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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Toxicity of Carbon Nanotubes and Its Implications for Occupational and Environmental Health

Carbon nanotubes (CNTs), which possess desirable electrical and mechanical properties, potentially have wide industrial applications. CNTs exist in two forms, single-wall (SW) and multi-wall (MW). There has been great concern that if CNTs enter the work environment as suspended respirable particulate matter (PM), they could pose an inhalation hazard. The results of recent rodent studies have collectively shown that CNTs can produce inflammation, epithelioid granulomas, fibrosis, and biochemical changes in the lungs. Studies in mice given equal amounts of test dusts showed that CNTs were more toxic than quartz and produced lesions that became progressively more pronounced. These results have led us to recommend that respirable CNT dust be considered a serious occupational health hazard, and that exposure limits be established in the expectation of expanded industrial applications. CNTs, which are totally insoluble and fibrous, would be expected to be more biopersistent than mineral fibers. Biopersistence is the key factor determining the long-term toxicity of mineral fibers and certainly of CNTs too. We have postulated that the electrical and fibrous properties of CNTs also play important roles in the toxicity of CNTs in the lungs. Recently, MWCNTs have been found in ultrafine PM aggregates in combustion streams of methane, propane, and natural-gas flames of typical stoves; indoor and outdoor fine (< 2.5 micron) PM samples were reported to contain significant fractions of MWCNTs. Environmental fine PM is mainly formed from combustion of fuels, and fine PM has been reported to be a major contributor to the induction of cardiopulmonary diseases by pollutants. Given that manufactured SWCNTs and/or MWCNTs have elicited pathological changes in the lungs and heart, we have postulated that exposure to combustion-generated MWCNTs in fine PM in the air may play a significant role in air pollution-related cardiopulmonary diseases. Therefore, CNTs from manufacturing and combustion sources in the environment could have adverse effects on human health.

Lam, Chiu-wing↗
NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
DOE OSTI2024

Uncovering the Size-dependent Thermal Solid Transformation of Akaganéite

Investigating the structural evolution and phase transformation of iron oxides is crucial for gaining a deeper understanding of geological changes on diverse planets and preparing oxide materials suitable for industrial applications. In this study, we employed in-situ heating techniques in conjunction with transmission electron microscopy (TEM) observations and ex-situ characterization to thoroughly analyze the thermal solid-phase transformation of akaganéite one-dimensional (1D) nanostructures with varying diameters. Our findings offer compelling evidence for a size-dependent morphology evolution in akaganéite 1D nanostructures, which can be attributed to the transformation from akaganéite to maghemite (?-Fe2O3) and subsequent crystal growth. Specifically, we observed that akaganéite nanorods with a diameter of around 50 nm transformed into hollow polycrystalline maghemite nanorods, which demonstrated remarkable stability without arresting crystal growth under continuous heating. In contrast, smaller akaganéite nanoneedles or nanowires with a diameter ranging from 20 to 8 nm displayed a propensity for forming single-crystal nanoneedles or nanowires through phase transformation and densification. By manipulating the size of the precursors, we have developed a straightforward method for the synthesis of single-crystal and polycrystalline maghemite nanowires through solid-phase transformation. These significant findings provide new insights into the size-dependent structural evolution and phase transformation of iron oxides at the nanoscale.

Wang, Xiang↗
NASA NTRS1998

The Successful Transfer of Space Derived Convergent Spray: An Application for Industrial Roof Coatings and Interstate Bridge Repair

A partnership was formed between the National Aeronautics and Space Administration (NASA), the Environmental Protection Agency (EPA), and United Technologies USBI Company to develop, demonstrate, and evaluate a technology that uses a solventless spray process for depositing a lightweight, highly filled roof coating on low-sloped commercial roofs. Although the levels of volatile organic compound (VOC) emissions from industrial roof coating's and paint operations have been reduced in recent years, this partnership,was an effort to further reduce VOC emission levels and to also demonstrate the use of reclaimed automobile tire rubber as a filler material in roof coating systems. Different materials and coatings were evaluated and tested before the final selection used to coat the roofs of two small buildings at NASA's Marshall Space Flight Center during fiscal year 1997. The project successfully leveraged the investment of EPA, NASA and private sector resources to demonstrate a pre-commercial roofing coating process. This process utilizes the Convergent Spray Technologies process, which was initially developed by USBI to apply highly-filled, thermal protection coatings to the Space Shuttle Solid Rocket Boosters. A second partnership between the NASA, Federal Highway Administration, Alabama Department of Transportation and USBI Company was formed to develop and demonstrate the Convergent Spray Technology as a method of applying a skid resistant coating to interstate roads and concrete bridge decking's. Again, different materials and coatings were evaluated and tested before the final selection of ground flint and resin. Two field demonstrations were performed during fiscal year 1997 for the purpose of evaluating the coating system under actual highway conditions. These coatings were applied on Interstate 65 near Huntsville Alabama, and in the Mobile Bankhead tunnel. The system performed this task without the use of harmful solvents, and accomplished the process in a single application. This project successfully leveraged the investment of NASA, Federal Highway Administration, the Alabama Department of Transportation, and private sector funding.

