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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Integrating renewable energy into mining operations: Opportunities, challenges, and enabling approaches

Mining is one of the most energy-intensive industries worldwide. It also provides a critical source of raw materials for the manufacturing, transportation, construction, and energy sectors. Demand for raw materials is projected to increase as the world population grows and many low-income economies become middle-income countries. This growth in mineral demand, coupled with falling mineral ore grade, will likely increase the mining industry’s energy demand, used for activities across exploration, extraction, beneficiation and processing, and refining. At the time of this writing, mine operations are – due to their remoteness – dependent on fossil fuels such as diesel, heavy oils, and coal. In principle, mining could use energy recovery, renewable energy, and carbon capture to supplement, replace, or mitigate the impacts of fossil fuel use. However, a combination of renewable-energy technologies would be required. We explore challenges, opportunities, and enabling approaches to integrate renewable energy technologies into mining operations by examining the literature, including academic work, technical reports, and data produced by international agencies. Here, we find that despite numerous opportunities, technical issues still need to be considered, but solutions can tailor renewables to the mining industry. Further research should focus on identifying specific opportunities, technologies, and implementation strategies across the value chain of a variety of minerals with similar operational procedures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Convolutional neural networks for intra-hour solar forecasting based on sky image sequences

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%-39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 x 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed.

14 SOLAR ENERGY↗
DOE OSTI2023

Interfacial engineering via laser ablation for high-performing PEM water electrolysis

A rationalized interfacial design strategy was applied to tailor the porous transport layer (PTL)-catalyst layer (CL) contact and the PTL bulk-phase architecture. Particularly, at the PTL-CL interface, our results reveal that laser ablated sintered titanium power-based PTLs improve electrolyzer performance at both the H2NEW Consortium baseline catalyst loading of 0.4 mgIr cm -2 as well as at the ultra-low catalyst loading of 0.055 mgIr cm -2 . Under ultra-low catalyst loadings, the laser ablated PTL demonstrates maximum reduction of 230 mV compared to the commercial PTL at 4 A cm -2 , and reduces by 68 mV at 3.2 A cm -2 under H2NEW baseline loading. Laser ablation alters the titanium phase at the interface, so it forms more uniform structure like a microporous layer or a backing layer, leading to an increase in the surface area in contact with the catalyst layer while preventing the membrane from deforming into the PTL. Moreover, we reveal that bulk-phase architecture modification of the PTL by ablating patterned pores at the flow field-PTL interface improves mass transport without sacrificing contact at the CL-PTL interface. Overall, laser ablation of the PTL is an effective method to customize interfacial design to enhance proton exchange membrane electrolyzer performance.

08 HYDROGEN↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2024

Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S.

Weather data plays a critical role in renewable energy analysis. Compared to using multiple Actual Meteorological Years, simulations using a single typical year require significantly fewer computational resources. Previous efforts to create typical weather datasets for renewable energy analysis either lack justified or optimized strategies for selecting and weighting different weather parameters or are limited to a few specific locations. Here, in this study, we developed a dataset comprising Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for over 2000 locations across the U.S., based on data from NASA's POWER project. The strategies for creating TSYs and TWYs were optimized based on the simulated outputs of various PV systems and wind turbines in 16 representative cities. This dataset provides an efficient means for the rapid evaluation and optimization of renewable energy systems throughout the entire U.S. Additionally, the optimal strategies identified in this study can be directly applied to create near-optimal TSYs and TWYs for most locations worldwide. However, readers can also employ the optimization approach presented in this work to develop optimal strategies tailored for particular regions.

NASA POWER↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

UrbanPop: A spatial microsimulation framework for exploring demographic influences on human dynamics

Ensuring the social equity of planning measures in social systems requires an understanding of human dynamics, particularly how individual relationships, activities, and interactions intersect with individual needs. Spatial microsimulation models (SMSMs) support planning for human security goals by representing human dynamics through realistic, georeferenced synthetic populations, that a) provide a complete representation of social systems while b) also protecting individual privacy. In this paper, we present UrbanPop, an open and reproducible SMSM framework for analysis of human dynamics with high spatial, temporal, and demographic resolution. UrbanPop creates synthetic populations of demographically detailed worker and student agents, positioning them first at probable nighttime locations (home), then moving them to probable daytime locations (work/school). Summary aggregations of these populations match the granular detail available at the census block group level in the American Community Survey Summary File (SF), providing realistic approximations of the actual population. UrbanPop users can select particular demographic traits important in their application, resulting in a highly tailored agent population. We first lay out UrbanPop's baseline methodology, including population synthesis, activity modeling, and diagnostics, then demonstrate these capabilities by developing case studies of shifting population distributions and high-risk populations in Knox County, TN during the global COVID-19 pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Hierarchical thermal-conductive polymer nanocomposites for thermal management

