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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SMART Mobility. Urban Science Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The Urban Science (US) Pillar focuses on maximum-mobility and minimum-energy opportunities associated with emerging transportation and transportation-related technologies specifically within the urban context. Such technologies, often referred to as automated, connected, efficient (or electrified), and shared (ACES), have the potential to greatly improve mobility and related quality of life in urban areas. Although all the SMART Mobility research pillars share some commonalities, Urban Science strives to model, analyze, and gain insights from the perspective of human settlements (the “city”) as a living organism. This is especially critical as the United States is one of the most urbanized countries, and as more and more of the global population migrates to urban areas.1 The urban mobility system consists of a complex network that reaches well beyond roads and vehicles and includes significant investments in public transit, private mobility services (such as taxis and transportation network companies, or TNCs), significant parking reserves, and curb management practices, not to mention the abundance of emerging on-demand micromobility services for the movement of people and goods such as e-bikes and scooters, which make the urban space a dynamic laboratory for mobility. Urban spaces also concentrate employment, markets, services, and attractions, which are the destinations for most trips. The concentration of human activities and ensuing density also creates the need and emphasis for space efficiency in urban environments, which is often less of a constraint in suburban or rural contexts. This mixture of transportation and mobility infrastructure and practice, combined with global urbanization trends, make urban spaces a critical focus of research for developing energy-efficient mobility systems (EEMS).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Mesolytic cleavage of homobenzylic ethers for programmable end-of-life function in redoxmers

Irreparable chemical damage to redox-active monomers, oligomers, and polymers (i.e. redoxmers) limits the lifetime of energy storage devices (e.g. redox flow batteries) by causing electrode and membrane fouling, as well as irreversible capacity loss. To predictably restore device and materials performance, it is desirable to add programmed destruction capabilities into these damage-prone materials. Here, we report the use of triggerable retrograde reactions to achieve programmable end-of-life function in redoxmers. Retrograde reactions are intended to break up damaged redoxmer materials into more soluble small-molecule constituents to prevent irreparably fouling battery interfaces. We investigated the redox-triggered mesolytic cleavage of homobenzylic ethers (HBEs) for this purpose. Combining experimental methods with simulations, we probed the influence of ring substituents on the programmable bond-scission behavior of HBEs. Variation of para-substituents on the HBEs allowed us to modify the oxidation potential, degradation pathway, and electrochemical mechanisms of the generated products. Given its ideal oxidation potential, we selected the para-methoxy-based HBE as the cleavage scaffold and integrated it with dialkoxybenzene, TEMPO, viologen, and para-nitrobenzene redox centers for compatibility, bulk deconstruction, and electrode defouling tests. Upon applying a high oxidation potential, the redox-active pendant is cleaved from the backbone via HBE bonds; of the four redox centers tested, three of them remained fully redox active. Viologen-appended redox active polymers with HBE linkers were cleaved to fully separate the redox centers from the backbone. Polymer-filmed electrodes were partially restored following HBE oxidation defouling. Here these studies highlight the co-design of new materials functions for developing sustainable energy storage materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
NASA NTRS1985

An integrated development facility for the calibration of low-energy charged particle flight instrumentation

A system was developed for the calibration and development of thermal ion instrumentation. The system provides an extended beam with usable current rates, approx. 1 pA/sq cm, at beam energies as low as 1 eV, with much higher values available with increasing energy. A tandem electrostatic and variable geometry magnetic mirror configuration within the ion source optimizes the use of the ionizing electrons. The system is integrated under microcomputer control to allow automatic control and monitoring of the beam energy and composition and the mass and angle-dependent response of the instrument under test. The system is pumped by a combination of carbon vane and cryogenic sorption roughing pumps and ion and liquid helium operating pumps.

Biddle, A. P.↗
NASA NTRS1977

Technological development of cylindrical and flat shaped high energy density capacitors

Cylindrical wound metallized film capacitors rated 2 micron F 500 VDC that had an energy density greater than 0.3 J/g, and flat flexible metallized film capacitors rated at 2 micron F 500 VDC that had an energy density greater than 0.1 J/g were developed. Polysulfone, polycarbonate, and polyvinylidene fluoride (PVF2) were investigated as dielectrics for the cylindrical units. PVF2 in 6.0 micron m thickness was employed in the final components of both types. Capacitance and dissipation factor measurements were made over the range 25 C to 100 C, and 10 Hz to 10 kHz. No pre-life-test burning was performed, and six of ten cylindrical units survived a 2500 hour AC plus DC lift test. Three of the four failures were infant mortality. All but two of the flat components survived 400 hours. Finished energy densities were 0.104 J/g at 500 V and 0.200 J/g at 700 V, the energy density being limited by the availability of thin PVF2 films.

