2023 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2023) Proceedings

Name: 2023 Asia-Pacific International Symposium on Aerospace Technology (APISAT 2023) Proceedings
Price: 3740 SEK
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Volume II

AvSong Fu

2025

Del 1051 i serien Lecture Notes in Electrical Engineering

3 740 kr

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Beskrivning

This book is a compilation of peer-reviewed papers from the 2023 Asia-Pacific International Symposium on Aerospace Technology (APISAT2023). The symposium is a common endeavour among the four national aerospace societies in China, Australia, Korea and Japan, namely, Chinese Society of Aeronautics and Astronautics (CSAA), Royal Aeronautical Society Australian Division (RAeS Australian Division), Japan Society for Aeronautical and Space Sciences (JSASS) and Korean Society for Aeronautical and Space Sciences (KSAS). APISAT is an annual event initiated in 2009. It aims to provide the opportunity to Asia-Pacific nations for the researchers of universities and academic institutes, and for the industry engineers to discuss the current and future advanced topics in aeronautical and space engineering.This is the volume II of the proceedings.

Produktinformation

Utgivningsdatum:2025-07-06
Höjd:155 x 235 x 108 mm
Vikt:2 984 g
Språk:Engelska
Serie:Lecture Notes in Electrical Engineering
Antal sidor:1 971
Förlag:Springer Verlag, Singapore
EAN:9789819740123

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Mer om författaren

Dr. Song Fu received his B.Sc. degree from Imperial College London in 1983 and Ph.D. degree from UMIST in 1988. Since then he has been working at Tsinghua University. His research field covers turbulence modelling, flow instabilities, hypersonic aerodynamics, etc. He proposed realisable second moment closure and nonlinear eddy-viscosity model before. He also proposed a transition model for hypersonic boundary layer flows reflecting some of the physics of the second instability mode. Professor Song Fu was elected as an AIAA Fellow in 2014 and IACM Fellow in 2012.

Innehållsförteckning

Adaptive backstepping Control of Morphing Aircraft Based on RBF Neural Networks.- Investigation of Flow Field and Performance of a Tandem Flapping Wing Micro Air Vehicle.- Rendering Involved and Machine Learning based Environment Interpretation Integrated Geometry and Image.