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  • STRUCTURAL MECHANICS  (19)
  • Aircraft Design, Testing and Performance  (6)
  • Numerical Analysis  (3)
  • air-breathing engine  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Optimization of air-breathing propulsion engine concept (1997)
    Chichester : Wiley-Blackwell
    Communications in Numerical Methods in Engineering 13 (1997), S. 635-641 
    Details
    ISSN: 1069-8299
    Keywords: design optimization ; air-breathing engine ; wave rotor ; multiflow turbofan engine ; Engineering ; Numerical Methods and Modeling
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mathematics , Technology
    Notes: The design optimization of air-breathing propulsion engine concepts has been accomplished by soft-coupling the NASA Engine Performance Program (NEPP) analyser with the NASA Lewis multidisciplinary optimization tool COMETBOARDS. Engine problems, with their associated design variables and constraints, were cast as non-linear optimization problems with thrust as the merit function. Because of the large number of mission points in the flight envelope, the diversity of constraint types, and the overall distortion of the design space, the most reliable optimization algorithm available in COMETBOARDS, when used by itself, could not produce satisfactory, feasible, optimum solutions. However, COMETBOARDS' unique features-which include a cascade strategy, variable and constraint formulations, and scaling devised especially for difficult multidisciplinary applications-successfully optimized the performance of subsonic and supersonic engine concepts. Even when started from different design points, the combined COMETBOARDS and NEPP results converged to the same global optimum solution. This reliable and robust design tool eliminates manual intervention in the design of air-breathing propulsion engines and eases the cycle analysis procedures. It is also much easier to use than other codes, which is an added benefit. This paper describes COMETBOARDS and its cascade strategy and illustrates the capabilities of the combined design tool through the optimization of a high-bypass-turbofan wave-rotor-topped subsonic engine and a mixed-flow-turbofan supersonic engine. ©1997 John Wiley & Sons, Ltd.
    Additional Material: 6 Ill.
    Type of Medium: Electronic Resource
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    Paper (German National Licenses)
  • 2
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    Computational micromechanics of woven composites (1991)
    In:  Other Sources
    Details
    Publication Date: 2011-08-24
    Description: The bounds on the equivalent elastic material properties of a composite are presently addressed by a unified energy approach which is valid for both unidirectional and 2D and 3D woven composites. The unit cell considered is assumed to consist, first, of the actual composite arrangement of the fibers and matrix material, and then, of an equivalent pseudohomogeneous material. Equating the strain energies due to the two arrangements yields an estimate of the upper bound for the material equivalent properties; successive increases in the order of displacement field that is assumed in the composite arrangement will successively produce improved upper bound estimates.
    Keywords: STRUCTURAL MECHANICS
    Type: In: Mechanics of composites at elevated and cryogenic temperatures; Proceedings of the Symposium, ASME Applied Mechanics Conference, Columbus, OH, June 16-19, 1991 (A93-32451 12-39); p. 145-155.
    Format: text
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    NASA TECHNICAL REPORTS
  • 3
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    Structural dynamics: Probabilistic structural analysis methods. Program overview (1991)
    In:  CASI
    Details
    Publication Date: 2013-08-31
    Description: A brief description is provided of the fundamental aspects of a quantification process. Progress since the last structural durability conference in 1989 is summarized. The methodology to date and that to be developed during the life of the program is presented. The uncertain factors are presented. The approach is outlined that is required to achieve component and/or system certification in the shortest possible time for affordable reliability risk. Two new elements appear in a block diagram: (1) uncertainties in human factor, and (2) uncertainties in the computer code. Research to quantify the uncertainties in the human factor was initiated and is discussed.
    Keywords: STRUCTURAL MECHANICS
    Type: Structural Integrity and Durability of Reusable Space Propulsion Systems; p 65-70
    Format: application/pdf
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    NASA TECHNICAL REPORTS
  • 4
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    A brief overview of computational structures technology related activities at NASA Lewis Research Center (1992)
    In:  CASI
    Details
    Publication Date: 2013-08-29
    Description: The presentation gives a partial overview of research and development underway in the Structures Division of LeRC, which collectively is referred to as the Computational Structures Technology Program. The activities in the program are diverse and encompass four major categories: (1) composite materials and structures; (2) probabilistic analysis and reliability; (3) design optimization and expert systems; and (4) computational methods and simulation. The approach of the program is comprehensive and entails exploration of fundamental theories of structural mechanics to accurately represent the complex physics governing engine structural performance, formulation, and implementation of computational techniques and integrated simulation strategies to provide accurate and efficient solutions of the governing theoretical models by exploiting the emerging advances in computer technology, and validation and verification through numerical and experimental tests to establish confidence and define the qualities and limitations of the resulting theoretical models and computational solutions. The program comprises both in-house and sponsored research activities. The remainder of the presentation provides a sample of activities to illustrate the breadth and depth of the program and to demonstrate the accomplishments and benefits that have resulted.
    Keywords: STRUCTURAL MECHANICS
    Type: NASA. Langley Research Center, Computational Structures Technology for Airframes and Propulsion Systems; p 81-89
    Format: text
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  • 5
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    Thermoviscoplastic nonlinear constitutive relationships for structural analysis of high-temperature metal matrix composites (1988)
    In:  Other Sources
    Details
    Publication Date: 2011-08-19
    Description: A set of thermoviscoplastic nonlinear constitutive relationships (TVP-NCR) developed for application to high-temperature metal matrix composites (HT-MMC) is described. The structural response of a turbine blade, made from fiber-reinforced superalloy HT-MMC and subject to representative loading conditions, is evaluated. Results indicate that this set of TVP-NCR is computationally effective.
    Keywords: STRUCTURAL MECHANICS
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  • 6
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    A unique set of micromechanics equations for high-temperature metal matrix composites (1988)
    In:  Other Sources
    Details
    Publication Date: 2011-08-19
    Description: A unique set of micromechanic equations is presented for high-temperature metal matrix composites. The set includes expressions to predict mechanical properties, thermal properties and constituent microstresses for the unidirectional fiber reinforced ply. The equations are derived based on a mechanics of materials formulation assuming a square array unit cell model of a single fiber, surrounding matrix and an interphase to account for the chemical reaction which commonly occurs between fiber and matrix. A three-dimensional finite element analysis was used to perform a preliminary validation of the equations. Excellent agreement between properties predicted using the micromechanics equations and properties simulated by the finite element analyses are demonstrated. Implementation of the micromechanics equations as part of an integrated computational capability for nonlinear structural analysis of high temperature multilayered fiber composites is illustrated.
    Keywords: STRUCTURAL MECHANICS
    Format: text
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    NASA TECHNICAL REPORTS
  • 7
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    Neural Network and Regression Approximations Used in Aircraft Design (1999)
    In:  CASI
    Details
    Publication Date: 2018-06-05
    Description: NASA Lewis Research Center's CometBoards Test Bed was used to create regression and neural network models for a High-Speed Civil Transport (HSCT) aircraft. Both approximation models that replaced the actual analysis tool predicted the aircraft response in a trivial computational effort. The models allow engineers to quickly study the effects of design variables on constraint and objective values for a given aircraft configuration. For example, an engineer can change the engine size by 1000 pounds of thrust and quickly see how this change affects all the output values without rerunning the entire simulation. In addition, an engineer can change a constraint and use the approximation models to quickly reoptimize the configuration. Generating the neural network and the regression models is a time-consuming process, but this exercise has to be carried out only once. Furthermore, an automated process can reduce calculations substantially.
    Keywords: Aircraft Design, Testing and Performance
    Type: Research and Technology 1998; NASA/TM-1999-208815
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  • 8
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    Design Sensitivity for a Subsonic Aircraft Predicted by Neural Network and Regression Models (2005)
    In:  CASI
    Details
    Publication Date: 2018-06-02
    Description: A preliminary methodology was obtained for the design optimization of a subsonic aircraft by coupling NASA Langley Research Center s Flight Optimization System (FLOPS) with NASA Glenn Research Center s design optimization testbed (COMETBOARDS with regression and neural network analysis approximators). The aircraft modeled can carry 200 passengers at a cruise speed of Mach 0.85 over a range of 2500 n mi and can operate on standard 6000-ft takeoff and landing runways. The design simulation was extended to evaluate the optimal airframe and engine parameters for the subsonic aircraft to operate on nonstandard runways. Regression and neural network approximators were used to examine aircraft operation on runways ranging in length from 4500 to 7500 ft.
    Keywords: Aircraft Design, Testing and Performance
    Type: Research and Technology 2004; NASA/TM-2005-213419
    Format: application/pdf
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  • 9
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    Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft (2003)
    In:  CASI
    Details
    Publication Date: 2018-06-02
    Description: The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.
    Keywords: Aircraft Design, Testing and Performance
    Type: Research and Technology 2002; NASA/TM-2003-211990
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  • 10
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    Neural Network and Regression Soft Model Extended for PAX-300 Aircraft Engine (2002)
    In:  CASI
    Details
    Publication Date: 2018-06-02
    Description: In fiscal year 2001, the neural network and regression capabilities of NASA Glenn Research Center's COMETBOARDS design optimization testbed were extended to generate approximate models for the PAX-300 aircraft engine. The analytical model of the engine is defined through nine variables: the fan efficiency factor, the low pressure of the compressor, the high pressure of the compressor, the high pressure of the turbine, the low pressure of the turbine, the operating pressure, and three critical temperatures (T(sub 4), T(sub vane), and T(sub metal)). Numerical Propulsion System Simulation (NPSS) calculations of the specific fuel consumption (TSFC), as a function of the variables can become time consuming, and numerical instabilities can occur during these design calculations. "Soft" models can alleviate both deficiencies. These approximate models are generated from a set of high-fidelity input-output pairs obtained from the NPSS code and a design of the experiment strategy. A neural network and a regression model with 45 weight factors were trained for the input/output pairs. Then, the trained models were validated through a comparison with the original NPSS code. Comparisons of TSFC versus the operating pressure and of TSFC versus the three temperatures (T(sub 4), T(sub vane), and T(sub metal)) are depicted in the figures. The overall performance was satisfactory for both the regression and the neural network model. The regression model required fewer calculations than the neural network model, and it produced marginally superior results. Training the approximate methods is time consuming. Once trained, the approximate methods generated the solution with only a trivial computational effort, reducing the solution time from hours to less than a minute.
    Keywords: Numerical Analysis
    Type: Research and Technology 2001; NASA/TM-2002-211333
    Format: application/pdf
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