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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

System and method for ablation assisted nanostructure formation for graded index surfaces for optics

A system and method is disclosed for forming a graded index (GRIN) on a substrate. In one implementation the method may involve applying a metal layer to the substrate. A fluence profile of optical energy applied to the metal layer may be controlled to substantially ablate the metal layer to create a vaporized metal layer. The fluence profile may be further controlled to control a size of metal nanoparticles created from the vaporized metal layer as the vaporized metal layer condenses and forms metal nanoparticles, the metal nanoparticles being deposited back on the substrate to form a GRIN surface on the substrate.

Yoo, Jae Hyuck↗
DOE OSTI2020

Charge Management for an Inductively Charged On-Demand Battery-Electric Shuttle Service with High Penetration of Renewable Energy

This paper presents a charge management control strategy for an on-demand battery-electric shuttle van operating at the National Renewable Energy Laboratory (NREL) campus and supported by day-time inductive charging at the vehicle's waiting spot. A new control algorithm has been proposed for reducing the demand charge costs incurred from wireless charging of the on-demand shuttle. A custom controller has been developed to monitor the shuttle, wireless charger, renewable energy generation, and various loads at NREL's campus, and regulate charging behavior for demand response. The intermittent renewable generation and sporadic operation of the on-demand shuttle service contribute to a high level of uncertainty in expected campus load profile, which must be carefully managed. The control algorithm predicts energy profile to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller has been designed and optimized using a Simulink model for the entire system. Next, it has been implemented and tested in real-time on the NREL campus. Two primary vehicle-use cases, charge sustaining and charge depletion operation, are tested under different load profiles and drive cycles to assess the controller's effectiveness at reducing peak demand and therefore demand charges. The proposed controller showed robust performance under different driving scenarios with high correlation between simulated and experimental data. The results showed that proper demand response can be achieved with an average of 94% reduction of charging loads during peak demand events.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2026

Flight Arrival Data for Each Airport

This dataset contains flight arrival data for each airport, which serves as the input for generating load profiles and estimating energy needs and fleet and charger requirements. ![image](distribution-flight-arrivals.png) Distribution of annual flight arrivals across different airport categories.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The Role of Blockchains in Multi-Stakeholder Transactive Energy Systems

Power grids are undergoing major changes due to rapid growth in renewable energy and improvements in battery technology. Prompted by the increasing complexity of power systems, decentralized solutions are emerging that arrange local communities into transactive microgrids. This paper addresses the problem of implementing transactive energy mechanisms in a distributed setting, providing both privacy and safety. Specifically, we design and implement an automated auction and matching system that ensures safety (e.g., satisfaction of line capacity constraints), preserves privacy, and promotes local trade and market efficiency for transactive energy systems. This design problem is challenging because safety, market efficiency, and privacy are competing objectives. We implement our solution as a decentralized trading platform built on blockchain technology and smart contracts. To demonstrate the viability of our platform, we analyze the results of experiments with dozens of embedded devices and energy production and consumption profiles using an actual dataset from the transactive energy domain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none have investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Energy Snapshot - St. Kitts and Nevis

This profile provides a snapshot of the energy landscape of the Federation of St. Christopher (St. Kitts) and Nevis - two islands located in the Leeward Islands in the West Indies.

44 EE - Strategic Programs, Energy Transition Init↗
DOE OSTI2020

Energy Snapshot – Sint Maarten / Saint Martin

This profile provides a snapshot of the energy landscape of the northeast Caribbean island Saint Martin. The island is divided between two nations, France in the north (Saint-Martin) and the Netherlands in the south (Sint Maarten).

44 EE - Strategic Programs, Energy Transition Init↗
DOE OSTI2025

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

deep learning↗
DOE OSTI2026

Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways

Quantitative simulation of trivalent ƒ-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac 3+ –DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CN Ac,O w ≈ 1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CN Ac,O w ≈ 1 and CN Ac,O w ≈ 2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac 3+ –DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

Actinium↗
DOE OSTI2026

Hybrid-Energy-Powered Electrochemical Ocean Alkalinity Enhancement Model: Plant Operation, Cost, and Profitability

Electrochemical ocean alkalinity enhancement is a form of marine carbon dioxide removal, a rapidly growing industry that is powered by efficient onshore or offshore energy sources. As more and larger deployments are being planned, it is important to consider how variable energy sources like tidal energy can impact plant performance and costs. An open-source Python-based generalizable model for electrodialysis-based ocean alkalinity enhancement has been developed that can capture key system-level insights of the electrochemistry, ocean chemistry, acid disposal, and co-product creation of these plants under various conditions. The model additionally accounts for hybrid energy system performance profiles and costs via the National Laboratory of the Rockies’ H2Integrate tool. The model was used to analyze an example theoretical plant deployment in North Admiralty Inlet, including how the plant is impacted by the available energy sources in the region and the scale at which plant costs are covered by the co-products it generates, such as recycled concrete aggregates, without requiring carbon credits. The results show that the example plant could be profitable without carbon credits at commercial scales of 100,000 to 1 million tons of carbon dioxide removal per year, so long as it uses low-cost electricity sources and either sells acid or recovers recycled concrete aggregates with about 1 molar acid concentrations, though more research is needed to confirm these results.

hybrid energy↗
DOE OSTI2020

A methodology for customizing implantation profiles of light ions using a single thin foil energy degrader

