Short Courses

Date: Saturday, June 8th, 2024
Course Length: Approximately 6 Hours
Instructor: Associate Professor Xudong Qian and Dr Wie Min Gho
Please bring the following materials with you: Pen and Paper

Course Description: This short course aims to provide engineers, researchers, and professionals with an enhanced understanding of the fatigue design principles and methodologies for support structures of offshore wind turbines. This course is separated into two parts and covers the following topics: fatigue overview; environmental loads; fatigue stresses and stress ranges; load cycle counting method; damage accumulation; fatigue mechanisms; S-N approach; load spectra and fatigue under variable loading; simplified fatigue assessment; spectral-based fatigue assessment. The short course caters to a broad audience with an interest in advancing their expertise in the design and fatigue considerations of support structures for offshore wind turbines, fostering collaboration and knowledge exchange within the industry.

Course Instructors

Dr. QIAN Xudong is currently an Associate Professor in the Department of Civil and Environmental Engineering (CEE) at the National University of Singapore (NUS), Deputy Head of CEE at NUS, Group Head of the Structural and Offshore Engineering, Director for Centre for Offshore Research and Engineering (CORE), and Director for Centre for Advanced Materials and Structures (CAMS) NUS. Prof Qian’s research interest focuses on experimental fracture mechanics, data driven digital twins for welded structures, fracture, fatigue and integrity assessment for large-scale structures, welded connections, steel-concrete-steel composite structures, and polymer composite materials. Prof Qian is a member of the editorial board in a number of premier journals including Engineering Fracture Mechanics, Fatigue and Fracture of Engineering Materials and Structures, etc.

Dr. GHO Wie Min has 35 years of experience in civil engineering, marine offshore, and energy industries. He is VP of Engineering and Technical Director for LNG, hydrogen, ammonia, and renewable energy infrastructure development at LNG Alliance and Aslan Energy Capital Pte Ltd. He leads technological innovation to enhance energy efficiency and foster business development for future sustainability. Apart from providing engineering research and consultancy services in the area of fatigue assessment of civil and marine offshore structures for the past many years, He has conducted regular training courses for professional development and is also a guest lecturer at Nanyang Technological University lecturing a course module on fatigue and fracture of steel structures for postgraduates.

If you registered for this course, please reach out to the registration company to select an alternative course. Email: OMAE@seatoskymeetings.com

Date: Saturday, June 8th, 2024
Course Length: Approximately 7-8 Hrs
Instructor: Rajeev K. Jaiman, Professor – The University of British Columbia, Vancouver
Course pre-requisite: Instructor provided slides and reading materials will be provided.

Course Description: This course focuses on the integration of mechanics and dynamical systems with machine learning (ML) via data-driven modeling and physics-based ML (PBML). The course begins with a review of calculus, linear algebra, regression analysis and various statistical methods. In the first part, prominent reduced-order models and model reductions techniques for physical systems will be systematically covered via coding examples. It is known that general-purpose black-box ML techniques do not perform well beyond the data they are being trained and they lack physical interpretability and reliability for engineering applications. To address these issues, this course will introduce hybrid physics-based ML techniques using a variety of neural network architectures. New developments pertaining to physics-based ML techniques and software development will be taught using examples from diverse marine/offshore engineering applications.

What will students learn in your course:

• Be able to perform dimensionality reduction of CFD and experimental measurements using singular value decomposition & various projection-based reduced-order models
• Be able to intuitively understand and use deep neural networks for classification, control/optimization and flow-field predictions
• Learn integration of projection-based model reduction techniques with various deep learning architectures

Course Instructor

Rajeev K. Jaiman is currently an Associate Professor and NSERC/Seaspan Industrial Research Chair in the Department of Mechanical Engineering at the University of British Columbia (UBC), Vancouver, Canada. Prior to his current appointment at UBC, he was an assistant professor in the Department of Mechanical Engineering at the National University of Singapore (NUS). Before joining NUS, he was the Director of CFD Development at Altair Engineering, Inc., Mountain View, California. Dr. Jaiman earned his first degree in Aerospace Engineering from the Indian Institute of Technology, Mumbai. He received his master’s and doctorate degrees from the University of Illinois at Urbana-Champaign (UIUC) with a Computational Science and Engineering option. His research interests broadly include multiphysics simulations, fluid-structure interaction, high-performance computing, data-driven modeling and physics-based machine learning. He is currently an Associate Editor of ASME-OMAE Journal, guest editor of Physics of Fluids, a senior member of AIAA and members of APS, ASME, USACM, SIAM and SNAME.

