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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 NTRS2000

The Instrument Synthesis for Analysis Laboratory at the Goddard Space Flight Center

The objectives of the Instrument Synthesis and Analysis laboratory (ISAL) are to: 1) Provide a rapid and sustainable instrument development environment with clear efficient processes and tools, re-usable models and skilled engineers. 2) Provide a capability for quick and efficient trade studies of instrument architectures and concepts. 3) Streamline and optimize instrument systems design for the entire life cycle, including cost and technology assessment. 4) Provide detailed multi-disciplinary modeling and analysis for phase C/D design validation and for operational investigations.

Wood, H. John↗
NASA NTRS1992

Orbit transfer rocket engine integrated control and health monitoring system technology readiness assessment

The objectives of this task were to: (1) estimate the technology readiness of an integrated control and health monitoring (ICHM) system for the Aerojet 7500 lbF Orbit Transfer Vehicle engine preliminary design assuming space based operations; and (2) estimate the remaining cost to advance this technology to a NASA defined 'readiness level 6' by 1996 wherein the technology has been demonstrated with a system validation model in a simulated environment. The work was accomplished through the conduct of four subtasks. In subtask 1 the minimally required functions for the control and monitoring system was specified. The elements required to perform these functions were specified in Subtask 2. In Subtask 3, the technology readiness level of each element was assessed. Finally, in Subtask 4, the development cost and schedule requirements were estimated for bringing each element to 'readiness level 6'.

Bickford, R. L.↗
NASA NTRS2002

Improving the Accuracy of Satellite Sea Surface Temperature Measurements by Explicitly Accounting for the Bulk-Skin Temperature Difference

The focus of this research was to determine whether the accuracy of satellite measurements of sea surface temperature (SST) could be improved by explicitly accounting for the complex temperature gradients at the surface of the ocean associated with the cool skin and diurnal warm layers. To achieve this goal, work was performed in two different major areas. The first centered on the development and deployment of low-cost infrared radiometers to enable the direct validation of satellite measurements of skin temperature. The second involved a modeling and data analysis effort whereby modeled near-surface temperature profiles were integrated into the retrieval of bulk SST estimates from existing satellite data. Under the first work area, two different seagoing infrared radiometers were designed and fabricated and the first of these was deployed on research ships during two major experiments. Analyses of these data contributed significantly to the Ph.D. thesis of one graduate student and these results are currently being converted into a journal publication. The results of the second portion of work demonstrated that, with presently available models and heat flux estimates, accuracy improvements in SST retrievals associated with better physical treatment of the near-surface layer were partially balanced by uncertainties in the models and extra required input data. While no significant accuracy improvement was observed in this experiment, the results are very encouraging for future applications where improved models and coincident environmental data will be available. These results are included in a manuscript undergoing final review with the Journal of Atmospheric and Oceanic Technology.

Wick, Gary A.↗
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 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 NTRS2020

Enabling Assurance in the MBSE Environment

A number of specific benefits that fit within the hallmarks of effective development are realized with implementation of model-based approaches to systems and assurance. Model Based Systems Engineering (MBSE) enabled by standardized modeling languages (e.g., SysML®) is at the core. These benefits in the context of spaceflight system challenges can include [1]: • Improved management of complex development • Reduced risk in the development process • Improved cost management • Improved design decisions With appropriate modeling techniques the assurance community also can improve early oversight and insight into project development. NASA has shown the basic constructs of SysML in an MBSE environment offer several key advantages, within a Model Based Mission Assurance (MBMA) initiative [2, 3]. These include the following: • Model viewpoints that promote rapid and systematic assessment of requirements coverage, hazard tagging and risk management • Embedded safety assessments for launch vehicles • Deployment of model assisted development of reliability products - Failure Modes and Effects Analyses (FMEAs) and Fault Trees • Test Planning • Validation and Verification of complex functions • Support of Assurance Case development for complex systems In addition, while there are benefits to be harvested, there is a realization that these do not come without effort and cost. Enabling model-based approaches requires structure, not only in an organizational context, but in a modeling context as well. There can be a steep learning curve and costs associated to train skilled modelers. But, on the other hand, not all of the assurance community need to be modelers. Models themselves must conform to ontologies that enable assurance. This places constraints upon the models and modelers. Optimums have yet to be developed where resources and constraints on modeling must be traded off in the organization and modeling efforts for projects. A number of barriers need to be overcome, as well, which pose challenges to the developers of the software that supports MBSE/MBMA. Information and data must be made to flow seamlessly through the life cycle. Because there is a wide variety of tools used in the community, to avoid the problems of the past of silos, delays, and diverging interests, information should flow among these tools to support the “single source of truth” paradigm of MBSE. This will greatly facilitate MBMA and advancement of assurance functions.

