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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

PTC MathCAD and Workgroup Manager: Implementation in a Multi-Org System

In this presentation, the presenter will review what was done at Kennedy Space Center to deploy and implement PTC MathCAD and PTC Workgroup Manager in a multi-org system. During the presentation the presenter will explain how they configured PTC Windchill to create custom soft-types and object initialization rules for their custom numbering scheme and why they choose these methods. This presentation will also include how to modify the EPM default soft-type file in the PTC Windchill server codebase folder. The presenter will also go over the code used in a start up script to initiate PTC MathCAD and PTC Workgroup Manager in the proper order, and also set up the environment variables when running both PTC Workgroup Manager and PTC Creo. The configuration.ini file the presenter used will also be reviewed to show you how to set up the PTC Workgroup Manager and customized it to their user community. This presentation will be of interest to administrators trying to create a similar set-up in either a single org or multiple org system deployment. The big take away will be ideas and best practices learned through implementing this system, and the lessons learned what to do and not to do when setting up this configuration. Attendees will be exposed to several different sets of code used and that worked well and will hear some limitations on what the software can accomplish when configured this way.

Multi-Org System↗
DOE OSTI2019

Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. Data-driven feature analysis was performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross-correlation, multinomial logistical regression, and classification and regression trees, were integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrated the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.

42 ENGINEERING↗
NASA NTRS1986

The computerization of programming: Ada (R) lessons learned

One of the largest systems yet written in Ada has been constructed. This system is the Intermetrics Ada compiler. Many lessons have been learned during the implementation of this Ada compiler. Some of these lessons, concentrating on those lessons relevant to large system implementations are described. The characteristics of the Ada compiler implementation project at Intermetrics are also described. Some specific experiences during the implementation are pointed out.

Struble, Dennis D.↗
NASA NTRS2016

Lessons Learned from the Advanced Topographic Laser Altimeter System

The ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) instrument is an upcoming Earth Science mission focusing on the effects of climate change. The flight instrument passed all environmental testing at GSFC (Goddard Space Flight Center) and is now ready to be shipped to the spacecraft vendor for integration and testing. This presentation walks through the lessons learned from design, hardware, analysis and testing perspective. ATLAS lessons learned include general thermal design, analysis, hardware, and testing issues as well as lessons specific to laser systems, two-phase thermal control, and optical assemblies with precision alignment requirements.

Thermal↗
NASA NTRS2018

Metabrain for Embedded Cognition (MBEC)

This study presents the application of Hidden Markov Models (HMM) to determine specialized features without expert input. Specifically, the application of such a method for classification of high multi-path fading is targeted, for demonstrating the feasibility of such an approach. This is the first step in the development of a meta-brain for embedded cognition (M-BEC) suite that can be used to apply machine learning to various communication systems at NASA GRC. The project explores the concept of fading and how it affects communication systems in a negative way. Currently, supervised learning methods are used to study the effects of fading on space links. However, such models rely on expert features to make predictions as to the state of a link and whether fading is present. This project offers the possibility of having the HMM learn what characteristics are important and make predictions based on those characteristics. This project explores Hidden Markov Models, their theory and applications to various problems, as well as the underlying equations and assumptions. A preliminary result is presented and recommendations are made as to the use of such an approach for communications systems.

Propagation↗
NASA NTRS2015

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Machine learning↗
DOE OSTI2021

Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on preparation and augmentation of data as well as selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 h with the highest probabilities.

25 ENERGY STORAGE↗
NASA NTRS2010

Automata learning algorithms and processes for providing more complete systems requirements specification by scenario generation, CSP-based syntax-oriented model construction, and R2D2C system requirements transformation

Systems, methods and apparatus are provided through which in some embodiments, automata learning algorithms and techniques are implemented to generate a more complete set of scenarios for requirements based programming. More specifically, a CSP-based, syntax-oriented model construction, which requires the support of a theorem prover, is complemented by model extrapolation, via automata learning. This may support the systematic completion of the requirements, the nature of the requirement being partial, which provides focus on the most prominent scenarios. This may generalize requirement skeletons by extrapolation and may indicate by way of automatically generated traces where the requirement specification is too loose and additional information is required.