Managing heat in electrical conductors is a major challenge to meet the demands for sustainable energy use and electrical reliability, most notably power electronics and energy-critical electrical machines. Achieving such disparate functionalities, such as high temperature thermal and electrical reliability, require rational design and manufacturing of thermal conductor material and its hierarchical structures. Here we present hierarchical thermal-conductive nanocomposites, consisting of nanostructured ceramic conformal coating and aligned ultrahigh molecular weight polyethylene fiber, to tailor heat dissipation in electric conductors. The hybrid aligned thermal interface exhibits a highly desirable temperature dependent anisotropic high thermal conductivity with 0.98W m –1 K –1 and dielectric strength with 3.4. In addition, electrically insulating thermal interfaces demonstrate high-performing and reliable electrical systems under the dynamic load conditions. The surface temperature of heterogeneous ceramic-polymer encapsulated conductor is 17.8 °C lower than that of polymer-encapsulated conductor at the same electrical load. Consequently, the findings shown here hold great promises for directing heat extraction in electrical machine systems, advancing thermal management for emerging electronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Lithiation bridged molecular conducting magnets

Combining ferromagnetism and metallic conductivity has been extensively studied in contemporary materials science. However, building such multifunctionalities in molecular chemistry is particularly challenging, on which two collective properties are long considered as mutually exclusive. Here, we report molecular conducting magnet consisting of two-dimensional graphene and molecular planar magnetic networks bridged by electrochemical lithiation, exhibiting the co-existence of distinct magnetism and conductivity and magneto-electric coupling effects. Here, the room temperature magnetoresistance and magnetoelectric coupling coefficient of such complex solid reach 10.7% and 1.77 V/Oe cm, respectively, which can be further tailored by modulating the interaction between graphene and molecular magnetic network. In addition, the graphene passivation simultaneously introduces environmental stability of molecular mag-nets. The electrochemical lithiation offers a pathway towards coupling magnetism and conductivity in molecular complexes.

2D organic complexes↗
DOE OSTI2024

Coal-derived conductive pavement for winter de-icing: prototype, modeling, and simulation

Existing pavement de-icing methods result in high installation and maintenance costs, traffic delays, excessive weariness and corrosion, and negative environmental and safety impacts. To overcome these challenges, this paper presents an innovative pathway to designing and constructing smart self-heating pavements using a low-cost coal-char bearing asphalt material. The conductive asphalt incorporates coal char, i.e., a key byproduct of the coal pyrolysis process, into the Stone Mastic Asphalt (SMA) mixture. This asphalt containing coal-derived solid carbon exhibits highly tailorable electrical conductivity, satisfactory mechanical and thermophysical properties, and superior cost-efficiency as compared to other conductive pavement materials. The de-icing performance was also demonstrated by laboratory experiments on a bench-scale prototype. Furthermore, an efficient thermal network model was developed and validated by experiments to investigate the transient thermal behavior and energy performance of the Ohmic heating pavement system. Furthermore, the whole-year energy simulation case studies were conducted on a bridge pavement with an annual energy use of 24.6–1444.8 kWh/m 2 , showcasing its potential in field applications across cool humid, cold humid, and subarctic/arctic climate zones.

42 ENGINEERING↗
DOE OSTI2026

A methodology for domain overlapping coupling of thermal-hydraulic systems

Multi-scale coupling has increasingly drawn attention as a promising approach for modeling thermal systems. Traditional system codes provide fast and robust predictions at the plant scale, while high-fidelity computational fluid dynamics (CFD)-based tools resolve localized flow and heat transfer phenomena with greater accuracy. By combining these complementary strengths, co-simulations enable multi-scale analysis that would otherwise be computationally prohibitive for a standalone CFD code. Here, this work introduces a robust and problem-agnostic domain overlapping (DO) coupling between the system thermal-hydraulic (STH) code System Analysis Module (SAM) and the coarse-mesh CFD code Pronghorn. Both applications belong to the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite, a code suite in active development at the Idaho National Laboratory (INL), tailored for multi-physics analysis of advanced reactors. Unlike previous approaches, BlueCRAB supports an agnostic interface between codes based on different fidelity, while its coupling formulation can address arbitrary flow geometries with multiple inlets and outlets in coupled components. The implemented method leads to consistent pressure drops, enthalpies, and scalar concentrations between coupled SAM and Pronghorn simulations. The methodology is demonstrated through two verification tests, which ensure the numerical consistency and conservation across the codes, and through one validation test against experimental data. The proposed problems explore different physical aspects inherent to thermal systems, with particular attention given to nuclear reactor analysis. These include buoyancy-driven flows, complex flow patterns, and setups with multiple inlets and outlets, representing challenges in advanced reactor applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Polymer-iron oxide hybrid films for controlling electrokinetic properties

