Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

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GE. The simulations make use of the multiblock capabilities of APG and APNASA, allowing treatment of multiple flow regions, such as the core flow, bypass flow, and bleed flows. The grid generator and flow solver have been run at GE on a networked system of workstations. The flow solver is also commonly run on the NASA Origin system at NASA Ames. Performance of the flow solver on the two systems will be compared. Additional information is contained in the original. Author Compressors; Computational Fluid Dynamics; Grid Generation (Mathematics); Parallel Processing (Computers); Turbofans; Computerized Simulation 20000064598 General Electric Co., Aircraft Engines, Cincinnati, OH USA Integrated Multidisciplinary Design Bailey, Michael W., General Electric Co., USA; Irani, Rohinton K., General Electric Co., USA; Finnigan, Peter M., General Electric Co., USA; Rohl, Peter J., General Electric Co., USA; Badhrinath, Krishnakumar, General Electric Co., USA; Welcome to the NASA High Performance Computing and Communications Computational Aerosciences (CAS) Workshop 2000; February 2000; In English; See also 20000064579; No Copyright; Abstract Only; Available from CASI only as part of the entire parent document An integrated multidisciplinary approach to system design of aircraft engines has been the goal for many years. Frequently referred to as concurrent engineering, this has been a predominantly manual process since the computer structure to support it has been unavailable. Historically the approach has been to achieve this multidisciplinary integration by linking aerodynamic and structural code inputs and outputs and typically generating geometry in the form of an IGES files as required. This approach does not provide a seamless flow of information from Conceptual through to Preliminary, Detail Design and manufacturing. Simplification at the Concept and Preliminary Design phases when commitments are made to the customer are often invalidated at the Detail Design phase. This results in rework, sub-optimal designs and potentially low customer satisfaction. Master Model or Common Geometry is a geometric centric approach that provides a linked associative environment from concept to manufactured product. Here the intent is to create 3-Dimensional solid geometry at engine concept which would flow seamlessly into detail design, digital engine mockup and manufacturing. This paper describes the move towards a more integrated CAD/CAM/CAE approach based on the concept of a common geometric representation of the product being the design integrator. Everyone is familiar with the concept of building up a system from its constituent parts. Less well understood is how the system level constraints and requirements flow down to these parts with resultant feedback to the system. This has traditionally been the goal of concurrent engineering but the degree of concurrency necessary to provide optimization at the system level has never been achieved. The ultimate goal is an infrastructure that will support concurrent design by analysis. This will be complimented by robust design techniques which use a probabilistic approach to account for the uncertainty associated with a design. GEAE is currently implementing a Common Geometry infrastructure that will support the product from creation to manufacturing and ultimately engine services. This infrastructure will also support the move from deterministic to probabilistic design. Several pilot projects have been successfully completed paving the way to a successful initial implementation. An integral part of this whole process is CAD Integration with Analysis where analytical models are created by applying boundary conditions to the same geometry using context models or views of geometry. Manufacturing also uses the same geometry combined with context models to create in-process models. A prerequisite in this is the creation of 3-Dimensional parametric feature based models. The goal is to significantly shorten the design cycle not only by automation but by creating a degree of concurrency previously unachievable. Results from these pilots, the initial implementation and the productivity gains demonstrated will be discussed. GEAE initiated the Common Geometry project two years ago. The ground rules were that the system would be Unigraphics based and be implemented in commercial software wherever possible. The Common Geometry environment should support the product creation from concept to manufacturing and ultimately services. An integral part of the system would be a Product Data Management System (PDMS) which would support the storage and retrieval of both data related directly to geometry and other metadata. This PDMS would permit the different activities to function concurrently and permit updates to flow down to all aspects of the design activity. This paper will build on the approach described in another publication. In every design there exists a performance floor and cost ceiling between which multiple solutions exist. The purpose of a design is to create a product that will provide customer satisfaction in terms of expectations and technical requirements. In the military world this is the ability to complete a specific mission and in the commercial world this is the ability to produce a revenue stream. The challenge is to translate these customer Critical to Quality requirements into hardware that will comprise a system. Consequently an understanding of the flowdown of the customer CTQ’s to individual parts is essential if customer satisfaction is to be achieved. This represents the challenge in GEAE’s Design For Six Sigma Initiative and is driving the shift from deterministic to probabilistic design methodologies. Common Geometry is a key enabling technology to achieving these goals. Shortening the design cycle by designing by analysis and minimizing testing will significantly reduce engine develop- 22
