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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

NREL Fleet Analysis Support Through Technology Integration Collaboration

This study leveraged the partnership between the United States Department of Energy's (DOE) Clean Cities Coalition Network and the Association for the Work Truck Industry (NTEA) to launch a vehicle and fleet analysis project that assisted fleets in identifying opportunities to save energy, improve efficiency, reduce costs, and meet environmental goals via short term data logging and analysis. The National Renewable Energy Laboratory (NREL) sought to establish a process that included initial data acquisition, provided data storage, and developed analytic methods to inform fleets of areas of opportunity based on approximately 30 days of in use vehicle performance data. However, long-term the project will require ongoing funding to fully develop and maintain the data sharing platform and to produce more complex analysis.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Phenomenon Identification and Ranking Table Development for Future Application Figure-of-Merit Studies on Thermal Energy Storage Integrations with Light Water Reactors

There are no standard prioritization criteria for evaluating thermal energy storage (TES) options for use in integrated energy systems. A framework for proposing, analyzing, and presenting energy storage integration with power producers and users is presented along with a specific figure-of-merit (FOM) study based in this framework. This basis for evaluating storage technologies can provide a structure for the energy industry to analyze and prioritize energy storage in different applications and environments. The phenomena identification and ranking table (PIRT) presents a series of design questions specific to energy storage applications. The FOM study, built in this PIRT framework based on a nuclear-renewable hybrid energy system using TES to produce power and provide process energy for a secondary user, successfully identified specific technologies to use based on the project requirements. Expanding the library of projects using this framework will expand the deployable options for energy storage and increase its potential for energy security.

25 ENERGY STORAGE↗
DOE OSTI2020

Flexible Nuclear Energy for Clean Energy Systems

As part of the Nuclear Innovation: Clean Energy Future initiative, this report describes flexibility in nuclear systems, the value it can bring, and international experiences surrounding flexible nuclear energy. Flexible nuclear energy for this report is defined as “The ability of nuclear energy generation to economically provide energy services at the time and location they are needed by end-users. These energy services can include both electric and non-electric applications utilizing both traditional nuclear power plants and advanced integrated systems.” Flexibility in nuclear systems is enabled by three main mechanisms: core ramping, integrated energy systems with multiple byproducts, and thermal storage. The value of this flexibility on a $\$$/MW and a $\$$/MWh basis can be estimated through a combination of physics and economics modeling. International agencies describe their experiences in operating flexible nuclear systems and describe their plans for increasing nuclear flexibility in a clean energy system, likely with a high penetration of variable renewable energy.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Genetically engineered poplar wood effectively enhances the efficiency of deep eutectic solvent-mediated one-pot processing

Although lignocellulosic biomass is a renewable resource with the potential to replace fossil-derived fuels and chemicals, its recalcitrance, largely due to lignin, limits its utilization. Recent advancements in genetic engineering have produced transgenic trees with reduced lignin content and/or modified lignin structure without compromising growth traits. Here, three engineered poplar varieties are evaluated as feedstocks using a biocompatible one-pot deep eutectic solvent-mediated process that integrates biomass fractionation and enzymatic saccharification within a single reactor, eliminating water washing and reconditioning. All transgenic poplars exhibit higher fermentable sugar yields than wild-type (WT) trees. Notably, QsuB poplar, incorporating 3,4-dihydroxybenzoate in lignin, achieves the highest glucose conversion yield of 91.3% (vs. 73.0% from WT). AT5 and MdCHS3 poplars, incorporating ferulate esters and naringenin, also demonstrate improved glucose yields (86.7 and 84.7%, respectively), confirming reduced biomass recalcitrance. Additionally, residual lignins are valorized via hydrogenolysis into phenolic compounds, with comparable alkylphenol production across all lines. These findings demonstrate that the transgenic poplar lines not only serve as superior feedstocks for sugar conversion but also provide a rich resource for phenolic compound production, enhancing the operational and economic viability of integrated biorefinery processes.

biomasses↗
DOE OSTI2025

AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Semiconductor Nanostructures by Scientific Design (Final Technical Report)

