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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Common Scientific and Technological Interests Between Astrobiology and Space Biology

The disciplines of astrobiology (AB) and space biology (SB) clearly have common interests, however they have not been pursued jointly. SB and AB are inextricably linked, both intellectually and technologically. They can now be effectively linked operationally. Cross-cutting joint collaborations will enhance innovation and increase cost effectiveness. Session topics include joint science questions, technologies, instrumentation, and missions. Examples include life detection, overlapping planetary protection concerns, biofilms, radiation, hyper- and hypogravity, applications of artificial intelligence and machine learning, interoperable databases, facilities (i.e., spacecraft, lunar surface efforts, simulation chambers, analog sites, etc.), training opportunities, and other topics relevant to AB and SB joint ventures. We welcome contributions on this very broad topical area to facilitate cross-fertilization of these disciplines that are of great importance to NASA.

astrobiology↗
NASA NTRS2022

Design Optimization of Energized Composite Using Simulation and Experimental Methods

As electric vehicles (EVs) are evolving, innovative technologies like “energized composite” that can store energy in the car's body helps extend its range per charge. The composite's unique ability to function as both structural body panel and charge storage medium stems from its unique pattern design between “electrochemical areas (EcA)” and “epoxy area (EpA)”. Herein, a design optimization study is presented to obtain a balanced ratio between EcA versus EpA to maximize the charge storage ability of the composite while maintaining a decent tensile and bending strength. Simulations using ANSYS software and experimental confirmation using universal testing machines and electrochemical analyzers are used to derive optimum ratios between EcA and EpA. Uniaxial tension test and 3-point bend test have been performed to optimize the tensile and bend strengths, whereas cyclic voltammetry, galvanic charge–discharge, and electrochemical impedance spectroscopy are used to determine the electrochemical performance of various design configurations by modulating the ratios of EcA versus EpA. Overall, the highest achieved energy storage per lamina is 2531 mWh m−2 for a maximum of 81.6% EcA with a tensile strength of 417.73 MPa and bending strength of 263.13 MPa. This study is highly beneficial for EVs and aerospace applications.

energy storage↗
NASA NTRSDate not supplied

A NASA Perspective on Quantum AI, Error Correction, and Beyond

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. We discuss the prospects for quantum computing and AI, highlighting advances in algorithms, both near- and longer-term. Quantum error correction is critical to the realization of any such vision. The talk with touch on some recent exciting advanced in quantum error correction, particularly in dynamical codes. The talk will conclude with an example of how the combination of quantum computing and artificial intelligence can help probe fundamental aspect of quantum physics.

quantum optimization algorithms and sampling↗
NASA NTRS2007

A New High-Speed Oil-Free Turbine Engine Rotordynamic Simulator Test Rig

A new test rig has been developed for simulating high-speed turbomachinery rotor systems using Oil-Free foil air bearing technology. Foil air bearings have been used in turbomachinery, primarily air cycle machines, for the past four decades to eliminate the need for oil lubrication. The goal of applying this bearing technology to other classes of turbomachinery has prompted the fabrication of this test rig. The facility gives bearing designers the capability to test potential bearing designs with shafts that simulate the rotating components of a target machine without the high cost of building "make-and-break" hardware. The data collected from this rig can be used to make design changes to the shaft and bearings in subsequent design iterations. This paper describes the new test rig and demonstrates its capabilities through the initial run with a simulated shaft system.

Howard, Samuel A.↗
NASA NTRS1998

A Domain-Decomposed Multi-Level Method for Adaptively Refined Cartesian Grids with Embedded Boundaries

The work presents a new method for on-the-fly domain decomposition technique for mapping grids and solution algorithms to parallel machines, and is applicable to both shared-memory and message-passing architectures. It will be demonstrated on the Cray T3E, HP Exemplar, and SGI Origin 2000. Computing time has been secured on all these platforms. The decomposition technique is an outgrowth of techniques used in computational physics for simulations of N-body problems and the event horizons of black holes, and has not been previously used by the CFD community. Since the technique offers on-the-fly partitioning, it offers a substantial increase in flexibility for computing in heterogeneous environments, where the number of available processors may not be known at the time of job submission. In addition, since it is dynamic it permits the job to be repartitioned without global communication in cases where additional processors become available after the simulation has begun, or in cases where dynamic mesh adaptation changes the mesh size during the course of a simulation. The platform for this partitioning strategy is a completely new Cartesian Euler solver tarcreted at parallel machines which may be used in conjunction with Ames' "Cart3D" arbitrary geometry simulation package.