McMillan, Vernotto C.↗
DOE OSTI2026

Cyclic moisture reactivation of calcium sorbents for long duration thermochemical energy storage

The transition to a flexible and reliable energy infrastructure, using electro-thermal energy generation technologies such as geothermal, concentrated solar power, and nuclear, usually demands simultaneous advancement of thermal energy storage (TES) to support on-demand electricity generation and industrial applications while mitigating the inherent intermittency of renewable energy sources and power outages from direct energy generation. Among TES technologies, thermochemical energy storage (TCES) based on calcium looping emerges as a compelling high-power energy storage candidate due to its high reaction enthalpy, compatibility with elevated operating temperatures, and abundance of low-cost materials. However, the long-term durability of calcium-based sorbents for TCES is hindered by surface sintering and particle aggregation, leading to performance degradation over repeated thermal cycles. This study explores a moisture hydration-based strategy to regenerate a degraded calcium sorbent and mitigate performance degradation for long duration TCES. The addition of moisture transforms calcium oxide into calcium hydroxide and produces intercalation water layers, associated with a regenerated surface area and reduced calcium oxide crystallite size. Both these effects are beneficial in restoring the sorbents' reactivity for carbonization. Additionally, an optimized hydration-assisted reactivation protocol balances the recovered energy storage capacity with heating penalty required for moisture removal from hydrated samples, resulting in an enhanced energy storage capacity up to 176% compared to benchmark sorbents that undergo cycling without reactivation after 60 cycles. In conclusion, these results highlight the potential of hydration-assisted reactivation to enhance the long-term performance of TCES, providing an effective pathway to advancing electro-thermal storage technologies.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Solar energy for process heat: Design/cost studies of four industrial retrofit applications

Five specific California plants with potentially attractive solar applications were identified in a process heat survey. These five plants were visited, process requirements evaluated, and conceptual solar system designs were generated. Four DOE (ERDA) sponsored solar energy system demonstration projects were also reviewed and compared to the design/cost cases included in this report. In four of the five cases investigated, retrofit installations providing significant amounts of thermal energy were found to be feasible. The fifth was rejected because of the condition of the building involved, but the process (soap making) appears to be an attractive potential solar application. Costs, however, tend to be high. Several potential areas for cost reduction were identified including larger collector modules and higher duty cycles.

French, R. L.↗
NASA NTRS1980

Parametric instabilities of rotor-support systems with application to industrial ventilators

Rotor support systems interaction with parametric excitation is considered for both unequal principal shaft stiffness (generators) and offset disc rotors (ventilators). Instability regions and types of instability are computed in the first case, and parametric resonances in the second case. Computed and experimental results are compared for laboratory machine models. A field case study of parametric vibrations in industrial ventilators is reported. Computed parametric resonances are confirmed in field measurements, and some industrial failures are explained. Also the dynamic influence and gyroscopic effect of supporting structures are shown and computed.

Parszewski, Z.↗
NASA NTRS1981

Commercial/industrial photovoltaic module and array requirement study. Low-cost solar array project engineering area

Design requirements for photovoltaic modules and arrays used in commercial and industrial applications were identified. Building codes and referenced standards were reviewed for their applicability to commercial and industrial photovoltaic array installation. Four general installation types were identified - integral (replaces roofing), direct (mounted on top of roofing), stand-off (mounted away from roofing), and rack (for flat or low slope roofs, or ground mounted). Each of the generic mounting types can be used in vertical wall mounting systems. This implies eight mounting types exist in the commercial/industrial sector. Installation costs were developed for these mounting types as a function of panel/module size. Cost drivers were identified. Studies were performed to identify optimum module shapes and sizes and operating voltage cost drivers. The general conclusion is that there are no perceived major obstacles to the use of photovoltaic modules in commercial/industrial arrays.

Source record↗
NASA NTRS1982

An overview of the value of parabolic dish solar thermal systems in industrial cogeneration applications

The essential elements of the cogeneration system configuration to be captured were the displacement of thermal energy by collection and use of the Brayton exhaust stream, and the sale back to the utility of any electricity production in excess of on-site requirements. In contrast to simply dumping these energy flows, their use or sale obviously serves, by itself, to increase gross value of the solar thermal energy system. Net allowable cost of the parabolic dish modules may or may not be increased, however. A consideration is that the waste heat capture and delivery subsystems are not free. This study does not address the incremental cost of adding waste heat capture, transport, and conversion (to steam, if necessary). It does compute a value for the thermal energy thereby displaced. This value can serve as a first-round input to any detailed economic evaluation of waste heat recovery.

