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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Medical/Scientific Illustration And Production Of Otological Health Awareness Materials

Over the past year, I have worked for my mentor, Beth Cooper, on a large variety of projects. Beth is the Manager of the Acoustical Testing Laboratory, which tests the acoustical emissions of payloads destined for the International Space Station. She is also responsible for educating, and developing new methods of educating, people of all occupational and educational backgrounds in hearing conservation. Beth spends much of her time developing new materials and strategies with which to train people and teach other people to train people in hearing conservation and noise emissions control. I have been helping Beth develop and market these materials by way of graphic design and scientific illustration. Last summer, I spent much of my time creating educational illustrations that visually explained particular concepts in Beth's presentations. Sometimes these illustrations were small "comics" while, at other times, they were an instructional series of illustrations. Since then, Beth and her lab have been developing and updating some materials which will be distributed free to hearing conservation and noise control professionals and others in related fields. I have helped with these projects by designing their packaging. In each instance, it was my responsibility to develop an aesthetically appealing package that would also, through its imagery, describe or summarize the contents of the product. I did this for 3 CD's (Auditory Demonstrations 11, MACSUG, and JeopEARdy) and saw them through their actual production and distribution. In addition to working with Beth, I work with the Imaging Technology Center on various imaging projects. Some of my activities include photo retouching and manipulation for videos and print. This summer, I also had the opportunity to develop a screen saver that would show of some of the photography contained on the soon-to-be-released "Highlights of the GRC Image Archives, vol. 2". I was also able to utilize my medical training to help several of ITC s videographers identify the best histological examples of cancerous cells for incorporation in one of their videos. Over the last part of this summer and then throughout the school year, I will be working with Beth to develop a "pre-packaged" lecture series about the physics of acoustics in the context of hearing conservation. These lectures will be used to teach people of all backgrounds the fundamental concepts involved in acoustical physics so they might be better aware of their own and others auditory health in and out of the work place, and, in the case of payload developers, to design and build more quiet science experiments for the ISS. Even though it may not seem as such, this project is precisely what I am learning to do as a student of the Cleveland Institute of Art's Medical Illustration Department. From my perspective, this project is about taking technical information and translating it into terms that anyone, regardless of background, can understand.

Hawes, Nicholas E.↗
NASA NTRS1983

Polymeric Applications in Electronics

Training manual shows how to use polymeric materials in fabrication electronic components. Report discusses polymeric applications in fabrication of electronic components and equipment. Intended as training manual for 6-day quality assurance and technician certification course in use of polymeric materials.

Read, W. S.↗
NASA NTRS2013

Polymers Advance Heat Management Materials for Vehicles

For 6 years prior to the retirement of the Space Shuttle Program, the shuttles carried an onboard repair kit with a tool for emergency use: two tubes of NOAX, or "good goo," as some people called it. NOAX flew on all 22 flights following the Columbia accident, and was designed to repair damage that occurred on the exterior of the shuttle. Bill McMahon, a structural materials engineer at Marshall Space Flight Center says NASA needed a solution for the widest range of possible damage to the shuttle s exterior thermal protection system. "NASA looked at several options in early 2004 and decided on a sealant. Ultimately, NOAX performed the best and was selected," he says. To prove NOAX would work effectively required hundreds of samples manufactured at Marshall and Johnson, and a concerted effort from various NASA field centers. Johnson Space Center provided programmatic leadership, testing, tools, and crew training; Glenn Research Center provided materials analysis; Langley Research Center provided test support and led an effort to perform large patch repairs; Ames Research Center provided additional testing; and Marshall provided further testing and the site of NOAX manufacturing. Although the sealant never had to be used in an emergency situation, it was tested by astronauts on samples of reinforced carbon-carbon (RCC) during two shuttle missions. (RCC is the thermal material on areas of the shuttle that experience the most heat, such as the nose cone and wing leading edges.) The material handled well on orbit, and tests showed the NOAX patch held up well on RCC.

Source record↗
NASA NTRS1996

Role of physical countermaneuvers in the management of orthostatic hypotension: efficacy and biofeedback augmentation

OBJECTIVE: To evaluate the efficacy of various physical countermaneuvers in reducing orthostatic hypotension and its associated symptoms and to assess the efficacy of biofeedback training in enhancing the effectiveness of physical countermaneuvers. MATERIAL AND METHODS: In nine study subjects with neurogenic orthostatic hypotension, four training sessions on physical countermaneuvers were performed after tilt-up, three with visual feedback on the effect of physical countermaneuvers on blood pressure and other cardiovascular variables. Blood pressure change and orthostatic symptoms during tilt-up were determined, as were the changes in total peripheral resistance, stroke index, and heart rate. RESULTS: The five female and four male patients had a mean age of 53 years and a mean duration of symptoms of 4.2 years. On an orthostatic symptom scale of 0 to 10, these patients had a mean symptom score of 7.3. The increment in systolic blood pressure was better for some maneuvers (such as leg crossing and a combination) than others (such as neck flexion and abdominal contraction). Three patterns of responses to biofeedback were found. Simple maneuvers such as squatting did not improve with training; visual feedback was needed for maneuvers such as thigh contraction, and performance declined without biofeedback; the third pattern, seen in maneuvers such as leg crossing, showed continued improvement with training, even without biofeedback. A survey at 3 to 4 months after training revealed continued use of physical maneuvers (3.8 +/- 3.1 per day), increased standing time with each episode of presyncopal symptoms (8.3 +/- 5.8 minutes), and continued global symptomatic improvement. Total peripheral resistance, but not heart rate or stroke index, showed significant regression with blood pressure improvement. CONCLUSION: Physical countermaneuvers are efficacious in reducing orthostatic hypotension, can be augmented by use of biofeedback, and may significantly improve the functional outcome. The major mechanism of improvement is an increase in total peripheral resistance, presumably by reducing the vascular capacitance.

NASA Program Space Physiology and Countermeasures↗
NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which heavily depend on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results that is findable and usable, along with integrated, efficient toolsets for effectively passing information across various length and time scales across such models. At the NASA Glenn Research Center under the Transformational Tools and Technologies Project, significant recent efforts have been directed towards establishing the required cyberinfrastructure to enable optimized ICME processes and the design of “fit-for-purpose” materials to achieve the goals outlined in the NASA Vision 2040 report. Such efforts include development of multiscale physics-based material models, which can be used to train highly efficient surrogate machine learning models, development of best practices and infrastructure for effective, traceable materials information management, and development of toolsets that integrate with physics-based codes, machine learning models, and an information management system to enable high throughput of materials data collection and analysis, establishment of digital twins and the digital thread, and automation of the ICME design process for material optimization.

Machine Learning↗
NASA NTRS2014

Some Challenges in the Design of Human-Automation Interaction for Safety-Critical Systems

Increasing amounts of automation are being introduced to safety-critical domains. While the introduction of automation has led to an overall increase in reliability and improved safety, it has also introduced a class of failure modes, and new challenges in risk assessment for the new systems, particularly in the assessment of rare events resulting from complex inter-related factors. Designing successful human-automation systems is challenging, and the challenges go beyond good interface development (e.g., Roth, Malin, & Schreckenghost 1997; Christoffersen & Woods, 2002). Human-automation design is particularly challenging when the underlying automation technology generates behavior that is difficult for the user to anticipate or understand. These challenges have been recognized in several safety-critical domains, and have resulted in increased efforts to develop training, procedures, regulations and guidance material (CAST, 2008, IAEA, 2001, FAA, 2013, ICAO, 2012). This paper points to the continuing need for new methods to describe and characterize the operational environment within which new automation concepts are being presented. We will describe challenges to the successful development and evaluation of human-automation systems in safety-critical domains, and describe some approaches that could be used to address these challenges. We will draw from experience with the aviation, spaceflight and nuclear power domains.

systems engineering↗
NASA NTRS1993

Functional approximation using artificial neural networks in structural mechanics

The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

Alam, Javed↗
NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗
NASA NTRS1979

Materials for helicopter gears

Some of the power train transmission gears in helicopter drive systems can become critical components as performance requirements are increased; accordingly, increasing attention must be paid to new alloys in order to obtain required performance reliability and survivability. Candidate advanced alloys, with improved high temperature properties, while increasing the resistance to scoring and scuffing, tend to have lower ductility and fracture toughness. An attempt is made to identify design materials, and process problems and requirements. In addition, it is recommended that the characterization of candidate steels be accelerated; preliminary investigation indicates that new alloys may provide improved capability against surface distress.

