Structural and acoustical performances of oil palm trunk waste - Elastomeric thermoplastic polyurethane composite.

In this report, naturally available materials have been utilized in the development of acoustic absorbers. This work presents the study of the effect of oil palm trunks dust (OPTD) loading to the mechanical and acoustical properties of elastomeric thermoplastic polyurethane (TPU). Four composite sheets of 3-mm thickness were prepared by varying the OPTD loadings with 10-40% wt into the polyurethane. Density, modulus elasticity, sound absorption coefficient and sound transmission loss of the samples were measured according to corresponding standards. The OPTD is found to reduce the density of the elastomeric polyurethane and at the same time, it increases the Young's modulus up to 215 MPa. The composite material can be applied as sound absorber panel installed in front of a rigid wall with an air gap. Increasing the air gap, thus lowering the air stiffness, shifts the absorption peak to a lower frequency. With OPTD loadings, the formation of micro-pores in the inner structure helps to improve the peak of sound absorption of the panel at the resonant frequency which can reach above 0.9. As the OPTD loading has effect on density, the effect on the sound transmission loss at the mass-controlled region is also apparent.

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics.

Complex computer codes are frequently used in engineering to generate outputs based on inputs, which can make it difficult for designers to understand the relationship between inputs and outputs and to determine the best input values. One solution to this issue is to use design of experiments (DOE) in combination with surrogate models. However, there is a lack of guidance on how to select the appropriate model for a given data set. This study compares two surrogate modelling techniques, polynomial regression (PR) and kriging-based models, and analyses critical issues in design optimisation, such as DOE selection, design sensitivity, and model adequacy. The study concludes that PR is more efficient for model generation, while kriging-based models are better for assessing max-min search results due to their ability to predict a broader range of objective values. The number and location of design points can affect the performance of the model, and the error of kriging-based models is lower than that of PR. Furthermore, design sensitivity information is important for improving surrogate model efficiency, and PR is better suited to determining the design variable with the greatest impact on response. The findings of this study will be valuable to engineering simulation practitioners and researchers by providing insight into the selection of appropriate surrogate models. All in all, the study demonstrates surrogate modelling techniques can be used to solve complex engineering problems effectively.