Robust and Reliability-Based Design Optimisation in Aerospace Engineering
Design optimisation has become a key ingredient in many engineering fields. Here, the increasing competition pressure is making the added value of optimised products and processes clear. These products and processes can offer improved performance and cost effectiveness which are difficult to obtain with traditional design approaches.
Uncertainties also play a very important role in the design process, because an optimal solution might not be optimal in terms of robustness, reliability or resilience. Different types of uncertainties can be considered, such as aleatory or epistemic uncertainties. Aleatory uncertainties can be described in terms of probabilistic models and reflect the intrinsic stochastic nature of certain physical properties. Epistemic uncertainties are due to a lack of knowledge. They are very important in preliminary phases of the design process, where the lack of knowledge is greater, but can be reduced as in later phases when a clearer insight is defined. Uncertainty quantification is a necessary step to assess the impact of the uncertainty on the optimal solutions. Robust design optimisation on the other hand deals with minimising the impact of the uncertainties on the optimal solution.
All this is particularly true in space and aerospace engineering, where complex systems often need to operate optimally in harsh and inhospitable environment with a high level of reliability. Traditional approaches that make use of safety margins to account for uncertainty in design and manufacturing tolerances are not adequate to fully capture the growing complexity of engineering systems and provide reliable and optimal solutions.
- Dr Stefan Goertz (DLR, Germany)
- Dr Mariapia Marchi (ESTECO, Italy)
- Prof Boris Naujoks (THK, Germany)
This symposium aims at bringing together researchers and practitioners in the field of design under aleatory and epistemic uncertainty (including applications to aerospace engineering problems) to share their knowledge and experiences and discuss problems and challenges, and to facilitate further improvements in this challenging field.
Authors are invited to submit papers on one or more of the following topics (or other related topics not included in this list):
- (Multi/many-objective) optimisation under uncertainty
- Robustness, reliability and resilience measures
- Efficient methods for uncertainty quantification
- Adjoint methods
- Gradient-based optimisation
- Evolutionary methods or heuristics
- Surrogate-assisted methods
- Artificial intelligence and machine learning approaches
- High-dimensional problems
- Complex systems
- Aerospace applications