Multiobjective Design Optimization using Nash Games

International audienceIn the area of pure numerical simulation of multidisciplinary coupled systems, the computational cost to evaluate a configuration may be very high. A fortiori, in multi- disciplinary optimization, one is led to evaluate a number of different configurations to iterate on the design parameters. This observation motivates the search for the most in- novative and computationally efficient approaches in all the sectors of the computational chain : at the level of the solvers (using a hierarchy of physical models), the meshes and geometrical parameterizations for shape, or shape deformation, the implementation (on a sequential or parallel architecture; grid computing), and the optimizers (deterministic or semi-stochastic, or hybrid; synchronous, or asynchronous). In the present approach, we concentrate on situations typically involving a small number of disciplines assumed to be strongly antagonistic, and a relatively moderate number of related objective functions. However, our objective functions are functionals, that is, PDE-constrained, and thus costly to evaluate. The aerodynamic and structural optimization of an aircraft configuration is a prototype of such a context, when these disciplines have been reduced to a few major objectives. This is the case when, implicitly, many subsystems are taken into account by local optimizations. Our developments are focused on the question of approximating the Pareto set in cases of strongly-conflicting disciplines. For this purpose, a general computational technique is proposed, guided by a form of sensitivity analysis, with the additional objective to be more economical than standard evolutionary approaches

Two dimensional airfoil optimisation using CFD in a grid computing environment

In this paper, a two-dimensional airfoil shape optimisation problem is investigated using CFD within a grid computing environment (GCE) implemented in Matlab. The feature-based parametric CAD tool ProEngineer is used for geometry modelling. The industrial level mesh generation tool Gambit and flow solver Fluent are employed as remote services using the Globus Toolkit as the low level API. The objective of the optimisation problem is to minimize the drag-to-lift coefficient ratio for the given operating condition. A Matlab interface to the design exploration system (OPTIONS) is used to obtain solutions for the problem. The adoption of grid technologies not only simplifies the integration of proprietary software, but also makes it possible to harness distributed computational power in a consistent and flexible manner

On the design of morphing airfoils using spinal structures

In this paper the design of spinal structures for the control of morphing airfoils is examined. The aim is to find structures that, when suitably loaded, can alter the aerodynamic shape of a cladding that forms the airfoil. Morphing through different cambered airfoils to achieve aerodynamic properties for different maneuvers is then possible by exploiting a range of incremental non-linear structural solutions. Further, by using structures that are acting in the post-buckling regime, it is possible to obtain significant changes in shape with only modest changes in applied load. The structure also presents enhanced aeroelastic properties. Results are formulated in terms of the aerodynamic properties of the morphed airfoils using a shape optimized beam as the spinal structure with fixed aerodynamic cladding

Aeroelastic Design and Optimization of Unconventional Aircraft Configurations in a Distributed Design Environment

A Multidisciplinary Design and Optimization (MDO) methodology is presented, which uses a physics-based modeling approach for the preliminary structural design of unconventional aircraft configurations. Therein, static as well as dynamic aeroelastic stability constraints are accounted for at the early stage of the design process. A functional parametrization is applied for the description of the aircraft’s geometry. Several physics based analysis modules are orchestrated by an engineering framework to enable distributed multidisciplinary analysis and optimization. The method builds on DLR’s collaborative design environment, which uses the central data model CPACS to provide consistent model information in the analysis workflow. A knowledge based aeroelastic engine is developed to accelerate the integration of the disciplinary models and the subsequent aeroelastic analysis, and to automate the disciplinary couplings. The approach is tested in optimization test cases for a conventional wing design as well as for a Blended Wing Body configuration