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Field
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of Computational Fluid Dynamics CFD environment and simulations including: - Computation of the microwave field, Coupling of the microwave field with the plasma - Computation of elementary ionization, recombination
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Project Overview Iterative RANS-based CFD design is approaching its practical limits. While high-fidelity simulation remains essential, the repeated geometry-CFD-evaluation loop that dominates
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cryogenic modelling, two-phase CFD, and AI-based reduced-order models to accelerate modelling capability in net‑zero aerospace technologies. Motivation Hydrogen research has accelerated to address the need
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CAD modelling CFD Demonstrated ability to work independently and to formulate and tackle research problems. Demonstrated academic research experience as evidenced by publications in high quality
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(FLICE) technique. .-Numerical simulation (CFD) of Newtonian and non-Newtonian fluid flows in the developed microdevices. .-Characterization of Newtonian and non-Newtonian fluid flows using flow
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Desirable skills - Experience with numerical simulation tools (e.g., COMSOL, CFD frameworks) - Knowledge of machine learning, especially PINNs or scientific ML - Experience with particle-based simulations
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for the turbomachinery design optimization process conducted by a parallel PhD student at LMFA. The numerical solver involved is ProLB. It is an innovative Computational Fluid Dynamics (CFD) software solution developed
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solar deployment and contribute to the future co location of solar and wind arrays. The studentship offers full training in experimental methods, data analysis (MATLAB), CFD, offshore renewable energy
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realistic engine-relevant conditions, with particular emphasis on the role of purge flows. Using CFD, the research will investigate how purge flow modifies secondary-flow behaviour and contributes to loss
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about shape optimization and the new opportunities offered by numerical design in turbomachinery. Prior experience in CFD and/or CFD code development (Python, C++, Fortran) would be a significant