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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Industrial Assessment Center – integration of education and practice. Final progress report

The goal of the DOE’s Industrial Assessment Center (IAC) program is twofold: first, to help US manufacturing competitiveness by providing assessments and recommendations for small and medium-sized enterprises (SMEs) on energy efficiency, productivity, sustainability and competitiveness – including measuring the impacts of these recommendations on reducing greenhouse gas emissions; and second, to address a growing shortage of engineering professionals with applied energy and manufacturing-related skills by training a diverse cross-section of engineering students through hands-on involvement in these assessments. IUPUI has an IAC, which was established in 2011, in the Department of Mechanical and Energy Engineering, Purdue School of Engineering and Technology, IUPUI. The IAC has won the awards twice. We have built the center that has the expertise and infrastructure for quality energy assessments to manufactures and commercial building owners and aligned our current research and teaching activities to the DOE’ training mission with programs to train the next generation of industrial energy efficiency experts, with theory and hands-on experience in conducting energy assessment for small and median manufacturing enterprises. We have made recommendations that have the potential to save about $20M annually. We also initiated research projects to meet DOE priorities, specifically in the areas of smart manufacturing and cybersecurity. On the energy assessment side, the Center has provided energy assessments to 144 qualified companies, primarily located in Indiana. On the training side, the center has trained students in various programs, such as the department’s Bachelor of Science (BS) programs in Mechanical Engineering (ME) and BS in Energy Engineering (EEN), both are ABET accredited engineering programs, as well as a graduate level Energy Management and Assessment certificate program. At the present, the center has trained 95 students, 43 or them received the DOE issued certificates. Research projects were developed within the center to advance energy efficiency related technologies, which provided excellent opportunities for our trainees. The center has also been building a professional network with utilities, Manufacturing Extension Partnership (MEP), government agencies, and manufacturing companies, to increase our client base and promote collaborations. The center received the 2019 Center of Excellence of the Year Award and three students received the “Outstanding Achievement in Energy Engineering by an IAC Student” awards in the past five years.

42 ENGINEERING↗
DOE OSTI2022

Short-term solar radiation forecast using total sky imager via transfer learning

Ground-based sky cameras, which capture hemispherical images, have been extensively used for localized monitoring of clouds. This paper proposes a short-term forecasting approach based on transfer learning using Total Sky-Imager (TSI) images of the Southern Great Plains (SGP) site obtained from the Atmospheric Radiation Measurement (ARM) dataset. An accurate estimation of solar irradiance using TSI is key for short-term solar energy generation forecasting and optimal energy consumption planning. We make use of deep neural network architectures such as AlexNet and ResNet-101 to extract the underlying deep convolution features from TSI images and then train using an ensemble learning approach to model and forecast solar radiation. We demonstrate the performance of the proposed approach by showcasing the best and worst cases. Thus, the transfer learning approach significantly reduces the time and resources required for modeling solar radiation. We outperform with reference to another state-of-art technique for solar modeling using TSI images at different forecast lead times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data

The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Uncovering the Network Modifier for Highly Disordered Amorphous Li‐Garnet Glass‐Ceramics

Abstract Highly disordered amorphous Li 7 La 3 Zr 2 O 12 (aLLZO) is a promising class of electrolyte separators and protective layers for hybrid or all‐solid‐state batteries due to its grain‐boundary‐free nature and wide electrochemical stability window. Unlike low‐entropy ionic glasses such as Li x PO y N z (LiPON), these medium‐entropy non‐Zachariasen aLLZO phases offer a higher number of stable structure arrangements over a wide range of tunable synthesis temperatures, providing the potential to tune the LBU‐Li + transport relation. It is revealed that lanthanum is the active “network modifier” for this new class of highly disordered Li + conductors, whereas zirconium and lithium serve as “network formers”. Specifically, within the solubility limit of La in aLLZO, increasing the La concentration can result in longer bond distances between the first nearest neighbors of Zr─O and La─O within the same local building unit (LBU) and the second nearest neighbors of Zr─La across two adjacent network‐former and network‐modifier LBUs, suggesting a more disordered medium‐ and long‐range order structure in LLZO. These findings open new avenues for future designs of amorphous Li + electrolytes and the selection of network‐modifier dopants. Moreover, the wide yet relatively low synthesis temperatures of these glass‐ceramics make them attractive candidates for low‐cost and more sustainable hybrid‐ or all‐solid‐state batteries for energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Origin of increased helium density inside bubbles in Ni (1–$x$) Fe $x$ alloys

