SEARCH · Engineering Papers

Results for “machine committee”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS1955

Thirty-Ninth Annual Report of the National Advisory Committee for Aeronautics: Administrative Report Including Technical Reports Nos. 1111 to 1157

This is the fiftieth year since Wilbur and Orville Wright at Kitty Hawk N. C., made their powered flight. That airplane was a fragile and unsteady machine of no immediate utility. It flew for only a minute but it disclosed the solution of the age-old problem of human flight. The Wrights were the first in the history of man to fly. There was no one to teach them. They had to discover principles and to learn the art by cautious and methodical experimenting. From their own research they obtained the practical information needed to design their successful flying machine. The Wrights received no effective aid from the theoretical studies of flight made by the mathematicians of the nineteenth century. The science of aerodynamics was developed in response to the practical demands of aeronautics in the years to follow. In 1908, the Wrights demonstrated at Fort Myer, Va., a vastly improved flyer, the first military airplane. It carried a passenger and flew for more than an hour. Following this public demonstration, the development of the airplane was taken up vigorously. At first France and Germany took the lead, then Great Britain, but the United States lagged behind in the furthering of this greatest American development of the century. With war clouds in view in 1915, the Congress established the National Advisory Committee for Aeronautics to undertake the scientific study of the problems of fight with a view to their practical solution. President Wilson appointed the members of the first Committee, consisting of the heads of the military and civil agencies of the Government concerned with aeronautics and experts from private life.

Source record↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment of Machine Learning and Artificial Intelligence for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

How Artificial Intelligence and Machine Learning Transform the Human Condition

Our July 2021 symposium, “How Artificial Intelligence and Machine Learning Transform the Human Condition,” was hosted through a partnership between Los Alamos National Laboratory and the National Academies of Sciences, Engineering, and Medicine’s Committee on Science, Technology, and Law. The symposium is part of a broader initiative focusing on harnessing transformative technologies, and builds on our September 2020 symposium titled, “COVID-19: Harnessing a Transformational Pandemic.” Topics such as systems biology and artificial intelligence not only represent compelling research frontiers but also highlight national security challenges with social, ethical, and legal implications.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1969

Interdisciplinary Approach to the Lubrication of Concentrated Contacts

In an effort to further the advances in the complex and multidisciplinary subject of lubrication, the National Aeronautics and Space Administration (NASA) inaugurated in 1967 an interdisciplinary lubrication symposium series. The first meeting of the series, held in San Antonio, Texas, on November 28-30, 1967, was devoted to a critical appraisal of the level of understanding and the needed research in the area of sliding friction and wear under unlubricated and boundary-lubrication conditions. The second meeting, held in Cleveland, Ohio, on November 19-21, 1968, again emphasized unlubricated and boundary-lubricated sliding friction and wear but attempted to focus attention on some selected theoretical and practical problems by discussing in small working group their implications and possible methods of attack. Having thus provided for an examination of sliding friction and wear under unlubricated and boundary-lubrication conditions in considerable breadth and depth, the Steering Committee recommended to ASA in September 196 that further attention be turned to an evaluation of the collective understanding and the needed research in the lubrication of highly loaded machine elements in a combined rolling and sliding situation. The recommendation was accepted by ASA and, accordingly, a symposium entitled Interdisciplinary Approach to the Lubrication of Concentrated Contacts was held on the p-rounds of Rensselaer Polytechnic Institute, Troy, New York, on July 15-17, 1969.

P M Ku↗

NASA NTRS1951

The Hydrodynamic Lubrication of Cyclically Loaded Bearings

Explore the source record for details and available documents.

LUBRICATION - BEARINGS, SLEEVE↗

NASA NTRSDate not supplied

Data-Centric Operational Design Domain Characterization for Machine Learning-Based Aeronautical Products

We give for Machine Learning (ML)-based aeronautical products, a first rigorous characterization of Operational Design Domains (ODDs). Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, using a top-down approach starting from system specifications, and a bottom-up approach starting from detailed ML Model (MLM) designs. Then we give a categorization of the data that ML-based applications can encounter in operation, identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: (1) the requirements necessary to drive the design of MLMs; (2) the potential effects on the MLM and higher levels of the system hierarchy; (3) the learning assurance processes that may be needed, and (4) system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope. The approach in this paper is one of the cornerstones of a future process guidance for development and certification/approval of safety-related aeronautical products implementing Artificial Intelligence (AI), currently being developed through aviation industry-based consensus, jointly by the SAE G-34 Committee for AI in aviation, and EUROCAE WG-114 for AI.

Aeronautical products↗

NASA NTRS1943

Some Investigations of the General Instability of Stiffened Metal Cylinders II : Preliminary Tests of Wire-braced Specimens and Theoretical Studies

This is the second of a series of reports covering an investigation of the general instability problem by the California Institute of Technology. The first five reports of this series cover investigations of the general instability problem under the loading conditions of pure bending and were prepared under the sponsorship of the Civil Aeronautics Administration. The succeeding reports of this series cover the work done on other loading conditions under the sponsorship of the National Advisory Committee for Aeronautics.

CYLINDERS, METAL - INSTABILITY↗

NASA NTRS1985

Government review of the Mod-2 wind turbine (as-built)

The findings and recommendations of the Government committee formed to conduct an as-built review of the three Mod-2 wind turbine units at Goldendale, Washington are given. The purpose of the review was to identify any critical deficiencies in machine components that could result in failure, and to recommend any necessary corrective action before resuming safe machine operation. The review concluded that one of the deficiencies identified would preclude planned attended or unattended operation, provided that certain corrective actions were implemented.

Johnson, W. R.↗

NASA NTRS2004

Low Emissions Alternative Power (LEAP) Project Office Business Team of the Aeropropulsion Research Program Office (ARPO) Org. 0140

The program for which I am working at this summer is Propulsion and Power/Low Emissions Alternative Power (P&P/LEAP). It invests in a fundamental TRL 1-6 research and technology portfolio that will enable the future of: Alternative fuels and/or alternative propulsion systems, non-combustion (electric) propulsion systems. P&P/LEAP will identify and capitalize on the highest potential concepts generated both internal and external to the Agency. During my 2004 summer at NASA Glenn Research Center, I worked with my mentor Barbara Mader, in the Project Office with the Business Team completing various tasks for the project and personnel. The LEAP project is a highly matrixed organization. The Project Office is responsible for the goals advocacy and dollar (budget) of the LEAP project. The objectives of the LEAP Project are to discover new energy sources and develop unconventional engines and power systems directed towards greatly reduced emissions, enable new vehicle concepts for public mobility, new science missions and national security. The Propulsion and PowerLow Emissions Alternative Power directly supports the environmental, mobility, national security objectives of the Vehicle Systems Program and the Aeronautics Technology Theme. Technology deliverables include the demonstration through integrated ground tests, a constant volume combustor in an engine system, and UAV/small transport aircraft all electric power system. My mentor serves as a key member of the management team for the Aeropropulsion Research Program Office (ARPO). She has represented the office on numerous occasions, and is a member of a number of center-wide panels/teams, such as the Space management Committee and is chair to the Business Process Consolidation Team. She is responsible for the overall coordination of resources for the Propulsion and Power Project - from advocacy to implementation. The goal for my summer at NASA was to document processes and archive program documents from the past years. I used the computer and office machines, and also worked with personnel in setting up a Cost Estimation Plan. I gained office experience in Word, Excel, and Power Point, with the completion of a variety of tasks. I made spreadsheets that pertained to the budget plan for Journey to Tomorrow, to name a few I have supported the office by tracking resource information: including programmatic travel, project budget at the center level to budgets for individual research sub-projects and grants. I also assisted the Program Support Office in their duties including, representing the office on numerous occasions on center-wide team/panels, such as the Space management committee, IFMP Budget Formulation, Journey to Tomorrow Committee, and the Vehicle Systems Program Business Process Team.

Buttler, Jennifer A.↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRS1994

Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 2

This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) on August 3-5, 1993, and held at JSC Gilruth Recreation Center. The symposium was cosponsored by NASA/JSC and U.S. Air Force Materiel Command. SOAR included NASA and USAF programmatic overviews, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations. More than 100 technical papers, 17 exhibits, a plenary session, several panel discussions, and several keynote speeches were included in SOAR '93. These proceedings, along with comments and suggestions made by panelists and keynote speakers, will be used to assess progress made in joint USAF/NASA projects and activities to identify future collaborative/jolnt programs. SOAR '93 was the responsibility of the USAF NASA Space Technology Interdependency Group Operations Committee. Symposium proceedings include papers presented by experts from NASA, the USAF, USA, and USN, U.S. Department of Energy, universities, and industry.

Navigation↗

NASA NTRS1975

Note on performance of tapered grip tensile loading devices

Alignment results are presented in terms of percent bending for a quick release, tapered grip, tensile loading device that has been proposed for testing sharply notched specimens of aluminum and magnesium alloys by a Task Group of the ASTM Committee E-24 on Fracture Testing of Metals. The results show that the bending introduced by the fixtures is strongly dependent on their relative rotational positions in respect to the loading rods which adapt them, to the tensile machine. For one set of tapered grips the highest bending was about 15%. Recommendations are made for improvement in the design of the tapered grips which should reduce the bending stresses substantially.