Evans, John W.↗
NASA NTRS1991

Software reliability report

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Unfortunately, the models appear to be unable to account for the random nature of the data. If the same code is debugged multiple times and one of the models is used to make predictions, intolerable variance is observed in the resulting reliability predictions. It is believed that data replication can remove this variance in lab type situations and that it is less than scientific to talk about validating a software reliability model without considering replication. It is also believed that data replication may prove to be cost effective in the real world, thus the research centered on verification of the need for replication and on methodologies for generating replicated data in a cost effective manner. The context of the debugging graph was pursued by simulation and experimentation. Simulation was done for the Basic model and the Log-Poisson model. Reasonable values of the parameters were assigned and used to generate simulated data which is then processed by the models in order to determine limitations on their accuracy. These experiments exploit the existing software and program specimens which are in AIR-LAB to measure the performance of reliability models.

Wilson, Larry↗
NASA NTRS1994

Vertical flight training: An overview of training and flight simulator technology with emphasis on rotary-wing requirements

The principal purpose of this publication is to provide a broad overview of the technology that is relevant to the design of aviation training systems and of the techniques applicable to the development, use, and evaluation of those systems. The issues addressed in our 11 chapters are, for the most part, those that would be expected to surface in any informed discussion of the major characterizing elements of aviation training systems. Indeed, many of the same facets of vertical-flight training discussed were recognized and, to some extent, dealt with at the 1991 NASA/FAA Helicopter Simulator Workshop. These generic topics are essential to a sound understanding of training and training systems, and they quite properly form the basis of any attempt to systematize the development and evaluation of more effective, more efficient, more productive, and more economical approaches to aircrew training. Individual chapters address the following topics: an overview of the vertical flight industry: the source of training requirements; training and training schools: meeting current requirements; training systems design and development; transfer of training and cost-effectiveness; the military quest for flight training effectiveness; alternative training systems; training device manufacturing; simulator aero model implementation; simulation validation in the frequency domain; cockpit motion in helicopter simulation; and visual space perception in flight simulators.

Alderete, Thomas S.↗
NASA NTRS2008

The NASA Evolutionary Xenon Thruster (NEXT): NASA's Next Step for U.S. Deep Space Propulsion

NASA s Evolutionary Xenon Thruster (NEXT) project is developing next generation ion propulsion technologies to enhance the performance and lower the costs of future NASA space science missions. This is being accomplished by producing Engineering Model (EM) and Prototype Model (PM) components, validating these via qualification-level and integrated system testing, and preparing the transition of NEXT technologies to flight system development. The project is currently completing one of the final milestones of the effort, that is operation of an integrated NEXT Ion Propulsion System (IPS) in a simulated space environment. This test will advance the NEXT system to a NASA Technology Readiness Level (TRL) of 6 (i.e., operation of a prototypical system in a representative environment), and will confirm its readiness for flight. Besides its promise for upcoming NASA science missions, NEXT may have excellent potential for future commercial and international spacecraft applications.

Schmidt, George R.↗
NASA NTRS2014

Recent Development Activities and Future Mission Applications of NASA's Evolutionary Xenon Thruster (NEXT)

NASAs Evolutionary Xenon Thruster (NEXT) project is developing next generation ion propulsion technologies to enhance the performance and lower the costs of future NASA space science missions. This is being accomplished by producing Engineering Model (EM) and Prototype Model (PM) components, validating these via qualification-level and integrated system testing, and preparing the transition of NEXT technologies to flight system development. This presentation is a follow-up to the NEXT project overviews presented in 2009-2010. It reviews the status of the NEXT project, presents the current system performance characteristics, and describes planned activities in continuing the transition of NEXT technology to a first flight. In 2013 a voluntary decision was made to terminate the long duration test of the NEXT thruster, given the thruster design has exceeded all expectations by accumulating over 50,000 hours of operation to demonstrate around 900 kg of xenon throughput. Besides its promise for upcoming NASA science missions, NEXT has excellent potential for future commercial and international spacecraft applications.

Electric propulsion↗
NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer

We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.

Machine Learning↗
NASA NTRS1994

Toward Automatic Scalability Analysis of Message Passing Programs: A Case Study

Scalability analysis forms an important component of any performance debugging cycle, for massively parallel machines. However, tools that help in performing such analysis for parallel programs are non-existent. The primary reason for lack of such tools is the complexity involved in capturing program dynamics such as communication-computation overlap, communication latencies and memory hierarchy reference patterns. In this paper, we highlight some simple techniques that can be used to study scalability of explicit message-passing parallel programs that consider the above issues. We start from the high level source code and use a methodology for deducing communication characteristics and its impact on the total execution time of the program. The approach is validated with the help of a pipelined method for solving scalar tri-diagonal systems, using both simulations and symbolic cost models on the Intel hypercube.

Sarukkai, Sekhar R.↗
NASA NTRS2008

NEXT Ion Propulsion System Development Status and Capabilities

NASA s Evolutionary Xenon Thruster (NEXT) project is developing next generation ion propulsion technologies to provide future NASA science missions with enhanced mission performance benefit at a low total development cost. The objective of the NEXT project is to advance next generation ion propulsion technology by producing engineering model system components, validating these through qualification-level and integrated system testing, and ensuring preparedness for transitioning to flight system development. As NASA s Evolutionary Xenon Thruster technology program completes advanced development activities, it is advantageous to review the existing technology capabilities of the system under development. This paper describes the NEXT ion propulsion system development status, characteristics and performance. A review of mission analyses results conducted to date using the NEXT system is also provided.

