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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Improved CLARAty Functional-Layer/Decision-Layer Interface

Improved interface software for communication between the CLARAty Decision and Functional layers has been developed. [The Coupled Layer Architecture for Robotics Autonomy (CLARAty) was described in Coupled-Layer Robotics Architecture for Autonomy (NPO-21218), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48. To recapitulate: the CLARAty architecture was developed to improve the modularity of robotic software while tightening coupling between planning/execution and basic control subsystems. Whereas prior robotic software architectures typically contained three layers, the CLARAty contains two layers: a decision layer (DL) and a functional layer (FL).] Types of communication supported by the present software include sending commands from DL modules to FL modules and sending data updates from FL modules to DL modules. The present software supplants prior interface software that had little error-checking capability, supported data parameters in string form only, supported commanding at only one level of the FL, and supported only limited updates of the state of the robot. The present software offers strong error checking, and supports complex data structures and commanding at multiple levels of the FL, and relative to the prior software, offers a much wider spectrum of state-update capabilities.

Estlin, Tara↗
NASA NTRS2009

Flight Test Performance of a High Precision Navigation Doppler Lidar

A navigation Doppler Lidar (DL) was developed at NASA Langley Research Center (LaRC) for high precision velocity measurements from a lunar or planetary landing vehicle in support of the Autonomous Landing and Hazard Avoidance Technology (ALHAT) project. A unique feature of this DL is that it has the capability to provide a precision velocity vector which can be easily separated into horizontal and vertical velocity components and high accuracy line of sight (LOS) range measurements. This dual mode of operation can provide useful information, such as vehicle orientation relative to the direction of travel, and vehicle attitude relative to the sensor footprint on the ground. System performance was evaluated in a series of helicopter flight tests over the California desert. This paper provides a description of the DL system and presents results obtained from these flight tests.

Pierrottet, Diego↗
NASA NTRS2012

The Relation between Coronal Holes and Coronal Mass Ejections during the Rise, Maximum, and Declining Phases of Solar Cycle 23

We study the interaction between coronal holes (CHs) and coronal mass ejections (CMEs) using a resultant force exerted by all the coronal holes present on the disk and is defined as the coronal hole influence parameter (CHIP). The CHIP magnitude for each CH depends on the CH area, the distance between the CH centroid and the eruption region, and the average magnetic field within the CH at the photospheric level. The CHIP direction for each CH points from the CH centroid to the eruption region. We focus on Solar Cycle 23 CMEs originating from the disk center of the Sun (central meridian distance ≤15deg) and resulting in magnetic clouds (MCs) and non-MCs in the solar wind. The CHIP is found to be the smallest during the rise phase for MCs and non-MCs. The maximum phase has the largest CHIP value (2.9 G) for non-MCs. The CHIP is the largest (5.8 G) for driverless (DL) shocks, which are shocks at 1 AU with no discernible MC or non-MC. These results suggest that the behavior of non-MCs is similar to that of the DL shocks and different from that of MCs. In other words, the CHs may deflect the CMEs away from the Sun-Earth line and force them to behave like limb CMEs with DL shocks. This finding supports the idea that all CMEs may be flux ropes if viewed from an appropriate vantage point.

CMEs↗
DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2018

Multiple-objective Reinforcement Learning for Inverse Design and Identification

Inverse chemical design using generative deep learning (DL) models is an emerging tool, which has early success in generating molecular structures with desired properties. We enhance this tool by developing heuristics for curriculum-learning based multiple-objective (20+ objectives) reinforcement learning, and apply it to the context of chemical identi?cation. We develop a generative DL framework that utilizes constraints pertaining to the unknown molecule’s mass and functional group composition, and show that multiple-objective RL-based generative DL models can correctly identify unknown molecules with a 80% success rate, compared to the baseline approach of 0%. Lastly, the heuristics developed are not limited to just chemistry research challenges; we anticipate any problem that utilizes reinforcement learning with multiple-objectives will bene?t from it.

Artificial Intelligence, Data Science, deep learni↗
DOE OSTI2020

Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

Bi, Sirui↗
DOE OSTI2023

GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.

Sarkar, Sulagna↗
DOE OSTI2022

M379A Mutant Tyrosine Phenol–lyase from Citrobacter freundii Has Altered Conformational Dynamics

The M379A mutant of Citrobacter freundii tyrosine phenol-lyase (TPL) has been prepared. M379A TPL is a robust catalyst to prepare a number of tyrosines substituted at the 3-position with bulky groups that cannot be made with wild type TPL. The three dimensional structures of M379A TPL complexed with L-methionine and 3-bromo-DL-phenylalanine have been determined by X-ray crystallography. Methionine is bound as a quinonoid complex in a closed active site in 3 of 4 chains of homotetrameric M379A TPL. M379A TPL reacts with L-methionine about 8-fold slower than wild type TPL. The temperature dependence shows that the slower reaction is due to less positive activation entropy. The structure of the M379A TPL complex of 3-bromo-DL-phenylalanine has a quinonoid complex in two subunits, with an open active site conformation. The effects of the M379A mutation on TPL suggest that the mutant enzyme has altered the conformational dynamics of the active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Finally, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Here, we present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

