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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗
NASA NTRS1989

Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

Lan, C. Edward↗
NASA NTRS1986

Weight estimation of unconventional structures by structural optimization

Automated techniques are presented that are used in structural optimization technology, with emphasis on modifications of finite element models to obtain an optimal material distribution for minimum weight while satisfying the prescribed design requirements. It is anticipated that the future development of computer aided engineering (CAE) system will provide environments where structural analysis, a design optimization, and weight evaluation modules are integrated, sharing a common data base. Structural optimization capabilities obtained by integrating a finite element structural analysis program and a numerical optimization code are developed and applied to two illustrative examples: marine gear housing structural weight minimization and joined wing structures.

Miura, Hirokazu↗
NASA NTRS1988

A new gravitational model for the earth from satellite tracking data - GEM-T1

A computation of a terrestrial gravitational field model called the Goddard Earth Model GEM-T1 is discussed and compared to previous models, including the GEM-L2. The software tools were redesigned for the model, allowing for the optimization of the technique of relative data weighting and model estimation used in GEM solutions. The GEM-T1 model provides a simultaneous solution for a gravity model in spherical harmonics complete to degree and order 36, a subset of 66 ocean tidal coefficients for the long-wavelength components of 12 major tides, and 5-day averaged earth rotation and polar motion parameters for the 1980 period on. GEM-T1 was derived from satellite tracking data acquired on 17 different satellites whose inclinations ranged from 15 degrees to polar. A simulation of the TOPEX/POSEIDON orbit using the covariances of the GEM-T1 model was made. Estimated radial error for the simulation was reduced to less than 30 cm rms.

Marsh, J. G.↗
NASA NTRS1986

On the pilot's behavior of detecting a system parameter change

The reaction of a human pilot, engaged in compensatory control, to a sudden change in the controlled element's characteristics is described. Taking the case where the change manifests itself as a variance change of the monitored signal, it is shown that the detection time, defined to be the time elapsed until the pilot detects the change, is related to the monitored signal and its derivative. Then, the detection behavior is modeled by an optimal controller, an optimal estimator, and a variance-ratio test mechanism that is performed for the monitored signal and its derivative. Results of a digital simulation show that the pilot's detection behavior can be well represented by the model proposed here.

Morizumi, N.↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS2021

Aircraft Flaps Modeling in OpenMDAO

The goal of this project was to develop a model for a single subsystem in the aerodynamics discipline, in this case the flaps of an aircraft. Flaps are high-lift devices used by planes to allow for quicker takeoffs, and slower landings. A computer-based aircraft model of the flaps of an aircraft was developed using the Python based open-source framework OpenMDAO. OpenMDAO is used to develop multi-disciplinary aircraft models using gradient-based optimization; design optimization (MDO) is concerned with solving design problems involving numerical models of complex engineering systems. There were 4 components in the model; each has input values, output variables, and equations to calculate said outputs. The variables and equations are sourced from NASA Fortran code from the 1970s, in a project called the General Aviation Synthesis Program (GASP). These variables and equations which create the model are being converted to Python for ease of use. The flaps model developed will be integrated into a larger model of a conventional aircraft’s flight phases. All subsystems of the model will first be built using the parameters of a Boeing 737 MAX-8, to validate its functionality and accuracy. Then, the aircraft model will be used for hybrid-electric research; running optimizations to improve efficiency, minimize fuel burn, and advance hybrid-electric technology in the aerospace field.

Computer-based aircraft modeling↗
NASA NTRS2018

Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

Analysi↗
NASA NTRS1979

Air breathing engine/rocket trajectory optimization

This research has focused on improving the mathematical models of the air-breathing propulsion systems, which can be mated with the rocket engine model and incorporated in trajectory optimization codes. Improved engine simulations provided accurate representation of the complex cycles proposed for advanced launch vehicles, thereby increasing the confidence in propellant use and payload calculations. The versatile QNEP (Quick Navy Engine Program) was modified to allow treatment of advanced turboaccelerator cycles using hydrogen or hydrocarbon fuels and operating in the vehicle flow field.

Smith, V. K., III↗
NASA NTRS1975

Results of a space shuttle vehicle ferry configuration afterbody fairing optimization study using a 140A/B 0.0405-scale model orbiter (43-0) in the Rockwell International 7.75 by 11.0 ft low speed wind tunnel (OA124)

Experimental aerodynamic investigations were conducted on a dual-strut mounted 0.0405-scale representation of the 140A/B outer mold line space shuttle orbiter vehicle. The tests, conducted from 11 Oct., 1974 through 22 Oct., 1974, were primarily to investigate aerodynamic stability and control characteristics of the space shuttle orbiter ferry configuration. Four afterbody fairing configurations and various additions to them in the form of horizontal and ventral fins strakes and other aerodynamic protuberances were tested. Base line data on the basic orbiter with MPS nozzles and bodyflap were recorded. The drag of the optimum ferry configuration was increased to the level of the basic orbiter for possible flight test configurations by the addition of two sizes of perforated speed brakes on the tail cone surface.

Houlihan, S. R.↗
NASA NTRS1988

Optimal post-experiment estimation of poorly modeled dynamic systems

Recently, a novel strategy for post-experiment state estimation of discretely-measured dynamic systems has been developed. The method accounts for errors in the system dynamic model equations in a more general and rigorous manner than do filter-smoother algorithms. The dynamic model error terms do not require the usual process noise assumptions of zero-mean, symmetrically distributed random disturbances. Instead, the model error terms require no prior assumptions other than piecewise continuity. The resulting state estimates are more accurate than filters for applications in which the dynamic model error clearly violates the typical process noise assumptions, and the available measurements are sparse and/or noisy. Estimates of the dynamic model error, in addition to the states, are obtained as part of the solution of a two-point boundary value problem, and may be exploited for numerous reasons. In this paper, the basic technique is explained, and several example applications are given. Included among the examples are both state estimation and exploitation of the model error estimates.

Mook, D. Joseph↗
NASA NTRS1991

RSM 1.0 user's guide: A resupply scheduler using integer optimization

The Resupply Scheduling Model (RSM) is a PC based, fully menu-driven computer program. It uses integer programming techniques to determine an optimum schedule to replace components on or before a fixed replacement period, subject to user defined constraints such as transportation mass and volume limits or available repair crew time. Principal input for RSJ includes properties such as mass and volume and an assembly sequence. Resource constraints are entered for each period corresponding to the component properties. Though written to analyze the electrical power system on the Space Station Freedom, RSM is quite general and can be used to model the resupply of almost any system subject to user defined resource constraints. Presented here is a step by step procedure for preparing the input, performing the analysis, and interpreting the results. Instructions for installing the program and information on the algorithms are given.

Viterna, Larry A.↗
NASA NTRS1997

Optimization of a Parallel Ocean General Circulation Model

One of the principal roles of the ocean in the global heat balance is storage of heat, which moderates seasonal extremes and leads to the contrast between ocean and continent temperatures.

3-dimensional ocean general circulation models (OG↗