Jianqiao Chen

A surrogate-based particle swarm optimization algorithm for solving optimization problems with expensive black box functions

In engineering applications, computer experiments such as finite element analysis and computational fluid dynamics are often used to model and analyse structural behaviours. In this article, a surrogate-based particle swarm optimization algorithm is proposed for solving optimization problems with expensive black box functions. An approximate optimization problem in which the black box functions are replaced by the hybrid surrogate models is efficiently solved to search and adjust the global optimum position during the iterative process. Since the presented method combines the merits of traditional optimization algorithms and particle swarm optimization, only a small number of particles is needed to achieve the optimal position after several iterations. Therefore, the method shows great advantages in solving engineering optimization problems with expensive black box functions. Several examples are presented to demonstrate the feasibility and effectiveness of the proposed method.

Probabilistic optimal design of laminates using improved particle swarm optimization

A new approach to the particle swarm optimization (PSO) is proposed for the solution of non-linear optimization problems with constraints, and is applied to the reliability-based optimum design of laminated composites. Special mutation-interference operators are introduced to increase swarm variety and improve the convergence performance of the algorithm. The reliability-based optimum design of laminated composites is modelled and solved using the improved PSO. The maximization of structural reliability and the minimization of total weight of laminates are analysed. The stacking sequence optimization is implemented in the improved PSO by using a special coding technique. Examples show that the improved PSO has high convergence and good stability and is efficient in dealing with the probabilistic optimal design of composite structures.

Effects of welding parameters on mechanical properties and microstructure of resistance spot welded DP600 joints

Abstract This paper experimentally investigates the effects of welding parameters including electrode force, welding current and welding time on the mechanical properties and microstructure of resistance spot welded DP600 joints. The experimental results show that there exists an optimum value for each set of welding parameters that can maximise the mechanical properties and welding nugget size. The welding nugget size is a key factor controlling the mechanical properties of resistance spot welded DP600 joints; there is a relationship among welding nugget size, tensile shear load and failure energy. A larger welding nugget size leads to a greater probability of pullout failure. In addition, welding parameters also affect the microstructure, grain size, crack initiation and microhardness of resistance spot welded DP600 joints.

APPLICATION OF SURROGATE BASED PARTICLE SWARM OPTIMIZATION TO THE RELIABILITY-BASED ROBUST DESIGN OF COMPOSITE PRESSURE VESSELS

A surrogate based particle swarm optimization (SBPSO) algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability-based robust design (RBRD) of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method (performed by software ANSYS). An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.

Optimal Strength Design for Fiber-Metal Laminates and Fiber-reinforced Plastic Laminates:

Optimal strength design of fiber-reinforced plastic (FRP) laminates and fiber-metal laminates (FML) is studied in this article. An optimization approach that integrates the particle swarm optimization algorithm and a general finite element code ANSYS was developed. ANSYS is utilized to obtain the failure index as fitness function and the optimum fitness is obtained by altering the fiber orientations. The strength behavior of FRP and FML under in-plane load and out-of-plane load is compared based on the optimization results. Results show that for in-plane load, due to the substituting of metal alloy sheet for prepreg layer, the strength behavior in transverse direction is enhanced and FML has better resistance to biaxial load. For out-of-plane point load, FML offers strength performance superior to that of FRP and is more stable for all the boundary conditions investigated.Optimal strength design of fiber-reinforced plastic (FRP) laminates and fiber-metal laminates (FML) is studied in this article. An optimization approach that integrates the particle swarm optimization algorithm and a general finite element code ANSYS was developed. ANSYS is utilized to obtain the failure index as fitness function and the optimum fitness is obtained by altering the fiber orientations. The strength behavior of FRP and FML under in-plane load and out-of-plane load is compared based on the optimization results. Results show that for in-plane load, due to the substituting of metal alloy sheet for prepreg layer, the strength behavior in transverse direction is enhanced and FML has better resistance to biaxial load. For out-of-plane point load, FML offers strength performance superior to that of FRP and is more stable for all the boundary conditions investigated.