42 ENGINEERING↗
DOE OSTI2013

Flow and axial dispersion in a sinusoidal-walled tube: Effects of inertial and unsteady flows

Dispersion in porous media flows has been the subject of much experimental, theoretical and numerical study. Here we consider a wavy-walled tube (a three-dimensional tube with sinusoidally-varying diameter) as a simplified conceptualization of flow in porous media, where constrictions represent pore throats and expansions pore bodies. A theoretical model for effective (macroscopic) longitudinal dispersion in this system has been developed by volume averaging the microscale velocity field. Direct numerical simulation using computational fluid dynamics (CFD) methods was used to compute velocity fields by solving the Navier-Stokes equations, and also to numerically solve the volume averaging closure problem, for a range of Reynolds numbers (Re) spanning the low-Re to inertial flow regimes, including one simulation at Re = 449 for which unsteady flow was observed. Dispersion values were computed using both the volume averaging solution and a random walk particle tracking method, and results of the two methods were shown to be consistent. Our results are compared to experimental measurements of dispersion in porous media and to previous theoretical results for the low-Re, Stokes flow regime. In the steady inertial regime we observe an power-law increase in effective longitudinal dispersion (DL) with Re, consistent with previous results. This rapid rate of increase is caused by trapping of solute in expansions due to flow separation (eddies). For the unsteady case (Re = 449), the rate of increase of DL with Re was smaller than that observed at lower Re. Velocity fluctuations in this regime lead to increased rates of solute mass transfer between the core flow and separated flow regions, thus diminishing the amount of tailing caused by solute trapping in eddies and thereby reducing longitudinal dispersion.

CFD↗
DOE OSTI2018

Butylphthalide ameliorates experimental autoimmune encephalomyelitis by suppressing PGAM5-induced necroptosis and inflammation in microglia

Highlights: • The Dl-3-n-butylphthalide ameliorates the disease severity of multiple sclerosis. • Necroptosis and inflammation play important roles in the function of NBP on EAE. • PGAM5 functions as a key mediator for the protective role of NBP on the progression of EAE. Multiple sclerosis (MS) is a long-lasting autoimmune disease of the central nervous system. Currently, the etiology of MS is not known. Experimental autoimmune encephalomyelitis (EAE), has been recognized as the most widely used animal models to study the molecular mechanisms underlying MS and the efficacy of potential drugs for treatment of MS. In the present study, we found that Dl-3-n-butylphthalide (NBP), a neuroprotective drug in ischemic brain injury, prevented development of disease in experimental autoimmune encephalomyelitis (EAE) and significantly reduced inflammatory factors and necroptosis-associated genes, including PGAM5 in the spinal cord tissues. Similarly, silence of PGAM5 in spinal cord also ameliorated the disease severity in the mice with EAE. Moreover, re-expression of PGAM5 counteracted the protective effect of NBP on the pathogenesis of EAE. Importantly, we found that both NBP and silence of PGAM5 inhibited cellular necroptosis and inflammation in microglia induced by TNFα plus zVAD-fmk. Meanwhile, overexpression of PGAM5 reactivated cellular necroptosis and inflammation suppressed by NBP in vitro. Taken together, our findings provide evidence that NBP can attenuate the progression of EAE by suppressing PGAM5-induced necroptosis and inflammation in microglia and represents a new therapeutic strategy for treating autoimmune diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

