Morphing Wing Structural Optimization Using Opposite-Based Population-Based Incremental Learning and Multigrid Ground Elements

This paper has twin aims. Firstly, a multigrid design approach for optimization of an unconventional morphing wing is proposed. The structural design problem is assigned to optimize wing mass, lift effectiveness, and buckling factor subject to structural safety requirements. Design variables consist of partial topology, nodal positions, and component sizes of a wing internal structure. Such a design process can be accomplished by using multiple resolutions of ground elements, which is called a multigrid approach. Secondly, an opposite-based multiobjective population-based incremental learning (OMPBIL) is proposed for comparison with the original multiobjective population-based incremental learning (MPBIL). Multiobjective design problems with single-grid and multigrid design variables are then posed and tackled by OMPBIL and MPBIL. The results show that using OMPBIL in combination with a multigrid design approach is the best design strategy. OMPBIL is superior to MPBIL since the former provides better population diversity. Aeroelastic trim for an elastic morphing wing is also presented

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

Sine-cosine optimization algorithm for the conceptual design of automobile components

In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm, and sine-cosine algorithm are used for shape optimization. The results show the ability of the sine-cosine optimization algorithm to optimize automobile components in the industry.King Fahd University of Petroleum and MineralsKhon Kaen Universit

An Iterated Tabu Search Approach for the Clique Partitioning Problem

Given an edge-weighted undirected graph with weights specifying dissimilarities between pairs of objects, represented by the vertices of the graph, the clique partitioning problem (CPP) is to partition the vertex set of the graph into mutually disjoint subsets such that the sum of the edge weights over all cliques induced by the subsets is as small as possible. We develop an iterated tabu search (ITS) algorithm for solving this problem. The proposed algorithm incorporates tabu search, local search, and solution perturbation procedures. We report computational results on CPP instances of size up to 2000 vertices. Performance comparisons of ITS against state-of-the-art methods from the literature demonstrate the competitiveness of our approach

The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components

As a result of the requirements imposed by international organizations and governments on fuel emissions, there is a growing interest in the design of lightweight vehicles with low-fuel emissions. Metaheuristic methods have been widely used for the optimum design of vehicle components in recent years for which successful results have been reported. Encouraged by such results obtained from the methods mentioned, the Henry gas solubility optimization algorithm (HGSO), a recently developed optimization method, is used to solve the shape optimization of a vehicle brake pedal to prove how HGSO can be used for solving shape optimization problems. This paper is the first application of the HGSO in connection with real-world optimization problems in the literature. The results show HGSO's ability to design better optimal components in the automotive industry.Pandit Deendayal Petroleum UniversityKing Fahd University of Petroleum and Mineral

Seagull optimization algorithm for solving real-world design optimization problems

In this research paper, a new surrogate-assisted metaheuristic for shape optimization is proposed. A seagull optimization algorithm (SOA) is used to solve the shape optimization of a vehicle bracket. The design problem is to find structural shape while minimizing structural mass and meeting a stress constraint. Function evaluations are carried out using finite element analysis and estimated by using a Kriging model. The results show that SOA has outstanding features just as the whale optimization algorithm and salp swarm optimization algorithm for designing optimal components in the industry.King Fahd University of Petroleum and Mineral

Structural optimization using multi-objective modified adaptive symbiotic organisms search

© 2019 Elsevier Ltd Multiple objective structural optimization is a challenging problem in which suitable optimization methods are needed to find optimal solutions. Therefore, to answer such problems effectively, a multi-objective modified adaptive symbiotic organisms search (MOMASOS) with two modified phases is planned along with a normal line method as an archiving technique for designing of structures. The proposed algorithm consists of two separate improved phases including adaptive mutualism and modified parasitism phases. The probabilistic nature of mutualism phase of MOSOS lets design variables to have higher exploration and higher exploitation simultaneously. As search advances, a stability between the global search and a local search has a significant effect on the solutions. Therefore, an adaptive mutualism phase is added to the offer MOASOS. Also, the parasitism phase of MOSOS offers over exploration which is a major issue of this phase. The over exploration results in higher computational cost since the majority of the new solutions gets rejected due to inferior objective functional values. In consideration of this issue, the parasitism phase is upgraded to a modified parasitism phase to increase the possibility of getting improved solutions. In addition, the proposed changes are comparatively simple and do not need an extra parameter setting for MOSOS. For the truss problems, mass minimization and maximization of nodal deflection are considered as objective functions, elemental stresses are considered as behavior constraints and (discrete) elemental sections are considered as side constraints. Five truss optimization problems validate the applicability of the considered meta-heuristics to solve complex engineering. Also, four constrained benchmark engineering design problems are solved to demonstrate the effectiveness of MOMASOS. The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase with a normal line method as an archiving technique provide superior and competitive results than the former obtained results

Aeroelastic and Structural Design of an All-Moveable Aircraft Fin

The Lagrange Multi-Disciplinary Design Optimisation (MDO) code is used for the preliminary study into design aspects of a generic all-movable aircraft fin. It is shown how the control effectiveness at high speeds can be increased through variation of the attachment position and stiffness, whilst still meeting strength and aeroelastic stability constraints