planning studies and tools require detailed information, extensive resources, and skills that can be a significant barrier to urban areas for finding the required charging infrastructure to support a targeted EV market share. This study generates regression models to estimate the number of direct current fast charging stations and the chargers to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, developed recently by the same authors, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan. This simulated data set includes the number of charging stations and chargers for each market share, technology advancement scenario, and the transportation network topology. The results show that the number of charging stations reduces with battery size and charging power and increases with EV market share and the road network lane length. The number of chargers reduces with charging power, whereas it increases with battery size, EV market share, and vehicle miles traveled in the system. The model developed here can be applied to any state having urban characteristics and weather conditions similar to Michigan.

Engineering↗
DOE OSTI2024

Insights into the action of phylogenetically diverse microbial expansins on the structure of cellulose microfibrils

Microbial expansins (EXLXs) are non-lytic proteins homologous to plant expansins involved in plant cell wall formation. Due to their non-lytic cell wall loosening properties and potential to disaggregate cellulosic structures, there is considerable interest in exploring the ability of microbial expansins (EXLX) to assist the processing of cellulosic biomass for broader biotechnological applications. Herein, EXLXs with different modular structure and from diverse phylogenetic origin were compared in terms of ability to bind cellulosic, xylosic, and chitinous substrates, to structurally modify cellulosic fibrils, and to boost enzymatic deconstruction of hardwood pulp. Five heterogeneously produced EXLXs (Clavibacter michiganensis; CmiEXLX2, Dickeya aquatica; DaqEXLX1, Xanthomonas sacchari; XsaEXLX1, Nothophytophthora sp.; NspEXLX1 and Phytophthora cactorum; PcaEXLX1) were shown to bind xylan and hardwood pulp at pH 5.5 and CmiEXLX2 (harboring a family-2 carbohydrate-binding module) also bound well to crystalline cellulose. Small-angle X-ray scattering revealed a 20–25% increase in interfibrillar distance between neighboring cellulose microfibrils following treatment with CmiEXLX2, DaqEXLX1, or NspEXLX1. Correspondingly, combining xylanase with CmiEXLX2 and DaqEXLX1 increased product yield from hardwood pulp by ~ 25%, while supplementing the TrAA9A LPMO from Trichoderma reesei with CmiEXLX2, DaqEXLX1, and NspEXLX1 increased total product yield by over 35%. This direct comparison of diverse EXLXs revealed consistent impacts on interfibrillar spacing of cellulose microfibers and performance of carbohydrate-active enzymes predicted to act on fiber surfaces. These findings uncover new possibilities to employ EXLXs in the creation of value-added materials from cellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2026

1977 Baltimore Travel Survey

The Baltimore Travel Dataset—conducted by the U.S. Federal Highway Administration and the University of California, Berkeley—is a detailed survey of demographic characteristics and travel patterns for 967 households in Baltimore, Maryland. The survey was conducted in May and June of 1977. The original goal of the survey was to collect an “ideal” dataset for disaggregate travel demand analysis.

1Hz data↗
DOE OSTI2026

1993 Baltimore Household Travel Survey

The 1993 Baltimore Household Travel Survey is a detailed survey of demographic characteristics and travel patterns for 2,692 households in Baltimore, Maryland. The original goal of the survey was to collect an “ideal” dataset for disaggregate travel demand analysis.

1Hz data↗
DOE OSTI2026

Transit Rider/Travel Behavior Inventory Survey - Minneapolis-St. Paul Metro - 2010

The Metropolitan Council administered a system-wide transit on-board survey between September and November 2010 as part of the 2010 Travel Behavior Inventory to provide detailed transit usage patterns and rider information to support modeling and planning efforts. The study’s focus was to capture only the most current and reliable data necessary to determine future public transportation needs in the Minneapolis region. The study was structured to collect detailed transit ridership data for different routes during different times of day to develop a disaggregate transit trip table that will support advanced travel demand modeling. The study was designed to leverage existing data sources, such as the 2005 on-board survey, to provide the best quality data to update the Travel Behavior Inventory in the Minneapolis-Saint Paul metropolitan area.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗
DOE OSTI2024

gcamfaostat: An R package to prepare, process, and synthesize FAOSTAT data for global agroeconomic and multisector dynamic modeling

The gcamfaostat R package is designed for the preparation, processing, and synthesis of the Food and Agriculture Organization (FAO) Statistics (FAOSTAT) agroeconomic data. The primary purpose is to facilitate FAOSTAT data use in global economic and multisector dynamic models while ensuring transparency, traceability, and reproducibility. Here, we provide an overview of the development of gcamfaostat (v1.0) and demonstrate its capabilities in generating and maintaining agroeconomic data required for the Global Change Analysis Model (GCAM). Our initiative seeks to enhance the quality and accessibility of data for the global agroeconomic modeling community, with the aim of fostering more robust and harmonized outcomes in a collaborative, efficient, and open-source framework. One of the important features of the package is the possibility to construct the FAO Food Balance Sheets at the disaggregated commodity level (with over 500 commodities), which provides a comprehensive and detailed data input for a variety of analytical and modeling applications. The processed data and visualizations offered by gcamfaostat can also be valuable to a broader audience interested in gaining insights into the intricacies of global agriculture.

97 MATHEMATICS AND COMPUTING↗