Due to virtually no solubility, He atoms implanted or created inside materials tend to form bubbles, which are known to damage material properties through embrittlement. Higher He density in nano-sized bub- bles was observed both experimentally and computationally in Ni (1–$x$) Fe $x$ -alloy samples compared to Ni. The bubbles in the Ni (1–$x$) Fe $x$ -alloys were observed to be faceted, whereas in elemental Ni they were more spherical. Molecular dynamics simulations showed that stacking fault structures formed around bubbles at maximum He density. Higher Fe concentrations stabilize stacking fault structures, suppress evolution of dislocation network around bubbles and suppress complete dislocation emission, leading to higher He density.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Mitochondrial-related gene expression profiles suggest an important role of PGC-1alpha in the compensatory mechanism of endemic dilated cardiomyopathy

Keshan disease (KD) is an endemic dilated cardiomyopathy with unclear etiology. In this study, we compared mitochondrial-related gene expression profiles of peripheral blood mononuclear cells (PBMCs) derived from 16 KD patients and 16 normal controls in KD areas. Total RNA was isolated, amplified, labeled and hybridized to Agilent human 4×44k whole genome microarrays. Mitochondrial-related genes were screened out by the Third-Generation Human Mitochondria-Focused cDNA Microarray (hMitChip3). Quantitative real-time PCR, immunohistochemical and biochemical parameters related mitochondrial metabolism were conducted to validate our microarray results. In KD samples, 34 up-regulated genes (ratios≥2.0) were detected by significance analysis of microarrays and ingenuity systems pathway analysis (IPA). The highest ranked molecular and cellular functions of the differentially regulated genes were closely related to amino acid metabolism, free radical scavenging, carbohydrate metabolism, and energy production. Using IPA, 40 significant pathways and four significant networks, involved mainly in apoptosis, mitochondrion dysfunction, and nuclear receptor signaling were identified. Based on our results, we suggest that PGC-1alpha regulated energy metabolism and anti-apoptosis might play an important role in the compensatory mechanism of KD. Our results may lead to the identification of potential diagnostic biomarkers for KD in PBMCs, and may help to understand the pathogenesis of KD. Highlights: • Thirty-four up-regulated genes were detected in KD versus health controls. • Forty pathways and four networks were detected in KD. • PGC-1alpha regulated energy metabolism and anti-apoptosis in KD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Impact of selenium addition to the cadmium-zinc-telluride matrix for producing high energy resolution X-and gamma-ray detectors

Abstract Both material quality and detector performance have been steadily improving over the past few years for the leading room temperature radiation detector material cadmium-zinc-telluride (CdZnTe). However, although tremendous progress being made, CdZnTe still suffers from high concentrations of performance-limiting defects, such as Te inclusions, networks of sub-grain boundaries and compositional inhomogeneity due to the higher segregation coefficient of Zn. Adding as low as 2% (atomic) Se into CdZnTe matrix was found to successfully mitigate many performance-limiting defects and provide improved compositional homogeneity. Here we report record-high performance of Virtual Frisch Grid (VFG) detector fabricated from as-grown Cd 0.9 Zn 0.1 Te 0.98 Se 0.02 ingot grown by the Traveling Heater Method (THM). Benefiting from superior material quality, we achieved superb energy resolution of 0.77% at 662 keV (as-measured without charge-loss correction algorithms) registered at room temperature. The absence of residual thermal stress in the detector was revealed from white beam X-ray topographic images, which was also confirmed by Infra-Red (IR) transmission imaging under cross polarizers. Furthermore, neither sub-grain boundaries nor their networks were observed from the X-ray topographic image. However, large concentrations of extrinsic impurities were revealed in as-grown materials, suggesting a high likelihood for further reduction in the energy resolution after improved purification of the starting material.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Synchronized monochromator and insertion device energy scans at SLS

