Aeromat

Applying state-of-the-art evolutionary optimization and surrogate modeling techniques to the automated design of aerodynamic structures and vehicles.
From: 
01.10.2016
to
30.09.2019
Type of funding: 
Publicly Funded Research

Project description

A sustainable energy future requires that we both do more with less, and that we fully exploit the renewable energy sources we have available. In this project we explore a common thread between these two approaches, developing tools to better explore and understand aerodynamic design. On the one hand our tools can be used to improve the performance of aerodynamic vehicles, and on the other improving our ability to harvest energy from wind. We develop automated methods for the design of complete aerodynamic structures, using machine-learning techniques to guide iterative experimentation with novel designs.

We focus on:

  1. Optimization of entire structures, rather than iterative improvement on existing designs
  2. Human-machine collaborative design exploration, to discover innovative design concepts
  3. Inclusion of structural mechanics and fluid structure interaction into the optimization, design, and modeling process
  4. Modeling techniques to support these goals, using data-driven approaches to approximate computationally intensive techniques and simulations

In particular we face challenges when creating tools which address these issues in tandem, such as:

  • modeling the performance of designs produced with non-traditional parameterizations
  • broad exploration of possible designs in computationally demanding contexts
  • optimization and modeling of aerodynamic and structural properties simultaneously

 

Project manager at H-BRS

Prof. Dr Dirk Reith

Computational Science and Engineering
Hochschule Bonn-Rhein-Sieg
Email: 
dirk.reith [at] h-brs.de

Sankt Augustin

Room: 
B 223

Co-operating professors

Research associates

Publications

Hochschule Bonn-Rhein-Sieg
Nine of the hundreds of near-optimal designs which vary in volume and curvature, produced in a single run of the surrogate-assisted illumination (SAIL) algorithm.

Aerodynamic Design Exploration through Surrogate-Assisted
Illumination

(Best Student Paper -- Multidisciplinary Design Optimization)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. AIAA Aviation and Aeronautics Forum 2017 

Hochschule Bonn-Rhein-Sieg
An overview of the airfoil design space. Produced through the surrogate-assisted illumination algorithm (SAIL).

Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted
Illumination

(Best Paper -- Complex Systems)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. Genetic and Evolutionary Computation Conference 2017

Hochschule Bonn-Rhein-Sieg
Predicting performance based on features rather than parameters.

Hierarchical Surrogate Modeling for Illumination Algorithms
Alexander Hagg. Genetic and Evolutionary Computation Conference 2017

Sponsors

Co-operation partners