Multiobjective optimization of a steering linkage

In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria.

     

Multi-objective reliability-based topology optimization of structures using a fuzzy set model

This research proposes a multi-objective reliability-based topology optimization (MORBTO) for structural design, which considers uncertain structural parameters based on a fuzzy set model. The new technique is established in the form of multi-objective optimization where the equivalent possibilistic safety index (EPSI) is included as one of the objective functions along with mass, and compliance. This technique can reduce complexity due to a double-loop nest problem used previously due to performing single objective optimization. The present technique can accomplish within one optimization run using a multi-objective approach. Two design examples are used to demonstrate the present technique, which have the objectives as structural mass and compliance with the constraint of structural strength. The results show the proposed technique is effective and simple compared to previous techniques.

     

New conceptual design of aeroelastic wing structures by multi-objective optimization

Internal structural layouts and component sizes of aircraft wing structures have a significant impact on aircraft performance such as aeroelastic characteristics and mass. This work presents an approach to achieve simultaneous partial topology and sizing optimization of a three-dimensional wing-box structure. A multi-objective optimization problem is assigned to optimize lift effectiveness, buckling factor and mass of a structure. Design constraints include divergence and flutter speeds, buckling factor and stresses. The topology and sizing design variables for wing internal components are based on a ground element approach. The design problem is solved by multi-objective population-based incremental learning (MOPBIL). The Pareto optimum results lead to unconventional wing structures that are superior to their conventional counterparts.     

Aircraft morphing wing design by using partial topology optimization

A morphing wing concept has been investigated over the last decade because it can effectively enhance aircraft aerodynamic performance over a wider range of flight conditions through structural flexibility. The internal structural layouts and component sizes of a morphing aircraft wing have an impact on aircraft performance i.e. aeroelastic characteristics, mechanical behaviors, and mass. In this paper, a novel design approach is proposed for synthesizing the internal structural layout of a morphing wing. The new internal structures are achieved by using two new design strategies. The first design strategy applies design variables for simultaneous partial topology and sizing optimization while the second design strategy includes nodal positions as design variables. Both strategies are based on a ground structure approach. A multiobjective optimization problem is assigned to optimize the percentage of change in lift effectiveness, buckling factor, and mass of a structure subject to design constraints including divergence and flutter speeds, buckling factors, and stresses. The design problem is solved by using multiobjective population-based incremental learning (MOPBIL). The Pareto optimum results of both strategies lead to different unconventional wing structures which are superior to their conventional counterparts. From the results, the design strategy that uses simultaneous partial topology, sizing, and shape optimization is superior to the others based on a hypervolume indicator. The aeroelastic parameters of the obtained morphing wing subject to external actuating torques are analyzed and it is shown that it is practicable to apply the unconventional wing structures for an aircraft.