Location: Monash University Malaysia > School of Engineering
Applications accepted all year round; Funded PhD Project (Students Worldwide)
Keywords: Subang Jaya; Malaysia; Lattice-Boltzmann Methods; Computational Mathematics; Machine Learning; Mechanical Engineering.
In the past three decades, the lattice-Boltzmann method (LBM) has emerged as a promising simulation candidate to model fluid flows as well as other various transport phenomena, due to its strong roots in fundamental physical theory and relative ease of implementation. In its development, various refining models have been proposed and incorporated into the LBM to increase its range of capabilities. However, in so doing, this has introduced a significant number of arbitrary user-defined parameters which are generally difficult to optimise and tune for effective simulations. Some examples of these include the relaxation parameters for higher-order moments in the multiple relaxation time collision operator (Chávez-Modena et al., 2018), microscopic population solutions for open outflow boundary conditions (Junk and Yang, 2009), and tuning parameters for convergence acceleration schemes e.g., the preconditioned LBM (Izquierdo and Fueyo, 2009).
Hence, this proposed study aims to utilize recent advances in machine learning algorithms to tackle the highly multidimensional nature of the current problem. This is in parallel with recent efforts (Bedrunka et al., 2021 and Corbetta et al., 2023) to better equip the LBM with more robust and streamlined kernel operators for improved operation. It is anticipated that a successful application of the above will (i) provide a guiding framework for future users of the algorithm to deploy the LBM more effectively and efficiently, (ii) improve the simplicity and clarity of the algorithm for its potential use in the industry, and (iii) obtain a deeper insight into the fundamental workings of the LBM through the simplification of selected higher-dimensional mathematical operations in the algorithm.
A first-class bachelor’s degree in a relevant area of engineering, e.g. Mechanical, E&E/Computer or Chemical Engineering), or in mathematics
Solid understanding of computer coding (MATLAB), and computational mathematics. Experience in projects related to computational fluid dynamics and/or machine learning will be beneficial
Having prior research experience and a good publication record will also be advantageous
Independent, proactiveness in learning, and with good written communication skills
The eligible candidate may be funded with a tuition fee waiver and living stipend under the Graduate Research Excellence Scholarship programme
Contact Person: Dr Chiew Yeong Shiong ([Email Address Removed])
Please send in a complete CV, academic transcripts, and other supporting documents