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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

On wavelength-dependent exciton lifetime distributions in reconstituted CP29 antenna of the photosystem II and its site-directed mutants

To provide more insight on the excitonic structure and exciton lifetimes of the wild type (WT) CP29 complex of Photosystem II (PSII) we measured high-resolution (low temperature) absorption, emission, and hole burned spectra for the A2 and B3 mutants, which lack chlorophylls a612 and b614 (Chls), respectively. Experimental and modeling results obtained for the WT CP29 and A2/B3 mutants provide new insight on the mutation-induced changes at the molecular level and shed more light on energy transfer dynamics. Simulations of the A2 and B3 optical spectra, using the second-order non-Markovian theory, and comparison with improved fits of WT CP29 optical spectra, provide more insight on their excitonic structure, mutation induced changes, and frequency-dependent distributions of exciton lifetimes (T 1 ). A new Hamiltonian obtained for WT CP29 reveals that deletion of Chls a612 or b614 induces changes in the site-energies of all remaining Chls. Hamiltonians obtained for A2 and B3 mutants are discussed in the context of the energy landscape of chlorophylls, excitonic structure and the transfer kinetics. Our data suggest that the lowest exciton states in A2 and B3 mutants are contributed by a611(57%), a610(17%), a615(15%) and a615(58%), a611(20%), a612(15%) Chls, respectively, although other compositions of lowest energy states are also discussed. Lastly, we argue that the calculated exciton decay times are consistent with both the hole-burning and recent transient absorption measurements. Wavelength-dependent T 1 distributions offer more insight into the interpretation of kinetic traces commonly described by discrete exponentials in global analysis/global fitting of transient absorption experiments.

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

Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment

The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.

Rodiles Delgado, Brian G.↗
DOE OSTI2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗
DOE OSTI2022

The mitochondrial Cu + transporter PiC2 (SLC25A3) is a target of MTF1 and contributes to the development of skeletal muscle in vitro

The loading of copper (Cu) into cytochrome c oxidase (COX) in mitochondria is essential for energy production in cells. Extensive studies have been performed to characterize mitochondrial cuproenzymes that contribute to the metallation of COX, such as Sco1, Sco2, and Cox17. However, limited information is available on the upstream mechanism of Cu transport and delivery to mitochondria, especially through Cu-impermeable membranes, in mammalian cells. The mitochondrial phosphate transporter SLC25A3, also known as PiC2, binds Cu + and transports the ion through these membranes in eukaryotic cells, ultimately aiding in the metallation of COX. We used the well-established differentiation model of primary myoblasts derived from mouse satellite cells, wherein Cu availability is necessary for growth and maturation, and showed that PiC2 is a target of MTF1, and its expression is both induced during myogenesis and favored by Cu supplementation. PiC2 deletion using CRISPR/Cas9 showed that the transporter is required for proliferation and differentiation of primary myoblasts, as both processes are delayed upon PiC2 knock-out. The effects of PiC2 deletion were rescued by the addition of Cu to the growth medium, implying the deleterious effects of PiC2 knockout in myoblasts may be in part due to a failure to deliver sufficient Cu to the mitochondria, which can be compensated by other mitochondrial cuproproteins. Co-localization and co-immunoprecipitation of PiC2 and COX also suggest that PiC2 may participate upstream in the copper delivery chain into COX, as verified by in vitro Cu + -transfer experiments. These data indicate an important role for PiC2 in both the delivery of Cu to the mitochondria and COX, favoring the differentiation of primary myoblasts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1965

Spin-off from space.

NASA information system to assist transfer of technological data from space programs to potential beneficiaries

NASA PROGRAM↗
NASA NTRS1969

IBM-1620 monitor 2-D disk-storage subroutines

Set of subroutines provides the FORTRAN user with protected, permanent, disk storage of data on an IBM 1620 Monitor 11-D system. The program consists of a set of four subroutines and a utility program. It allows block data to be transferred directly between assigned core locations and disk storage.