We developed and used quantum simulation techniques to predict structural, electronic, optical and thermal properties affecting the scientific design of semiconductor nanostructures. We focused on the study of surfaces and interfaces at the nanoscale, in particular embedding and solvation of semiconductor nanoparticles, and provided a microscopic description of nanostructured materials in realistic environments, directly comparable with experimental conditions. We addressed three major problems: (i) the design of realistic structural models of integrated nanostructures, e.g. quantum dots embedded in solid matrices and nanostructures immersed in fluids; (ii) the development of efficient and accurate calculations to describe the excited state properties of nanostructures, beyond standard Density Functional Theory, so as to understand and predict electronic properties involved in renewable energy applications, for example solar conversion devices. In particular we developed and used methods based on a many body perturbation theory. (iii) the development of calculations of heat transport properties of nanostructured materials, for thermoelectric applications, including predictive calculations of thermal and electrical conductivity and of Seebeck coefficients. Specifically, we developed and used methods based on the Boltzmann transport equation and molecular dynamics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Advance Distribution Management System (ADMS)

This presentation explores the transition from traditional distribution management systems to Advanced Distribution Management Systems (ADMS) as a foundation for smart grid development. It highlights the key benefits of ADMS, including enhanced reliability, improved operational efficiency, and increased situational awareness. The presentation also addresses common implementation challenges such as system integration, data management, and organizational readiness. It concludes with a forward-looking perspective on the evolving role of ADMS and essential takeaways for utilities and stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Wind 2H2 Project (CRADA Final Report)

The Xcel-NREL Wind Electrolysis R&D Project is designed to gain knowledge and insight about producing hydrogen from electricity for use as a vehicle fuel and as a “stored wind”. The project will analyze producing hydrogen directly from wind as well as from the electric grid. In addition, it will look at storing hydrogen and then using it to put electricity back onto the grid. Modifications were made to the original scope to also include electric vehicles integration with the electric grid. This report addresses Task 8 portion of the CRADA, focused on testing and measuring the attributes of plug-in electric vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2020

Techno-Economic Modelling of Tidal Energy Converter Arrays in the Tacoma Narrows

Hydrokinetic tidal energy converter (TEC) technology is yet to become cost competitive with other renewable energy sources. Understanding the interaction between energy production and the costs incurred harvesting that energy may unlock the economic potential of this technology. Although hydrodynamic simulation of TEC arrays has matured over time, including demonstration of how small and large arrays affect the resource, integration of cost modelling is often limited. The advanced ocean energy array techno-economic modelling tool ‘DTOcean’ enables designers to calculate and improve the levelised cost of energy (LCOE) of an array through parametric simulation of the energy extraction, design of the electrical network, moorings and foundations, and simulation of the installation and lifetime operations and maintenance of the array. This work presents a verification of DTOcean’s ability to simulate the techno-economic performance of TEC arrays by reproducing the hypothetical RM1 reference model, a semi-analytical model of a TEC array based in the Tacoma Narrows of Washington state, U.S.A. It is demonstrated that DTOcean can produce a reasonable estimate to the LCOE predicted by the reference model, giving (in Euro cents per kiloWatt hour) 36.69 ¢/kWh against the reference model’s 34.612 ¢/kWh for 10 TECs, while for 50 TECs, DTOcean calculated 20.34 ¢/kWh compared to 17.34 ¢/kWh for the reference model.

Topper, Mathew B. R. (ORCID:0000000347324347)↗
DOE OSTI2021

Heat Based Power Augmentation for Modular Pumped Hydro Storage in Smart Buildings Operation

In the U.S., building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground-level. This unique design eliminates the geographical limitation associated with existing state of the art energy storage technologies. It is easy to be scaled for building level, community level and grid level applications. Using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g. from thermal storage, Combined Cooling, Heat and Power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Chen, Yang↗
DOE OSTI2023

