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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Carbon Coating Influence on the Formation of Percolating Electrode Networks for Silicon Anodes

Previous studies have demonstrated that chemical vapor deposition carbon coating on silicon (Si@C) can enhance the electrochemical performance of lithium-ion batteries with Si-based anodes. However, the underlying mechanisms contributing to this improvement have not been fully explored. Here, we address this knowledge gap by applying a suite of characterization methods to evaluate Si@C anodes prepared by reducing acetylene on ball-milled Si particles. Raman mapping measurements show that the C coating (<5 nm thick) enables a homogeneous Si and carbon distribution during the slurry casting process, thereby promoting Si utilization during cycling. The coating’s microstructure and morphology were evaluated using X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy, and neutron reflectivity experiments. Electrochemical impedance spectroscopy measurements upon cycling indicate that carbon coating also reduces the overall resistance as benchmarked against bare Si anodes. Galvanostatic cycling in half-cell studies revealed higher initial Coulombic efficiency and specific capacities with increasing carbon coating time. However, solid electrolyte interphase (SEI) investigations using XPS showed that the coated and uncoated samples have very similar characteristics, suggesting that the SEI may only play a minor role in enhancing the performance of Si@C. Full-cell evaluation of the Si electrodes was consistent with half-cell results relating to performance and SEI properties, further supporting the conclusion that electronic and ionic percolation, enabled by effective electrode manufacturing, are the dominant factors contributing to the favorable performance of Si@C.

25 ENERGY STORAGE↗
NASA NTRS1982

Application of In Situ Fiberization for fabrication of improved strain isolation pads and graphite epoxy composites

The feasibility of applying the in situ fiberization process to the fabrication of strain isolation pads (SIP) for the Space Shuttle and to the fabrication of graphite-epoxy composites was evaluated. The ISF process involves the formation of interconnected polymer fiber networks by agitation of dilute polymer solutions under controlled conditions. High temperature polymers suitable for SIP use were fiberized and a successful fiberization of polychloro trifluoroethylene, a relatively high melting polymer, was achieved. Attempts to fiberize polymers with greater thermal stability were unsuccessful, apparently due to characteristics caused by the presence of aromaticity in the backbone of such materials. Graphite-epoxy composites were fabricated by interconnecting two dimensional arrays of graphite fiber with polypropylene IS fibers with subsequent epoxy resin impregnation. Mechanical property tests were performed on laminated panels of this material to evaluate intralaminar and interlaminar shear strength, and thus fracture toughness. Test results were generally unpromising.

Rosser, R. W.↗
NASA NTRS1983

A system for the simulation and evaluation of satellite communication networks

With the emergence of a new era in satellite communications brought about by NASA's thrust into the Ka band with multibeam and onboard processing technologies, new and innovative techniques for evaluating these concepts and systems are required. To this end, NASA, in conjunction with its extensive program for advanced communications technology development, has undertaken to develop a concept for the simulation and evaluation of a complete communications network. Incorporated in this network will be proof of concept models of the latest technologies proposed for future satellite communications systems. These include low noise receivers, matrix switches, baseband processors, and solid state and tube type high power amplifiers. To accomplish this, numerous supporting technologies must be added to those aforementioned proof of concept models. These include controllers for synchronization, order wire, and resource allocation, gain compensation, signal leveling, power augmentation, and rain fade and range delay simulation. Taken together, these will be assembled to comprise a system capable of addressing numerous design and performance questions. The simulation and evaluation system as planned will be modular in design and implementation, capable of modification and updating to track and evaluate a continuum emerging concepts and technologies.

Bagwell, J. W.↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Predicting the von Neumann entanglement entropy using a graph neural network

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of $3.6\,\times 10^{-3}$ when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.

graph neural networks↗
DOE OSTI2021

Hydrodechlorination of 1,2-Dichloroethane on Platinum Catalysts: Insights from Reaction Kinetics Experiments, Density Functional Theory, and Microkinetic Modeling

