No abstract

In this article, analysis of average Nusselt number (Nu avg), which indicates the heat removal from the battery pack cooled by flowing fluid is carried out considering coupled heat transfer condition at the pack and coolant interface. Five categories of coolant, mainly gases, common oils, thermal oils, nanofluids, and liquid metals, are selected. In each coolant category, five fluids (having different Prandtl number Pr) are selected and passed over the Li-ion battery pack. The analysis is made for different conductivity ratio (Cr), heat generation term (Q gen), Reynolds number (Re), and Pr. Pr varying in the range 0.0208-511.5 (25 coolants) and Cr for each category of coolant having its own upper and lower limit are used to analyze the heat removed from the battery pack. Using single feed-forward network and integrating two feed-forward networks having multi-layers with backpropagation is employed for artificial neural network (ANN) modelling. In this modelling, the concept of the main network and space network is devised for multiple back propagation (MBP). The numerical analysis revealed that the temperature distribution in battery and fluid is greatly affected by increasing Cr. The maximum temperature located close to the upper edge of battery is found to get reduced significantly with the increase of Cr, but upto a certain limit above which reduction is marginal. The analysis carried out reveals that Cr and Q gen have no role in improving Nu avg while Pr and Re vary it significantly in each step. Moreover, Nu avg is found to increase with Re continuously irrespective of any Cr and Q gen. While, for oils with an increase in Pr and Re, Nu avg was found to reduce significantly. Nanofluids are found to be more effective in improving heat transfer from the battery pack when cooled by flowing nano-coolants over it. The MBP networks proposed are successfully trained, and hence they can be used for prediction of Nu avg .

Model updating is concerned about the correction of finite element models by processing the record of dynamic response from test structures in order to have an accurate model for any simulated analysis. Finite element model updating had emerged years ago as an important subject in structural dynamics. It has been used frequently and has been successfully applied to many fields especially in detecting the dynamic stiffness of a structure. The purpose of this study is to perform model updating of a go-kart chassis structure in order to reduce the percentage of error between the experimental modal analysis (EMA) and finite element analysis (FEA). Modal properties (natural frequency, mode shapes, and damping ratio) of the go-kart chassis structure were determined using both EMA and FEA. Correlation of the modal parameters gathered in FEA and EMA was carried out before optimizing the data from finite element. By adjusting the selective parameters, incongruities between those two analyses are generally reduced. The sensitivity of selected parameters is also obtained. The significant reduction in percentage of error before and after model updating procedure was carried out in this study clearly shows that model updating technique is a reliable method in reducing the discrepancies between EMA and FEA. Therefore, in cases of high discrepancies between analytical and actual test data, model updating can be considered as an option in order to obtain better correlation between those two sets of data.

This is a repository copy of Transversal crack and delamination of laminates using XFEM.