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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Sample Complexity of Power System State Estimation using Matrix Completion: Preprint

In this paper, we propose an analytical framework to quantify the amount of data samples needed to obtain accurate state estimation in a power system-a problem known as \emph{sample complexity} analysis in computer science. Motivated by the increasing adoption of distributed energy resources into the distribution-level grids, it becomes imperative to estimate the state of distribution grids in order to ensure stable operation. Traditional power system state estimation techniques mainly focus on the transmission network which involve solving an overdetermined system and eliminating bad data. However, distribution networks are typically \emph{underdetermined} due to the large number of connection points and high cost of pervasive installation of measurement devices. In this paper, we consider the recently proposed state-estimation method for underdetermined systems that is based on \emph{matrix completion}. In particular, a \emph{constrained} matrix completion algorithm was proposed, wherein the standard matrix completion problem is augmented with additional equality constraints representing the physics (namely power-flow constraints). We analyze the sample complexity of this general method by proving an upper bound on the sample complexity that depends directly on the properties of these constraints that can lower number of needed samples as compared to the unconstrained problem. To demonstrate the improvement that the constraints add to state estimation, we test the method on a 141-bus distribution network case study and compare it to the traditional least squares minimization state estimation method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Frontiers and opportunities in bioenergy crop microbiome research networks

Researchers from across the four U.S. Department of Energy Bioenergy Research Centers engaged in a microbiome workshop that focused on identifying challenges and collaboration opportunities to better understand bioenergy-relevant plant–microbe interactions. The virtual workshop included hands-on educational sessions and a keynote address on current best practices in microbiome science and community microbiome standards, as well as breakout sessions aimed at identifying microbiome-related data and measurements that should be prioritized, opportunities for and barriers to integrating plant metabolites to microbiome research, and strategies for more effectively integrating microbiome data and processes into existing models. Based on participant discussion, key findings of the workshop were the need to prioritize scaling data sharing across BRCs and the broader research community and securing collaborative infrastructure in the areas of microbiome-ecosystem modeling and molecular plant-microbe interactions. This workshop review highlights additional main findings from this event, to encourage cross-site and more holistic meta-analyses while promoting wide scientific community engagement across plant microbiome sciences.

09 BIOMASS FUELS↗
DOE OSTI2011

First principles calculation of finite temperature magnetism in Fe and Fe3C

Density functional calculations have proven to be a useful tool in the study of ground state properties of many materials. The investigation of finite temperature magnetism, on the other hand, has to rely usually on the usage of empirical models that allow the large number of evaluations of the systems Hamiltonian that are required to obtain the phase space sampling needed to obtain the free energy, specific heat, magnetization, susceptibility, and other quantities as function of temperature. We have demonstrated a solution to this problem that harnesses the computational power of today s large massively parallel computers by combining a classical Wang Landau Monte-Carlo calculation [F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001)] with our first principles multiple scattering electronic structure code [Y. Wang et al., Phys. Rev. Lett. 75, 2867 (1995)] that allows the energy calculation of constrained magnetic states [M. Eisenbach et al., Proceedings of the Conference on High Performance Computing, Networking, Storage and Analysis (ACM, New York, 2009)]. We present our calculations of finite temperature properties of Fe and Fe3C using this approach and we find the Curie temperatures to be 980 and 425K, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

electrical and electronic engineering↗
DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗
DOE OSTI2022

Tempering kinetics during multilayer laser additive manufacturing of a ferritic steel

