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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates
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spend secondment periods at SJTU, focusing on CFD model development and validation studies against model test results from SJTU. At NTNU, the candidate will be part of the Marine Structures research group
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institutes, and industrial partners, will provide interdisciplinary training spanning chemical and mechanical engineering, computational fluid dynamics (CFD), experimental combustion diagnostics, and techno
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universities, research institutes, and industrial partners, will provide interdisciplinary training spanning chemical and mechanical engineering, computational fluid dynamics (CFD), experimental combustion
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initiation in the following areas are helpful: Strong background on computational fluid dynamics (CFD) and design software using ANSYS and SolidWorks. Experience on high performance computing is desirable
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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• Skilled in prototyping and experimental investigations for marine vehicles • Skilled in using mainstream CFD and autonomous system softwares (e.g. Star-CCM+, OpenFoam) • Research experience in
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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on
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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method