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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Visualizing a Vulnerability: Its Connections to Hardware and Software

All Hazards Analysis (AHA) is a framework developed by Idaho National Laboratory that provides capabilities to collect, store, analyze, and visualize critical infrastructure information. A core function of AHA is its ability to simulate faults or outages in networks of infrastructure originating from a plethora of causes, ranging from natural disasters to cyberattacks. AHA utilizes Hardware and Software Bills of Material (HBOM and SBOM, respectively) along with Known Exploited Vulnerabilities (KEVs) to document the potential attack vectors for each piece of infrastructure. The objective of this contribution to AHA was to create a visualization tool that could capture the small details held in each individual artifact as well as preserve the large-scale connections that link them together to aid threat modeling.

58 GEOSCIENCES↗
DOE OSTI2025

Deployment of neural-network-based neutron microscopic cross sections in the Griffin reactor physics application

The capability to utilize neural networks to predict macroscopic and microscopic cross section parametric spaces has been developed for the Griffin reactor physics application. The LibTorch interface enables Griffin's MOOSE-based materials to interact with LibTorch-trained models, allowing for the evaluation of complex macroscopic or microscopic cross section spaces, which are then used to evaluate the neutronic properties of the Griffin finite element model. This study benchmarks traditional ISOXML-formatted tabulation libraries against neural network-based models for 279 nuclides on 20,160 grid points for zero-dimensional and two-dimensional reactor models. Benchmark metrics include the fundamental mode eigenvalue, fission and absorption rates, and various temperature coefficients of reactivity (isothermal, fuel, and moderator). From the perspective of storage space, the complete set of LibTorch models uses 11 MB on disk, compared to the 10 GB for the ISOXML multigroup library that covers the same grid space. For the two-dimensional performance case considered in Griffin, the Torch model uses 97% less RAM than the reference ISOXML dataset while runtime increases by a factor of 3 when using the LibTorch model compared to the ISOXML dataset with multi-linear interpolation. The LibTorch model consistently yields errors within 0.01% for most analyzed quantities except for the temperature coefficients of reactivity where the maximum discrepancies are up to 0.3 $\frac{pcm}{K}$. Due to the neural network attempting to best predict quantities with no regard for a positive or negative bias for any given quantity, predictions may experience random fluctuations, resulting in both positive and negative errors. Future work will entail both depletion and coupled transient analysis to determine the predictive capabilities of Griffin with neural network-based cross sections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1975

Networks systems and operations

The application of wideband communication techniques for data links with satellites is discussed. A diagram of the demand assigned voice communications system is provided. The development of prototype integrated spacecraft paramps at S- and C-bands is described and the performance of space-qualified paramps is tabulated. The characteristics of a dual parabolic cylinder monopulse zoom antenna for use with the tracking and data relay satellite system (TDRSS) are analyzed. The development of a universally applicable transponder at S-band is reported. A block diagram of the major subassemblies of the S-band transponder is included. The technology aspects of network timing and synchronization of communication systems are to show the use of the Omega navigation system. The telemetry data compression system used during the Skylab program is evaluated.

Source record↗
DOE OSTI2023

Lower and upper threshold limit for artificial neural network based chilled and condenser water temperatures set-point control in a chilled water system

In this study, an ANN (artificial neural network) based real-time optimized control algorithm for a chilled water cooling system was developed and applied in an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, the cooling tower’s CndWT (condenser water temperature) and the chiller’s ChWT (chilled water temperature) were set as system control variables. To evaluate algorithm performance, the electric consumption and the COP (coefficient of performance) were compared and analyzed when ChWT and CndWTs were controlled conventionally and controlled based on the ANN. During the analysis, unexpected abnormal data was observed due to insufficient training data and limited consideration of OWBT (outdoor air wet-bulb temperature) when determining the CndWT set-point. Therefore, it is necessary to further build training data from a wider range of conditions and to set the lower limit of CndWT set-point to at least +3.6 °C above OWBT when the OWBT is higher than 23 °C, so that further energy savings can be achieved.