Zelik, J. A.↗
DOE OSTI2023

Quantifying conditional probabilities of fish-turbine encounters and impacts

Tidal turbines are one source of marine renewable energy but development of tidal power is hampered by uncertainties in fish-turbine interaction impacts. Current knowledge gaps exist in efforts to quantify risks, as empirical data and modeling studies have characterized components of fish approach and interaction with turbines, but a comprehensive model that quantifies conditional occurrence probabilities of fish approaching and then interacting with a turbine in sequential steps is lacking. We combined empirical acoustic density measurements of Pacific herring ( Clupea pallasii ) and when data limited, published probabilities in an impact probability model that includes approach, entrainment, interactions, and avoidance of fish with axial or cross-flow tidal turbines. Interaction impacts include fish collisions with stationary turbine components, blade strikes by rotating blades, and/or a collision followed by a blade strike. Impact probabilities for collision followed by a blade strike were lowest with estimates ranging from 0.0000242 to 0.0678, and highest for blade strike ranging from 0.000261 to 0.40. Maximum probabilities occurred for a cross-flow turbine at night with no active or passive avoidance. Estimates were lowest when probabilities were conditional on sequential events, and when active and passive avoidance was included for an axial-flow turbine during the day. As expected, conditional probabilities were typically lower than analogous independent events and literature values. Estimating impact probabilities for Pacific herring in Admiralty Inlet, Washington, United States for two device types illustrates utilization of existing data and simultaneously identifies data gaps needed to fully calculate empirical-based probabilities for any site-species combination.

collision risk↗
DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

PotLib 2023: New version of a potential energy surface library for chemical systems

POTLIB is a library of global and semiglobal potential energy surface subprograms. The library currently features 410 entries, including both single-state entries and multi-state entries. When one calls the routine of a single-state entry, it returns the ground-electronic-state adiabatic potential energy surface at the input geometry. In addition, some entries also return the gradient of the surface. Further, when one calls a multi-state entry, it returns a diabatic potential energy matrix (DPEM). If the entry also has the gradient of the DPEM, one can compute adiabatic surfaces, their gradients, and the nonadiabatic coupling vectors (NACs) from the DPEM and its gradient by diagonalization. Some but not all the routines conform to one of a set of standard interfaces. The goal is to facilitate chemical dynamics research by collecting and disseminating a comprehensive collection of state-of-the-art potential energy routines (developed by a wide, international group of researchers) with systematic and well-defined interfaces for use with chemical dynamics programs. Systems in the library include CHArO 2 , CHN 2 O + , CH 2 O, CH 2 O 2 , CH 3 N 2 , CH 3 O, CH 4 , CH 4 Br, CH 4 Cl, CH 4 F, CH 4 O, CH 4 OCl, CH 4 OF, CH 4 OH, CH 5 , CH$^{+}_{5}$, CH 5 N, CH 5 O 2 , CH 8 O 2 , C 2 H 2 N 2 O, C 2 H 2 O, C 2 H 4 N, C 2 H 4 O 4 , C 2 H 6 Cl, C 2 H 6 F, C 2 H 6 H, C 2 H 6 O, C 2 H 6 OH, C 2 H 7 , C 2 O 2 , C 3 H 4 O 2 , C 3 H 7 NO, C 6 H 6 O, C 6 H 6 S, C 7 H8S, HBrCl, HCl 2 , HF 2 , HI 2 , HLiF, HNaF, HO 2 , HO 3 , HOBr, HSiO, H 2 Br, H 2 ClO, H 2 F, H 2 F 2 , H 2 FO, H 2 Na, H 2 O, H 2 O 2 , H 2 OBr, H 3 , H 3 Cl, H 3 ClN, H 3 ClO, H 3 N, H 3 O, H 3 O 2 , H$^{+}_{3}$, H 3 S, H 4 ClSi, H 4 N, H 4 NO, H 4 O 2 , H 5 GeO, H 5 Si, H$^{+}_{7}$, Al m H n , Al n , Ar N O, K 2 Rb 2 , NO 2 , N 2 O, N 2 O 2 , N 3 , N 4 , O 3 , and O 4 .

