Browsing by Author "Demange, Jean"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access A comparison study of two multifidelity methods for aerodynamic optimization(Elsevier, 2019-11-29) Kontogiannis, Spyridon G.; Demange, Jean; Savill, Mark A.; Kipouros, TimoleonIndustrial aerodynamic design applications require multiobjective optimization tools able to provide design feedback to the engineers. This is true especially when optimization studies are carried out during the conceptual design stage. The need for fast optimization methods has led to the development of multifidelity methods in a surrogate based optimization environment. Multifidelity tools have the potential to accelerate the design process, primarily due to the lower cost associated with the low fidelity tool. In addition to this, the design stage is shortened as mature and reliable high fidelity design information is provided earlier in the design cycle. Despite this high potential of these methods, there is no explicit comparison available in the literature between multifidelity surrogate based optimization tools for industrial aerodynamic problems. This paper aims at providing a direct comparison between two multiobjective multifidelity surrogate based optimization methods developed by our group. The first approach uses a trust region formulation for efficient multiobjective that does not require gradients. The second is using the concept of expected improvement to perform fast design space exploration based on a novel Kriging modification for multifidelity data. The tools are applied in two aerodynamic design problems: optimization of a high lift configuration in respect to maximum lift maximization and an airfoil design for transonic cruising conditions. These problems feature characteristics of industrial interest. They involve difficult physical analyses in the case of the high lift configuration and a more complex optimization formulation due to the increased dimensionality in the case of the transonic airfoil. Our presented methods are compared against a CFD-based optimization, a surrogate based optimization using only high fidelity data and a multifidelity surrogate based optimization based on Co-Kriging. Early results suggest that the trust region method can quickly provide improved designs leading to an efficient Pareto front. The expected improvement based method shows fast exploration attributes and a wide Pareto front.Item Open Access Multifidelity multiobjective trust-region-based optimisation for high-lift devices.(2018-02) Demange, Jean; Savill, Mark A.; Kipouros, TimoleonThis thesis addresses the maximum lift prediction early in the design stage. To do so, the wing high-lift element positions is optimised to estimate the maximum achievable lift. Aircraft design is currently done in at least two main stages: a first high-level design work is undertaken using fast but not very reliable tools which is then improved in a subsequent loop using more accurate tools but requiring more computational time. The present work uses synergies between those models to quickly estimate the maximum lift capabilities of an aerofoil with the use of a derivative-free method along with a trust region framework to perform the multiobjective optimisation while ensuring the convergence towards the most accurate tool optima. The method is first applied on a single-objective formulation and shows significant time saving when the aerodynamic problem is simple. A decrease in benefits is observed when applied to the high-lift devices optimisation because of the increased differences between the low- and the high-fidelity models and a decrease in the tools robustness. A co-Kriging model instead of the additive correction is shown to be beneficial for the accuracy without reducing time saving. Two trust region definitions are compared and shown not to be equivalent: the Euclidean trust region is more accurate but usually more expensive whereas the step-based one uses a more approximated solution of the subproblem which decreases the cost. It is also shown, in accordance with literature, that the multifidelity method increases the variability in convergence because it exacerbates the suboptimiser stochastic characteristics. The main contribution to knowledge is the extension to multiobjective problems. Because of the low correlation between the low- and high-fidelity tools, the method with an additive correction is shown to be dominated by the high-fidelity-only optimiser, albeit the multifidelity is more diversified. The use of a co-Kriging model shows a significant improvement of the Pareto front optimality and extent. Single-fidelity Surrogate Based Optimisation however may provide similar benefits. A novel visualisation approach to compare two models of different fidelity is introduced and a qualitative analysis of the low-fidelity accuracy effect on the multifidelity convergence is done: the most sensitive variables should be correctly captured by the lower fidelity model whereas the less sensitive ones will be corrected by the model correction.