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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Particle size and shape effect of Crumbler® rotary shear-milled granular woody biomass on the performance of Acrison® screw feeder: A computational and experimental investigation

Physical experiments and discrete element model (DEM) simulations are conducted to evaluate particle characteristics and operation parameter effects on screw feeding performance for rotary shear-milled Douglas fir. Three performance metrics are used: mass flow rate, shaft driving torque, and specific energy consumption. The impact of particle size, particle size distribution (PSD), shaft rotational speed (rpm), and hopper dimensions on the performance are investigated. All employed performance metrics reveal the superior flowability of the 2-mm particles in contrast to the larger 6-mm counterpart. Remarkably, wider PSD results in poorer flowability than the two mono-sized particles, proving the flowability enhancement achieved by narrower PSD of the woody feedstock. More importantly, DEM simulations unveil PSD-induced degradation in flowability is attributed to mechanical interlocking and particle segregation effects. Furthermore, higher shaft rpm causes higher mass flow rate at the cost of higher specific energy consumption due to viscous dissipation and changes in flow pattern.

09 BIOMASS FUELS↗
DOE OSTI2022

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Conserving asphalt resources: Rethinking rejuvenator performance evaluation through peptizing efficiency

Timely rejuvenation and restoration of asphalt are essential conservation practices that help preserve and extend the service life of roads, bridges, driveways, and parking lots. The performance of asphalt rejuvenators is often assessed based on their diffusion rates and softening power, yet these metrics alone fail to capture true rejuvenation potential. Here, this study integrates density functional theory (DFT) modeling with experimental analysis to show that rejuvenation effectiveness is primarily governed by molecular interactions with oxidized asphaltene nanoaggregates. DFT results revealed that amide- and unsaturated-chain compounds, such as hexadecanamide, oleic acid, and 9,17-octadecadienal, act cooperatively to disrupt π–π stacking and exfoliate asphaltene layers, reducing binding strength and enhancing dispersion. Unlike bulky, rigid molecules that remain trapped, these components remain mobile and repeatedly interact with multiple aggregation sites. Experimental validation using rheometry and FTIR confirmed that such molecularly compatible rejuvenators restore the binder flexibility and polydispersity, even when diffusion is relatively slow. Building on this mechanistic foundation, six rejuvenators (A2, A5, A7, A8, A9, and A10) were evaluated using a three-metric performance framework encompassing cracking resistance (Glover–Rowe parameter), UV stability, and surface hydrophobicity. Among the six rejuvenators evaluated, A9 exhibited the highest overall performance, reducing the Glover–Rowe cracking parameter by 85% (from 351 kPa to 53 kPa), demonstrating the greatest resistance to UV-induced aging with a stability index of 4.17 h·kPa⁻¹, and increasing surface hydrophobicity to a contact angle of 103.6°. Its superior performance is primarily attributed to its amide- and unsaturated-chain components, which act cooperatively to disrupt π–π stacking interactions and exfoliate asphaltene layers, thereby promoting molecular deagglomeration and enhancing long-term durability. These results shift the criteria for selecting rejuvenators: effective candidates must pair the electronic capability to unlock aged asphaltenes with sufficient structural stability to resist secondary aging and restore hydrophobicity. Restoring hydrophobicity is critical, as aging reduces the asphalt’s water repellency and increases water diffusion, which in turn accelerates moisture-related damage; consequently, a high-performing rejuvenator must effectively restore the binder’s hydrophobic characteristic. Collectively, these findings provide a framework for the rational design of next-generation of bio-based rejuvenators that enhance pavement longevity and promote long-term sustainability.

Aging↗
DOE OSTI2026

Artificial intelligence-driven municipal solid waste sortation and its significance on downstream waste valorization in the United States: Techno-economic and life cycle assessment

Municipal solid waste (MSW) generation is quickly increasing due to population growth, industrialization, and urbanization. Current sorting technologies, e.g., optical sorters have low sortation efficiency and produce contaminated sorted materials unsuitable for downstream valorization. This study evaluates a novel artificial neural network (ANN)-based MSW sortation system through sorting efficiencies, economic performance, and environmental impacts. ANN system can sort up to 8 plastic types and all organic waste (i.e., food, yard, and paper waste) with 99% and 93% recovery efficiency, respectively, compared to optical sortation which could separate only polyvinyl chloride (PVC) plastic ultimately producing mixed plastics (except PVC) and organics streams as products. Nonetheless, their sortation costs are similar, $28.7 for ANN-based sortation and $28 for optical sortation per metric ton (Mt) of MSW at processing capacity of 94,860 metric tons (Mt)/yr of MSW in the United States. Additionally, ANN system produced high purity feedstocks for downstream valorization. A biochar pyrolysis plant modeled to process 3.6 and 14 Mt/h of sorted yard waste outperformed biochar production from mixed organic fraction (MOF), achieving 8% lower production cost and 34% lower global warming impact (1.37 kg CO 2 eq. per kg) compared to biochar derived from mixed organic feedstock (2.08 kg CO 2 eq.).

