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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ubiquitous Traffic Volume Estimation System

The Ubiquitous Traffic Volume Estimation System uses Machine Learning (ML) algorithms to provide accurate estimates of number vehicles on the roadway (ubiquitous traffic volume) based on inputs such as commercial probe traffic data that includes average vehicle speed and probe counts, roadway characteristics, weather, time of day, and day of week, and reference volume counts from a limited number of automatic traffic recorder stations. The Ubiquitous Traffic Volume Estimation System estimates traffic volumes 24 hours a day, 7 days a week, and 365 days a year on all major corridors and freeways in Denver metropolitan area and is being expanded to the whole state of Colorado. The methodology and code developed in this effort can be easily customized to other states and cities where similar data is available.

Young, Stanley↗
DOE OSTI2023

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. Here, we demonstrate improved processing and laser control performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Knowledge management information tool Web analytics - 15182

The Knowledge Management Information Tool (KM-IT) is a web-based [01] system developed to maintain and preserve the Department of Energy's (DOE's) D and D knowledge base. The system was developed by Florida International University's Applied Research Center (FIU-ARC) with the support of the D and D community including the DOE Office of Environmental Management, and with the active collaboration and support of the DOE's Energy Facility Contractors Group (EFCOG). The KM-IT system is a D and D community driven system tailored to serve the technical issues faced by the D and D workforce across the DOE Complex. The D and D KM-IT is an interactive web-based system operational and available for free access at www.dndkm.org. [02] The D and D KM-IT is currently composed of the following tailored D and D modules: Web Crawler, Hotline, Technology, Document Library (including Hanford's and Savannah River Sites' ALARA Center Reports), Specialist Directory, Lessons Learned, Best Practices, Video/Picture Library, Vendors, Collaboration Tools, Training, and Mobile Systems. The Web Crawler dynamically searches through the D and D KM-IT repository as well as the web and displays search results based on the search criteria. The Hotline allows registered users to post questions/problems related to D and D and to receive solutions from a subject-matter specialist. The Technology module serves as repository of D and D technologies and contains technology/ demonstration fact sheets and vendor information. D and D community members can add Lessons Learned, Best Practices, Documents, and Pictures/Videos to the D and D KM-IT repository, after a formal approval process. The Vendor module provides a directory of commercial vendors who provide D and D related technologies, supplies, and services. D and D KM-IT makes excellent use of the knowledge that exists within the D and D community by allowing D and D project managers around the DOE complex to collaborate by sharing innovative ideas, past experiences, and practices and by maintaining a directory of subject matter specialists. The mobile version of the KM-IT can be accessed from m.dndkm.org [03] and currently has six active modules: Vendor, Technology, Picture/Video Library, Hotline, Lessons Learned and Specialist Directory. Web analytics is a key tool being implemented by the D and D KM-IT project team in an effort to enhance the system to better meet users' needs. The D and D KM-IT project team incorporates web analytics as part of its operational elements to analyze information and key data points on how the system is being used. Web analytics is the measurement, collection, analysis and reporting of internet data for the purposes of understanding and optimizing web usage. The use of web analytics retains or attracts D and D community members to access and share knowledge through virtual collaboration on the KM-IT platform. D and D KM-IT web analytics measures details on the overall usage of the system without tracking personal information or specific user behavior: how many users visited the application, how many of those users were unique visitors, how visitors come to the system (i.e., by following a link to the system or by using the web site URL via bookmark or directly typing the address), what keywords were used to perform searches with the search engine, how long the average user stayed on a given screen or on the entire system and what links were clicked on. This information is used to identify which areas of the system are more popular than the others and provides information that can be used to streamline the application for a better user experience. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Interface learning in fluid dynamics: Statistical inference of closures within micro–macro-coupling models

Many complex multiphysics systems in fluid dynamics involve using solvers with varied levels of approximations in different regions of the computational domain to resolve multiple spatiotemporal scales present in the flow. The accuracy of the solution is governed by how the information is exchanged between these solvers at the interface and several methods have been devised for such coupling problems. In this article, we construct a data-driven model by spatially coupling a microscale lattice Boltzmann method (LBM) solver and macroscale finite difference method (FDM) solver for reaction-diffusion systems. The coupling between the micro-macro solvers has one to many mapping at the interface leading to the interface closure problem, and we propose a statistical inference method based on neural networks to learn this closure relation. The performance of the proposed framework in a bifidelity setting partitioned between the FDM and LBM domain shows its promise for complex systems where analytical relations between micro-macro solvers are not available.

42 ENGINEERING↗
DOE OSTI2024

Energy performance evaluation of the ASHRAE Guideline 36 control and reinforcement learning–based control using field measurements

This study evaluates the energy performance of ASHRAE Guideline 36–compliant control (ASHRAE 36 control) and reinforcement learning (RL)–based control through experimental field tests and a simulation study. Three field tests were conducted at Oak Ridge National Laboratory’s commercial building test facility in Oak Ridge, Tennessee: a baseline with a baseline conventional control, a test with ASHRAE 36 control, and a test with RL-based control. The selected ASHRAE 36 controls were trim and respond control, as well as variable air volume (VAV) box control. We compared the measured supply air temperature of the rooftop unit, VAV box supply air temperature, and VAV box supply airflow rate across the three test cases. The field data indicated that ASHRAE 36 controls operated as specified by ASHRAE Guideline 36. Based on these data, ASHRAE 36 control achieved a 45 % reduction in hourly averaged HVAC energy consumption compared with the baseline, and RL-based control achieved a 66 % reduction. These potential annual energy savings were confirmed using a calibrated whole-building energy model. Compared with the baseline, ASHRAE 36 control reduced HVAC energy consumption by 42 %, and RL-based control achieved a 54 % reduction. Furthermore, RL-based control reduced total HVAC energy consumption by 21 % more than ASHRAE 36 control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy

Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)–based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Challenges of nickel silicidation in CMOS technologies

In our paper, we review some of the key challenges associated with the Ni silicidation process in the most recent CMOS technologies. The introduction of new materials (e.g.SiGe), and of non-planar architectures bring some important changes that require fundamental investigation from a material engineering perspective. Following a discussion of the device architecture and silicide evolution through the last CMOS generations, we focus our study on a very peculiar defect, termed NiSi-Fangs. We describe a mechanism for the defect formation, and present a detailed material analysis that supports this mechanism. We highlight some of the possible metal enrichment processes of the nickel monosilicide such as oxidation or various RIE (Reactive Ion Etching) plasma process, leading to a metal source available for defect formation. Furthermore, we investigate the NiSi formation and re-formation silicidation differences between Si and SiGe materials, and between (1 0 0) and (1 1 1) orientations. Finally, we show that the thermal budgets post silicidation can lead to the formation of NiSi-Fangs if the structure and the processes are not optimized. Beyond the understanding of the defect and the discussion on the engineering solutions used to prevent its formation, the interest of this investigation also lies in the fundamental learning within the Ni–Pt–Si–Ge system and some additional perspective on Ni-based contacts to advanced microelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integrating Learning and Physics based Computation for Fast Online Transient Analysis

In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Integrating Hybrid-Clustering and Localized Regression for Time Synchronization of a Hierarchical Underwater Acoustic Sensor Array

Time synchronization is a critical requirement for the application of underwater acoustic sensor network (UWSN). Although a number of time synchronization protocols have been proposed for UWSN, none of them can be directly applied to autonomous acoustic receivers due to lack of hardware platform to communicate with each other. In this paper, we propose a machine learning-based time synchronization framework for autonomous receiver arrays, using the Juvenile Salmon Acoustic Telemetry System as a case study. The proposed framework consists of array partition and time synchronization. Using detections of receiver attached beacons as input, this framework synchronizes all receiver clocks to a root receiver clock. The framework has been successfully used in a field study at Trevallyn Dam forebay in Tasmania, Australia.

Fu, Tao↗
NASA NTRS1991

Paradigms for machine learning

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

Schlimmer, Jeffrey C.↗
NASA NTRS1998

An Intelligent Case-Based Help Desk Providing Web-Based Support for EOSDIS Customers

This paper describes a project that extends the concept of help desk automation by offering World Wide Web access to a case-based help desk. It explores the use of case-based reasoning and cognitive engineering models to create an 'intelligent' help desk system, one that learns. It discusses the AutoHelp architecture for such a help desk and summarizes the technologies used to create a help desk for NASA data users.

Mitchell, Christine M.↗
NASA NTRS2000

Bridging the Gap Between Space and Education

The vision of the Internationa Space Education Initiative (ISEI) - Phase I of III is to bring the excitement of the National Aeronautics and Space Administration's (NASA's) exploration of the Solar System to a diverse international audience in the context of formal classroom standards-based learning.

international educators Space education↗
NASA NTRS2017

Interpretable Categorization of Heterogeneous Time Series Data

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Drones↗
DOE OSTI2021

Protection of Radial Circuits with High Penetration Distributed Photovoltaics

The paper addresses the shortcomings of distribution protection systems as distribution level photovoltaic installations as well as other inverter based distributed resources are added to the system. Several protection schemes have been evaluated, including distance-based, estimation-based and learning-based. The paper summarizes the results and provides recommendations.

distance protection, radial distribution feeders, ↗
DOE OSTI2024

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, Shining↗
DOE OSTI2021

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea . We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py . Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

MW-Class SOFC Pilot System Development

The overall objective of this project is to develop a low-cost, efficient, and reliable MWe-class Solid Oxide Fuel Cell (SOFC) power system towards commercial deployment in natural-gas fueled distributed generation applications. This report details the successful completion of all project objectives. The main goals of the project were to develop the conceptual design of a MWe-class SOFC power system, and to complete a techno-economic analysis (TEA) to determine system costs at low-volume production levels of >100MW per year. The combination of the BoP equipment cost and SOFC stack module factory cost provided the net fabrication cost of the selected 1MW SOFC system configuration. The results of the study showed that the cost target of less than $6000/kW is achievable for a FOAK 1MW SOFC system, exclusive of the first-time engineering cost. Also, the project included an exploratory analysis of the factory cost of next generation stack technology based on the Compact Solid-oxide Architecture (CSA) design at a high annual manufacturing rate of one-GW produced in a giga-factory. The BoP capital cost for one giga-watt power production was estimated using learning-curve analysis based on the historical manufacturing cost data related to similar high temperature Molten Carbonate fuel cell systems built by FuelCell Energy (FCE). The giga-factory manufactured SOFC cost estimate combined with the BoP cost at high production rates led to the estimation of entire system capital cost. Ultimately, a Cost of Electricity (COE) analysis was performed including natural gas fuel prices, installation and O&M costs, and stack replacement cost. The results of the techno-economic analysis were summarized by parametric estimation of COE for 1MW SOFC NOAK (Nth-of-a-Kind) system with natural gas prices as a variable parameter.

08 HYDROGEN↗