Source record↗
NASA NTRS2010

Design and Testing of a Prototype Lunar or Planetary Surface Landing Research Vehicle (LPSLRV)

This handbook describes a two-semester senior design course sponsored by the NASA Office of Education, the Exploration Systems Mission Directorate (ESMD), and the NASA Space Grant Consortium. The course was developed and implemented by the Mechanical and Aerospace Engineering Department (MAE) at Utah State University. The course final outcome is a packaged senior design course that can be readily incorporated into the instructional curriculum at universities across the country. The course materials adhere to the standards of the Accreditation Board for Engineering and Technology (ABET), and is constructed to be relevant to key research areas identified by ESMD. The design project challenged students to apply systems engineering concepts to define research and training requirements for a terrestrial-based lunar landing simulator. This project developed a flying prototype for a Lunar or Planetary Surface Landing Research Vehicle (LPSRV). Per NASA specifications the concept accounts for reduced lunar gravity, and allows the terminal stage of lunar descent to be flown either by remote pilot or autonomously. This free-flying platform was designed to be sufficiently-flexible to allow both sensor evaluation and pilot training. This handbook outlines the course materials, describes the systems engineering processes developed to facilitate design fabrication, integration, and testing. This handbook presents sufficient details of the final design configuration to allow an independent group to reproduce the design. The design evolution and details regarding the verification testing used to characterize the system are presented in a separate project final design report. Details of the experimental apparatus used for system characterization may be found in Appendix F, G, and I of that report. A brief summary of the ground testing and systems verification is also included in Appendix A of this report. Details of the flight tests will be documented in a separate flight test report. This flight test report serves as a complement to the course handbook presented here. This project was extremely ambitious, and achieving all of the design and test objectives was a daunting task. The schedule ran slightly longer than a single academic year with the complete design closure not occurring until early April. Integration and verification testing spilled over into late May and the first flight did not occur until mid to late June. The academic year at Utah State University ended on May 8, 2010. Following the end of the academic year, testing and integration was performed by the faculty advisor, paid research assistants, and volunteer student help

Murphy, Gloria A.↗
NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗
NASA NTRS1990

Payload training methodology study

The results of the Payload Training Methodology Study (PTMS) are documented. Methods and procedures are defined for the development of payload training programs to be conducted at the Marshall Space Flight Center Payload Training Complex (PCT) for the Space Station Freedom program. The study outlines the overall training program concept as well as the six methodologies associated with the program implementation. The program concept outlines the entire payload training program from initial identification of training requirements to the development of detailed design specifications for simulators and instructional material. The following six methodologies are defined: (1) The Training and Simulation Needs Assessment Methodology; (2) The Simulation Approach Methodology; (3) The Simulation Definition Analysis Methodology; (4) The Simulator Requirements Standardization Methodology; (5) The Simulator Development Verification Methodology; and (6) The Simulator Validation Methodology.

Source record↗
NASA NTRS1999

Chemical Hygiene Program

The Chemical Management Team is responsible for ensuring compliance with the OSHA Laboratory Standard. The program at Lewis Research Center (LeRC) evolved over many years to include training, developing Standard Operating Procedures (SOPS) for each laboratory process, coordinating with other safety and health organizations and teams at the Center, and issuing an SOP binder. The Chemical Hygiene Policy was first established for the Center. The Chemical Hygiene Plan was established and reviewed by technical, laboratory and management for viability and applicability to the Center. A risk assessment was conducted for each laboratory. The laboratories were prioritized by order of risk, higher risk taking priority. A Chemical Management Team staff member interviewed the lead researcher for each laboratory process to gather the information needed to develop the SOP for the process. A binder containing the Chemical Hygiene Plan, the SOP, a map of the laboratory identifying the personal protective equipment and best egress, and glove guides, as well as other guides for safety and health. Each laboratory process has been captured in the form of an SOP. The chemicals used in the procedure have been identified and the information is used to reduce the number of chemicals in the lab. The Chemical Hygiene Plan binder is used as a training tool for new employees. LeRC is in compliance with the OSHA Standard. The program was designed to comply with the OSHA standard. In the process, we have been able to assess the usage of chemicals in the laboratories, as well as reduce or relocate the chemicals being stored in the laboratory. Our researchers are trained on the hazards of the materials they work with and have a better understanding of the hazards of the process and what is needed to prevent any incident. From the SOP process, we have been able to reduce our chemical inventory, determine and implement better hygiene procedures and equipment in the laboratories, and provide specific training to our employees. As a result of this program, we are adding labeling to the laboratories for emergency responders and initiating a certified chemical user program.

Mayor, Antoinette C.↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
NASA NTRS2011

ARC+(Registered Trademark) and ARC PC Welding Simulators: Teach Welders with Virtual Interactive 3D Technologies

123 Certification Inc., a Montreal based company, has developed an innovative hands-on welding simulator solution to help build the welding workforce in the most simple way. The solution lies in virtual reality technology, which has been fully tested since the early 90's. President and founder of 123 Certification Inc., Mr. Claude Choquet Ing. Msc. IWE. acts as a bridge between the welding and the programming world. Working in these fields for more than 20 years. he has filed 12 patents world-wide for a gesture control platform with leading edge hardware related to simulation. In the summer of 2006. Mr Choquet was proud to be invited to the annual IIW International Weld ing Congress in Quebec City to launch the ARC+ welding simulator. A 100% virtual reality system and web based training center was developed to simulate multi process. multi-materiaL multi-position and multi pass welding. The simulator is intended to train welding students and apprentices in schools or industries. The welding simulator is composed of a real welding e[eetrode holder (SMAW-GTAW) and gun (GMAW-FCAW). a head mounted display (HMD), a 6 degrees of freedom tracking system for interaction between the user's hands and head. as well as external audio speakers. Both guns and HMD are interacting online and simultaneously. The welding simulation is based on the law of physics and empirical results from detailed analysis of a series of welding tests based on industrial applications tested over the last 20 years. The simulation runs in real-time, using a local logic network to determine the quality and shape of the created weld. These results are based on the orientation distance. and speed of the welding torch and depth of penetration. The welding process and resulting weld bc.1d are displayed in a virtual environment with screenplay interactive training modules. For review. weld quality and recorded process values can be displayed and diagnosed after welding. To help in the le.tming process, a learning curve for each student and each Virtual Welding Class'" can be plotted, for an instructor's review or a required third party evaluation.

Choquet, Claude↗
NASA NTRSDate not supplied

A Machine Learning-Derived Atomistic Potential for Y2Si2O7

Incorporation of SiC/SiC ceramic matrix composite (CMC) hot section components into aircraft engines promises to increase efficiency and safety. However, SiC/SiC CMCs are subject to water vapor-induced oxidation and recession at the high temperatures of engine operation, and thus environmental barrier coatings (EBCs) are required to reduce this degradation and enable their widespread adoption. An understanding of EBCs failure mechanisms, including thermochemical and thermomechanical mechanisms, is essential as coating degradation leads to reduced CMC component service life. Computational modeling approaches can provide insight into EBC material properties important for coating design. However, density functional theory (DFT) is computationally expensive and atomistic potentials are lacking for materials of interest. In this work, we utilize a machine learning approach and DFT training data to parameterize atomistic potentials for two candidate EBC materials, Y2Si2O7 and Yb2Si2O7. These potentials enable near DFT-accurate calculations of thermodynamic and thermomechanical properties essential to EBC design.

Cameron J Bodenschatz↗
NASA NTRS2012

Command Center Training Tool (C2T2)

This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.

Jones, Phillip↗
NASA NTRS1967

Weld procedure produces quality welds for thick sections of Hastelloy-X

Welding program produces premium quality, multipass welds in heavy tube sections of Hastelloy-X. It develops semiautomatic tungsten/inert gas procedures, weld wire procurement specifications material weld properties, welder-operator training, and nondestructive testing inspection techniques and procedures.

Flens, F. J.↗