Development of frame finite element model for truss structures with semi-rigid connections

The problem of connecting truss structures is one of the major concerns in structural analysis and design. The behavior of truss structures is usually analyzed using a common finite element model, which considers each member as a two-force member. Each truss member connection is treated as a rotational pinned joint, but in the reality, the members of truss structures are usually connected with bolts or by welding. Alternatively, a designer may analyze such a structure using a frame finite element model where joint connections are considered fixed or rigid connections, which provide a connection that is stiffer than the inherent behavior. In this research, instead of using truss or frame finite element models, a substructure technique is employed to develop a more realistic finite element model. Each element is separated into three parts, a main element and two joint elements. The substructure technique is integrated into the frame finite element model to reduce design variables in global equations, to increase deformability of the joint elements, and make the proposed model more realistic. Young’s modulus values of the joints are reduced as a percentage of the modulus of the main elements. Comparison of the results obtained from the proposed model to the truss and frame finite element models are reported

Solving Partial Differential Equations Using a New Differential Evolution Algorithm

This paper proposes an alternative meshless approach to solve partial differential equations (PDEs). With a global approximate function being defined, a partial differential equation problem is converted into an optimisation problem with equality constraints from PDE boundary conditions. An evolutionary algorithm (EA) is employed to search for the optimum solution. For this approach, the most difficult task is the low convergence rate of EA which consequently results in poor PDE solution approximation. However, its attractiveness remains due to the nature of a soft computing technique in EA. The algorithm can be used to tackle almost any kind of optimisation problem with simple evolutionary operation, which means it is mathematically simpler to use. A new efficient differential evolution (DE) is presented and used to solve a number of the partial differential equations. The results obtained are illustrated and compared with exact solutions. It is shown that the proposed method has a potential to be a future meshless tool provided that the search performance of EA is greatly enhanced

Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle

Many-objective optimisation is a design problem, having more than 3 objective functions, which is found to be difficult to solve. Implementation of such optimisation on aircraft conceptual design will greatly benefit a design team, as a great number of trade-off design solutions are provided for further decision making. In this paper, a many-objective optimisation problem for an unmanned aerial vehicle (UAV) is posed with 6 objective functions: take-off gross weight, drag coefficient, take off distance, power required, lift coefficient and endurance subject to aircraft performance and stability constraints. Aerodynamic analysis is carried out using a vortex lattice method, while aircraft component weights are estimated empirically. A new self-adaptive meta-heuristic based on decomposition is specifically developed for this design problem. The new algorithm along with nine established and recently developed multi-objective and many-objective meta-heuristics are employed to solve the problem, while comparative performance is made based upon a hypervolume indicator. The results reveal that the proposed optimiser is the best performer for this design task.Defence Technology InstituteThailand Research Fun

Many‑objective meta-heuristic methods for solving constrained truss optimisation problems: A comparative analysis

Many-objective truss structure problems from small to large-scale problems with low to high design variables are investigated in this study. Mass, compliance, first natural frequency, and buckling factor are assigned as objective functions. Since there are limited optimization methods that have been developed for solving many-objective truss optimization issues, it is important to assess modern algorithms performance on these issues to develop more effective techniques in the future. Therefore, this study contributes by investigating the comparative performance of eighteen well-established algorithms, in various dimensions, using four metrics for solving challenging truss problems with many objectives. The statistical analysis is performed based on the objective function best mean and standard deviation outcomes, and Friedman's rank test. MMIPDE is the best algorithm as per the overall comparison, while SHAMODE with whale optimisation approach and SHAMODE are the runners-up. • A comparative test to measure the efficiency of eighteen state-of-the-practice methods is performed. • Small to large-scale truss design challenges are proposed for the validation. • The performance is measured using four metrics and Friedman's rank test

Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation

This paper proposes an optimisation process for the design of aircraft fuselage stiffeners using evolutionary optimisation. A new design problem is developed to find a layout for fuselage stiffeners (rings and stringers) such that the structural mass, compliance, and the first-mode natural frequency can be optimised, subject to structural constraints. The stiffeners are modelled as beam elements. Three multiobjective meta-heuristics are employed to solve the problem, and a comparative study of the results of these optimisers is carried out. It is found that the proposed layout synthesis problem for aircraft fuselage stiffeners leads to a set of efficient structural solutions, which can be used at the decision-making stage. It is an automated design strategy with high potential for further investigation.Thailand Research Fund (RTA6180010

Hybrid taguchi-levy flight dis-tribution optimization algorithm for solving real-world design optimization problems

The Levy flight distribution optimization algorithm is a recently developed meta-heuristic. In this study, the Levy flight distribution optimization algorithm and the Taguchi method are hybridized to solve the shape optimization problem, which is the final step in developing optimum structural components. The new method is termed the hybrid Levy flight distribution and Taguchi (HLFD-T) algorithm. Geometric dimensions are used as design variables in the optimization, and the problem is aimed at mass minimization. The constraint in the problem is the maximum stress value. The well-known Kriging meta-modeling approach and a specifically developed hybrid approach have been coupled in this paper to find the component's optimal geometry. The results show that the proposed hybrid algorithm (HLFD-T) has more robust features than the ant lion algorithm, the whale algorithm, and the Levy flight distribution optimization algorithm for obtaining an optimal component geometry.Bursa Uludağ ÜniversitesiKhon Kaen University, Khon KaenKing Fahd University of Petroleum Mineral