Grade 91 steel forms martensite during additive manufacturing and the extent of tempering of martensite significantly affects the mechanical properties of parts. Currently, there is a lack of quantitative understanding of the tempering kinetics for Grade 91 steel, and as a result, the effects of repeated thermal cycles on properties for different processing conditions cannot be determined. Here we evaluate the tempering kinetics by determining the constant terms in the Johnson Mehl Avrami kinetic equation from the tempering data available in the literature and the thermal cycles computed using a rigorously-tested heat and fluid flow model of multi-layer additive manufacturing. The raw tempering data are cleaned using a neural network to enhance accuracy. The lower layers experience repeating cycles of heating and cooling when the upper layers are added. As a result, the hardness is reduced owing to the tempering of martensite. In contrast, martensite formed in the upper layers is not tempered to the same extent and the hardness remains high. Therefore, the hardness of the part increases with the distance from the substrate. Variations in the heat input at different laser powers and scanning speeds significantly affect the extent of tempering. Finally, since the method used here can provide a quantitative understanding of the tempering of martensite and the spatial variation in hardness, it can be used to tailor the microstructure and hardness of heat treatable printed metallic parts.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Sustainable Energy Solutions Task 2.0: Wind Turbine Reliability and Maintainability Enhancement through System-wide Structure Health Monitoring and Modifications to Rotating Components

An evaluation of nondestructive structural health monitoring methods was completed with over 132 documents, 37 specifically about wind turbines, summarized into a technology matrix. This matrix lists the technology, what can be monitored with this technology, and gives a short summary of the key aspects of the technology and its application. Passive and active acoustic emission equipment from Physical Acoustics Corp. and Acellent Technologies have been evaluated and selected for use in experimental state loading and fatigue tests of composite wind turbine blade materials. Acoustic Emission (AE) and Active Ultrasonic Testing (AUT), were applied to composite coupons with both simulated and actual damage. The results found that, while composites are more complicated in nature, compared to metallic structures, an artificial neural network analysis could still be used to determine damage. For the AE system, the failure mode could be determined (i.e. fiber breakage, delamination, etc.). The Acellent system has been evaluated to work well with composite materials. A test-rig for reliability testing of the rotating components was constructed. The research on the types of bearings used in the wind turbines indicated that in most of the designs, the main bearings utilized to support the shaft are cylindrical roller bearings. The accelerated degradation testing of a population of bearings was performed. Vibration and acoustic emission data was collected and analyzed in order to identify a representative degradation signal for each bearing to identify the initiation of the degradation process in the bearings. Afterwards, the RMS of the vibration signal from degradation initiation up to the end of the useful life of the bearing was selected to predict the remaining useful life of the bearing. This step included fitting Autoregressive Moving Average (ARMA) models to the degradation signals and approximating the probability distribution function (PDF) of remaining useful life based on the results of Monte-Carlo simulation of the ARMA models. This step was performed for different percentages of the degradation signal of each bearing. The accuracy of the proposed approach then was assessed by comparing the actual life of the bearing and the estimated life of the bearing from the developed models. The results were impressive and indicated that the accuracy of the models improved as more data was utilized in developing the ARMA models (we get closer to the end of the life of the bearing).

17 WIND ENERGY↗
DOE OSTI2024

Computational synthesis of a new generation of 2D-based perovskite quantum materials

Perovskite-based optoelectronic devices have emerged as a promising energy source due to their potential for scalable production. This study introduces “perovskene,” a novel class of 2D materials derived from the ABC3-like perovskites, synthesized via a data-driven, high-throughput computational strategy. We harness machine learning and multitarget deep neural networks to systematically investigate the structure–property relations, paving the way for targeted material design and optimization in fields such as renewable energy, electronics, and catalysis. The characterization of over 1500 synthesized structures shows that more than 500 structures are stable, revealing properties such as ultra-low work function and large magnetic moment, underscoring the potential for advanced technological applications.

2D materials↗
DOE OSTI2018

SYP73 Is a Novel Membrane‐Associated Actin‐Binding Protein that Anchors the ER to the Actin Cytoskeleton in Arabidopsis