Jones, M. H.↗

DOE OSTI2025

2020 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is essential for addressing the increasingly complex national workforce demands stemming from the growth of computational science and engineering challenges. Computational science and engineering (CSE) takes a multidisciplinary approach that utilizes scientific computing to tackle practical problems and provide technical tools across the spectrum of scientific discovery. The DOE CSGF specifically highlights high-performance computing (HPC) as a critical enabling technology in CSE, driving advancements in science and engineering that are vital to both the DOE and the broader economy. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines have been transformed through the augmentation of scientific observation via HPC. At government laboratories, academic institutions, and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, quantum information systems, fusion-reactor design, machine learning, additive manufacturing, nano materials for next-generation batteries and transistors, and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing. Computational biology, machine learning, and quantum computing are among the subjects that began to swell in the ranks of CSGF applicants before the labs were hiring as high a percentage of employees in these categories.” The explosion of scientific and technological data has heightened the demand for advanced high-performance computing (HPC) to transform these data into meaningful scientific insights. As access to vast amounts of data increases, the fields of Machine Learning and Artificial Intelligence are experiencing a resurgence, enhancing the established practices of computational modeling and simulation. In its September 2020 subcommittee report on "AI/ML, Data Intensive Science, and High-Performance Computing," the DOE Advanced Scientific Computing Advisory Committee (ASCAC) specifically called for a fellowship program to train computational and data scientists to address exascale and data-intensive computing challenges. This integration of empirical and theoretical modeling will increasingly guide federal policymakers in making decisions that impact American society and future generations. It demands a workforce of highly skilled and intellectually agile computational scientists capable of navigating the rapid advancements in scientific computing within the DOE National Laboratory research environment. The DOE CSGF program has consistently addressed this critical need.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

An assessment of space shuttle flight software development processes

In early 1991, the National Aeronautics and Space Administration's (NASA's) Office of Space Flight commissioned the Aeronautics and Space Engineering Board (ASEB) of the National Research Council (NRC) to investigate the adequacy of the current process by which NASA develops and verifies changes and updates to the Space Shuttle flight software. The Committee for Review of Oversight Mechanisms for Space Shuttle Flight Software Processes was convened in Jan. 1992 to accomplish the following tasks: (1) review the entire flight software development process from the initial requirements definition phase to final implementation, including object code build and final machine loading; (2) review and critique NASA's independent verification and validation process and mechanisms, including NASA's established software development and testing standards; (3) determine the acceptability and adequacy of the complete flight software development process, including the embedded validation and verification processes through comparison with (1) generally accepted industry practices, and (2) generally accepted Department of Defense and/or other government practices (comparing NASA's program with organizations and projects having similar volumes of software development, software maturity, complexity, criticality, lines of code, and national standards); (4) consider whether independent verification and validation should continue. An overview of the study, independent verification and validation of critical software, and the Space Shuttle flight software development process are addressed. Findings and recommendations are presented.

Source record↗

NASA NTRS1987

Timing issues in the distributed execution of Ada programs

This paper examines, in the context of distributed execution, the meaning of Ada constructs involving time. In the process, unresolved questions of interpretation and problems with the implementation of a consistent notion of time across a network are uncovered. It is observed that there are two Ada mechanisms that can involve a distributed sense of time: the conditional entry call, and the timed entry call. It is shown that a recent interpretation by the Language Maintenance Committee resolves the questions for the conditional entry calls but results in an anomaly for timed entry calls. A detailed discussion of alternative implementations for the timed entry call is made, and it is aruged that: (1) timed entry calls imply a common sense of time between the machines holding the calling and called tasks; and (2) the measurement of time for the expiration of the delay and the decision of whether or not to perform the rendezvous should be made on the machine holding the called task. The need to distinguish the unreadiness of the called task from timeouts caused by network failure is pointed out. Finally, techniques for realizing a single sense of time across the distributed system (at least to within an acceptable degree of uncertainty) are also discussed.

Volz, Richard A.↗

NASA NTRS1988

Ergonomic Models of Anthropometry, Human Biomechanics and Operator-Equipment Interfaces

The Committee on Human Factors was established in October 1980 by the Commission on Behavioral and Social Sciences and Education of the National Research Council. The committee is sponsored by the Office of Naval Research, the Air Force Office of Scientific Research, the Army Research Institute for the Behavioral and Social Sciences, the National Aeronautics and Space Administration, and the National Science Foundation. The workshop discussed the following: anthropometric models; biomechanical models; human-machine interface models; and research recommendations. A 17-page bibliography is included.

Kroemer, Karl H. E.↗