Hinchey, Michael G.↗
DOE OSTI2025

Harnessing Machine Learning to Predict MoS 2 Solid Lubricant Performance

Physical vapor deposited (PVD) molybdenum disulfide (MoS 2 ) solid lubricant coatings are an exemplar material system for machine learning methods due to small changes in process variables often causing large variations in microstructure and mechanical/tribological properties. Here, in this work, a gradient boosted regression tree machine learning method is applied to an existing experimental data set containing process, microstructure, and property information to create deeper insights into the process-structure–property relationships for molybdenum disulfide (MoS 2 ) solid lubricant coatings. The optimized and cross-validated models show good predictive capabilities for density, reduced modulus, hardness, wear rate, and initial coefficients of friction. The contribution of individual deposition variables (i.e., argon pressure, deposition power, target conditioning) on coating properties is highlighted through feature importance. The process-property relationships established herein show linear and non-linear relationships and highlight the influence of uncontrolled deposition variables (i.e., target conditioning) on the tribological performance.

MoS2↗
DOE OSTI2021

Stiff neural ordinary differential equations

Neural Ordinary Differential Equations (ODEs) are a promising approach to learn dynamical models from time-series data in science and engineering applications. This work aims at learning neural ODEs for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODEs in the classical stiff ODE systems of Robertson’s problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems. We then present successful demonstrations in stiff systems of Robertson’s problem and an air pollution problem. The demonstrations show that the usage of deep networks with rectified activations, proper scaling of the network outputs as well as loss functions, and stabilized gradient calculations are the key techniques enabling the learning of stiff neural ODEs. The success of learning stiff neural ODEs opens up possibilities of using neural ODEs in applications with widely varying time-scales, such as chemical dynamics in energy conversion, environmental engineering, and life sciences.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

X-43A Fluid and Environmental Systems: Ground and Flight Operation and Lessons Learned

The X-43A Hyper-X program demonstrated the first successful flights of an airframe integrated scramjet powered hypersonic vehicle. The X-43A vehicles established successive world records for jet-powered vehicles at speeds of Mach 7 and Mach 10. The X-43A vehicle is a subscale version of proposed hypersonic reconnaissance strike aircraft. Scaled down to a length of 12 ft (3.66 m), the lifting body design with high fineness ratio resulted in very small internal space available for fluid systems and their corresponding environmental conditioning systems. Safe testing and operation of the X-43A fluid and environmental systems was critical for mission success, not only for the safety of the flight crew in the NASA B-52B carrier aircraft, but also to maintain the reliability of vehicle systems while exposed to dynamics and hostile conditions encountered during the boost trajectory. The X-43A fluid and environmental systems successfully managed explosive, pyrophoric, inert, and very high pressure gases without incident. This report presents a summary of the checkout and flight validation of the X-43A fluid systems. The testing used for mission assurance is summarized. System performance during captive carry and launch flights is presented. The lessons learned are also discussed.

Vachon, Michael Jacob↗
NASA NTRS2011

Transient Plume Model Testing Using LADEE Spacecraft Attitude Control System Operations

We have learned it is conceivable that the Neutral Mass Spectrometer on board the Lunarr Atmosphere Dust Environment Explorer (LADEE) could measure gases from surface-reflected Attitude Control System (ACS) thruster plume. At minimum altitude, the measurement would be maximized, and gravitational influence minimized ("short" time-of-flight (TOF) situation) Could use to verify aspects of thruster plume modeling Model the transient disturbance to NMS measurements due to ACS gases reflected from lunar surface Observe evolution of various model characteristics as measured by NMS Species magnitudes, TOF measurements, angular distribution, species separation effects

Woronowicz, Michael↗
DOE OSTI2023

Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting.

quantum computing↗
NASA NTRS1979

Solar heating and hot water system installed at Municipal Building complex, Abbeville, South Carolina

Information on the solar energy system installed at the new municipal building for the City of Abbeville, SC is presented, including a description of solar energy system and buildings, lessons learned, and recommendations. The solar space heating system is a direct air heating system. The flat roof collector panel was sized to provide 75% of the heating requirement based on an average day in January. The collectors used are job-built with two layers of filon corrugated fiberglass FRP panels cross lapped make up the cover. The storage consists of a pit filled with washed 3/4 in - 1 1/2 in diameter crushed granite stone. The air handler includes the air handling mechanism, motorized dampers, air circulating blower, sensors, control relays and mode control unit. Solar heating of water is provided only those times when the hot air in the collector is exhausted to the outside.

Source record↗
NASA NTRS1993

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

Fayyad, Usama M.↗