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

Advancing Clean Energy Equity

This year’s Financial Innovations Roundtable (June 16-17, 2022) focused on advancing clean energy equity and was co-hosted by the University of New Hampshire's Carsey School of Public Policy and the Federal Reserve Bank of New York. Clean energy technologies are better than ever, with costs continuing to decline. Yet the low-income and under-resourced communities – particularly communities of color, Native communities, and other traditionally marginalized populations – that are disproportionately impacted by climate and severe weather-related events lag in clean energy investments. Community Development Financial Institutions (CDFIs) have a long track record of providing access to capital to low- and moderate-income communities nationwide. Green Banks, established at state and local levels, use innovative financing to attract private capital and incentivize investments in clean and renewable energy. Together, the nation's network of more than 1,300 CDFIs and 21 Green Banks have the financing expertise and deep market understanding and relationships to finance a transition to clean energy. This event explored how Green Banks and CDFIs can funnel creditworthy projects to market and efficiently raise capital for them. Building on the Carsey School White Paper, Clean Energy Project Development for Low-Income Communities: Strengthening the Ecosystem for Delivering Solar Energy and Deep Efficiency Retrofits (Hangen, 2022), the FIR sought to identify options and create opportunities for Green Banks, CDFIs, and impact investors to collaborate in offering a range of products, approaches, and tools to better serve communities and individuals who have thus far been left out of the transition to clean energy. The event had 101 participants from a variety of sectors including CDFIs, Green Banks, mission-driven clean energy organizations, government agencies, banks, and impact investment professionals.

14 SOLAR ENERGY↗
DOE OSTI2024

Parameter development and characterization of laser powder directed energy deposition of Nb – Alloy C103 for thin wall geometries

This work focuses on the parameter development and microstructural characterization of Nb-based alloy C103 for thin wall structures produced via laser powder – directed energy deposition (LP – DED). Laser power and scanning speeds were varied as part of a design of experiments to identify adequate print parameters. Combinations were evaluated for relative density, porosity, and geometrical accuracy. A combination of a laser power of 1420 W and scanning speed of 14 mm/s resulted in a relative density >99%, exhibited a consistent weld bead profile and was used for further microstructural evaluation. A mix of optical and scanning electron microscopy (SEM) revealed small, slightly elongated grains along the edges and large epitaxial grains in the central region along the Build – Transverse view. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis revealed Hf rich columnar cells in the center that transition into an evenly spaced cellular structure towards the edges of the built sample. Electron backscatter diffraction (EBSD) scans show a sharp [001] texture when looking at the cross section of the sample along the build direction. The Build – Scan view revealed a zig – zag pattern that follows the back – and – forth deposition strategy that was used. Finally, microhardness measurements were taken in the as – built (AB) and stress relieved (SR) conditions to baseline preliminary mechanical properties. The AB condition exhibited a large amount of scatter in the data and averages up to 11% larger than the SR condition. The reduction in scatter upon applying the SR cycle are indicative of a large concertation of dislocations present in the AB condition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Renewable Energy Potential Model: Hawaii Geothermal Supply Curves

This dataset extends the development of the Renewable Energy Potential (reV) model to include geothermal energy, with a specific focus on Hawaii. Provided here are the results of two scenarios that were modeled for geothermal energy in Hawaii: binary enhanced geothermal systems (EGS) at a depth of 2.5 km and hydrothermal binary systems at a depth of 1.5 km. The resource data for both scenarios were derived from Lautze and Haskins (2024) using an exponential method. The PFA probability of heat map was used as a look up table for which temperature gradient to use (Lautze and Haskins, 2024). The dataset provides geospatial and techno-economic details for evaluating geothermal energy potential. It includes spatial coordinates, estimated capacity factors, developable area, resource potential, and annual energy production metrics. Economic details such as levelized cost of electricity (LCOE), site development costs, transmission costs, and fixed-charge rates are also included. The reV model, originally developed for wind and solar energy, incorporates these variables to evaluate deployment constraints related to land use, environmental and cultural factors, and grid integration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Binder-Free Wood Converted Carbon for Enhanced Water Desalination Performance

Here, the design and synthesis of high-performance and economical carbon electrodes play a critical role in developing energy-efficient water desalination technologies. As a sustainable approach, low-cost and abundant biomass materials are promising candidates to prepare porous carbon for capacitive deionization. In this study, binder-free porous carbon sheets are successfully prepared using natural balsa, pine, and basswood by thermal carbonization and treated by chemical activation. The carbon electrode materials converted from balsa and pine exhibit a comparable salt adsorption performance by capacitive deionization due to the extensive surface area, substantial electrical property, and superior hydrophilic performance. The following activation treatment of the balsa-converted carbon further enhances the surface and electrical properties and benefits the desalination performance. The salt adsorption capacity of the activated balsa electrode exhibits 12.45 mg g -1 . Additionally, 19.52 mg g -1 Pb 2+ and 20.06 mg g -1 Cr 3+ heavy metal adsorption capacity is also observed with the activated balsa electrode in 100 mg L -1 PbCl 2 and 50 mg L -1 CrCl 3 , respectively. To the best of authors’ knowledge, this is the highest NaCl adsorption capacity performance reported thus far by using pure wood converted carbon as the electrode, and these promising results indicate that activated balsa is an extraordinary material for water desalination.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Overcoming Anode Instability in Solid‐State Batteries through Control of the Lithium Metal Microstructure