Transient Modelling of HTGR Thermal Load Follow in Modelica

The ability of nuclear power plants to load follow electrical power demand is particularly important as we move to higher variable renewable (VRE) energy production on low carbon energy grids. High temperature gas reactors (HTGRs) are capable of load following by adjusting thermal output, but their high temperatures and thermal inertia significantly impact the power transients they are able to follow. To aid investigation into new integrated energy systems with advanced reactors, it is necessary to have an open-source transient model of a full HTGR primary and secondary. This paper introduces two new open-source models for a HTGR and corresponding balance of plant (BOP) written for the HYBRID library developed at Idaho National Laboratory. A transient thermal load follow test case was taken by the work from Brits et al. to benchmark our open-source model against transient operation results from other proprietary codes that exist. The control scheme for the two new models in combination is discussed and a comparison of the response in transient load follow between the two codes performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2023

Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands.

Chen, Yang↗
DOE OSTI2022

Development of integrated screening, cultivar optimization, and verification research (DISCOVR): A coordinated research-driven approach to improve microalgal productivity, composition, and culture stability for commercially viable biofuels production

To address major knowledge gaps and barriers to the commercial development of algal biomass for biofuels and co-products, a collaborative consortium, Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR), was established in 2016. Funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO), this consortium constitutes a partnership between four DOE national laboratories - Pacific Northwest National Laboratory (PNNL), Los Alamos National Laboratory (LANL), the National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) - and the Arizona Center for Algae Technology and Innovation (AzCATI) at Arizona State University. To address the barriers of strain selection for achieving high seasonal productivities with a suitable composition and culture resilience, a tiered strain down-selection pipeline is implemented. At Tier I, the temperature and salinity tolerance of strains is determined in flask cultures; at Tier II, the areal biomass productivity and composition is determined in climate-simulation photobioreactors; at Tier III, the productivity and culture stability are determined in outdoor raceways. The top performing strains move forward to long-term testing at the algae testbed site at AzCATI to generate annual biomass productivity data. Concurrent to the strain down-selection in the DISCOVR pipeline, hypotheses for increasing biomass productivity, shifting biomass composition to enhance intrinsic value, and improving culture stability and resistance to pests are also tested. Techno-economic analyses are carried out to determine whether promising findings from laboratory studies or proposed modifications in outdoor pond cultivation conditions translate into reductions in the minimum biomass selling price (MBSP). Notably, in the three years following the launch of DISCOVR, annual biomass productivity has increased from 11.7 to 17.6 g m -2 day -1 , resulting in an MBSP decrease from 824 to 611 $ ton -1 .

09 BIOMASS FUELS↗
DOE OSTI2025

Developing Fluorescence-Based Sensors to Support Rare Earth Element Separation

Rare earth elements (REEs) are essential to most renewable energy technologies. Unfortunately, as we transition to sustainable energy production, the demand for REEs is rapidly growing well beyond current rates of production. As a result, novel means of efficient, scalable, and easily adaptable methods for processing primary and recycle feedstocks are needed. Development and integration of sensors for highly selective in-line monitoring can support more efficient design and testing of such novel separation processes, as well as more cost-effective deployment of those separation flowsheets. Work here will explore the application of fluorescence spectroscopy, a highly sensitive and selective technique, to quantify multiple lanthanides in complex mixtures including known interferents or quenching agents. Results include identification of the optimal excitation wavelength and the limit of detection of various rare earth elements as well as the performance of data-science-based quantification approaches in streams where “unknowns” are present. Overall, the data science tools in conjunction with optical sensor data were able to quantify analytes in the presence of other lanthanides which can be anticipated in the actual industrial stream. Here we include characterization of lanthanides in a microfluidic device similar to those used in new process development. This study demonstrates the capability of utilizing fluorescence spectroscopy to quantify analytes in a complicated solution matrix, suggesting this is a successful approach for in-line monitoring to optimize the separation efficiency in an industrial stream.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Coupled Heat Power Operation of Smart Buildings via Modular Pumped Hydro Storage

In the United States, building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings’ energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low-grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high-efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground level. This unique design eliminates the geographical limitation associated with the existing state-of-the-art energy storage technologies. It is easy to be scaled for building level, community level, and grid level applications. By using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g., from thermal storage, combined cooling, heat and power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Here, several groups of comparison experiments have been conducted, and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