The structure and propagation of laminar flames under autoignitive conditions

The laminar flame speed s l is an important reference quantity for characterising and modelling combustion. Experimental measurements of laminar flame speed require the residence time of the fuel/air mixture (τ f ) to be shorter than the autoignition delay time (τ). This presents a considerable challenge for conditions where autoignition occurs rapidly, such as in compression ignition engines. As a result, experimental measurements in typical compression ignition engine conditions do not exist. Simulations of freely propagating premixed flames, where the burning velocity is found as an eigenvalue of the solution, are also not well posed in such conditions, since the mixture ahead of the flame can autoignite, leading to the so called “cold boundary problem”. In this paper, a numerical method for estimating a reference flame speed, s R , is proposed that is valid for laminar flame propagation at autoignitive conditions. Two isomer fuels are considered to test this method: ethanol, which in the considered conditions is a single-stage ignition fuel; and dimethyl ether, which has a temperature-dependent single- or two-stage ignition and a negative temperature coefficient regime for τ. Calculations are performed for the flame position in a one-dimensional computational domain with inflow-outflow boundary conditions, as a function of the inlet velocity U I and for stoichiometric fuel–air premixtures. The response of the flame position, L F , to U I shows distinct stabilisation regimes. For single-stage ignition fuels, at low U I the flame speed exceeds U I and the flame becomes attached to the inlet. Above a critical U I value, the flame detaches from the inlet and L f becomes extremely sensitive to U I until, for sufficiently high U I , the sensitivity decreases and L f corresponds to the location expected from a purely autoignition stabilised flame. The transition from the attached to the autoignition regimes has a corresponding peak dL f /dU I value which is proposed to be a unique reference flame speed s R for single-stage ignition fuels. For two-stage ignition fuels, there is an additional stable regime where a high-temperature flame propagates into a pool of combustion intermediates generated by the first stage of autoignition. This results in two peaks in dL f /dU I and therefore two reference flame speed values. The lower value corresponds to the definition of s R for single-stage ignition fuels, while the higher value exists only for two-stage ignition fuels and corresponds to a high temperature flame propagating into the first stage of autoignition and is denoted s R ' . Finally, a transport budget analysis for low- and high-temperature radical species is also performed, which confirms that the flame structures at U I = s R and U I = s R ' do indeed correspond to premixed flames (deflagrations), as opposed to spontaneous ignition fronts which do not have a unique propagation speed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Distinguishing interfacial double layer and oxide-based capacitance on gold and pre-oxidized Fe-Cr in 1-ethyl-3-methylimidazolium methanesulfonate room temperature ionic liquid aqueous mixture

The frequency-dependent impedance of a metal/oxide/room temperature ionic liquid (RTIL) interface was separated into oxide layer (C ox ) and double layer (C dl ) capacitances using electrochemical impedance spectroscopy (EIS). A pre-formed oxide of known initial thickness on Fe-20 at.% Cr was investigated in 1-ethyl-3-methylimidazolium methanesulfonate ([Emim][MeSO 3 ]) RTIL + aqueous mixtures. The oxide thickness was calculated from C ox using the complex capacitance representation and the power-law model. The result was additionally verified by cross-sectional analysis using transmission electron microscopy. The C dl calculated from the constant-phase element (CPE) was distinguishable from C ox for Fe-20Cr, which enables monitoring of the interfaces of structural metals with oxide layers in RTILs containing water.

Constant-phase element↗
DOE OSTI2021

Rapid failure mode classification and quantification in batteries: A deep learning modeling framework

Unique, rapid identification and quantification of the dominant aging modes in lithium-ion batteries (LiBs) with early and non-specialized test data is a significant scientific challenge. Leveraging synthetic-data, deep-learning (DL) techniques have great potential to enable fast and robust classification and quantification of battery aging modes that produce different patterns of cell aging. This study, for the first time, presents a synthetic–data-based DL modeling framework for rapid and automatic classification and quantification of battery-aging modes and resultant aging with experimental validation. Availing synthetic dQ.dV -1 curves for ~26000 initial conditions and aging modes, the framework classified the dominant aging modes, for cells undergoing fast charge, in fewer than 100 cycles. Upon classification, the framework quantified the evolution of the aging modes, which were often nonuniform with cycling, for 22 gr/NMC532 pouch cells tested up to 600 cycles at different charging rates (1C–9C).

25 ENERGY STORAGE↗
DOE OSTI2023

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Hyperpigmentation and higher incidence of cutaneous malignancies in moderate-high PCB- and dioxin exposed individuals

Highlights: • Elevated blood levels of PCBs and dioxins were found in workers of a transformer recycling company. • Higher incidence of acne and malignancies of the skin in PCB- and dioxin exposed workers. • A correlation between hyperpigmentation on the skin and exposure levels in the workers. Polychlorinated biphenyls (PCB) are well known persistent and toxic environmental pollutants. Our aim was to identify effects of moderate-high exposure to dioxin-like (dl) and non-dioxin-like (ndl)-PCBs on the skin in order to provide more insight in the pathophysiological effects of these compounds. We performed a dermatological examination on 92 former workers from a transformer recycling company with known elevated serum PCB and/or dioxin (polychlorinated dibenzo-p-dioxin/polychlorinated dibenzo-p-furan (PCDD/F)) levels. In addition, we performed a skin cancer screening over a period of seven years (2010–2016) on resp. 268, 271, 210, 149, 92, 129 and 79 participants. We found a higher incidence of acne and malignancies of the skin (malignant melanoma, basal cell carcinoma and mycosis fungoides) in the workers compared to normal population. The probability of having hyperpigmentation on the skin was statistically significantly higher in workers with higher sumPCBs- (OR:1.09(1.12–2.17)), dioxin-like (dl)-PCBs- (OR:1.56(1.12–2.17)) and dioxin (PCDD/Fs) (OR:1.09(1.02–1.16)) levels. Age was a confounding factor in this model. Formation of hyperpigmentation could be an indicator for (moderate-high) exposure to toxic compounds like PCBs. The higher incidence of cutaneous malignancies found in the workers might be associated with PCB- and dioxin exposure, warranting further investigation on larger cohorts.

54 ENVIRONMENTAL SCIENCES↗