Abstract Enabling the lithium metal anode (LMA) in solid‐state batteries (SSBs) is the key to developing high energy density battery technologies. However, maintaining a stable electrode–electrolyte interface presents a critical challenge to high cycling rate and prolonged cycle life. One such issue is the interfacial pore formation in LMA during stripping. To overcome this, either higher stack pressure or binary lithium alloy anodes are used. Herein, it is shown that fine‐grained ( d = 20 µm) polycrystalline LMA can avoid pore formation by exploiting the microstructural dependence of the creep rates. In a symmetric cell set‐up, i.e., LiǀLi 6.25 Al 0.25 La 3 Zr 2 O 12 (LLZO)ǀLi, fine‐grained LMA achieves > 11.0 mAh cm −2 compared to ≈ 3.6 mAh cm −2 for coarse‐grained LMA ( d = 295 µm) at 0.1 mA cm −2 and at moderate stress of 2.0 MPa. Smaller diffusion lengths (≈ 20 µm) and higher diffusivity pathway along dislocations ( D d ≈ 10 −7 cm 2 s −1 ), generated during cell fabrication, result in enhanced viscoplastic deformation in fine‐grained polycrystalline LMA. The electrochemical performances corroborate well with estimated creep rates. Thus, microstructural control of LMA can significantly reduce the required stack pressure during stripping. These results are particularly relevant for “anode‐free” SSBs wherein both the microstructure and the mechanical state of the lithium are critical parameters.

25 ENERGY STORAGE↗
DOE OSTI2023

Photoelectrochemical materials for solar energy conversion

Research and development on semiconductors for applications in solar energy conversion has witnessed rapid growth over the past several decades. With the aim of producing chemical fuels from sunlight, intense research efforts have focused on the discovery of semiconductors with crystalline structures and chemical compositions that have the potential to satisfy the complex set of required photoelectrochemical properties. This chapter focuses on the foundational structure-property relationships of selected oxide and nitride semiconductors relevant to their uses as n-/p-type photoelectrodes and as photocatalysts. Their solid-state structures and compositions are described, with a special emphasis on strategies proven effective at targeting suitable band gaps with strong visible-light absorption, efficient charge separation and diffusion of charge carriers, and optimal band edge energies for driving surface redox reactions for water splitting.

O’Donnell, Shaun↗
DOE OSTI2023

Harness the power of atomistic modeling and deep learning in biofuel separation

Biofuels offer a remarkable, sustainable energy source for a future of clean energy. The development of efficient biofuel separation plays a crucial role in achieving cost-effective utilization of biofuel. In this chapter, we provide an overview of the recent advancements in atomistic-level modeling and deep learning in the rational design of novel, efficient biofuel separation. The fundamental principles of quantum and statistical mechanics are covered in appropriate detail to highlight their underlying differences in theory. The methodologies of several molecular representations and deep learning algorithms applicable to biofuel separation are briefly demonstrated as well. The applications, successes, and risks of employing density functional theory, ab initio molecular dynamics, classical molecular dynamics, and deep learning are provided to showcase their recent accomplishments in biofuel separation as well as potential improvements in both methodology and application. Lastly, a vision for the future growth of these methods is illustrated.

deep learning, artificial intelligence, biofuels, ↗
DOE OSTI2021

A record high average ZT over a wide temperature range in a Single-layer Sb 2 Si 2 Te 6

Conversion of waste heat into usable energy requires development of thermoelectric materials with high efficiency in a wide temperature range. Here, using first principles theory and Boltzmann transport theory, we show that the thermoelectric performance of the p-type single-layer Sb 2 Si 2 Te 6 has a high figure of merit ZT of 2.62 at 900 K and a record high average ZT of ~1.93 (corresponding the conversion efficiency of ~23.2%) in the temperature range of 300-900 K. These values are significantly higher than the recently measured average ZT of ~0.57 in the temperature range of 310–823 K [Luo et al., Joule 4, 159–175 (2020)] in layered bulk Sb 2 Si 2 Te 6 . We attribute the large enhancement of ZT in the single layer material to the increase in the thermoelectric power factor resulting from the complex Fermi surface. Our work reveals the great potential of a single-layer Sb 2 Si 2 Te 6 for wide-temperature-range thermoelectric applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Active learning strategy for high fidelity short-term data-driven building energy forecasting

The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning—an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Furthermore, building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