42 ENGINEERING↗
DOE OSTI2022

Randomized Cholesky Preconditioning for Graph Partitioning Applications

A graph is a mathematical representation of a network; we say it consists of a set of vertices, which are connected by edges. Graphs have numerous applications in various fields, as they can model all sorts of connections, processes, or relations. For example, graphs can model intricate transit systems or the human nervous system. However, graphs that are large or complicated become difficult to analyze. This is why there is an increased interest in the area of graph partitioning, reducing the size of the graph into multiple partitions. For example, partitions of a graph representing a social network might help identify clusters of friends or colleagues. Graph partitioning is also a widely used approach to load balancing in parallel computing. The partitioning of a graph is extremely useful to decompose the graph into smaller parts and allow for easier analysis. There are different ways to solve graph partitioning problems. For this work, we focus on a spectral partitioning method which forms a partition based upon the eigenvectors of the graph Laplacian (details presented in Acer, et. al.). This method uses the LOBPCG algorithm to compute these eigenvectors. LOBPCG can be accelerated by an operator called a preconditioner. For this internship, we evaluate a randomized Cholesky (rchol) preconditioner for its effectiveness on graph partitioning problems with LOBPCG. We compare it with two standard preconditioners: Jacobi and Incomplete Cholesky (ichol). This research was conducted from August to December 2021 in conjunction with Sandia National Laboratories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Security Vulnerability and Mitigation in Photovoltaic Systems

Software and firmware vulnerabilities pose security threats to photovoltaic (PV) systems. When patches are not available or cannot be timely applied to fix vulnerabilities, it is important to mitigate vulnerabilities such that they cannot be exploited by attackers or their impacts will be limited when exploited. However, the vulnerability mitigation problem for PV systems has received little attention. This paper analyzes known security vulnerabilities in PV systems, proposes a multi-level mitigation framework and various mitigation strategies including neural network-based attack detection inside inverters, and develops a prototype system as a proof-of-concept for building vulnerability mitigation into PV system design.

14 SOLAR ENERGY↗
DOE OSTI2021

Deep active learning for classifying cancer pathology reports

Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Homomorphic Encryption for Machine Learning and Artificial Intelligence Applications

Third-party and expert analysis is a cost-effective solution for solving specialized problems or processing large datasets related to reactor structural health monitoring and nondestructive evaluation. However, when handling proprietary information, third-party and expert analysts pose a privacy risk. To address this challenge, Homomorphic Encryption (HE) permits arithmetic operations on encrypted data without exposing the underlying data. Implementations of Machine Learning (ML) and Artificial Intelligence (AI) algorithms using HE greatly enhances the capabilities of third-party analysts while maintaining a low security risk. This paper details current success in applying Principal Component Analysis (PCA) and Fully Connected Neural Networks (NN) using the Microsoft SEAL implementation of the popular CKKS Fully Homomorphic Encryption (FHE) algorithm. The MNIST Handwritten Dataset is analyzed as a proof-of-concept demonstration of the implementations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1979

Technical assistance for law-enforcement communications: Case study report

Methods developed to improve police communications systems are described. Use of queueing analysis shows several ways of improving time of response to inquiries made from the field for license plate checks and for information on current wants and warrants, through a state multiple switcher network. Design criteria for more efficient centralized switching equipment are developed. A message load problem experienced in a dispatch center is analyzed, showing that communications could be improved by adding communications channels, not by adding people.

Reilly, N. B.↗
NASA NTRS1986

A reliable pipelining protocol for the message service of the Mobile Satellite Experiment

This paper describes and analyzes a pipelining protocol for the data message communications of MSAT-X, a proposed experimental satellite-based mobile communications network. A demand assigned multiple access protocol using pure ALOHA for making reservation requests has been developed for MSAT-X under error-free assumptions. Preliminary propagation studies indicate that the short term bit error rate of satellite channels in a mobile environment can be as high as 0.001. Therefore, error-control schemes must be developed to ensure reliable transmissions. A retransmission scheme using selective repeat to minimize the end-to-end delay is proposed. Slotted ALOHA for making reservation requests is used to increase the overall system throughput. Since the number of channels available for reservation and data channels is essentially fixed for a given voice call blocking probability and a fixed call arrival rate, the analysis presented in this paper is also applicable to the integrated voice and data services of MSAT-X. Various operational scenarios have been investigated.

Li, V. O. K.↗
NASA NTRS1989

The role of the host in a cooperating mainframe and workstation environment, volumes 1 and 2

In recent years, advancements made in computer systems have prompted a move from centralized computing based on timesharing a large mainframe computer to distributed computing based on a connected set of engineering workstations. A major factor in this advancement is the increased performance and lower cost of engineering workstations. The shift to distributed computing from centralized computing has led to challenges associated with the residency of application programs within the system. In a combined system of multiple engineering workstations attached to a mainframe host, the question arises as to how does a system designer assign applications between the larger mainframe host and the smaller, yet powerful, workstation. The concepts related to real time data processing are analyzed and systems are displayed which use a host mainframe and a number of engineering workstations interconnected by a local area network. In most cases, distributed systems can be classified as having a single function or multiple functions and as executing programs in real time or nonreal time. In a system of multiple computers, the degree of autonomy of the computers is important; a system with one master control computer generally differs in reliability, performance, and complexity from a system in which all computers share the control. This research is concerned with generating general criteria principles for software residency decisions (host or workstation) for a diverse yet coupled group of users (the clustered workstations) which may need the use of a shared resource (the mainframe) to perform their functions.

