Multidisciplinary multi-objective design optimization of an active morphing wing section

Abstract

Design and optimization of morphing wings are of current research interest as they promise increasing efficiency and flexibility of future aircraft. A challenging task is to find structural layouts of morphing wings that enable aerodynamically optimized shape changes without defining the target shape a priori. The current paper addresses this task and presents a method that combines the optimization of the active structure of a wing section, parameterized by Lindenmayer cellular systems, with an aerodynamic evaluation. Neither the structural layout nor the target shape has to be defined a priori. This aim is achieved by a multidisciplinary optimization using evolutionary algorithms with aerodynamic and structural objectives. The developed method allows to optimize the topology of the internal structure, the placement of linear contraction, and expansion actuators as well as the setting of their actuation degree concurrently. It is shown that the present approach allows to find optimized internal layouts containing active structural elements for morphing wing sections.

Numerical optimization-based design studies on biaxial tensile tests

“…23 GEOpS2 is based on Evolutionary Algorithms and has been successfully applied to various aerostructural optimization tasks. 24–27 High efficiency is gained by massive parallelization of the evaluation of designs.…”

“…23 GEOpS2 is based on Evolutionary Algorithms and has been successfully applied to various aerostructural optimization tasks. [24][25][26][27] High efficiency is gained by massive parallelization of the evaluation of designs. GEOpS2 uses Genetic Algorithms (GA), Evolution Strategies (ES) and Differential Evolution (DE) as optimization algorithms.…”

Design studies for a light aircraft wing with highly integrated load-bearing hydrogen tanks using multi-objective optimization methods

“…A recent application example for this tool is the optimization of morphing wings. 23 The optimization algorithms are Genetic Algorithms (GA), Evolutionary Strategies (ES), and Differential Evolution (DE), which are all used in parallel. The number of discrete and continuous design variables of the multi-objective optimization problem (2) are dependent on the specific case.…”

“…FiPPS 2 . For the Finite Element simulation, the in-house Finite Element solver FiPPS 2 , which was recently successfully applied on morphing wing sections by Dexl et al, 23 is used. With this solver, linear static solutions as well as buckling calculations are possible.…”

Multidisciplinary Lightweight Optimization for Front Impact Structure of Body Frame Based on Active and Passive Safety

“…It fully considers the coordination mechanism between various disciplines in the process of complex product design. It can shorten the design cycle and obtain better design schemes by obtaining the optimal value of conflicting objectives [21][22][23][24][25].…”