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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 OSTI2022

Physics Informed Reinforcement Learning for Power Grid Control using Augmented Random Search

Wide adoption of deep reinforcement learning need to overcome several challenges in energy system domain, including scalability, learning from limited samples, and high-dimensional continuous state and action spaces. In this paper, we integrated physics-based information from the normal generator operation state formula in the reinforcement learning agent's neural network loss function, and applied an augmented random search agent to optimize the generator control under dynamic contingency. Simulation results demonstrated the reliability performance improvements in training speed, reward convergence, sampling efficiency, scalability, and transferability.

physics informed ML, Physics Informed Neural Netwo↗
NASA NTRS2014

Anomalous Thrust Production from an RF Test Device Measured on a Low-Thrust Torsion Pendulum

This paper describes the eight-day August 2013 test campaign designed to investigate and demonstrate viability of using classical magnetoplasmadynamics to obtain a propulsive momentum transfer via the quantum vacuum virtual plasma. This paper will not address the physics of the quantum vacuum plasma thruster, but instead will describe the test integration, test operations, and the results obtained from the test campaign. Approximately 30-50 micro-Newtons of thrust were recorded from an electric propulsion test article consisting primarily of a radio frequency (RF) resonant cavity excited at approximately 935 megahertz. Testing was performed on a low-thrust torsion pendulum that is capable of detecting force at a single-digit micronewton level, within a stainless steel vacuum chamber with the door closed but at ambient atmospheric pressure. Several different test configurations were used, including two different test articles as well as a reversal of the test article orientation. In addition, the test article was replaced by an RF load to verify that the force was not being generated by effects not associated with the test article. The two test articles were designed by Cannae LLC of Doylestown, Pennsylvania. The torsion pendulum was designed, built, and operated by Eagleworks Laboratories at the NASA Johnson Space Center of Houston, Texas. Approximately six days of test integration were required, followed by two days of test operations, during which, technical issues were discovered and resolved. Integration of the two test articles and their supporting equipment was performed in an iterative fashion between the test bench and the vacuum chamber. In other words, the test article was tested on the bench, then moved to the chamber, then moved back as needed to resolve issues. Manual frequency control was required throughout the test. Thrust was observed on both test articles, even though one of the test articles was designed with the expectation that it would not produce thrust. Specifically, one test article contained internal physical modifications that were designed to produce thrust, while the other did not (with the latter being referred to as the "null" test article). Test data gathered includes torsion pendulum displacement measurements which are used to calculate generated force, still imagery in the visible spectrum to document the physical configuration, still imagery in the infrared spectrum to characterize the thermal environment, and video imagery. Post-test data includes static and animated graphics produced during RF resonant cavity characterization using the COMSOL Multiphysics® software application. Excerpts from all of the above are included and discussed in this paper. Lessons learned from test integration and operations include identification of the need to replace manual control of the resonant cavity target frequency with an automated frequency control capability. Future test plans include the development of an automatic frequency control circuit. Test results indicate that the RF resonant cavity thruster design, which is unique as an electric propulsion device, is producing a force that is not attributable to any classical electromagnetic phenomenon and therefore is potentially demonstrating an interaction with the quantum vacuum virtual plasma. Future test plans include independent verification and validation at other test facilities.

Brady, David↗
NASA NTRS1992

Integration and use of Microgravity Research Facility: Lessons learned by the crystals by vapor transport experiment and Space Experiments Facility programs

The Crystals by Vapor Transport Experiment (CVTE) and Space Experiments Facility (SEF) are materials processing facilities designed and built for use on the Space Shuttle mid deck. The CVTE was built as a commercial facility owned by the Boeing Company. The SEF was built under contract to the UAH Center for Commercial Development of Space (CCDS). Both facilities include up to three furnaces capable of reaching 850 C minimum, stand-alone electronics and software, and independent cooling control. In addition, the CVTE includes a dedicated stowage locker for cameras, a laptop computer, and other ancillary equipment. Both systems are designed to fly in a Middeck Accommodations Rack (MAR), though the SEF is currently being integrated into a Spacehab rack. The CVTE hardware includes two transparent furnaces capable of achieving temperatures in the 850 to 870 C range. The transparent feature allows scientists/astronauts to directly observe and affect crystal growth both on the ground and in space. Cameras mounted to the rack provide photodocumentation of the crystal growth. The basic design of the furnace allows for modification to accommodate techniques other than vapor crystal growth. Early in the CVTE program, the decision was made to assign a principal scientist to develop the experiment plan, affect the hardware/software design, run the ground and flight research effort, and interface with the scientific community. The principal scientist is responsible to the program manager and is a critical member of the engineering development team. As a result of this decision, the hardware/experiment requirements were established in such a way as to balance the engineering and science demands on the equipment. Program schedules for hardware development, experiment definition and material selection, flight operations development and crew training, both ground support and astronauts, were all planned and carried out with the understanding that the success of the program science was as important as the hardware functionality. How the CVTE payload was designed and what it is capable of, the philosophy of including the scientists in design and operations decisions, and the lessons learned during the integration process are descussed.

