PhD Development of a Multi-Scale Optimisation Framework for Nanoparticle-Enhanced Closed-Loop Geothermal Systems

Details

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

Geothermal energy systems can be broadly divided into two semi-independent systems: extraction of thermal energy from subsurface environment system and transformation of enthalpy into power. The former involves injection of cold brine into variable depths with partial or complete vaporisation of the brine and further extraction in production wells.

Numerical reservoir simulations are constrained by computational cost, which limits the feasible grid resolution.

Detailed geological models of formations often contain hundreds of millions of cells (~108), making direct simulation intractable. In these models, each cell is assigned fluid (e.g., density, viscosity) and rock properties (e.g., porosity, permeability) that are used to solve the governing flow equations. A common strategy to overcome this limitation is upscaling, where cell properties are statistically averaged to coarsen the grid into larger, more computationally manageable volumes. While this technique enables the simulation of large fields, it often oversimplifies critical smallscale heterogeneities, leading to inaccurate flow predictions.

This PhD project aims to develop a comprehensive computational framework to maximise the thermal performance of closed-loop geothermal heat exchangers. The core innovation lies in integrating nanofluid heat transfer enhancement with the strategic optimisation of wellbore placement and system operation. The research will be pursued through two interconnected thrusts:

• System-Level Optimisation via Machine Learning and Reduced-Order Modelling: A robust optimisation model will be developed to determine the optimal wellbore placement and operational parameters. This model will leverage high-fidelity simulations to train a POD-based ROM, enabling rapid performance evaluation. This ROM will be integrated with adjoint methods for efficient gradient calculation and reinforcement learning (RL) algorithms to discover optimal control policies within complex geological environments.

• Component-Level Enhancement through Nanofluid Modelling: A high-fidelity computational model will be developed to predict heat transfer enhancement within the wellbore. This model will incorporate a multi-phase formulation to simulate the dynamics of nanoparticle suspension, aggregation, and sedimentation, and their impact on convective heat transfer, thermal conductivity, and pressure drop.

The synergy between these thrusts will be embedded in a model framework: the nanofluid model will provide accurate performance data to the optimiser, while the optimiser will define the ideal system configuration to leverage the nanofluid’s enhanced properties.

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Physics, Mathematics, Computer Sciences, Mechanical/Chemical Engineering.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr J Gomes at jefferson.gomes@abdn.ac.uk. If you require any additional assistance in submitting your application or have any queries about the application process, please don’t hesitate to contact us at researchadmissions@abdn.ac.uk