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Field
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-state physics, fluid dynamics, solid-dynamics, and fracture/degradation; all in a highly transient and non-linear system. In this project we will extend multi-component, multi-phase field frameworks
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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
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areas. Cranfield is part of the national testbed for 6G, researching in the following areas of interest: Real-time specification of 6G telecommunication and edge computing services using Large Language
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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project offers a unique opportunity to develop autonomous microswimmers, which are bioinspired structures at the micrometre scale that can propel themselves through fluids, mimicking natural swimming
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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Computational Fluid Dynamics (CFD) and Conjugate Heat Transfer (CHT) modelling, which captures both the fluid & solid domains, as required to develop this understanding for engine-representative geometries and
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“lattice” version of space and time, similar to the finite difference approach in computational fluid dynamics. Using this Lattice QCD method, Centre Vortex fields will be analysed to understand particles
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This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
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Location: South Kensington About the role: The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple