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:

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Sum total of downloads: 35

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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1 image of flag of Germany Germany 16 45.71%
2 image of flag of Russian Federation Russian Federation 5 14.29%
3 image of flag of Czech Republic Czech Republic 5 14.29%
4 image of flag of India India 2 5.71%
5 image of flag of United Kingdom United Kingdom 2 5.71%
6 image of flag of China China 2 5.71%
7 image of flag of Norway Norway 1 2.86%
8 image of flag of Hong Kong Hong Kong 1 2.86%
9 image of flag of France France 1 2.86%

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