Data-driven modelling of thermally coupled fluids and hydraulic efficiency optimisation in pipe flows
Lead Research Organisation:
Swansea University
Department Name: College of Engineering
Abstract
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.
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.
People |
ORCID iD |
| Daniela Segura Galeana (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/T517987/1 | 30/09/2020 | 29/09/2025 | |||
| 2511798 | Studentship | EP/T517987/1 | 01/01/2021 | 31/12/2023 | Daniela Segura Galeana |