- Long Chen, Technical University of Munich
- Nicolas R. Gauger, Technical University of Kaiserslautern
- Kai-Uwe Bletzinger, Technical University of Munich
Shape optimization is an important branch of design optimization that finds useful applications in the emerging field of computational engineering and has already been considered a necessity in several industries, such as aerospace and automobile. However, open problems and challenges remain at the frontiers of shape optimization, especially when dealing with large-scale problems. Does a complex engineering application have a shape-optimal solution? What is the appropriate shape parameterization for a particular application? How to perform shape optimization that requires solving very expensive governing equations? For decades, shape optimization has attracted the interests of mathematicians, computer and computational scientists, and engineers, rendering its interdisciplinary nature. While areas such as numerical analysis, geometric modeling, scientific computing, mathematical programming, and control theory are classical and center to shape optimization. Opportunities are open to emerging areas, such as machine learning.
This mini-symposium aims to bring together researchers from industry and academia to discuss a variety of related computational efforts, bridging the gap between the two, and foster future collaborations.
Scope: ""Large-scale problems"" are broadly defined, including shape optimizations, where expensive simulations, uncertainty quantification are involved, or the formulated optimization problem is large-scale and challenging.
Thus, suitable topics include, but are not limited to:
1. Non-parametric shape optimization
2. Adjoint methods, multidisciplinary shape optimization
3. Acceleration of shape optimization via model order reduction
4. High-performance computing in shape optimization
5. Shape optimization with level-set methods
6. Efficient shape regularization methods
7. Shape parameterization methods for large and complex geometries
8. Data-driven, machine learning approaches to shape optimization
9. Mathematical programming for large-scale shape optimizations
List of tentative titles and speakers (indicated by *):
Mini-Symposium Part I, Topic 1000 and 1300
Session Theoretical Foundations:
1 Keynote. Basic examinations of non-parametric shape optimization problems
Hideyuki Azegami*,
Department of Complex Systems Science, Nagoya University
2. Accelerated constrained shape optimization
Long Chen*, Kai-Uwe Bletzinger,
Chair of Structural Analysis, TU Munich
3. Higher-order shape optimization based on variational derivatives
Stephan Schmidt*,
Department of Mathematics, Humboldt University of Berlin
4. Dicrete adjoints for multiphysics problems
Ole Burghardt*, Chair for Scientific Computing, TU Kaiserslautern
Pedro Gomes, Department of Aeronautics, Imperial College London
Nicolas R. Gauger, Chair for Scientific Computing, TU Kaiserslautern
Rafael Palacios, Department of Aeronautics, Imperial College London
5. Aerodynamic and acoustic design optimization of a multiple propeller combination for distributed electrical propulsion
Antonio Visingardi, Mattia Barbarino, Domenico Quagliarella*,
CIRA -- Italian Aerospace Research Centre
Mini-Symposium Part II, Topic 1000 and 1300
Session Industrial Applications:
6 Keynote. Structural optimization in ANSYS
Gerogios Michailidis*, M. Albertelli, and A. Faure,
Ansys, Inc.
7. Adaptive Vertex Morphing filtering for large node-based shape optimization problems
Ihar Antonau*, Chair of Structural Analysis, TU Munich
Majid Hojjat, BMW Group
Kai-Uwe Bletzinger, Chair of Structural Analysis, TU Munich
8. Adjoint-based shape optimization for industrial heat exchangers
Tobias Kattmann*, Robert Bosch GmbH
Ole Burghardt, Chair for Scientific Computing, TU Kaiserslautern
Nicolas R. Gauger, Chair for Scientific Computing, TU Kaiserslautern,
Nijso Beishuizen, Robert Bosch GmbH
9. Data-driven analysis, design, and optimization in fluids engineering
Koji Shimoyama*,
Institute of Fluid Science, Tohoku University
10. Large-scale industrial shape optimization applications in maritime two-phase flows - Learning from the adjoint
Niklas Kühl*, Thomas Rung,
Hamburg University of Technology