Kusmanoff, Antone↗
NASA NTRS2004

An Integrated Centroid Finding and Particle Overlap Decomposition Algorithm for Stereo Imaging Velocimetry

An integrated algorithm for decomposing overlapping particle images (multi-particle objects) along with determining each object s constituent particle centroid(s) has been developed using image analysis techniques. The centroid finding algorithm uses a modified eight-direction search method for finding the perimeter of any enclosed object. The centroid is calculated using the intensity-weighted center of mass of the object. The overlap decomposition algorithm further analyzes the object data and breaks it down into its constituent particle centroid(s). This is accomplished with an artificial neural network, feature based technique and provides an efficient way of decomposing overlapping particles. Combining the centroid finding and overlap decomposition routines into a single algorithm allows us to accurately predict the error associated with finding the centroid(s) of particles in our experiments. This algorithm has been tested using real, simulated, and synthetic data and the results are presented and discussed.

McDowell, Mark↗
NASA NTRS2010

Carbon Dioxide (CO2) Retrievals from Atmospheric Chemistry Experiment (ACE) Solar Occultation Measurements

The Atmospheric Chemistry Experiment ACE satellite (SCISAT-1) was launched into an inclined orbit on 12 August 2003 and is now recording high signal-to-noise 0.02 per centimeter resolution solar absorption spectra covering 750-4400 per centimeter (2.3-13 micrometers). A procedure has been developed for retrieving average dry air CO2 mole fractions (X(sub CO2)) in the altitude range 7-10 kilometers from the SCISAT-1 spectra. Using the N2 continuum absorption in a window region near 2500 per centimeter, altitude shifts are applied to the tangent heights retrieved in version 2.2 SCISAT-1 processing, while cloudy or aerosol-impacted measurements are eliminated. Monthly-mean XCO2 covering 60 S to 60 N latitude for February 2004 to March 2008 has been analyzed with consistent trends inferred in both hemispheres. The ACE XCO2 time series have been compared with previously-reported surface network measurements, predictions based on upper tropospheric aircraft measurements, and space-based measurements. The retrieved X(sub CO2) from the ACE-FTS spectra are higher on average by a factor of 1.07 plus or minus 0.025 in the northern hemisphere and by a factor of 1.09 plus or minus 0.019 on average in the southern hemisphere compared to surface station measurements covering the same time span. The ACE derived trend is approximately 0.2% per year higher than measured at surface stations during the same observation period.

Rinsland, Curtis P.↗
NASA NTRS2011

The Habitat Demonstration Unit Project: A Modular Instrumentation System for a Deep Space Habitat

NASA is focused on developing human exploration capabilities in low Earth orbit (LEO), expanding to near Earth asteroids (NEA), and finally to Mars. Habitation is a crucial aspect of human exploration, and a current focus of NASA activities. The Habitation Demonstration Unit (HDU) is a project focused on developing an autonomous habitation system that enables human exploration of space by providing engineers and scientists with a test bed to develop, integrate, test, and evaluate habitation systems. A critical feature of the HDU is the instrumentation system, which monitors key subsystems within the habitat. The following paper will discuss the HDU instrumentation system performance and lessons learned during the 2010 Desert Research and Technology Studies (D-RaTS). In addition, this paper will discuss the evolution of the instrumentation system to support the 2011 Deep Space Habitat configuration, the challenges, and the lessons learned of implementing this configuration. In 2010, the HDU was implemented as a pressurized excursion module (PEM) and was tested at NASA s D-RaTS in Arizona [1]. For this initial configuration, the instrumentation system design used features that were successful in previous habitat instrumentation projects, while also considering challenges, and implementing lessons learned [2]. The main feature of the PEM instrumentation system was the use of a standards-based wireless sensor node (WSN), implementing an IEEE 802.15.4 protocol. Many of the instruments were connected to several WSNs, which wirelessly transmitted data to the command and data handling system via a mesh network. The PEM instrumentation system monitored the HDU during field tests at D-RaTS, and the WSN data was later analyzed to understand the performance of this system. In addition, several lessons learned were gained from the field test experience, which fed into the instrumentation design of the next generation of the HDU.

Rojdev, Kristina↗