Patterson, Michael J.↗
NASA NTRS2018

Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a diverse team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This report describes the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); identified gaps and actions to close those gaps; and major recommendations) which constitutes a community consensus document as it is a result of over 450 professionals input obtain via: 1) four society workshops (AIAA, NAFEMS, and two TMS), 2) community-wide survey, and 3) the establishment of 9 expert panels (one per KE) consisting on average of 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. The study envisions the development of a cyber-physical-social ecosystem comprised of experimentally verified and validated computational models, tools, and techniques, along with the associated digital tapestry, that impacts the entire supply chain to enable cost-effective, rapid, and revolutionary design of fit-for-purpose materials, components, and systems. Although the vision focused on aeronautics and space applications, it is believed that other engineering communities (e.g., automotive, biomedical, etc.) can benefit as well from the proposed framework with only minor modifications. Finally, it is TTT's hope and desire that this vision provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.

multi-scale modeling↗
NASA NTRS1993

Development and use of computational techniques in Army Aviation research and development programs for crash resistant helicopter technology

During the 1960's over 30 full-scale aircraft crash tests were conducted by the Flight Safety Foundation under contract to the Aviation Applied Technology Directorate (AATD) of the U.S. Army Aviation Systems Command (AVSCOM). The purpose of these tests were to conduct crash injury investigations that would provide a basis for the formulation of sound crash resistance design criteria for light fixed-wing and rotary wing aircraft. This resulted in the Crash Survival Design Criteria Designer's Guide which was first published in 1967 and has been revised numerous times, the last being in 1989. Full-scale aircraft crash testing is an expensive way to investigate structural deformations of occupied spaces and to determine the decelerative loadings experienced by occupants in a crash. This gave initial impetus to the U.S. Army to develop analytical methods to predict the dynamic response of aircraft structures in a crash. It was believed that such analytical tools could be very useful in the preliminary design stage of a new helicopter system which is required to demonstrate a level of crash resistance and had to be more cost effective than full-scale crash tests or numerous component design support tests. From an economic point of view, it is more efficient to optimize for the incorporation of crash resistance features early in the design stage. However, during preliminary design it is doubtful if sufficient design details, which influence the exact plastic deformation shape of structural elements, will be available. The availability of simple procedures to predict energy absorption and load-deformation characteristics will allow the designer to initiate valuable cost, weight, and geometry tradeoff studies. The development of these procedures will require some testing of typical specimens. This testing should, as a minimum, verify the validity of proposed procedures for providing pertinent nonlinear load-deformation data. It was hoped that through the use of these analytical models, the designer could optimize aircraft design for crash resistance from both a weight and cost increment standpoint, thus enhancing the acceptance of the design criteria for crash resistance.

Burrows, Leroy T.↗
NASA NTRS1989

Space biology initiative program definition review. Trade study 1: Automation costs versus crew utilization

A significant emphasis upon automation within the Space Biology Initiative hardware appears justified in order to conserve crew labor and crew training effort. Two generic forms of automation were identified: automation of data and information handling and decision making, and the automation of material handling, transfer, and processing. The use of automatic data acquisition, expert systems, robots, and machine vision will increase the volume of experiments and quality of results. The automation described may also influence efforts to miniaturize and modularize the large array of SBI hardware identified to date. The cost and benefit model developed appears to be a useful guideline for SBI equipment specifiers and designers. Additional refinements would enhance the validity of the model. Two NASA automation pilot programs, 'The Principal Investigator in a Box' and 'Rack Mounted Robots' were investigated and found to be quite appropriate for adaptation to the SBI program. There are other in-house NASA efforts that provide technology that may be appropriate for the SBI program. Important data is believed to exist in advanced medical labs throughout the U.S., Japan, and Europe. The information and data processing in medical analysis equipment is highly automated and future trends reveal continued progress in this area. However, automation of material handling and processing has progressed in a limited manner because the medical labs are not affected by the power and space constraints that Space Station medical equipment is faced with. Therefore, NASA's major emphasis in automation will require a lead effort in the automation of material handling to achieve optimal crew utilization.

Jackson, L. Neal↗
NASA NTRS2005

Deformation Response and Life of Metallic Composites

The project was initially funded for one year (for $100,764) to investigate the potential of particulate reinforced metals for aeropropulsion applications and to generate fatigue results that quantify the mean stress effect for a titanium alloy matrix material (TIMETAL 21S). The project was continued for a second year (for $85,000) to more closely investigate cyclic deformation, especially ratcheting, of the titanium alloy matrix at elevated temperature. Equipment was purchased (for $19,000) to make the experimental program feasible; this equipment included an extensometer calibrator and a multi-channel signal conditioning amplifier. The project was continued for a third year ($50,000) to conduct cyclic relaxation experiments aimed at validating the elastic-viscoelastic-viscoplastic model that NASA GRC had developed for the titanium alloy. Finally, a one-year no cost extension was granted to enable continued analysis of the experimental results and model comparisons.

Lissenden, Cliff J.↗