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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 55 records · Page 3

The procedure safety system

Telerobotic operations, whether under autonomous or teleoperated control, require a much more sophisticated safety system than that needed for most industrial applications. Industrial robots generally perform very repetitive tasks in a controlled, static environment. The safety system in that case can be as simple as shutting down the robot if a human enters the work area, or even simply building a cage around the work space. Telerobotic operations, however, will take place in a dynamic, sometimes unpredictable environment, and will involve complicated and perhaps unrehearsed manipulations. This creates a much greater potential for damage to the robot or objects in its vicinity. The Procedural Safety System (PSS) collects data from external sensors and the robot, then processes it through an expert system shell to determine whether an unsafe condition or potential unsafe condition exists. Unsafe conditions could include exceeding velocity, acceleration, torque, or joint limits, imminent collision, exceeding temperature limits, and robot or sensor component failure. If a threat to safety exists, the operator is warned. If the threat is serious enough, the robot is halted. The PSS, therefore, uses expert system technology to enhance safety thus reducing operator work load, allowing him/her to focus on performing the task at hand without the distraction of worrying about violating safety criteria.

Obrien, Maureen E.↗

Applications of thermal energy storage to process heat storage and recovery in the paper and pulp industry

Applications of Thermal Energy Storage (TES) in a paper and pulp mill power house were studied as one approach to the transfer of steam production from fossil fuel boilers to waste fuel of (hog fuel) boilers. Data from specific mills were analyzed, and various TES concepts evaluated for application in the process steam supply system. Constant pressure and variable pressure steam accumulators were found to be the most attractive storage concepts for this application.

Carr, J. H.↗

Industrial storage applications overview

The implementation of a technology demonstration for the food processing industry, development and technology demonstrations for selected near-term, in-plant applications and advanced industrial applications of thermal energy storage are overviewed.

Duscha, R. A.↗

Signal Processor

Macrodyne, Inc.'s Model 3100 Frequency Domain Processor is an innovative instrument that significantly increases the amount and accuracy of information available from laser velocimeter flow measurement systems. The innovation permits use of laser velocimetry in industrial applications where extremely precise measurements are required; it has applicability in industrial controls and in improvement of transmission standards in the automotive industry.

Source record↗

GRCop-84: A High Temperature Copper-based Alloy For High Heat Flux Applications

While designed for rocket engine main combustion chamber liners, GRCop-84 (Cu-8 at.% Cr-4 at.% Nb) offers potential for high heat flux applications in industrial applications requiring a temperature capability up to approximately 700 C (1292 F). GRCop-84 is a copper-based alloy with excellent elevated temperature strength, good creep resistance, long LCF lives and enhanced oxidation resistance. It also has a lower thermal expansion than copper and many other low alloy copper-based alloys. GRCop-84 can be manufactured into a variety of shapes such as tubing, bar, plate and sheet using standard production techniques and requires no special production techniques. GRCop-84 forms well, so conventional fabrication methods including stamping and bending can be used. GRCop-84 has demonstrated an ability to be friction stir welded, brazed, inertia welded, diffusion bonded and electron beam welded for joining to itself and other materials. Potential applications include plastic injection molds, resistance welding electrodes and holders, permanent metal casting molds, vacuum plasma spray nozzles and high temperature heat exchanger applications.

Ellis, David L.↗

Infrared Heaters

The heating units shown in the accompanying photos are Panelbloc infrared heaters, energy savers which burn little fuel in relation to their effective heat output. Produced by Bettcher Manufacturing Corporation, Cleveland, Ohio, Panelblocs are applicable to industrial or other facilities which have ceilings more than 12 feet high, such as those pictured: at left the Bare Hills Tennis Club, Baltimore, Maryland and at right, CVA Lincoln- Mercury, Gaithersburg, Maryland. The heaters are mounted high above the floor and they radiate infrared energy downward. Panelblocs do not waste energy by warming the surrounding air. Instead, they beam invisible heat rays directly to objects which absorb the radiation- people, floors, machinery and other plant equipment. All these objects in turn re-radiate the energy to the air. A key element in the Panelbloc design is a coating applied to the aluminized steel outer surface of the heater. This coating must be corrosion resistant at high temperatures and it must have high "emissivity"-the ability of a surface to emit radiant energy. The Bettcher company formerly used a porcelain coating, but it caused a production problem. Bettcher did not have the capability to apply the material in its own plant, so the heaters had to be shipped out of state for porcelainizing, which entailed extra cost. Bettcher sought a coating which could meet the specifications yet be applied in its own facilities. The company asked The Knowledge Availability Systems Center, Pittsburgh, Pennsylvania, a NASA Industrial Applications Center (IAC), for a search of NASA's files

Source record↗

Two Strategies for Microbial Production of an Industrial Enzyme-Alpha-Amylase

Extremophiles are microorganisms that thrive in, from an anthropocentric view, extreme environments including hot springs, soda lakes and arctic water. This ability of survival at extreme conditions has rendered extremophiles to be of interest in astrobiology, evolutionary biology as well as in industrial applications. Of particular interest to the biotechnology industry are the biological catalysts of the extremophiles, the extremozymes, whose unique stabilities at extreme conditions make them potential sources of novel enzymes in industrial applications. There are two major approaches to microbial enzyme production. This entails enzyme isolation directly from the natural host or creating a recombinant expression system whereby the targeted enzyme can be overexpressed in a mesophilic host. We are employing both methods in the effort to produce alpha-amylases from a hyperthermophilic archaeon (Thermococcus) isolated from a hydrothermal vent in the Atlantic Ocean, as well as from alkaliphilic bacteria (Bacillus) isolated from a soda lake in Tanzania. Alpha-amylases catalyze the hydrolysis of internal alpha-1,4-glycosidic linkages in starch to produce smaller sugars. Thermostable alpha-amylases are used in the liquefaction of starch for production of fructose and glucose syrups, whereas alpha-amylases stable at high pH have potential as detergent additives. The alpha-amylase encoding gene from Thermococcus was PCR amplified using carefully designed primers and analyzed using bioinformatics tools such as BLAST and Multiple Sequence Alignment for cloning and expression in E.coli. Four strains of Bacillus were grown in alkaline starch-enriched medium of which the culture supernatant was used as enzyme source. Amylolytic activity was detected using the starch-iodine method.

Bernhardsdotter, Eva C. M. J.↗

Spinal Bracing

Dr. Arthur Copes of the Copes Foundation, Baton Rouge, LA, says that 35 percent of the 50 technical reports he received from the NASA/Southern University Industrial Applications Center in Baton Rouge and the Central Industrial Applications Center, Durant, OK, were vital to the development of his Copes Scoliosis Braces, which are custom designed and feature a novel pneumatic bladder that exerts constant corrective pressure to the torso to slowly reduce or eliminate the spinal curve.

Source record↗

High-Temperature/Vacuum Fiber-Pushout Apparatus

Fiber-pushout tests performed at temperatures up to 1,100 degrees C. Apparatus performs fiber-pushout tests using radiant heating in vacuum. Vacuum maintained by turbopump prevents significant specimen oxidation during test. Successfully applied to metal and ceramic matrix composites being developed for jet engines. Applicable to many industrial applications, such as automotive components and energy-conversion devices. Previous Brief, "New Testing Tool for Composite Interfaces" (LEW-15297), describes apparatus designed to perform fiber-pushout testing at room temperature.

Eldridge, Jeffrey I.↗

Advanced Triangulation Displacement Sensors

Advanced optoelectronic triangulation displacement sensors undergoing development. Highly miniaturized, more stable, more accurate, and relatively easy to use. Incorporate wideband electronic circuits suitable for real-time monitoring and control of displacements. Measurements expected to be accurate to within nanometers. In principle, sensors mass-produced at relatively low unit cost. Potential applications numerous. Possible industrial application in measuring runout of rotating shaft or other moving part during fabrication in "zero-defect" manufacturing system, in which measured runout automatically corrected.

Poteet, Wade M.↗

General purpose optimization software for engineering design

The author has developed several general purpose optimization programs over the past twenty years. The earlier programs were developed as research codes and served that purpose reasonably well. However, in taking the formal step from research to industrial application programs, several important lessons have been learned. Among these are the importance of clear documentation, immediate user support, and consistent maintenance. Most important has been the issue of providing software that gives a good, or at least acceptable, design at minimum computational cost. Here, the basic issues developing optimization software for industrial applications are outlined and issues of convergence rate, reliability, and relative minima are discussed. Considerable feedback has been received from users, and new software is being developed to respond to identified needs. The basic capabilities of this software are outlined. A major motivation for the development of commercial grade software is ease of use and flexibility, and these issues are discussed with reference to general multidisciplinary applications. It is concluded that design productivity can be significantly enhanced by the more widespread use of optimization as an everyday design tool.

Vanderplaats, G. N.↗

Current and Future Developments in Air Traffic Control

Current and future energy demands, end uses, and cost used to characterize typical applications services in the industrial sector of the United States are examined. A review and evaluation of existing industrial energy data bases was undertaken to assess their potential for supporting SERI research market suitability analysis; (2) market development; (3) end use matching; (4) industrial application studies; and (5) identification of cost and performance goals for solar systems and typical information requirements for industrial energy end use. The focus was on fuels and electric energy used for heat and power purchased by the manufacturing subsector and listed by 2, 3, and 4 digit SIC, primary fuel. The effects of federal and state industrial energy conservation programs on future industrial sector demands were assessed.

Jackson, Joseph↗

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↗

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↗

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↗

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↗

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.↗