09 BIOMASS FUELS↗
DOE OSTI2026

Measuring Success: A Refined Methodology for Estimating Long-term Continuous Improvement

Successful resource management systems require current and detailed feedback on operational and corporate-level performance. As corporate accountability concerns intensify, the precision and reliability of these performance metrics have become crucial. Traditional savings estimation methods can be difficult to understand, particularly linear regression, and can provide varying results. This paper reviews common efficiency metrics and highlights underlying mathematical inconsistencies when estimating total and percent savings with current methods. A refined approach to calculating long-term utility savings is proposed that simplifies current methodologies utilizing ratios to define an adjusted baseline, allowing for consistent and fair aggregation of results across multiple scales from resources to corporate performance. A simplified example demonstrates how the proposed methodology improves upon existing methods, especially in intermediate years. This paper’s major contributions are the simplified approach for converting modeled utility usage into estimated savings and the consistent roll-up methodology enabling more comparable, aggregable, and actionable results across scales.

Price, Chris [ORNL] (ORCID:0000000202007906)↗
DOE OSTI2020

Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. In this work, we used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Medium-temperature phase change material integration in domestic heat pump water heaters for improved thermal energy storage

In this review, we examine state-of-the-art developments in integrating phase change materials (PCMs) for thermal energy storage (TES) in domestic heat pump water heaters (HPWHs). The component design optimization and control optimization of HPWHs and TES are reviewed for insight into improving the thermal capacity and efficiency of a PCM-integrated HPWH. The state-of-the-art review is categorized by the stage of development of the PCM for deployment in HPWHs. To select appropriate PCMs for HPWHs, a six-factor down-selection process is used to determine the best material(s) for integration in HPWHs with appropriate heat exchanger design. Ultimately, food-grade PCMs appear to be the best candidate for integration of TES in domestic HPWHs because they are nontoxic, highly cyclable, and have heat transfer properties accommodable to water heating when integrated in a manner to overcome the thermal conductivity limitations of the material. A key parameter of water heating performance is thermal heating power of the PCM component, which is not often reported. Many studies report significant improvement in capacity and efficiency. Furthermore, many performance metrics are identified from the literature to quantify the system performance, but agreement across studies is not found. Unified energy factor and first hour rating performance tests are required for commercially available HPWHs, and these performance metrics could homogenize the literature. Ultimately, we find that select PCMs are ready for deployment with HPWHs for performance improvement, and component design and control optimizations are new avenues of research and development required for a commercially viable PCM-integrated HPWH system.

25 ENERGY STORAGE↗
DOE OSTI2024

Techno-economic analysis of renewable energy generation at the South Pole

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. Here, this study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all -diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration

Demand Flexibility Assessment Tool (DFAT) is an open source web-based tool that estimates the demand flexibility potential of common control strategies in commercial buildings. The toolkit features a demand flexibility estimation tool that contains two calculators, basic and advanced, based on the level of input of customer data. The basic version calculates demand shed metrics for the control strategy “global temperature adjustment” and “cycle on/off compressors” using customer building information, local weather data, and electrical meter data. The advanced version, which uses detailed HVAC equipment data, calculates demand flexibility metrics for control strategies such as static pressure reset, global temperature adjustment, and cycle on/off compressors. In addition to the demand flexibility estimation tool, this toolkit offers a benchmarking tool that helps facility operators, aggregators, and utility resource managers assess demand flexibility opportunities, quantify/verify performance, and compare their performance against that of their peers.