The endoplasmic reticulum (ER) is an essential organelle that spreads throughout the cytoplasm and undergoes extensive remodeling. In plants, the remodeling and motility of the ER rely mainly on actin and to a minor extent on microtubules. Although the actomyosin system has been suggested functional for ER streaming in plant cells, the mechanisms underlying stable interaction of the ER membrane with actin are unknown. We demonstrate that SYP73, a member of the plant Syp7 subgroup of SNARE proteins is a novel ER membrane‐associated actin‐binding protein. We show that overexpression of SYP73 causes a striking rearrangement of the ER over actin and that loss of SYP73 reduces ER streaming and affects overall ER network morphology and plant growth. A potential mechanism that regulates SYP73 functions has been investigated. Although conserved domain prediction tools did not detect any actin‐binding domain in SYP73, we identified a fragment that is mainly responsible for SYP73 actin binding. Furthermore, the role of SYP73 in regulating actin reorganization has also been examined. We propose a model for plant ER remodeling whereby the dynamic rearrangement and streaming of the ER network depend on the propelling action of myosin‐XI over actin coupled with a SYP73‐mediated bridging, which dynamically anchors the ER membrane with actin filaments. Support or Funding Information This work is support by the Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, US Department of Energy, the National Science Foundation, and MSU AgBioResearch. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Cao, Pengfei↗
DOE OSTI2016

TU-AB-BRC-07: Efficiency of An IAEA Phase-Space Source for a Low Energy X-Ray Tube Using Egs++

Purpose: To extend the capability of the EGSnrc C++ class library (egs++) to write and read IAEA phase-space files as a particle source, and to assess the relative efficiency gain in dose calculation using an IAEA phase-space source for modelling a miniature low energy x-ray source. Methods: We created a new ausgab object to score particles exiting a user-defined geometry and write them to an IAEA phase-space file. A new particle source was created to read from IAEA phase-space data. With these tools, a phase-space file was generated for particles exiting a miniature 50 kVp x-ray tube (The INTRABEAM System, Carl Zeiss). The phase-space source was validated by comparing calculated PDDs with a full electron source simulation of the INTRABEAM. The dose calculation efficiency gain of the phase-space source was determined relative to the full simulation. The efficiency gain as a function of i) depth in water, and ii) job parallelization was investigated. Results: The phase-space and electron source PDDs were found to agree to 0.5% RMS, comparable to statistical uncertainties. The use of a phase-space source for the INTRABEAM led to a relative efficiency gain of greater than 20 over the full electron source simulation, with an increase of up to a factor of 196. The efficiency gain was found to decrease with depth in water, due to the influence of scattering. Job parallelization (across 2 to 256 cores) was not found to have any detrimental effect on efficiency gain. Conclusion: A set of tools has been developed for writing and reading IAEA phase-space files, which can be used with any egs++ user code. For simulation of a low energy x-ray tube, the use of a phase-space source was found to increase the relative dose calculation efficiency by factor of up to 196. The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant No. 432290).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Cellobiose Consumption Uncouples Extracellular Glucose Sensing and Glucose Metabolism in Saccharomyces cerevisiae

Glycolysis is central to energy metabolism in most organisms and is highly regulated to enable optimal growth. In the yeast Saccharomyces cerevisiae, feedback mechanisms that control flux through glycolysis span transcriptional control to metabolite levels in the cell. Using a cellobiose consumption pathway, we decoupled glucose sensing from carbon utilization, revealing new modular layers of control that induce ATP consumption to drive rapid carbon fermentation. Alterations of the beta subunit of phosphofructokinase-1 (PFK2), H + -plasma membrane ATPase (PMA1), and glucose sensors (SNF3 and RGT2) revealed the importance of coupling extracellular glucose sensing to manage ATP levels in the cell. Controlling the upper bound of cellular ATP levels may be a general mechanism used to regulate energy levels in cells, via a regulatory network that can be uncoupled from ATP concentrations under perceived starvation conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Liquid Sunshine to Fuel Your Car (A "Life at the Frontiers of Energy Research" contest entry from the 2011 Energy Frontier Research Centers (EFRCs) Summit and Forum)