Aftosmis, M. J.↗
NASA NTRS1963

Integrated Mission Simulation for Long Term Space Flight

The problems of evaluating pilot performance, pilot training, and the integration of the man-machine system for proper functioning become increasingly difficult as the length of projected space flights increases. Further, actual in-flight experience becomes harder to obtain and the major means for training and evaluation becomes simulation. The first major source of difficulty in any simulation stems from a basic requirement of leaning theory: that for increased proficiency of performance to occur within any biologically capable system, the stimuli which are the bases for the expected behavior must be presented in an amenable form to the learner. Further, the sequence of presentation of the pertinent stimuli must be such that the learner can form the appropriate concepts and interactions required for the ultimate use of the learned material. It can then be seen that two basic factors of learning complex skills are: 1) Fidelity of simulation of pertinent stimuli. 2) Appropriate sequencing of stimuli during learning for proper concept formation.

SPACE FLIGHT↗
NASA NTRS1982

Design, simulation and evaluation of advanced display concepts for the F-16 control configured vehicle

Advanced display concepts to augment the tracking ability of the F-16 Control Configured Vehicle (CCV) were designed, simulated, and evaluated. A fixed-base simulator was modified to represent the F-16 CCV. An isometric sidearm control stick and two-axis CCV thumb button were installed in the cockpit. The forward cockpit CRT was programmed to present an external scene (numbered runway, horizon) and the designed Heads Up Display. The cockpit interior was modified to represent a fighter and the F-16 CCV dynamics and direct lift and side force modes were programmed. Compensatory displays were designed from man-machine considerations. Pilots evaluated the Heads up Display and compensatory displays during simulated descents in the presence of several levels of filtered, zero-mean winds gusts. During a descent from 2500 feet to the runway, the pilots tracked a point on the runway utilizing the basic F-16, F-16 CCV, and F-16 CCV with advanced displays. Substantial tracking improvements resulted utilizing the CCV modes, and the displays were found to even further enhance the tracking ability of the F-16 CCV.

Klein, R. W.↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2010

Designing and Implementing an OVERFLOW Reader for ParaView and Comparing Performance Between Central Processing Units and Graphical Processing Units

In the Applied Aerosciences and CFD branch at Johnson Space Center, computational simulations are run that face many challenges. Two of which are the ability to customize software for specialized needs and the need to run simulations as fast as possible. There are many different tools that are used for running these simulations and each one has its own pros and cons. Once these simulations are run, there needs to be software capable of visualizing the results in an appealing manner. Some of this software is called open source, meaning that anyone can edit the source code to make modifications and distribute it to all other users in a future release. This is very useful, especially in this branch where many different tools are being used. File readers can be written to load any file format into a program, to ease the bridging from one tool to another. Programming such a reader requires knowledge of the file format that is being read as well as the equations necessary to obtain the derived values after loading. When running these CFD simulations, extremely large files are being loaded and having values being calculated. These simulations usually take a few hours to complete, even on the fastest machines. Graphics processing units (GPUs) are usually used to load the graphics for computers; however, in recent years, GPUs are being used for more generic applications because of the speed of these processors. Applications run on GPUs have been known to run up to forty times faster than they would on normal central processing units (CPUs). If these CFD programs are extended to run on GPUs, the amount of time they would require to complete would be much less. This would allow more simulations to be run in the same amount of time and possibly perform more complex computations.

Chawner, David M.↗
NASA NTRS1987

Design of a force reflecting hand controller for space telemanipulation studies

The potential importance of space telemanipulator systems is reviewed, along with past studies of master-slave manipulation using a generalized force reflecting master arm. Problems concerning their dynamic interaction with the human operator have been revealed in the use of these systems, with marked differences between 1-g and simulated weightless conditions. A study is outlined to investigate the optimization of the man machine dynamics of master-slave manipulation, and a set of specifications is determined for the apparatus necessary to perform this investigation. This apparatus is a one degree of freedom force reflecting hand controller with closed loop servo control which enables it to simulate arbitrary dynamic properties to high bandwidth. Design of the complete system and its performance is discussed. Finally, the experimental adjustment of the hand controller dynamics for smooth manual control performance with good operator force perception is described, resulting in low inertia, viscously damped hand controller dynamics.