ment cost and create better products. Common geometry will provide the computer infrastructure to make true concurrent engineering a reality. Author Computer Aided Design; Concurrent Engineering; Computerized Simulation; Three Dimensional Models 20000064602 NASA Glenn Research Center, Cleveland, OH USA Modular Multi-Fidelity Simulation Methodology for Multiple Spool Turbofan Engines Hall, Edward J., Rolls-Royce Allison, USA; VanDrei, Don, NASA Glenn Research Center, USA; Townsend, Scott, NASA Glenn Research Center, USA; February 2000; In English; See also 20000064579; No Copyright; Abstract Only; Available from CASI only as part of the entire parent document This paper describes the development of a component-based simulation environment for multiple-spool turbofan gas turbine engines. Engine performance simulations are based on coupled models of individual components (fans, compressors, combustors, turbines, nozzles, etc.) employing multiple levels of fidelity. During the course of the simulation, the performance of each component may be derived through computational models of varying levels of fidelity including: (1) 0-D performance map lookup (2) Steady 2-D CFD analysis (3) Steady 3-D CFD analysis (4) Time-dependent 3-D CFD analysis This study builds on previous efforts directed at whole engine performance simulation which modeled the low pressure (LP) and high pressure (HP) components separately, using different solution methodologies. The ultimate strategy in the current development effort is to provide an environment whereby the traditional model for engine performance simulations (the cycle analysis) can be extended to provide the level of fidelity available in modern three-dimensional turbomachinery CFD codes. Traditional cycle analysis provides a representative framework for the overall engine simulation whereby individual components are represented via zero-dimensional block elements. The performance of each element is traditionally computed based on table or map look-up. This is the essential capability provided in the Numerical Propulsion System Simulation (NPSS Version 1.0) at it’s lowest level of fidelity. The interest in the current study lies in extending the level of fidelity available in each of the block components to three dimensions and beyond. This capability enhancement is derived from computational results from the ADPAC CFD code based on realistic 3-D component models of each of the block elements in the engine cycle analysis. The simulation environment exploits paralellism at several different levels. Individual CFD simulations employ multiple processors and provide message passing between individual blocks of a single component simulation. Coupled simulations of multiple components provide yet another level of parallelism. Finally, simultaneous low-fidelity and high-fidelity simulations yield yet another opportunity for parallelism. Simulation models have been developed and applied to two modem engine designs: the General Electric Energy Efficient Engine (EEE) and the Rolls- Royce Allison AE3007 engine. Both of these engines employ a dual spool configuration (separate spools for HP and LP components). Low fidelity (0-D) NPSS models were developed for each of these engine configurations. Simultaneously, high fidelity CFD models (3-D) were also developed on a component by component basis. Cooperative simulation between codes with varying levels of physics is provided through sophisticated inter-code communication environments using the CORBA data exchange model. Both JAVA-based and C++-based executive programs have been developed to drive the overall simulation employing mixed models of component representation. The system has been demonstrated for performance simulations of both the EEE and AE3007 engines to explore the multidisciplinary aspects of the simulation environment. Both aerodynamic and mechanical coupling are provided between common shaft-mounted components via a shaft power balance procedure. Dual spool shaft power balance procedures based on 3-D CFD results have also been developed to provide a nearly complete 3-D representation of the entire engine flowfield. Additional information is contained in the original. Author Computational Fluid Dynamics; Computerized Simulation; Gas Turbine Engines; Mathematical Models; Simulation; Turbofan Engines; Turbomachinery 20000064604 ASE Technologies, Inc., Cincinnati, OH USA Multi-Stage Simulation of Advanced Gas Turbine Engines Vitt, Paul, ASE Technologies, Inc., USA; Subramanian, S. Mani, ASE Technologies, Inc., USA; Cherry, David, General Electric Co., USA; Turner, Mark, General Electric Co., USA; February 2000; In English; See also 20000064579; No Copyright; Abstract Only; Available from CASI only as part of the entire parent document Next generation gas turbine engines are being developed to increase thrust and fuel economy, and to be environmentally friendly, all while reducing cost and weight, for both military and commercial applications. NASA’s Ultra Efficient Engine Technology (UEET), and DoD’s Integrated High Performance Turbine Engine Technology (IHPTET) and Joint Technology Advanced Gas Generator (JTAGG) programs, are supporting these technology goals. The beneficiaries of these developmental programs include commercial aircraft, such as the Boeing 737NG and the Next Generation Regional Transport, and military programs like the Joint Strike Fighter (JSF). to support these technology programs related to propulsion systems, advanced computa- 23
Page 1 and 2: Scientific and Technical Aerospace
Page 3 and 4: Introduction Scientific and Technic
Page 5 and 6: Subject Divisions Table of Contents
Page 7 and 8: ground equipment and systems. For a
Page 9 and 10: Subject Categories of the Division
Page 11 and 12: 48 Oceanography 139 Includes the ph
Page 13 and 14: 72 Atomic and Molecular Physics 191
Page 15 and 16: Document Availability Information T
Page 17 and 18: Avail: NASA Public Document Rooms.
Page 19 and 20: NASA CASI Price Tables — Effectiv
Page 21 and 22: ALABAMA AUBURN UNIV. AT MONTGOMERY
Page 23 and 24: ��
Page 25 and 26: 20000061433 Pisa Univ., Dipartiment
Page 27 and 28: English; 34th; 34th Thermophysics C
Page 29 and 30: ciency of the generated derivative
Page 31 and 32: 20000063509 NASA Glenn Research Cen
Page 33 and 34: 05 AIRCRAFT DESIGN, TESTING AND PER
Page 35 and 36: A long tradition of aerodynamic des
Page 37 and 38: sonic and transonic cases will be p
Page 39 and 40: 20000061449 British Aerospace Publi
Page 41 and 42: 20000064593 NASA Langley Research C
Page 43: performance required for a proposed
Page 47 and 48: tion (NPSS), is focused on the inte
Page 49 and 50: surface measurement techniques used
Page 51 and 52: with emphasis on the search for lin
Page 53 and 54: 20000063520 NASA Kennedy Space Cent
Page 55 and 56: primary issues for it’s adaptatio
Page 57 and 58: Footage shows the night vertical ta
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Page 61 and 62: necessary for ignition modeling, or
Page 63 and 64: engine lifetime, and high power ope
Page 65 and 66: 24 COMPOSITE MATERIALS ��
Page 67 and 68: ments. For the screening of liner m
Page 69 and 70: formation of a neutral, fluorescent
Page 71 and 72: 20000065655 Ames Lab., IA USA Funda
Page 73 and 74: This is a second Triennial Conferen
Page 75 and 76: of completely charged PSSNa solutio
Page 77 and 78: 20000064100 NASA Langley Research C
Page 79 and 80: Spacecraft in low Earth orbit (LEO)
Page 81 and 82: Pressure gradients, i.e. pressure h
Page 83 and 84: agreed that the NO(sub x) levels co
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Page 87 and 88: 20000064078 Physics and Electronics
Page 89 and 90: 20000064925 Institute for Human Fac
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Added Analysis (Risk Reduction); 6)
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shipboard electrical power generati
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20000067664 Jet Propulsion Lab., Ca
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other, The theoretical framework go
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figures compare the lift and pitchi
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ment phase demonstrate desired feat
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esolution mode with resolving power
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to track 1% or 0.5% changes was hig
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20000067643 NASA Marshall Space Fli
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37 MECHANICAL ENGINEERING ��
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solver based on overset grid techno
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20000064621 Rensselaer Polytechnic
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that for a specific example of a be
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km x 1000 km). As with the nearly c
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20000064527 Analytical Imaging and
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20000064533 Austin State Univ., Dep
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20000064539 California Univ., Inst.
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in Yellowstone National Park. We ar
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terns of community hysteresis based
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tion model (DEM) fitting to the sca
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The Carolina slate belt is a 10- to
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20000063373 Los Alamos National Lab
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20000064912 New Energy and Industri
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and discusses the issues and resear
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The objective of this study was to
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distributions with engine test resu
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7.6, while the 1926 events appear t
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We investigate the compositional di
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Flight Center, USA; Moore, T. E., N
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This report presents the Applied Me
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in Goa, India, by 3 - 4 days. Wind
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51 LIFE SCIENCES (GENERAL) Includes
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20000062717 Joint Inst. for Nuclear
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20000064015 Institute for Human Fac
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during 50% (SD 4%) of the breathing
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20000064711 Massachusetts Inst. of
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The analysis of this smaller data s
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vided a functional helmet mount. Th
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61 COMPUTER PROGRAMMING AND SOFTWAR
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The goal of multi-image classificat
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20000063526 Defence Science and Tec
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85 dB amplifier design evolved by o
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20000064594 New Mexico Univ., Elect
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20000064615 NASA Ames Research Cent
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declarations with a pattern recogni
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containing point where interpolatio
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has his own responsibility, while t
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the people, documents and projects
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and experimental technology, many o
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tems. In particular this applicatio
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Over the past decade, high performa
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expensive than a single analysis. I
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20000064726 Carnegie-Mellon Univ.,
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20000064099 Teledyne Brown Engineer
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20000066597 Geophysical Observatory
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20000065633 Institute TNO of Applie
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important conclusion is that for su
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est frame of the string. The modifi
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isotopes. The method and program we
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A detailed study is undertaken, usi
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20000063497 Kaiser Electro-Optics,
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delivers 60% of the x-ray flux from
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76 SOLID-STATE PHYSICS ��
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20000064660 Abdus Salam Internation
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20000064703 Institute of Atomic Ene
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The BRST approach is applied to the
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20000067671 Hampton Univ., VA USA A
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82 DOCUMENTATION AND INFORMATION SC
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ecoming almost too cumbersome to be
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physical world. These are the two p
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with WFPC2 data. Tests of HSTphot
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A brief description is given of the
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20000064070 NASA Ames Research Cent
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gas, show variations that have rema
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egy, called Power In that would all
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20000065631 NASA Kennedy Space Cent
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20000064089 Smithsonian Astrophysic
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A ABERRATION, 143, 198 ABRASION, 22
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COMPRESSIBLE FLOW, 83 COMPRESSION L
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FATIGUE (MATERIALS), 50, 93, 96 FAT
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LASING, 90 LATE STARS, 224 LATITUDE
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PATCH ANTENNAS, 74 PATHOLOGY, 98 PA
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SPACE PLATFORMS, 38 SPACE PROBES, 2
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WIND PROFILES, 137 WIND TUNNEL TEST
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Brown, Andy, 38 Brown, April S., 20
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Grosvenor, C. A., 70 Grosvenor, J.
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Lu, Gui-Ying, 50 Lugg, Desmond J.,
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Robertson, Tony, 39 Robinson, Jeffr
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Warner, J. D., 63 Warren, James, 16
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