The dislocation-free zone at a mode I crack tip

Abstract The distribution of dislocations in the vicinity of a mode I crack tip is formulated based on the observation of a single crystal specimen of aluminium by transmission electron microscopy (TEM). Closed form expressions of the dislocation density function and the dislocation-free zone (DFZ) condition for a mode I crack are derived. The relationship between the size of the crack, dislocation-free zone and plastic zone is obtained as a function of the applied stress. The characteristic of this model is compared with that of the model proposed by Chang and Ohr for a mode III crack.

Time-Dependent Reliability Model of Deteriorating Structures Based on Stochastic Processes and Bayesian Inference Methods

AbstractPerformance and reliability of structures will deteriorate with time as a result of the effects of various loads and the environment. This paper aims to develop a time-dependent reliability model of deteriorating structures that considers both aging effects and random shocks. First, a deteriorating model is proposed in which the aging effect is modeled as a gamma process while random shock is described by a Poisson process. The time-dependent reliability of the structural components is then evaluated based on the model. To incorporate the effects of model uncertainties, Bayesian inference methods are further integrated with the reliability model to update the uncertain parameters in the model using sampling data. Numerical examples demonstrate that the proposed model provides a reasonable method for evaluating the reliability of deteriorating structures containing model uncertainties.

Propagation of small surface cracks in stainless steel at high temperature

Abstract Growth behavior of small surface cracks in stainless steel is experimentally examined at 500°C and room temperature. The crack is confirmed to maintain a semi-elliptic shape during its growth. Cyclic deformation behavior of the material is investigated to evaluate elasto-plastic fracture mechanics parameters. It is found that crack growth rates have a good correlation with the strain intensity factor range ΔK e and J -integral range ΔJ .

Dome shape optimization of filament-wound composite pressure vessels based on hyperelliptic functions considering both geodesic and non-geodesic winding patterns

Filament-wound composite pressure vessels, owing to the advantages of their high specific strength, specific modulus and fatigue resistance, as well as excellent design performance, have been widely used in energy engineering, chemical industry and other fields. A filament-wound composite pressure vessel generally consists of two parts, a cylindrical drum part and the dome parts. In the cylindrical drum part, the filament winding angle and the winding layer thickness can be easily determined due to the regular shape. In the dome parts, however, both the winding angle and the thickness vary along the meridian line. Performance of the dome parts, which strongly depends on the effect of end-opening and the winding mode, dominates the performance of a pressure vessel. In this paper, optimum design of the dome parts is studied by considering both geodesic winding and non-geodesic winding patterns. A hyperelliptic function is adopted as the basis function for describing the meridian of the dome shape. The dome contour is optimized by taking the shape factor (S.F.) as the objective and parameters in the basis function as the design variables. A specific composite pressure vessel is taken as the numerical analysis example with varying dome shape which is to be optimized. The optimum design solution is obtained through the particle swarm optimization algorithm. It shows that an optimized dome with non-geodesic winding has better S.F. as compared with geodesic winding. Influences of the slippage coefficient and the polar opening on the S.F. are also discussed.

Sequential algorithms for structural design optimization under tolerance conditions

A deterministic optimization usually ignores the effects of uncertainties in design variables or design parameters on the constraints. In practical applications, it is required that the optimum solution can endure some tolerance so that the constraints are still satisfied when the solution undergoes variations within the tolerance range. An optimization problem under tolerance conditions is formulated in this article. It is a kind of robust design and a special case of a generalized semi-infinite programming (GSIP) problem. To overcome the deficiency of directly solving the double loop optimization, two sequential algorithms are then proposed for obtaining the solution, i.e. the double loop optimization is solved by a sequence of cycles. In each cycle a deterministic optimization and a worst case analysis are performed in succession. In sequential algorithm 1 (SA1), a shifting factor is introduced to adjust the feasible region in the next cycle, while in sequential algorithm 2 (SA2), the shifting factor is replaced by a shifting vector. Several examples are presented to demonstrate the efficiency of the proposed methods. An optimal design result based on the presented method can endure certain variation of design variables without violating the constraints. For GSIP, it is shown that SA1 can obtain a solution with equivalent accuracy and efficiency to a local reduction method (LRM). Nevertheless, the LRM is not applicable to the tolerance design problem studied in this article.