Paines, J. D. B.↗
NASA NTRS1993

An architecture and model for cognitive engineering simulation analysis - Application to advanced aviation automation

The process of designing crew stations for large-scale, complex automated systems is made difficult because of the flexibility of roles that the crew can assume, and by the rapid rate at which system designs become fixed. Modern cockpit automation frequently involves multiple layers of control and display technology in which human operators must exercise equipment in augmented, supervisory, and fully automated control modes. In this context, we maintain that effective human-centered design is dependent on adequate models of human/system performance in which representations of the equipment, the human operator(s), and the mission tasks are available to designers for manipulation and modification. The joint Army-NASA Aircrew/Aircraft Integration (A3I) Program, with its attendant Man-machine Integration Design and Analysis System (MIDAS), was initiated to meet this challenge. MIDAS provides designers with a test bed for analyzing human-system integration in an environment in which both cognitive human function and 'intelligent' machine function are described in similar terms. This distributed object-oriented simulation system, its architecture and assumptions, and our experiences from its application in advanced aviation crew stations are described.

Corker, Kevin M.↗
NASA NTRSDate not supplied

A Gaussian Process Enhancement to Linear Parameter Varying Models

Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets — all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility, where rapidly emerging vehicle designs are significantly more complex, while having to be both cost-effective and safe. To meet these engineering challenges, machine learning methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency. This paper examines the use of Gaussian process machine learning to generalize and enhance the commonly used class of quasi-Linear Parameter Varying models for fast full-envelope simulation while also supporting control system design and analysis with model uncertainty. Gaussian process machine learning is selected because it: can fuse multiple data sets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require starting from a large training data set. To demonstrate the benefits of the approach, a robust stability analysis with Gaussian process uncertainty is shown for a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.

Gaussian Process↗
NASA NTRS1988

Automatic mathematical modeling for real time simulation system

A methodology for automatic mathematical modeling and generating simulation models is described. The models will be verified by running in a test environment using standard profiles with the results compared against known results. The major objective is to create a user friendly environment for engineers to design, maintain, and verify their model and also automatically convert the mathematical model into conventional code for conventional computation. A demonstration program was designed for modeling the Space Shuttle Main Engine Simulation. It is written in LISP and MACSYMA and runs on a Symbolic 3670 Lisp Machine. The program provides a very friendly and well organized environment for engineers to build a knowledge base for base equations and general information. It contains an initial set of component process elements for the Space Shuttle Main Engine Simulation and a questionnaire that allows the engineer to answer a set of questions to specify a particular model. The system is then able to automatically generate the model and FORTRAN code. The future goal which is under construction is to download the FORTRAN code to VAX/VMS system for conventional computation. The SSME mathematical model will be verified in a test environment and the solution compared with the real data profile. The use of artificial intelligence techniques has shown that the process of the simulation modeling can be simplified.

Wang, Caroline↗
NASA NTRS1973

Methods for study of cardiovascular adaptation of small laboratory animals during exposure to altered gravity

Several new techniques are reported for studying cardiovascular circulation in small laboratory animals kept in metabolic chambers. Chronical cannulation, miniaturized membrane type heart-lung machines, a prototype walking chamber, and a fluorocarbon immersion method to simulate weightlessness are outlined. Differential hypothermia work on rat cancers provides localized embedding of radionuclides and other chemotherapeutical agents in tumors and increases at the same time blood circulation through the warmed tumor as compared to the rest of the cold body. Some successful clinical applications of combined chemotherapy and differential hypothermia in skin cancer, mammary tumors, and brain gliomas are described.

Popovic, V.↗
NASA NTRS1975

Machine processing of S-192 and supporting aircraft data: Studies of atmospheric effects, agricultural classifications, and land resource mapping

Two tasks of machine processing of S-192 multispectral scanner data are reviewed. In the first task, the effects of changing atmospheric and base altitude on the ability to machine-classify agricultural crops were investigated. A classifier and atmospheric effects simulation model was devised and its accuracy verified by comparison of its predicted results with S-192 processed results. In the second task, land resource maps of a mountainous area near Cripple Creek, Colorado were prepared from S-192 data collected on 4 August 1973.

Thomson, F.↗