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conducted using high-fidelity CFD model such as Large-Eddy Simulation (LES). The work will include simulation of shallow boundary layer flows such as atmospheric flows with differing thermal stability and of
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian You will be part of a dynamic research team working on topics relevant
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the pulsed electric field so that all microalgae in the solution receive the electric field for equal durations. The aim is to continue the numerical study (CFD) already undertaken in this field to find
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to carry out high-performance numerical simulations using our in-house CFD code, extract physical insights from simplified flow models, and characterise synchronisation thresholds and the robustness
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parameters, optimising these and various process conditions to enhance the overall performance of the gas turbine employing specific indicators. Used with a complementary CFD modelling approach, the research
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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applications. The project integrates: Computational Fluid Dynamics (CFD) and multiphase flow modeling Radiative heat transfer Machine learning and reduced-order modeling Data-driven optimization for industrial
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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support for process development from laboratory to pilot to demonstration scale Working with a wide range of simulation tools such as CFD, numerial optimisation and artificial intelligence Topic-independent
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collaborators. Under this job title, you will develop a CFD modelling platform to evaluate the combustion, flammable and toxic gases emission and dispersion characteristics resulted from battery fires/thermal