We report a method was developed to quantify the spatial distribution and implantation depth of energy-degraded light ions with a thin foil rotating energy degrader for use during multiple ion beam irradiation. The methodology covers three physical phenomena: ions passing through the thin foil, ions travelling through the vacuum to the target, and ion implantation into the target, and accounts for the distribution of ions both in depth and in plane. The processes of energy straggling and scattering were calculated using SRIM. The effects of raster-scanning, and the geometry of the system were implemented in scripts handling the SRIM output files. Elastic backscattering (EBS) using 2.38 MeV H+ protons was used to measure the helium depth profiles after implantation with and without thin foil energy degradation. Defect analysis with transmission electron microscopy confirmed the implantation profiles measured with EBS and calculated with SRIM.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se 2 Solar Cells

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first used to determine the formation energy and diffusion energy barriers of dominant defects in Cu(In,Ga)Se 2 . Next, continuum reaction–diffusion models are developed to analyze the redistribution of defects during manufacturing processes. We estimate defect capture cross sections using a first-principles-based approach. Here, these cross sections are combined with our calculated defect profiles and trap energy levels to parameterize a Shockley–Read–Hall recombination model, which we implement into a device simulator to predict carrier lifetimes and device performance. In that way, a predictive technology computer aided design model is built to predict and optimize the performance of Cu(In,Ga)Se 2 solar cells.

14 SOLAR ENERGY↗
DOE OSTI2020

Benefit Analysis of Long-Duration Energy Storage in Power Systems with High Renewable Energy Shares

The integration of high shares of variable renewable energy raises challenges for the reliability and cost-effectiveness of power systems. The value of long-duration energy storage, which helps address variability in renewable energy supply across days and seasons, is poised to grow significantly as power systems shift to larger shares of variable generation such as wind and solar. This study explores the system-level services and associated benefits of long-duration energy storage on the 2050 Western Interconnection (WI). The operation of the future WI system with 85% renewable penetration is simulated using a two-stage production cost model. The impact of long duration energy storage on systemwide operations is examined for the 2050 WI system, using a range of round-trip efficiencies corresponding to four different energy storage technologies. The analysis projects the energy storage dispatch profile, system-wide production cost savings (from both diurnal and seasonal operation), and impacts on generation mix, and change in renewable generation curtailment.

25 ENERGY STORAGE↗
DOE OSTI2025

Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

New generation bunch shape monitor for ion accelerators

Measuring longitudinal beam parameters is important for operation and development of high intensity linear accelerators, but it is notoriously difficult for proton and ion beams at non-relativistic energies. The Bunch Shape Monitor (BSM) is a device used for measuring the longitudinal bunch distribution in ion linacs. The existing BSM models have poor electron collection efficiency from the wire and are limited to one-dimensional measurements of the phase coordinate. In response to this problem, we have developed a new generation BSM with improved performance. The proposed design incorporates three major innovations: First, the collection efficiency was improved by adding a focusing field between the wire and the entrance slit, which will also allow measurements over a much wider dynamic range. Second, an improvement in the measurement speed was achieved by sampling longitudinal profiles of multiple energy slices simultaneously, where the BSM wire is placed at the exit of an ion spectrometer so that ions with different energies hit the wire at different horizontal coordinates along the wire. Finally, the design incorporates a motion system that can shift the wire and deflecting cavity together, enabling transverse profile measurements like a wire scanner. Here, in this paper, we will provide the design of the new BSM and report on its beam test results at the Spallation Neutron Source facility in Oak Ridge National Laboratory.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Cosmological constraints from the Planck cluster catalogue with DES shear profiles and Chandra observations

We present cosmological constraints from the Planck PSZ2 cosmological cluster sample, using weak-lensing shear profiles from Dark Energy Survey (DES) data and X-ray observations from the Chandra telescope for the mass calibration. We compute hydrostatic mass estimates for all clusters in the PSZ2 sample with a scaling relation between their Sunyaev-Zeldovich signal and X-ray derived hydrostatic mass, calibrated with the Chandra data. We introduce a method to correct these masses with a hydrostatic mass bias using shear profiles from wide-field galaxy surveys. We simultaneously fit the number counts of the PSZ2 sample and the mass calibration with the DES data, finding $Ω_\text{m}=0.312^{+0.018}_{-0.024}$, $σ_8=0.777\pm 0.024$, $S_8\equiv σ_8 \sqrt{Ω_\text{m} / 0.3}=0.791^{+0.023}_{-0.021}$, and $(1-b)=0.844^{+0.055}_{-0.062}$ for our baseline analysis when combined with BAO data. When considering a hydrostatic mass bias evolving with mass, we find $Ω_\text{m}=0.353^{+0.025}_{-0.031}$, $σ_8=0.751\pm 0.023$, and $S_8=0.814^{+0.019}_{-0.020}$. We verify the robustness of our results by exploring a variety of analysis settings, with a particular focus on the definition of the halo centre used for the extraction of shear profiles. We compare our results with a number of other analyses, in particular two recent analyses of cluster samples obtained from SPT and eROSITA data that share the same mass calibration data set. We find that our results are in overall agreement with most late-time probes, in very mild tension with CMB results (1.6$σ$), and in significant tension with results from eROSITA clusters (2.9$σ$). We confirm that our mass calibration is consistent with the eROSITA analysis by comparing masses for clusters present in both Planck and eROSITA samples, eliminating it as a potential cause of tension.

Aymerich, G. [Orsay, IAS; AIM, Saclay] (ORCID:0009↗