Research Fellow, Mechanical Engineering
Research Fellow in Computational Flow Modelling of Hydrodynamic Loads on Offshore and Marine Superstructures
We have an immediate position for a suitable candidate with research and development experience in applying Smooth Particle Hydrodynamics (SPH) to model air-sea and wave interactions with floating and moored offshore and marine structures. Candidates at the level of PhD with relevant postdoctoral experience and demonstrated capability in single and two-phase SPH are the minimum qualifications required to be considered for this opportunity to work in an on-going project for about 2.5 years involving computational modelling to obtain flow field and pressure distributions over offshore superstructure models to validate numerical simulation results. Candidate should also have an interest in participating (and a willingness to learn) and contributing towards the development of physics informed machine learning surrogates for the same problem. The successful candidate must be highly motivated for research, able to work independently as well as part of a multidisciplinary team, have excellent communication skills and willing to learn and acquire new skills during the project.
- Candidate must have a strong background in the creation and analysis of computational data from High Fidelity Multi-Physics Methods based on Computational Continuum Mechanics (CCM) i.e. CFD, CSD, 6-DOF Dynamics using open source solvers for multi-phase fluid dynamics of the air-sea interface to provide estimates of unsteady aerodynamic, wave and current loading on offshore superstructures, creation of reduced order models from these high fidelity data to train physics informed deep learning networks for fast and reliable hydrodynamic and wave load predictions on marine offshore superstructures.
- While the candidate should have experience working SPH (DualSPHysics), familiarity with open source CCM tools such as OpenFOAM, Fluidity, and FENICS, the Linux OS and parallel computing technologies is also expected. Candidate must be adept at using High Performance Computing Platforms such as multi-core and GPU systems at the NUS-HPC Platform and the National Supercomputing Centre (NSCC) facility for the high fidelity large scale computational simulations in this project.
- Familiarity with model order reduction techniques and the ability to use these data to construct reliable machine learning surrogates for prediction using Tensorflow / deep learning / Python tools will be needed for contributing towards the development of the next generation CCM Informed AI driven Engineering Prediction Tools.
- The successful candidate is expected to hit the ground running in the computational work.
Location: Kent Ridge Campus
Department : Mechanical Engineering