Heizer, Barbara L.↗
NASA NTRS2006

CloudSat Safety Operations at Vandenberg AFB

CloudSat safety operations at Vendenberg AFB is given. The topics include: 1) CloudSat Project Overview; 2) Vandenberg Ground Operations; 3) Delta II Launch Vehicle; 4) The A-Train; 5) System Safety Management; 6) CALIPSO Hazards Assessment; 7) CALIPSO Supplemental Safeguards; 8) Joint System Safety Operations; 9) Extended Stand-down; 10) Launch Delay Safety Concerns; and 11) Lessons Learned.

CloudSat↗
NASA NTRS1990

Prelaunch processing scientific payloads since Challenger - Lessons learned exercise

The 'lessons learned' process that follows each NASA payload-processing operation is described with attention given to the development of a knowledge base from the results. The process is based on the subjective evaluation of operations problems by test-team members following a mission. The lessons learned from four Space Shuttle missions - STS-26R, -29R, -30R, and -30 - are examined with categorizations of incidents which is based on operational, documentation, hardware, and software categories. Recommendations for ways to address the incidents are categorized similarly, with operational categories such as admonitory, documentation modifications, and support changes. A basic numerical dataset is developed based on the results, and the data show that STS-26R had the highest number of incidents. The process is found to be an effective educational tool in payload-processing operations because it disseminates key individual experiences.

Schuiling, R. L.↗
DOE OSTI2023

Applications of physics informed neural operators

Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code , an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗
DOE OSTI2021

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this study we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this work we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Assumption Generation for the Verification of Learning-Enabled Autonomous Systems

Providing safety guarantees for autonomous systems is difficultas these systems operate in complex environments that require the use of learning-enabled components, such as deep neural networks (DNNs) for visual perception. DNNs are hard to analyze due to their size (they can have thousands or millions of parameters), lack of formal specifications (DNNs are typically learnt from labeled data, in the absence of any formal or informal requirements), and sensitivity to small changes in the environment. We present an assume-guarantee style compositional approach for the formal verification of system-level safety properties of such autonomous systems. Our insight is that we can analyze the system in the absence of the DNN perception components by automatically synthesizing assumptions on the DNN behaviour that guarantee the satisfaction of the required safety properties. The synthesized assumptions are the weakest in the sense that they characterize the output sequences of all the possible DNNs that, plugged into the autonomous system, guarantee the required safety properties. The assumptions can be leveraged as run-time monitors over a deployed DNN to guarantee the safety of the overall system; they can also be mined to extract local specifications for use during training and testing of DNNs. We illustrate our approach on a case study taken from the autonomous airplanes domain that uses a complex DNN for perception

Autonomous systems↗
NASA NTRS2014

A Flight Research Overview of WSPR, a Pilot Project for Sonic Boom Community Response