"Liquid Sunshine to Fuel Your Car" was submitted by the Center for Lignocellulose Structure and Formation (CLSF) to the "Life at the Frontiers of Energy Research" video contest at the 2011 Science for Our Nation's Energy Future: Energy Frontier Research Centers (EFRCs) Summit and Forum. Twenty-six EFRCs created short videos to highlight their mission and their work. CLSF is directed by Daniel Cosgrove at Pennsylvania State University and is a partnership of scientists from three institutions: Penn State (lead), North Carolina State University, and Virginia Tech University. The Office of Basic Energy Sciences in the U.S. Department of Energy's Office of Science established the 46 Energy Frontier Research Centers (EFRCs) in 2009. These collaboratively-organized centers conduct fundamental research focused on 'grand challenges' and use-inspired 'basic research needs' recently identified in major strategic planning efforts by the scientific community. The overall purpose is to accelerate scientific progress toward meeting the nation's critical energy challenges. The mission of the Center for Lignocellulose Structure and Formation is 'to dramatically increase our fundamental knowledge of the formation and physical interactions of bio-polymer networks in plant cell walls to provide a basis for improved methods for converting biomass into fuels.' Research topics are: biofuels (biomass), membrane, interfacial characterization, matter by design, and self-assembly.

09 BIOMASS FUELS↗
DOE OSTI2024

K–Co–Mo–S x chalcogel: high-capacity removal of Pb 2+ and Ag + and the underlying mechanisms

Chalcogenide-based aerogels, known as chalcogels, represent a novel class of nanoparticle-based porous amorphous materials characterized by high surface polarizability and Lewis base properties, exhibiting promising applications in clean energy and separation science. This work presents a K–Co–Mo–S x (KCMS) chalcogel as a highly efficient sorbent for heavy metal ions and details its sorption mechanisms. Its incoherent structure comprises Mo 2 V (S 2 ) 6 and Mo 3 IV S(S 6 ) 2 anion-like clusters with four- and six-coordinated Co–S polyhedra, forming a Co–Mo–S covalent network that hosts K + ions through electrostatic attraction. The interactions of KCMS with heavy metal ions, particularly Pb 2+ and Ag + , reveal that KCMS is exceptionally effective in removing these ions from ppm concentrations down to trace levels (≤5 ppb). KCMS rapidly removes Ag + (≈81.7%) and Pb 2+ (≈99.5%) within five minutes, achieving >99.9% removal within an hour, with a distribution constant K d ≥10 8 mL g -1 . KCMS exhibits an impressive removal capacity of 1378 mg g -1 for Ag + and 1146 mg g -1 for Pb 2+ , establishing it as one of the most effective materials known to date for heavy metal removal. This material is also effective for the removal of Ag + and Pb 2+ along with Hg 2+ , Ni 2+ , Cu 2+ , and Cd 2+ from various water sources even in the presence of highly concentrated and chemically diverse cations, anions, and organic species. Analysis of the post-interacted KCMS by synchrotron X-ray pair distribution function (PDF), X-ray photoelectron spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS) revealed that the sorption of Pb 2+ , Ag + , and Hg 2+ mainly occurs by the exchange of K + and Co 2+ . Despite being amorphous, this material exhibits unprecedented ion-exchange mechanisms both for the ionically and covalently bound K + and Co 2+ , respectively. In conclusion, this discovery advances our knowledge of amorphous gels and guides material synthesis principles for the highly selective and efficient removal of heavy metal ions from water.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Metal sulfide electrodes and energy storage devices thereof

The present invention generally relates to energy storage devices, and to metal sulfide energy storage devices in particular. Some aspects of the invention relate to energy storage devices comprising at least one flowable electrode, wherein the flowable electrode comprises an electroactive metal sulfide material suspended and/or dissolved in a carrier fluid. In some embodiments, the flowable electrode further comprises a plurality of electronically conductive particles suspended and/or dissolved in the carrier fluid, wherein the electronically conductive particles form a percolating conductive network. An energy storage device comprising a flowable electrode comprising a metal sulfide electroactive material and a percolating conductive network may advantageously exhibit, upon reversible cycling, higher energy densities and specific capacities than conventional energy storage devices.

25 ENERGY STORAGE↗