Catalytic hydrodechlorination is a promising strategy for treating industrial 1,2-dichloroethane wastes, for which Pt and Pt-based alloy catalysts are widely used. Here, we performed a detailed mechanistic study for 1,2-dichloroethane hydrodechlorination on Pt using a synergistic approach combining density functional theory (DFT) calculations, reaction kinetics experiments, and microkinetic modeling. Using planewave DFT calculations, we evaluated the reaction energy and activation energy barrier of each elementary step involved in the reaction network on Pt(111). The calculated energetics were then incorporated into a comprehensive mean-field microkinetic model accounting for a total of 65 elementary steps. The model-predicted reaction rates were compared with the results from our reaction kinetics experiments on SiO2-supported Pt catalysts. Our results indicated that the hydrodechlorination of 1,2-dichloroethane on Pt(111) starts with a H-removal step; then, it proceeds through a sequence of alternating dechlorination and dehydrogenation steps until vinylidene (CH 2 C*) is formed; finally, CH 2 C* is hydrogenated to the final product, ethane, sequentially via vinyl (CH 2 CH*), ethylene, and ethyl (CH 3 CH 2 *) intermediates. After model parameter adjustments, we achieved good agreement between our theoretical model and experimental results; the adjustments to the calculated parameters are consistent with the typically anticipated coverage effects. Furthermore, our study offers valuable mechanistic insights, which are useful for improving catalysts for this chemistry.

1,2-dichloroethane↗
DOE OSTI2022

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Synthetic Electricity Market Data Generation and HERON Use Case Setup of Advanced Nuclear Reactors Coupled with Thermal Energy Storage Systems

This study evaluates and optimizes advanced nuclear reactors coupled with thermal energy storage (TES) systems in an Integrated Energy System (IES) architecture to enable advanced nuclear power plants (A NPP) to participate in multi-commodity markets, thus enhancing their economic competitiveness. Nuclear-TES coupling scenarios studied herein are designed attenuate the nuclear heat dynamics and defer energy delivery to a later time, enabling the nuclear reactor to continue operating at or near steady-state design conditions as usual while also enabling flexible generation. Three A-NPPs, namely, an advanced light-water reactor (A LWR), a high temperature gas-cooled reactor (HTGR) and a liquid-metal fast reactor (LMFR) were selected as the initial use cases for demonstrating the technoeconomic of thermally balanced energy storage coupling design for thermal power extraction. Each of the reactor technologies were evaluated in two different electricity markets. Stochastic optimization approach was adopted which included the evaluation of price signals from the Pennsylvania-New Jersey-Maryland (PJM) market, and Electric Reliability Council of Texas (ERCOT), using an autoregressive moving average (ARMA) model. Risk Analysis Virtual Environment (RAVEN) tool and its dispatch optimization plugin, the Holistic Energy Resource Optimization Network (HERON), were used to perform dispatch and capacity optimization, using the price data provided by the ARMA models. The results from the Nuclear-TES use cases will be used to design and characterize dynamic integrated system behavior and feedback.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1991

Measuring earth orientation with the Global Positioning System

A globally distributed network of high-precision receivers which obtain data from the full Global Positioning System (GPS) configuration of 18 or more satellites may soon become an efficient and economical method for the rapid determination of short-term variations in earth orientation. A covariance analysis has been performed to evaluate the errors associated with GPS monitoring of earth orientation. Earth orientation parameters were modeled either as constants over observing windows of various lengths, or as stochastic process-noise variables. The sensitivity of earth orientation estimates to systematic errors in selected model parameters was also examined. GPS measurements appear to be highly competitive with those from other techniques, and have the potential to generate nearly continuous centimeter-level earth orientation information to aid both spacecraft navigation and the study of high-frequency earth orientation-related processes.

Freedman, Adam P.↗
NASA NTRS1994

Performance evaluation of the Engineering Analysis and Data Systems (EADS) 2

The Engineering Analysis and Data System (EADS)II (1) was installed in March 1993 to provide high performance computing for science and engineering at Marshall Space Flight Center (MSFC). EADS II increased the computing capabilities over the existing EADS facility in the areas of throughput and mass storage. EADS II includes a Vector Processor Compute System (VPCS), a Virtual Memory Compute System (CFS), a Common Output System (COS), as well as Image Processing Station, Mini Super Computers, and Intelligent Workstations. These facilities are interconnected by a sophisticated network system. This work considers only the performance of the VPCS and the CFS. The VPCS is a Cray YMP. The CFS is implemented on an RS 6000 using the UniTree Mass Storage System. To better meet the science and engineering computing requirements, EADS II must be monitored, its performance analyzed, and appropriate modifications for performance improvement made. Implementing this approach requires tool(s) to assist in performance monitoring and analysis. In Spring 1994, PerfStat 2.0 was purchased to meet these needs for the VPCS and the CFS. PerfStat(2) is a set of tools that can be used to analyze both historical and real-time performance data. Its flexible design allows significant user customization. The user identifies what data is collected, how it is classified, and how it is displayed for evaluation. Both graphical and tabular displays are supported. The capability of the PerfStat tool was evaluated, appropriate modifications to EADS II to optimize throughput and enhance productivity were suggested and implemented, and the effects of these modifications on the systems performance were observed. In this paper, the PerfStat tool is described, then its use with EADS II is outlined briefly. Next, the evaluation of the VPCS, as well as the modifications made to the system are described. Finally, conclusions are drawn and recommendations for future worked are outlined.

