Robust Aeroservoelastic Stability Analysis 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Robust Aeroservoelastic Stability Analysis

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Brenner, M. 作者
Springer Verlag
譯者
出版日期
205 頁數
$ 168.37 價格
HRD
叢書系列
9781852330965 圖書編碼

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發表於2024-11-11

Robust Aeroservoelastic Stability Analysis 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Robust Aeroservoelastic Stability Analysis 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Robust Aeroservoelastic Stability Analysis 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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Robust Aeroservoelastic Stability Analysis 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Robust Aeroservoelastic Stability Analysis 在線電子書 圖書描述

This book introduces a new method of analyzing aeroelasticity for flexible aircraft by applying robust stability theory. This method was developed to address several concerns with traditional methods and improve the efficiency of flight flutter testing. One advantage to this method is the ability to consider modeling errors in the analysis and generate a worst-case flutter margin. Another advantage is the ability to incorporate flight data directly into the analysis to ensure the model accounts for poorly modeled properties that are observed on the aircraft. The material covered provides a detailed explanation of the method to analyze robust stability margins using ??-analysis. It shows how to formulate aeroelastic models in the ?? framework using standard state-space concepts. It also presents several ways to represent modeling errors in this framework and discusses how these errors relate to typical errors in aeroelastic models. Algorithms are listed that demonstrate how to apply these robustness concepts to general aircraft models and compute worst-case stability margins. Engineers working with flight test programs will be particularly interested in the material detailing methods of using flight data to update theoretical models and associated uncertainty descriptions. These methods represent a dramatic improvement over traditional analysis that separately analyze flight data and theoretical models. Incorporating the flight data presents the distinct benefit of ensuring the uncertainty description is a realistic representation of modeling errors so the worst-case stability margins are neither overly optimistic or conservative.

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