Date: Sunday, June 9th, 2024
Course Length: Approximately 8 Hrs
Instructor: Guillaume Ducrozet, Full Professor, PhD, Ecole Centrale Nantes, LHEEA Lab. (France)
Course Materials: Please bring your laptop

Course Description: The design of marine structures is mainly driven by the loads induced by ocean waves. Then, the accurate description of those nonlinear waves is essential in ocean engineering. To understand, model and reproduce the propagation of realistic complex wave fields (irregular, short-crested) in domains of significant size, the most relevant approach relies on the use of nonlinear potential flow theory. Different approaches can be used to solve the problem numerically. This course aims to present one accurate and efficient methodology to address the fully nonlinear wave propagation problem, namely the High-Order Spectral (HOS) method.

It includes a short theoretical description with a focus on the numerical properties of the method. The advantages and drawbacks, compared to other existing approaches, are presented. Special emphasis is placed on the practical use of HOS models with recommended practices, taking as reference the open-source codes HOS-ocean and HOS-NWT. The course will be the opportunity to gain hands-on experience on the new versions (and features) of both codes that will be released in Spring 2024. This is complemented with different examples of combined use of Numerical and Experimental Wave Tanks to address ocean engineering problems. Finally, a specific section introduces the possibility of coupling between HOS and other models requiring the description of the velocity/pressure fields induced by the waves (such as CFD models to address the wave-structure interaction problems).

The course is primarily intended for industry professionals, researchers, and graduate students in marine and offshore engineering who desire an introduction on nonlinear wave propagation models as well as those who are eager to perform nonlinear wave simulations for specific applications: Digital Twin of an experimental wave tank, specific irregular sea state configuration in open ocean, wave-structure interactions, etc.

After this course, it is expected that students know how to use the open-source solvers HOS-ocean and HOS-NWT as well as how to choose the relevant numerical parameters. They will be able to perform numerical simulations of nonlinear wave propagation of different types of sea states (regular waves, irregular waves, long- and short-crested) in an open-ocean context as well as in a wave tank. They will have an idea of the possible combined use of numerical and experimental wave tanks. Finally they will know the proper methodology to couple the HOS solvers to other numerical codes such as CFD.

Course Instructor

After graduating with a PhD in Ocean Engineering from Ecole Centrale Nantes (ECN – France), in 2007, I occupied different post-doctoral positions, including a 1-year stay at DTU in 2009. Then, in 2010, I obtained a permanent position as an Assistant/Associate Professor at ECN in the LHEEA Laboratory. Since September 2023, I have been a Full Professor in Ocean Engineering at Ecole Centrale de Nantes (France) in the LHEEA Laboratory.

I have an interest in different research fields: Free-surface hydrodynamics; Nonlinear ocean waves; Numerical modelling; Pseudo-spectral methods (HOS); Rogue/freak waves; Wave-structure interactions; Experimental hydrodynamics I am the main developer of the open-source High-Order Spectral models HOS-ocean and HOS-NWT. I have authored 40+ publications in ISI peer-reviewed international journals. Since 2022, I have been deputy head of the LHEEA Lab.

Date: Sunday, June 9th, 2024
Course Length: 7.5 Hours
Instructors: Jichao Li, Research Scientist; Liu Xuan, Senior Research Engineer
Course pre-requisite: Before joining the course, please read this review article: Li, J., Du, X., & Martins, J. R. (2022). Machine learning in aerodynamic shape optimization. Progress in Aerospace Sciences, 134, 100849.

Please bring your laptop with phyton3.x.

What to learn:

• Introduction to aerodynamic shape optimization
• Efficient parameterization of 3D aerodynamic shapes using generative AI
• Data-driven modeling for aerodynamic analyses
• Experiment on various optimization examples (2D airfoils and 3D wind turbine blades)

Schedule:

Morning (9:00am - 12:15am: 3hrs + 15 mins break):
1. Introduction to fundamental issues in aerodynamic shape optimization.
2. Modal parameterization of 2D airfoils to address the dimensionality issue.
3. Data-driven aerodynamic analyses of 2D airfoils for rapid design optimization.

Afternoon (1:15pm - 4:30pm: 3hrs + 15 mins break):
4. Examples on 2D airfoil shape optimization using the ML-based methods.
5. Modal parameterization of 3D wind turbine shape.
6. Examples on data-driven wind turbine shape optimization.

Course Instructor

Dr. Jichao Li is a research scientist at the A*STAR Institute of High Performance Computing. His research focuses on aerodynamic and hydrodynamic shape optimization using scientific machine learning, targeting effective design optimization of aircraft, wind turbines, and marine propellers, especially those with many design points, uncertain design variables, discontinuous merit functions, and multiple design objectives. He is a member of the American Institute of Aeronautics and Astronautics and the Association of Computational Mechanics Singapore. He is featured among the World's Top 2% Scientists in Aerospace Engineering of the year 2023.

Ms. Xuan Liu is a senior research engineer at A*STAR Institute of High Performance Computing. Her research focuses on numerical weather modelling and prediction (NWP) with a specification in urban sustainability, urban heat island study and air-ocean interactions. She has abundant experience in 3D model visualization and programming using Python and Fortran.