Debrunner, Linda S.↗
NASA NTRS2006

Interconnect Performance Evaluation of SGI Altix 3700 BX2, Cray X1, Cray Opteron Cluster, and Dell PowerEdge

We study the performance of inter-process communication on four high-speed multiprocessor systems using a set of communication benchmarks. The goal is to identify certain limiting factors and bottlenecks with the interconnect of these systems as well as to compare these interconnects. We measured network bandwidth using different number of communicating processors and communication patterns, such as point-to-point communication, collective communication, and dense communication patterns. The four platforms are: a 512-processor SGI Altix 3700 BX2 shared-memory machine with 3.2 GB/s links; a 64-processor (single-streaming) Cray XI shared-memory machine with 32 1.6 GB/s links; a 128-processor Cray Opteron cluster using a Myrinet network; and a 1280-node Dell PowerEdge cluster with an InfiniBand network. Our, results show the impact of the network bandwidth and topology on the overall performance of each interconnect.

Fatoohi, Rod↗
DOE OSTI2024

Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation (Rev.1)

Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to time and computational resource constraints. This paper introduces a novel approach for expressibility estimation of PQCs using Graph Neural Networks (GNNs). We demonstrate the predictive power of our GNN model with a dataset consisting of 25,000 samples from the noiseless IBM QASM Simulator and 12,000 samples from three distinct noisy quantum backends. The model accurately estimates expressibility, with root mean square errors (RMSE) of 0.05 and 0.06 for the noiseless and noisy backends, respectively. We compare our model’s predictions with reference circuits from Sim et al. and IBM Qiskit’s hardwareefficient ansatz sets to further evaluate our model’s performance. Our experimental evaluation in noiseless and noisy scenarios reveals a close alignment with ground truth expressibility values, highlighting the model’s efficacy. Moreover, our model exhibits promising extrapolation capabilities, predicting expressibility values with low RMSE for out-of-range qubit circuits trained solely on only up to 5-qubit circuit sets. This work thus provides a reliable means of efficiently evaluating the expressibility of diverse PQCs on noiseless simulators and hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
NASA NTRS1984

A system for the simulation and evaluation of satellite communication networks

With the emergence of a new era in satellite communications, brought about by NASA's thrust into the Ka band with multibeam and onboard processing technologies, new and innovative techniques for evaluating these concepts and systems are required. To this end, NASA, in conjunction with its extensive program for advanced communications technology development, has undertaken to develop a concept for the simulation and evaluation of a complete communications network. Incorporated in this network will be proof-of-concept models of the latest technologies proposed for future satellite communications systems. These include low noise receivers, matrix switches, baseband processors, and solid state and tube type high power amplifiers. To accomplish this, numerous supporting technologies must be added to those aforementioned proof-of-concept models. These include controllers for synchronization, order wire, resource allocation, gain compensation, signal leveling, power augmentation, and rain fade and range delay simulation. Taken together, these will be assembled to comprise a system capable of addressing numerous design and performance questions. The simulation and evaluation system, as planned, will be modular in design and implementation, capable of modification and updating to track and evaluate a continuum of emerging concepts and technologies. Previously announced in STAR as N84-13400

Bagwell, J. W.↗
NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗
NASA NTRS1980

A microprocessor based high speed space-borne packet switch

The design and performance evaluation of a single microprocessor based packet switch for use on-board a satellite are presented. The primary design criterion is to maximize the system throughput. A number of protocols and their impact on the switch design are discussed. Stem and processor hardware architectures are described. The flow charts of the software required to accomplish the essential functions are presented and modification needed for use in a multisatellite network are also indicated. Maximum throughput attainable is calculated, desirable characteristics of microprocessors for this application are pointed out. A queue theoretic model of the packet switch has been developed. Analytical relationships of the average waiting times and the queue sizes at the queues and the overall average response time with the various design parameters of the switch have been obtained. A number of graphs showing the effect of variation in the number of input and output lines, data rates, destination functions, speed of output lines and some other design parameters on the performance of the switch are presented and discussed.

Arozullah, M.↗
NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Recognition↗