Krejci, H. F.↗
NASA NTRS1971

An investigation of the normal momentum transfer for gases on tungsten

The near monoenergetic beam of neutral helium and argon atoms impinged on a single crystal tungsten target, with the (100) face exposed to the beam. The target was mounted on a torsion balance. The rotation of this torsion balance was monitored by an optical lever, and this reading was converted to a measurement of the momentum exchange between the beam and the target. The tungsten target was flashed to a temperature in excess of 2000 C before every clean run, and the vacuum levels in the final chamber were typically between 0.5 and 1 ntorr. The momentum exchange for the helium-tungsten surface and the argon-tungsten surface combination was obtained over approximately a decade of incoming energy (for the argon gas) at angles of incidence of 0, 30, and 41 deg on both clean and dirty (gas covered) surfaces. The results exhibited a significant variation in momentum transfer between the data obtained for the clean and dirty surfaces. The values of normal momentum accommodation coefficient for the clean surface were found to be lower than the values previously reported.

Moskal, E. J.↗
NASA NTRS1972

A summary of the OV1-19 satellite dose, depth dose, and linear energy transfer spectral measurements

Measurements of the biophysical and physical parameters in the near earth space environment, specifically, the Inner Van Allen Belt are discussed. This region of space is of great interest to planners of the Skylab and the Space Station programs because of the high energy proton environment, especially during periods of increased solar activity. Many physical measurements of charged particle flux, spectra, and pitch angle distribution have been conducted and are programmed in the space radiation environment. Such predictions are not sufficient to accurately predict the effects of space radiations on critical biological and electronic systems operating in these environments. Some of the difficulties encountered in transferring from physical data to a prediction of the effects of space radiation on operational systems are discussed.

Cervini, J. T.↗
NASA NTRS1972

Analytical design and performance studies of the nuclear light bulb engine

Analytical studies were conducted to investigate in detail the heat balance characteristics of the nuclear light bulb engine. Distributions of energy deposition to all engine components from the fission process, conduction and convection, and thermal radiation were considered. Where uncertainties in basic data or heat transfer characteristics were encountered, ranges of heat loads were calculated and reference values were selected. The influence of these heat loads on engine performance, space radiator requirements, and cooling sequence and cooling circuit designs was determined. The analyses resulted in revisions to the previously reported reference engine characteristics, principally in the heat loads to some engine components and in the cooling sequence. These revisions were incorporated in the engine dynamics digital computer simulation program. No significant changes occurred in the dynamic response of the engine to perturbations in fuel injection rate, reactivity or exhaust nozzle area.

Rodgers, R. J.↗
NASA NTRS1973

Attitude computation system

An attitude computation facility for the control of unmanned satellite missions is reported. The system's major components include: the ability to transfer the attitude data from the control center to the attitude computer at a rate of 2400 bps; an attitude computation center which houses communications, closed circuit TV, graphics devices and a data evaluation area; and the use of interactive graphics devices to schedule jobs and to control program flow.

Werking, R. D.↗
NASA NTRS1975

Electrostatic field changes and recovery curves produced by Florida lightning

Preliminary data on electric field changes produced by cloud-to-ground lightning and the subsequent field recoveries are presented. Both single-station and multiple-station observations were made. There is large variation in the data: total charge transfers range from 10 to 30 C and beyond, electric-field recovery times vary by a factor of 2 or more, and initial charge heights occur over a large interval. Only distance and angle dependencies are somewhat predictable, and then only in a few cases. Thus the field recovery mechanism is not a simple one.

Jacobson, E. A.↗
NASA NTRS1977

Application of remote sensing to state and regional programs

The problem includes data acquisition and transformation to products acceptable to the users. Optimized institutionalization of data management, product transfer, and education of the user community are also of major concern. With respect to the lattice, various structures were suggested and the fields of application are presented.

Miller, W. F.↗
NASA NTRS1977

Heat-transfer and pressure measurements on a simulated elevon deflected 30 deg near flight conditions at Mach 7

Heat transfer rates and pressures were obtained on an elevon plate (deflected 30 deg) and a flat plate upstream of the elevon in an 8 foot high-temperature structures tunnel. The flight Reynolds number and flight total enthalpy for altitudes of 26.8 km and 28.7 km at Mach seven were duplicated. The heat transfer and pressure data were used to establish heating and pressure loads. The measured heating was compared with several theoretical predictions, and the closest agreement obtained with a Schultz-Grunow reference enthalpy method of calculation.

Johnson, C. B.↗