Source record↗
NASA NTRS1984

Development of robots and application to industrial processes

An algorithm is presented for using a robot system with a single camera to position in three-dimensional space a slender object for insertion into a hole; for example, an electrical pin-type termination into a connector hole. The algorithm relies on a control-configured end effector to achieve the required horizontal translations and rotational motion, and it does not require camera calibration. A force sensor in each fingertip is integrated with the vision system to allow the robot to teach itself new reference points when different connectors and pins are used. Variability in the grasped orientation and position of the pin can be accomodated with the sensor system. Performance tests show that the system is feasible. More work is needed to determine more precisely the effects of lighting levels and lighting direction.

Palm, W. J.↗
NASA NTRS1985

A strategy for advancing tilt-rotor technology

Tilt-rotor technology has many features which make it a very promising development in aviation which might have application to a wide variety of transportation and logistics situations. However, aside from military applications and rather specialized industrial applications, little is known regarding the potential of tilt-rotor for commercial transportation and hence it is difficult to plan a development program which would gain support and be likely to produce a stream of significant benefits. The purpose is to attempt to provide some of this information in a manner that would be useful for preparing a strategy for development of tilt-rotor aircraft technology. Specifically, the objectives were: to identify promising paths of development and deployment of tilt-rotor aircraft technology in the air transportation system considering both benefits and disbenefits, and to identify any particular groups that are likely to benefit significantly and propose plans for gaining their support of research and development of this technology. Potential advantages of the tilt-rotor technology in the context of air transportation as a door-to-door system were identified, and then promising paths of development of such tilt-rotor systems were analyzed. These then lead to recommendations for specific studies, information dissemination and development of awareness of the tilt-rotor among specific transport-related groups.

Morlok, Edward K.↗
NASA NTRS1992

Applications of industrial engineering

Two separate and distinctly different projects are described within this paper: the stepping motion bearing tester, and the memo search and find project. The objective of the first project was to program the JSI Digital Servo Controller to control the motor using the stepping pattern prescribed by the AMSU-U2 Scan Profile. The objective of the second project was to develop a dBASE 3 Plus program that would allow the user to gain the necessary information to retrieve the memo(s) being sought upon supplying a word or group of words possibly found in the keyword list or upon supplying the author's name.

Yarbrough, Walthea V.↗
NASA NTRS1994

Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications

Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.

Hardy, Terry L.↗
NASA NTRS1995

Space Applications of Industrial Laser Systems (SAILS)

A program is under way to develop a YAG laser based materials processing workstation to fly in the cargo bay of the Space Shuttle. The system will be capable of cutting and welding steel, aluminum, and Inconel alloys of the type planned for use on Space Station Freedom. As well as demonstrating the ability of a YAG laser to perform remote (fiber-optic delivered) repair and fabrication operations in space, fundamental data will be collected on these interactions for comparison with terrestrial data and models. The flight system, scheduled to fly in 1995, will be constructed as two modules to fit into the standard Get Away Special (GAS) canisters. The first can holds the laser and its power supply, to be constructed by our industrial partner, Lumonics Industrial Processing Division. The second canister has the materials processing workstation and the command and data acquisition subsystems. These components will be provided by groups at the University of Tennessee Space Institute (UTSI) and the University of Waterloo. The cans are linked by a fiber-optic cable which transmits the beam from the laser head to the workstation.

Mueller, Robert E.↗
NASA NTRS1999

The Successful Transfer of Space Derived Convergent Spray Technology(TM): An Application for Industrial Roof Coatings and Interstate Bridge Repair

Although the levels of volatile organic compound (VOC) emissions from industrial roof coatings and paint operations have been reduced in recent years, the roof coating partnership was an effort to further reduce VOC emission levels and to also demonstrate the use of reclaimed automobile tire rubber as a filler material in roof coating systems. Different materials and coatings were evaluated and tested before the final selection was used to coat the roofs of two small buildings at NASA's MSFC (Marshall Space Flight Center) during fiscal year 1997. The primary objective of the Bridge repair partnership was to strategically leverage public and private sector funds for the purpose of developing a bridge repair process that was cheaper, better, and faster than conventional methods. It was also, imperative that this process be environmentally friendly and capable of performing under extreme conditions for a period of not less than 2 years.

McMillan, Vernotto↗