Leong, Michael↗
DOE OSTI2023

Influence of the air-cooled supercritical carbon dioxide recompression cycle design-point on molten salt power tower techno-economic performance

Here, this paper investigates the techno-economic performance of a molten salt power tower integrated with an air-cooled supercritical carbon dioxide recompression cycle. Prior research into the cycle design point showed trade-offs between efficiency, cost, and temperature difference across the heat input, resulting in trade-offs between the design-point generation of the molten salt power tower system and the cost of sensible-heat thermal energy storage. Prior research into cycle off-design showed that colder ambient temperature designs have higher efficiencies on cold days but lower efficiency and capacity degradation on hot days. We present a framework to evaluate how cycle design, cost, and corresponding off-design performance affect the techno-economic performance of the molten salt power tower system. We use a steady-state off-design cycle model assuming fixed shaft speeds, inventory control, and air-cooler fan power control and integrate the cycle model with the molten salt power tower model from the National Renewable Energy Laboratory's System Advisor Model. First, we present a surrogate model that uses a heuristic approach to correct the interpolation when the cycle reaches capacity limitations at ambient temperatures hotter than design. Next, we evaluate how the recompression cycle design-point ambient temperature, air-cooler approach temperature, air-cooler fan power, and recuperator conductance influence cycle and system design metrics. Finally, we complete annual simulations using an hourly "duck curve" pricing schedule and present annual performance metrics to evaluate the cumulative effects of the cycle design parameters and corresponding off-design performance on molten salt power tower techno-economics.

14 SOLAR ENERGY↗
DOE OSTI2023

Global fire modelling and control attributions based on the ensemble machine learning and satellite observations

Contemporary fire dynamics is one of the most complex and least understood land surface phenomena. Global fire controls related to climate, vegetation, and anthropogenic activity are usually intertwined, and difficult to disentangle in a quantitative way. Here, we leveraged an ensemble of five machine learning (ML) models and multiple satellite-based observations to conduct global fire modeling for three fire metrics (burned area, fire number, and fire size), and quantified driving mechanisms underlying annual fire changes in a spatially resolved manner for the period 2003–2019. Ensemble learning is a meta-approach that combines multiple ML predictions to improve accuracy, robustness, and generalization performance. We found that the optimized ensemble ML well reproduced annual dynamics of global burned area (R 2 = 0.90, P < 0.001), total fire numbers (R 2 = 0.86, P < 0.001), and averaged fire size (R 2 = 0.70, P < 0.001). Additionally, the ensemble ML captured key spatial patterns of multi-year mean magnitudes, annual variabilities, anomalies, and trends for different fire metrics. Our ML-based fire attributions further highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area (–1.9 Mha/yr, P < 0.01), followed by climate control (–1.3 Mha/yr, P < 0.01) and insignificant positive vegetation control (0.4 Mha/yr, P = 0.60). Spatially, climate dominated a much larger burned area (53.7%) than human (23.4%) or vegetation control (22.9%); however, the counteracting effects from regional wetting and drying trends weakened the net climate impacts on global burned area. The fire number and fire size exhibited similar spatial control patterns with burned area; globally, however, fire number tended to be more affected by climate while fire size more influenced by human activities. Overall, our study confirmed the feasibility and efficiency of ensemble ML in global fire modeling and subsequent control attributions, providing a better understanding of contemporary fire regimes and contributing to robust fire projections in a changing environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Aboveground woody biomass estimation of young bioenergy plantations of Populus and its hybrids using mobile (backpack) LiDAR remote sensing

Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10 th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.

AGB↗
DOE OSTI2021

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Assessment of simulated and observed cavitation-induced erosion damage in Spallation Neutron Source target vessels

Cavitation-induced erosion damage in different Spallation Neutron Source (SNS) target designs are simulated using explicit finite element–based techniques and compared with observations of erosion in targets after operation. The efficacy of the previously developed method, called saturation time, was evaluated using erosion-damaged samples from new target designs. A new metric called maximum bubble size was implemented under the rationale that larger cavitation bubbles will collapse more intensely. The maximum cavitation bubble size over 1 ms of simulated time was calculated based on the Rayleigh–Plesset equation for each element integration point and presented as a contour map at the vessel surface for assessing with erosion observations. SNS targets are now operated with helium gas injection to reduce cavitation damage. A simulation method using a material model for the mixture of mercury and gas bubbles was recently developed and used to account for the effect of small gas bubbles on the structural response of the target vessel. Furthermore, this work compares the new method's results with observed cavitation damage. Maps of the calculated maximum bubble size for targets operated with and without gas injection were compared with photographs of erosion damage observed in SNS targets. The patterns in maximum bubble size maps correlated well with observations of erosion patterns in target vessels after service. Advantages and challenges of the maximum bubble size simulation technique are provided, and differences between results from the previous and the newly proposed metric are discussed.

Jiang, Hao↗