Electrokinetic phenomena at polymer-water interfaces are central to technologies for water purification, ion separations, and energy conversion, yet the ability to systematically control polymer surface charge and associated electrokinetic processes remains limited. Here, we demonstrate a simple liquid-phase infiltration (LPI) method to synthesize polymer–metal oxide hybrid films with controllable interfacial properties. Hydroxy-terminated poly(2-vinylpyridine) (P2VP-OH) brushes grafted to silicon substrates were infiltrated with iron nitrate from ethanolic solution, followed by low-temperature thermal treatment to convert the infiltrated precursor into iron oxide. Spectroscopic ellipsometry, X-ray photoelectron spectroscopy, and thermogravimetric analysis confirmed oxide incorporation and hybrid film formation without polymer degradation. Electrokinetic measurements reveal that the hybrid films acquire the electrokinetic properties of the infiltrated oxide, with concentration-dependent streaming potentials and surface conductivities closely matching those of pure iron oxide films. These results establish metal oxide infiltration as a scalable and low-cost strategy for controlling interfacial charge in polymer surfaces. The approach introduces new materials and design parameters for tailoring ion selectivity, transport, and energy conversion, with broad implications for the development of advanced membranes, electrokinetic harvesting devices, and polymer-supported oxide electrodes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Traction-separation response of bilayer graphene interfaces: The role of an intercalated single atomic layer of water molecules and hydroxyl groups

Molecular dynamics simulations of the normal and shear traction-separation response of dry and wet bilayer graphene interfaces were performed. The influence of a single, atomic layer of water molecules sandwiched between graphene layers and hydroxyl functionalization on one or both layers were investigated. Hydroxyl groups provided a shielding effect during normal separation while favoring stick-slip friction under sliding conditions. The single, atomic layer of water molecules confined between hydrophilic and asymmetric hydrophobic/hydrophilic surfaces was found to ease sliding friction. The mechanisms arose from the balance between the attractive and repulsive graphene/water interactions through bridges of single water molecules between the two surfaces and the structure of the water monolayer under confinement for which an out-of-plane water/water hydrogen bond network could not be developed. The single, atomic layer of water molecules reduced the sliding friction the most when both surfaces were hydrophilic and lowered the sliding friction of the asymmetric hydrophobic/hydrophilic interfaces to that when both surfaces were hydrophobic. An optimal hydroxyl surface coverage of less than 10% may exist for efficient water lubrication, with diminishing effect with increasing number of hydroxyl groups. Finally, the findings offer a design strategy for assembling bilayer graphene interfaces with tailored friction and sliding contact properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Formation of pyrophosphates across grain boundaries induces the formation of mismatched but oriented interfaces in silver phosphate polypods

The interfaces and their misfit defects determine the materials properties for a wide range of applications, such as electronic devices, photocatalysis, and mechanical engineering, etc. However, current understanding of atomic interfacial structures is limited. Here we discover a special interfacial structure, mismatched but oriented interface via two distinct facets. Transmission and scanning electron microscopy results suggest that, in Ag 3 PO 4 polypods structures, interfaces of {1 0 0} and {1 1 0}, ({1 0 0}/(1 1 0}), {1 0 0}/{1 1 1}, {1 1 0}/{1 1 1}, and {1 0 0}/{1 0 0}, etc., have a certain orientation relationship, corresponding to the energy minima and coincident site lattice of interfacial atoms as demonstrated by molecular dynamics simulations. Density functional theory demonstrates that the formation of pyrophosphate and/or phosphates rotation to bond across the interface compensate the lattice mismatch at the interfaces, as well as deformations of Ag-O bonds. Furthermore, our work opens up a new avenue for a much wider range of interfacial structures, allow for a higher diversity of structures, and shine light on tailoring crystal structures, morphologies, and the resulting properties.

