Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Sheffield
- Cranfield University
- University of Birmingham
- ; University of Surrey
- Brunel University London
- King's College London Department of Engineering
- Manchester Metropolitan University;
- The University of Edinburgh
- The University of Manchester
- University of Birmingham;
- University of Exeter
- University of Newcastle
- University of Surrey
- 3 more »
- « less
-
Field
-
compatibility with traditional composite matrices. Explore complementary computational fluid dynamics-discrete element method (CFD-DEM) simulations as a tool to predict fibre-fluid interactions and inform
-
and modelling techniques. Real-World Impact: Contribute to transformative technologies in clean energy and carbon capture. Future job opportunities: Digital modelling and computational fluid dynamics
-
at the micrometre scale that can propel themselves through fluids, mimicking natural swimming organisms such as bacterial forms. Using biological building blocks found in cells and encapsulating them inside vesicles
-
hydrodynamics for novel marine vehicles, including large ships and small AUVs and offshore renewable energy systems including offshore wind. You are expected to perform advanced computational fluid dynamics
-
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
-
accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
-
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
-
with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience