Multidisciplinary Design Optimization and Its Application in Deep Manned Submersible Design

1. Auflage 2020
ISBN: 978-981-15-6457-4
Verlag: Springer Nature Singapore

Buch, Englisch, Band 13, 292 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 470 g

Reihe: Ocean Engineering & Oceanography

This book investigates Reliability-based Multidisciplinary Design Optimization (RBMDO) theory and its application in the design of deep manned submersibles (DMSs). Multidisciplinary Design Optimization (MDO) is an effective design method for large engineering systems like aircraft, warships, and satellites, which require designers and engineers from various disciplines to cooperate with each other. MDO can be used to handle the conflicts that arise between these disciplines, and focuses on the optimal design of the system as a whole. However, it can also push designs to the brink of failure. In order to keep the system balanced, Reliability-based Design (RBD) must be incorporated into MDO. Consequently, new algorithms and methods have to be developed for RBMDO theory.

This book provides an essential overview of MDO, RBD, and RBMDO and subsequently introduces key algorithms and methods by means of case analyses. In closing, it introduces readers to the design of DMSs and applies RBMDO methods to the design of the manned hull and the general concept design. The book is intended for all students and researchers who are interested in system design theory, and for engineers working on large, complex engineering systems.

Zielgruppe

Research

Autoren/Hrsg.

Cui, Weicheng

Pan, Binbin

Fachgebiete

• Geowissenschaften Geologie Marine Geologie, Ozeanographie (Meereskunde)
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Meerestechnik, ablandige Plattformen
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik

Inhaltsverzeichnis

Introduction.- Theory of Multidisciplinary Design Optimization,- Theory of Uncertainty .- Reliability and Its Analysis.- Multidisciplinary Design Optimization Based on Reliability.- Design of Deep Manned Submersible.- Application of Multidisciplinary Design Optimization in DMS.   

Über Autor(innen)

Dr. Binbin Pan is an associate professor at Shanghai Ocean University (SHOU), and deputy director of SHOU’s Hadal Science and Technology Research Center (HAST). He received his BSc from the Department of Engineering Mechanics at Tsinghua University in 2006 and his PhD from the China Ship Scientific Research Center (CSSRC) in 2013. From 2014 to 2016, he pursued post-doctoral research at the Oceanlab, University of Aberdeen, Scotland. At HAST, he is currently the leader of the Lander Team (LT), which focuses on the customization of landers for scientists and developing a new generation of deep-sea actuators and other devices based on smart materials and new technologies. He is interested in Multidisciplinary Design Optimization (MDO) theory and related algorithms. He is also interested in new methods and materials in the development of underwater engineering, especially deep-sea engineering.

Dr. Weicheng Cui is currently a chair professor at Westlake University and an adjunct professor at Shanghai Ocean University. He is the dean of the Hadal Science and Technology Research Center (HAST) of Westlake University and Shanghai Ocean University. He received his BSc from the Department of Engineering Mechanics at Tsinghua University in 1986 and his PhD from the University of Bristol, England in 1990. From 1990 to 1993, he pursued post-doctoral research at the Department of Aerospace Engineering, University of Bristol. He was the project leader and first deputy chief designer of the Jiaolong deep manned submersible. He has published more than 400 papers in various technical journals and conference proceedings. He was awarded the title of one of eight national “deep-diving heroes” in 2013 by the Chinese government and selected as one of ten “Science Stars of China” in 2016 by Nature. His Rainbowfish Challenging the Challenger Deep Project has received substantial media attention, e.g. from Nature magazine and the BBC.