A customized version of Fish School Search (FSS) algorithm and the innovative volitive operator of FSS (which is incorporated into the regular particle swarm optimization (PSO) algorithm) are applied to the finite element model (FEM) updating problem

Bayesian techniques have been widely used in finite element model (FEM) updating. The attraction of these techniques is their ability to quantify and characterize the uncertainties associated with dynamic systems. In order to update an FEM, the Bayesian formulation requires the evaluation of the posterior distribution function. For large systems, this function is difficult to solve analytically. In such cases, the use of sampling techniques often provides a good approximation of this posterior distribution function. The hybrid Monte Carlo (HMC) method is a classic sampling method used to approximate high-dimensional complex problems. However, the acceptance rate of HMC is sensitive to the system size, as well as to the time step used to evaluate the molecular dynamics trajectory. The shadow HMC technique (SHMC), which is a modified version of the HMC method, was developed to improve sampling for large system sizes by drawing from a modified shadow Hamiltonian function. However, the SHMC algorithm performance is limited by the use of a non-separable modified Hamiltonian function. Moreover, two additional parameters are required for the sampling procedure, which could be computationally expensive. To overcome these weaknesses, the separable shadow HMC (S2HMC) method has been introduced. This method uses a transformation to a different parameter space to generate samples. In this paper, we analyse the application and performance of these algorithms, including the parameters used in each algorithm, their limitations and the effects on model updating. The accuracy and the efficiency of the algorithms are demonstrated by updating the finite element models of two real mechanical structures. It is observed that the S2HMC algorithm has a number of advantages over the other algorithms; for example, the S2HMC algorithm is able to efficiently sample at larger time steps while using fewer parameters than the other algorithms.

The primary supporting structure of an automobile and its other vital systems is the chassis. The chassis structure is required to bear high shock, stresses, and vibration, and therefore it should possess adequate strength. The objective of current research is to analyze a heavy motor vehicle chassis using numerical and experimental methods. The CAD design and FE analysis is conducted using the ANSYS software. The design of the chassis is then optimized using Taguchi design of Experiments (DOE); the optimization techniques used are the central composite design (CCD) scheme and optimal space filling (OSF) design. Thereafter, sensitivity plots and response surface plots are generated. These plots allow us to determine the critical range of optimized chassis geometry values. The optimization results obtained from the CCD design scheme show that cross member 1 has a higher effect on the equivalent stresses as compared to cross members 2 and 3. The chassis mass reduction obtained from the CCD scheme is approximately 5.3%. The optimization results obtained from the OSF scheme shows that cross member 2 has a higher effect on equivalent stress as compared to cross members 1 and 3. The chassis mass reduction obtained from optimal space filling design scheme is approximately 4.35%.

A chassis is one of the vital parts of a heavy motor vehicle, which provides rigidity to the vehicle and improves structural stability and rigidity for accurate handling. The design and material of a chassis structure significantly affects its strength and weight. Optimization techniques can be used in systematic design improvement of chassis to meet industry requirements. The current research is intended to optimize the design of chassis using the Box–Behnken design scheme and the material tested is P100/6061 Al and Al GA 7-230 MMC. Different design points were generated using the design of the experiments. Equivalent stress, deformation and mass were evaluated for each design point. The variable selected for optimization using the Box–Behnken scheme was cross member width. The CAD modelling and FE simulation of the heavy motor vehicle chassis were conducted using ANSYS software. From the optimization conducted on the chassis design, response surface plots of equivalent stress, deformation and mass were generated, which enabled to determine the range of dimensions for which these parameters are maximum or minimum. The sensitivity plots of different variables were generated, which has shown that cross member 2’s width has a maximum effect on equivalent stress and cross member 3’s width has a minimum effect on equivalent stress, whereas for total deformation, cross member 3 shows the maximum sensitivity percentage, which signifies that cross member 3 has the maximum effect on total deformation, and vice versa. The use of the aluminium metal matrix composites P100/6061 Al and Al GA 7-230 MMC aided to reduce the weight of the chassis by 68% and 70%, respectively, without much reduction in the strength of the chassis.