In support of NASAs ongoing effort to bring supersonic commercial travel to the public, NASA Dryden Flight Research Center and NASA Langley Research Center, in cooperation with other industry organizations, conducted a flight research experiment to identify the methods, tools, and best practices for a large-scale quiet (or low) sonic boom community human response test. The name of the effort was Waveforms and Sonic boom Perception and Response. Such tests will go towards building a dataset that governing agencies like the Federal Aviation Administration and International Civil Aviation Organization will use to establish regulations for acceptable sound levels of overland sonic booms. Until WSPR, there had never been an effort that studied the response of people in their own homes and performing daily activities to non-traditional, low sonic booms.WSPR was a NASA collaborative effort with several industry partners, in response to a NASA Aeronautics Research Mission Directorate Research Opportunities in Aeronautics. The primary contractor was Wyle. Other partners included Gulfstream Aerospace Corporation, Pennsylvania State University, Tetra Tech, and Fidell Associates, Inc.A major objective of the effort included exposing a community with the sonic boom magnitudes and occurrences expected in high-air traffic regions with a network of supersonic commercial aircraft in place. Low-level sonic booms designed to simulate those produced by the next generation of commercial supersonic aircraft were generated over a small residential community. The sonic boom footprint was recorded with an autonomous wireless microphone array that spanned the entire community. Human response data was collected using multiple survey methods. The research focused on essential elements of community response testing including subject recruitment, survey methods, instrumentation systems, flight planning and operations, and data analysis methods.This paper focuses on NASAs role in the efforts logistics and operations including human response subject recruitment, the operational processes involved in implementing the surveys throughout the community, instrumentation systems, logistics, flight planning, and flight operations. Findings discussed in this paper include critical lessons learned in all of those areas. The paper also discusses flight operations results. Analysis of the accuracy and repeatability of planning and executing the unique aircraft maneuver used to generate low sonic booms concluded that the sonic booms had overpressures within 0.15 pounds-per-square-feet of the planned values for 76 of t he attempts. Similarly, 90 of the attempts to generate low sonic booms within the community were successful.

sonic booms↗
NASA NTRS2017

Flight Test Evaluation of an Unmanned Aircraft System Traffic Management (UTM) Concept for Multiple Beyond-Visual-Line-of-Sight (BVLOS) Operations

Many applications of small Unmanned Aircraft System (UAS) have been envisioned. These include surveillance of key assets such as pipelines, rail, or electric wires, deliveries, search and rescue, traffic monitoring, videography, and precision agriculture. These operations are likely to occur in the same airspace in the presence of many static and dynamic constraints such as airports, and high wind areas. Therefore, operations of small UAS need to be managed to ensure safety and operation efficiency is maintained. NASA has advanced a concept for UAS Traffic Management (UTM) and has initiated a research effort to refine that concept and develop operational and system requirements. A UTM research platform is in development and flight test activities to evaluate core functions and key assumptions focusing exclusively on UAS operations in different environments are underway. This seminar will present lessons learned from a recent flight test focused on enabling operations of multiple UAS in lower-risk environments within and beyond visual line of sight (BVLOS).

UTM↗
NASA NTRS2010

International Space Station Mechanisms and Maintenance Flight Control Documentation and Training Development

International Space Station (ISS) crew and flight controller training documentation is used to aid in training operations. The Generic Simulations References SharePoint (Gen Sim) site is a database used as an aid during flight simulations. The Gen Sim site is used to make individual mission segment timelines, data, and flight information easily accessible to instructors. The Waste and Hygiene Compartment (WHC) training schematic includes simple and complex fluid schematics, as well as overall hardware locations. It is used as a teaching aid during WHC lessons for both ISS crew and flight controllers. ISS flight control documentation is used to support all aspects of ISS mission operations. The Quick Look Database and Consolidated Tool Page are imagery-based references used in real-time to help the Operations Support Officer (OSO) find data faster and improve discussions with the Flight Director and Capsule Communicator (CAPCOM). A Quick Look page was created for the Permanent Multipurpose Module (PMM) by locating photos of the module interior, labeling specific hardware, and organizing them in schematic form to match the layout of the PMM interior. A Tool Page was created for the Maintenance Work Area (MWA) by gathering images, detailed drawings, safety information, procedures, certifications, demonstration videos, and general facts of each MWA component and displaying them in an easily accessible and consistent format. Participation in ISS mechanisms and maintenance lessons, mission simulation On-the-Job Training (OJT), and real-time flight OJT was used as an opportunity to train for day-to-day operations as an OSO, as well as learn how to effectively respond to failures and emergencies during mission simulations and real-time flight operations.

Daugherty, Colin C.↗
NASA NTRS2011

Orion Pad Abort 1 Flight Test - Ground and Flight Operations

This paper discusses the ground and flight operations aspects to the Pad Abort 1 launch. The paper details the processes used to plan all operations. The paper then discussions the difficulties of integration and testing, while detailing some of the lessons learned throughout the entire launch campaign. Flight operational aspects of the launc~ are covered in order to provide the listener with the full suite of operational issues encountered in preparation for the first flight test of the Orion Launch Abort System.