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

Enhanced optoelectronic and elastic responses in fluorinated penta-BCN

We report surface passivation in two-dimensional (2D) materials is one of the best approaches to modulate the structural, dynamical, and mechanical stabilities thereby enhancing chemical and physical properties for optoelectronic applications. Here, we report an ab-initio investigation on structurally, thermally, dynamically, and mechanically stable, and experimentally feasible fluorinated penta-BCN (F-BCN) monolayer. The structural reconstruction after fluorination, increases bond lengths and thickness to reduce the average stiffness and elasticity attaining the mechanical isotropy. Nevertheless, the value of 2D Young’s modulus of F-BCN is comparable to the pristine structure at 6% of compression without mechanical instability. A significant bandgap tailoring is achieved, similar to that of mechanical sensitivity, due to applied strain. Remarkably, following the fluorine passivation, the monolayer’s induced wide direct bandgap semiconducting behavior improves optical absorbance and reflectivity, decreases energy loss, changes optical anisotropy, and makes it a promising candidate material for optoelectronic and nanomechanical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Modification of LiMn 2 O 4 surfaces by controlling the Acid–Base surface chemistry of atomic layer deposition

In this combined theoretical and experimental study, we report the effects of Al precursor selection on the growth chemistry, interfacial structure, and electrochemistry of Al 2 O 3 coatings on spinel LiMn 2 O 4 (LMO) surfaces by atomic layer deposition (ALD). Five Al precursors, exhibiting a range of Lewis acid-base properties, were used to establish trends in the ALD Al 2 O 3 growth chemistry on LMO powders in comparison to redox-inactive planar silicon substrates. We show that the Lewis acid-base properties of the Al precursor ligands can be used to tune both the Mn oxidation states and the Al 2 O 3 coverage on the LMO surface. Density functional theory calculations are used to examine the reaction mechanism of each precursor on LMO surfaces, elucidating a correlation between the Lewis acidity of the ligands and the decomposition thermochemistry. In-depth X-ray photoelectron spectroscopy and in situ Fourier transform infrared spectroscopy measurements support these theoretical predictions and further reveal how different Al precursors modify the atomic and electronic structure near the LMO surface during ALD. While the Mn oxidation state is strongly influenced by the Lewis acidity of the precursor ligand, the surface coverage and thickness of the Al 2 O 3 coating are a more representative descriptor of the electrochemical performance measured in coin cell experiments. Finally, we show how the ligand acid-base properties of ALD precursors can be used to rationally tailor atomic layer growth mechanisms, which may enable atomic level control over the structure of functionalized interfaces for various applications related to catalysis, semiconductors, and energy storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ghost states and surface structures of the charge density wave kagome metal ScV 6 Sn 6

In this work, we investigate the high-temperature phase of the kagome metal ScV 6 Sn 6 using scanning tunneling microscopy/spectroscopy (STM/S) and density functional theory calculations. STM topographic images of the cleaved sample reveal two distinct surface terminations: flat islands with Sn termination and trenches terminated by kagome layers with Sn as the outermost atomic layer. STS measurements on the Sn-terminated and kagome-terminated surfaces show significant differences, in particular the presence of large density of states near the Fermi level in the former case. Our first-principles calculations reveal that the charge density on the kagome-terminated surface gives rise to “ghost states” which show intensity away from surface atoms, arising due to hybridization of orbitals above the surface. These states can obscure the intrinsic properties of the surface, potentially leading to misattribution of the surface termination. This underscores the need for careful interpretation in STM studies, especially when discerning surface states of localized states. Understanding the surface structure of this versatile quantum material provides essential information for interpreting surface-sensitive experiments, tailoring material properties, engineering interfaces, and controlling stability and reactivity. This knowledge paves the way for further exploration and potential applications of kagome lattice materials in various fields, including quantum computing, topological physics, and advanced electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A comparative study on cubic and tetragonal Ce-ZrO 2 supported Rh catalysts for N 2 O decomposition

Zirconium oxide (ZrO 2 ) exhibits strong synergy with cerium oxide (CeO 2 ), acting as a structural and electronic promoter during catalytic redox reactions. As a result, Ce-ZrO 2 composite oxides are widely used as supports in various catalytic systems. In our previous work, we demonstrated that the incorporation of Zr 4+ into the CeO 2 lattice significantly enhanced Rh dispersion, improved redox ability, and stabilized surface Rh species, which collectively boosted the de-N 2 O activity of Rh/Ce-ZrO 2 catalysts. Building on these findings, the present study emphasizes that the crystallographic phase of Ce-ZrO 2 , governed by the Ce/Zr ratio, plays a decisive role in tuning the physicochemical environment of Rh active sites and thereby optimizing catalytic performance. In conclusion, tailoring the Ce/Zr ratio to favor the cubic fluorite structure emerges as a promising strategy for the rational design of highly active and stable catalysts for N 2 O decomposition and potentially other redox-sensitive environmental applications.

36 MATERIALS SCIENCE↗