This project develops a three-level mathematical framework to solve the “no-go” paradox of the Liquid-Metal Vortex-Fluidic Device (LM-VFD), where standard centrifugal forces are theoretically 1,000 times too weak to break the metal’s protective oxide skin. By building a hierarchy of models, we aim to identify a “triple-mechanism bridge” comprising capillary stress from microscopic spikes, wave-mode collisions, and Marangoni surface-tension gradients. Preliminary analysis shows that these factors interact to amplify local stress, suggesting a productivity optimum at high RPM. Our framework will shift the experimental focus to measuring the stress-concentration factor ($k_{sc}$) as the primary driver of the reactor\u2019s high-throughput performance.<\/p>\n\n\n\n
Aims<\/p>\n\n\n\n
The primary aim of this project is to resolve the fundamental “no-go” paradox of the Liquid-Metal Vortex-Fluidic Device (LM-VFD) by identifying the exact physical mechanisms that allow for high-throughput material production. While standard centrifugal theory suggests that the forces generated within the spinning thin film are three orders of magnitude too weak to rupture the metal’s protective oxide skin, real-world results prove otherwise. This research aims to bridge that gap by developing a three-level model hierarchy\u2014ranging from scaling laws to a population ensemble simulator\u2014to accurately map the reactor’s operating landscape.<\/p>\n\n\n\n
Objectives<\/p>\n\n\n\n
Implement a coupled wave-stress and oxide-Markov simulator (Level 2) to quantitatively confirm the inadequacy of mean-field azimuthal modes in breaking the oxide skin. Validate a three-mechanism hypothesis\u2014incorporating spicular tip-radius sharpening, wave-mode collision impacts, and Marangoni surface-tension gradients\u2014to explain how local stress is multiplied by a factor of $10^3$. Utilise a Lagrangian Monte Carlo ensemble (Level 3) to refine productivity predictions by accounting for the distinct behaviours of freshly-ruptured versus aged-ruptured cells. Build and verify a computational framework using Python\/Julia to ensure all solver outputs are agnostic and accurate to a precision of $10^{-4}$.<\/p>\n\n\n\n
Significance<\/p>\n\n\n\n
This research is significant because it fundamentally reorients the scientific understanding of liquid-metal reactors from a focus on bulk material properties to the study of stress-concentration factors ($k_{sc}$). By identifying that the productivity optimum occurs at high RPM due to specific “force multipliers,” the framework will provide a predictive tool that matches empirical data from high-throughput labs. Furthermore, the project\u2019s sensitivity analysis identifies $k_{sc}$ as the highest-leverage parameter for future optimisation, effectively setting a new experimental agenda focused on high-speed video correlation and Marangoni differentials rather than simple yield strength.<\/p>\n\n\n\n
Ideal Candidate<\/p>\n\n\n\n
We are seeking a highly motivated HDR candidate with a strong foundation in numerical analysis and computational physics. The ideal applicant should possess advanced skills in Julia or Python, with a demonstrated ability to build and verify robust code. Familiarity with fluid dynamics, dimensionless scaling, or stochastic modelling (e.g., Monte Carlo methods) is highly desired. Candidates must have excellent analytical problem-solving skills to navigate complex multiphysics simulations and collaborate effectively with leading scientists at UWA. Additionally, the applicants should meet the eligibility criteria for entry into a PhD program at Curtin University.<\/p>\n\n\n\n
This project is open to International and Domestic applicants.<\/p>\n\n\n\n
Scholarship<\/p>\n\n\n\n
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship<\/a>.<\/p>\n\n\n\n Enquires and How to Apply<\/p>\n\n\n\n For enquires about this opportunity contact Professor Victor Calo at victor.calo@curtin.edu.au<\/a><\/p>\n\n\n\n