Reverse Engineering of Passenger Jets – Classified Design Parameters

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De Grave, Emiel: Reverse Engineering of Passenger Jets – Classified Design Parameters. Master Thesis. Hamburg : Aircraft Design and Systems Group (AERO), Department of Automotive and Aeronautical Engineering, Hamburg University of Applied Sciences, 2017. URN: https://nbn-resolving.org/urn:nbn:de:gbv:18302-aero2017-08-25.017

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/9322

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Sum total of downloads: 637




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This thesis explains how the classified design parameters of existing passenger jets can be determined. The classified design parameters are; the maximum lift coefficient for landing and take-off, the maximum aerodynamic efficiency and the specific fuel consumption. The entire concept is based on the preliminary sizing of jet powered civil aeroplanes. This preliminary sizing is explained in detail because it is the foundation of the final result. The preliminary sizing is combined using reverse engineering which is not a strict method. Therefore, only the basics are explained. By applying reverse engineering on the preliminary sizing and aiming for the classified design parameters as output, formulas are derived to calculate the maximum lift coefficients, the maximum aerodynamic efficiency and the specific fuel consumption. The goal is to calculate these parameters, using only aircraft specifications that are made public by the manufacturer. The calculations are complex with mutual relations, iterative processes and optimizations. Therefore, it is interesting to integrate everything in a tool. The tool is built in Microsoft Excel and explained in detail adding operating instructions. The program is executed for miscellaneous aeroplanes, supported with the necessary comments. Investigated aeroplanes are: Caravelle 10B (Sud-Aviation), Boeing 707-320C, BAe 146-200 (British Aerospance), A320-200 (Airbus), "The Rebel" (based on A320), Boeing SUGAR High, Boeing 747-400, Blended Wing Body VELA 2 (VELA) and Dassault Falcon 8X.
License of this version: CC BY-NC-SA 4.0 Unported
Document Type: Book
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 151 23.70%
2 image of flag of Russian Federation Russian Federation 106 16.64%
3 image of flag of United States United States 88 13.81%
4 image of flag of Czech Republic Czech Republic 66 10.36%
5 image of flag of United Kingdom United Kingdom 23 3.61%
6 image of flag of Turkey Turkey 16 2.51%
7 image of flag of India India 16 2.51%
8 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 15 2.35%
9 image of flag of Canada Canada 15 2.35%
10 image of flag of Ethiopia Ethiopia 13 2.04%
    other countries 128 20.09%

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