Hackenberg, Davis L.↗
DOE OSTI2020

Improving Gas Reactor Design with Complex Non-Standard Reaction Mechanisms in a Reactive Flow Model

Fluorinated gases are critical to a number of high-technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but existing methods to destroy them requires a significant amount of energy. The goal of this project was to develop a more energy efficient method of destroying these high GWP gases using computational fluid dynamics, but the computational requirements are significant. In this project, we bring together resources available at the national labs, including high-performance computing hardware and modern computational methods, in order to explore an industrially important problem with key environmental and economic impact. In this project, we examined the state-of-the-art models for the physical processes involved, and made subtle changes to arrive at alternative formulations considerably better suited for computation and study. We examined several representative scenarios and developed a set of analyses techniques and tools that could be used to explore the systems in much greater detail. This exploratory project has demonstrated to ALZETA that HPC can indeed provide a unique view into their complex engineering problem - one that is simply inaccessible by any other means. The tools allow us to explore the details of the chemical process, and allow one to construct "what-if" scenarios to address shortcomings of the physical device in its current operating modes. However, as with many sources of completely new information, many new questions are illuminated with this capability. It will take considerable time, effort and experience before we learn to probe the solutions, pose operating scenarios and confirm modifications to the device that lead to real engineering advances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge

Extremely low power neuromorphic systems are well-suited for deployment to the edge for many applications. In many use cases of neuromorphic computing for control, a spiking neural network is trained off-line using a simulation and then deployed to a neuromorphic system at the edge, where it will operate without ongoing training or learning. However, it may be desirable to continue training or learning at the edge to refine or adapt to the real-world system. In this work, we propose an approach for performing real-time evolutionary optimization for spiking neural networks for neuromorphic deployment at the edge. In particular, we propose a combination of simulation and real-world evaluations, along with feedback from the real-world environment, to train spiking neural networks for continuous deployment to the edge. We show that the real-time evolution at the edge approach achieves comparable performance to an evolution approach that requires constant evaluation in the realworld environment.

Schuman, Catherine↗
DOE OSTI2022

The COVID-19 High-Performance Computing Consortium

In March of 2020, recognizing the potential of High Performance Computing (HPC) to accelerate understanding and the pace of scientific discovery in the fight to stop COVID-19, the HPC community assembled the largest collection of worldwide HPC resources to enable COVID-19 researchers worldwide to advance their critical efforts. Amazingly, the COVID-19 HPC Consortium was formed within one week through the joint effort of the Office of Science and Technology Policy (OSTP), the U.S. Department of Energy (DOE), the National Science Foundation (NSF), and IBM to create a unique public–private partnership between government, industry, and academic leaders. This article is the Consortium's story–how the Consortium was created, its founding members, what it provides, how it works, and its accomplishments. We will reflect on the lessons learned from the creation and operation of the Consortium and describe how the features of the Consortium could be sustained as a National Strategic Computing Reserve to ensure the nation is prepared for future crises.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exploring Capability of Multimodal Foundation Model for Image-based Fault Detection of Photovoltaic Modules

Multimodal Foundation Model (MFM), like ChatGPT and Gemini, have emerged as powerful tools for their exceptional natural language processing capabilities and their emerging potential in image analysis. This paper investigates the application of MFMs for photovoltaic (PV) fault detection through image analysis, focusing on ChatGPT 4.0 and Gemini 1.5 Pro. Three types of PV images and the corresponding common PV faults are detected: bird droppings using visible images, cell cracks via electroluminescence (EL) images, and hotspots using infrared (IR) images. Among the two models, Gemini 1.5 Pro demonstrated superior performance, achieving near-perfect results with an average F1 score of 0.97, consistently outperforming ChatGPT 4.0 in accuracy and reliability. Unlike traditional machine learning (ML) models, MFMs can operate in a zero shot manner that does not require additional training by the user, and the input images are not limited by size, angle, scope, or PV technology. The strong adaptability and user-friendliness make MFM a promising tool for analyzing PV images and advancing health monitoring for PV modules.

Li, Baojie↗