Data-driven modelling of thermally coupled fluids and hydraulic efficiency optimisation in pipe flows

THE CONTEXT

This project is in collaboration with UKAEA Culham Science Centre, with the aim of maximizing heat energy output from the cooling pipe systems employed by the fusion reactor Tokamak facility.

The Tokamak is an experimental machine conceived in the form of a toroid and designed to harness the energy of fusion, that is, the energy resulting from the collision of light elements deuterium and tritium. The fusion reaction is facilitated by ensuring that these hydrogen isotopes are always confined in the form of an extreme temperature plasma, contained within an inner vacuum vessel, via the application of high-strength tailor-made magnetic fields generated through cutting-edge superconducting coils.

Within the Tokamak, a structural component, named the blanket, shields the vacuum vessel from the high-energy neutrons produced during the fusion reaction and thus protects the rest of the components of the tokamak from thermal degradation. In addition, the blanket is responsible for a) the transfer of thermal power output across its module components into an actively cooled pipe system and b) the breeding of tritium for the self-sustainability of the fusion reactor.

THE MOTIVATION

The general motivation is contributing towards carbon neutral energy. Specifically,

1. The design and thermal management of plasma facing structural components in a fusion reactor, such as blanket modules, is critical in order to prevent costly and irreparable damage in magnetically confined fusion.

2. Net power output of a fusion power plant can be increased through the minimisation of thermo-hydraulic losses (i.e. viscous, turbulence, conduction) in the cooling systems, a key aspect for the economic success of this type of energy.

3. Novel skin friction reducing technologies, such as shark skin, riblets or anisotropic porous wall media, are known for their effectiveness to reduce pressure drops (i.e. energy losses), thus maximising hydraulic efficiency, but their impact on heat transfer remains unquantified, particularly in the case of a fusion reactor environment.

THE AIM

Given the above three factors, the aim of this PhD research project is the design and implementation of a data-driven computational model for the analysis, assessment and optimisation of the thermo-hydraulic efficiency of skin friction reducing technologies in cooling pipe systems in the context of confined fusion.

THE OBJECTIVES

The above aim will be crystallised through the following objectives:

1. Design and implementation of a high-fidelity computational model for the analysis of the thermo-hydraulic efficiency of skin friction reducing technologies. Emphasis will be placed in the appraisal of the necessary components required to obtain credible results, include an optimal turbulence model, explore the incompressible to compressible flow regime, conjugate heat transfer flux conditions.

2. Validation and benchmarking of the computational model versus available experimental, semi-analytical and numerical results.

3. Development of a fast and computationally efficient surrogate reduced order data-driven computational model, through the exploitation of a new deep learning paradigm. The goal will be to maximise computation speed whilst preserving much of the accuracy of the high-fidelity computational model.

4. Existing (and possibly novel) topologies for reducing skin friction and increasing heat transfer will be quantified and compared using the new surrogate model.

5. Gain an insight into the underlying thermo-hydraulic mechanisms when using new skin friction technologies, enabling the possibility for new innovations in heat transfer surfaces.

6. As a final objective, if time allows, the use of the deep learning paradigm will be further exploited to drive adaptive simulations in order to find the best surface type geometry for a coolant pipe within a divertor or blanket cooling channel.