Synchronous monochromator and insertion device energy scans were implemented at the Surfaces/Interfaces:Microscopy (SIM) beamline in order to provide the users fast X-ray magnetic dichroism studies (XMCD). A simple software control scheme is proposed based on a fast monochromator run-time energy readback which quickly updates the insertion device requested energy during an on-the-fly X-ray absorption scan (XAS). In this scheme the Plain Grating Monochromator (PGM) motion control, being much slower compared with the insertion device (APPLE-II type undulator), acts as a 'master' controlling the undulator 'slave' energy position. This master-slave software implementation exploits EPICS distributed device control over computer network and allows for a quasi-synchronous motion control combined with data acquisition needed for the XAS or XMCD experiment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Boundary edge networks induced by bulk topology

Here, we introduce an effective edge network theory to characterize the boundary topology of coupled edge states generated from various types of topological insulators. Two examples studied are a two-dimensional second-order topological insulator and three-dimensional topological fullerenes, which involve multileg junctions. As a consequence of bulk-edge correspondence, these edge networks can faithfully predict properties such as the energy and fractional charge related to the bound states (edge solitons) in the aforementioned systems, including several aspects that were previously complicated or obscure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Hydrogen and Battery Storage Technologies for Low-Cost Energy Decarbonization in Distribution Networks

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. Here, a proton exchange membrane electrolyzer can produce H 2 and serves as a utility controllable load. The produced H 2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H 2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H 2 at $\$1$ /kg are presented showing that the distribution network could produce 100% renewable electricity and H 2 could be produced at a cost of $\$1$ /kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.

25 ENERGY STORAGE↗
DOE OSTI2016

Assessment of the American Recovery and Reinvestment Act Upon the Department of Energy Weatherization Assistance Program: Assessment of the ARRA

The Department of Energy (DOE) administers the national low-income Weatherization Assistance Program (WAP). Under this program, DOE provides grants to states (grantees), which then provide grants to local weatherization agencies (subgrantees), to weatherize income-eligible homes for free. The American Recovery and Reinvestment Act (ARRA) of 2009 allocated $5 billion in funding to WAP, a very significant increase from an annual appropriation that ranged in the $200–250 million range for many years. Furthermore, as part of a major evaluation of WAP, states and local weatherization agencies were surveyed, in part, to assess their experiences during the ARRA period. The substantial funding increase created a number of issues for the national weatherization network: the political visibility of their programs increased significantly, organizational responsibilities were shifted, new laws and regulations were passed that impacted their programs, media attention of their programs increased, federal oversight of their programs increased; and programmatic costs increased because of the increased oversight and because ARRA required WAP to operate under the provisions of the Davis-Bacon Act of 1931. There was also some concern within the national weatherization network that the influx of ARRA funds would permanently damage nonfederal funding leveraging relationships. However, two-thirds of grantees and over 40% of subgrantees stated that they believe that the long-term impacts of ARRA on leveraging relationships will be positive.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A biophysical approach to the performance diagnosis of human–building energy interaction: Information (bits) modeling, algorithm, and indicators of energy flow complexity

Highlights: • Energy-related human-building interaction is analyzed at the biophysical system scale. • Energy flow-networking model of environmental building performance is suggested. • Information is suggested as a holistic building performance metric. - Abstract: This article addresses the utmost upstream impacts of human–building energy interaction by proposing a network-based model, algorithms, and indicators. Hypothesizing that human behavior is a key factor for the symbiotic development of building and the geobiosphere system, the author seeks emergy (spelled with an “m”)-information integrated measures to indicate dynamic system-level performance of the interaction with building energy flow topology. To validate the hypothesis and methods, four representative building cases were tested on the building form (envelope) of (i) a building with no occupant intervention (baseline), (ii) a building controlled by responsive human behavior (bioclimatic adaptation), (iii) a building with reinforced insulation and behavior-dominated control (passive design), and (iv) a net-zero energy building (NZEB). The results demonstrate that adaptive human behavior in building operation increases the information content and complexity of energy-flow networking, improving performance and sustainability. Findings also reveal that increasing information, complexity, and power (energy availability over time) parallel the general energetic features of developing biophysical systems (greater feedback, internalization, and recycling of materials and energies). It becames clear that active behavioral response is a dominant agent of sustainable environments even on a far broader system scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Strain release through hydrogen bond–mediated layer twisting

Strain engineering, enabling the precise control over structure and functional properties, is a key strategy for the design of advanced materials. However, the mechanisms governing strain evolution and release at the nanoscale remain largely unexplored. In this study, we leverage in situ heating transmission electron microscopy and synchrotron x-ray spectroscopy to investigate the strain relaxation pathways of boehmite (γ-AlOOH) at 575 kelvin by revealing real-time structural dynamics. Through tracking the moiré pattern evolution, we identify distinct strain release mechanisms, including layer twisting, defect formation, and domain restructuring. Our neural network potential calculations reveal that energy fluctuations at small twist angles are dominated by an interference-like interaction modulation of hydrogen bonds between boehmite interlayers, with metastable twisted structures corresponding to local minima of the potential energy landscape. This work establishes a previously unidentified paradigm of two-dimensional layer twisting mediated by hydrogen bonding, offering insights into strain-driven transformation mechanisms, and thus may have broad implications for strain in material and earth sciences.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

Szu, Harold H.↗
DOE OSTI2020

Thermal Properties of Sodium Borosilicate Glasses as a Function of Sulfur Content

SO3 additions, up to 3.0 wt%, were systematically investigated for effects on the physical properties of sodium borosilicate glass melted in air, with a sulfur-free composition of 50SiO2-10Al2O3-12B2O3-21Na2O-7CaO (wt%). Solubility measurements, using electron microscopy chemical analysis, determined the maximum loading to be ~1.5 wt% SO3. It was found that sulfur (here as sulfate) in this glass increased the glass transition temperature, thermal diffusivity, heat capacity, and thermal conductivity, and decreased the mass density. Structural analysis, performed with Raman spectroscopy, indicated that the borosilicate network polymerized with sulfur additions, presumably due to Na2O being required to charge compensate the ionic SO42- additions, thus becoming unavailable to form non-bridging oxygen in the silicate network. It is postulated that this increased crosslinking of the borosilicate backbone lead to a structure with higher dimensionality and average bond energy. This increased the mean free paths and vibration frequency of the phonons, which resulted in the observed increase in thermal properties.

Thermal properties, Borosilicate glass, Sulfur sol↗
DOE OSTI2022

Biopolymer Modified Cementitious Systems with Radically Superior Strength and Durability

This project was a pioneering study of the effect of nanochitin additives on setting time, rheological, and mechanical properties of cementitious materials. Chitin is the second most abundant biopolymer in nature after cellulose and is readily available from byproducts of the growing seafood industry. In this study, chitin powder from shrimp shells was processed into chitin nanocrystals (ChNCs) and chitin nanofibers (ChNFs). Ch-Cs delayed initial and final set times with their high ionic surface charges likely by electrostatic repulsion. The viscosity of fresh cement paste increased with Ch-NFs, while Ch-NCs did not significantly affect rheological properties. The distinct effects of the two nanochitin forms are attributed to the mobility of the different morphologies of the rod-like ChNCs vs. the entangled and restrained network of the ChNFs. Nanochitin forms showed encouraging results by significantly increasing flexural strength by ~40% at the optimum concentration of 0.05 wt% and significant increases in fracture energy. These bio-additives were found promising and future studies should continue to evaluate their use in concrete.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-Time Testbed for Studying Cyberattacks and Defense in DER-integrated Smart Inverter Systems

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a gridtied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man in the middle attacker. The Man-in-the-Middle (MITM) attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from a DNP3 controller that exploit smart inverter grid support functions. We choose DNP3 and implement grid support functions according to the IEEE Std. 1547-2018 mandated for the interconnection and interoperability of DER power systems with associated power components. Furthermore, we develop a protocol payload agnostic attack detection framework that leverages the round-trip time (RTT) anomalies between DNP3 requests and responses and can detect the presence of attacks without having to analyze the payload’s contents, while balancing trade-offs between false alarm counts, missed detections, and time to detection. To facilitate further research, we publicly release benign and attack network traffic exchanged between various sensors, controllers, and actuators in our grid-tied inverter testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