Study level

  • PhD
  • Honours

Faculty/School

Faculty of Engineering

School of Mechanical, Medical and Process Engineering

Topic status

We're looking for students to study this topic.

Research centre

Supervisors

Professor Emilie Sauret
Position
Professor
Division / Faculty
Faculty of Engineering

External supervisors

  • Dr Sahan Kuruneru, CSIRO

Overview

The increase in global energy demand necessitates further advancement in photovoltaic (PV) systems. Advancements in PVs could potentially play a role to help meet the Paris Agreement of limiting global temperature increase to below 2 degrees Celsius. In conjunction with the rising demand for clean energy production, the global agricultural industry needs to keep pace with rising food demand which is expected to increase by 50% by 2050 to feed over a projected 10 billion people. The scarcity of fertile land and water scarcity in remote parts of Australia infers the need to deploy 'Agrivoltaic' systems which combines traditional farming activities with ground-mounted PV panels on the same land as a way of tackling the food-energy nexus.

The performance of ground-mounted PV panels commonly found in solar farms depends on a myriad of factors such as tilt angle, microclimate i.e. wind loads, shading, solar irradiance, and dust deposition. In particular, dust deposition of PV panels incurs high maintenance costs and operation downtime. Unfortunately, the combined effects of these entities on PV panel performance and crop yield is poorly understood and its study devoid in existing literature. This project aims to develop an advanced numerical model, namely coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM), backed by thorough experimental validation, to unravel the fundamental mechanisms of coupled air-dust particle transport and its effect on PV performance and farm crop yields. Parallel to CFD-DEM, PV field layout optimization software and/or Machine Learning/AI will be implemented to assess optimum PV solar farm layout under varying microclimate conditions such as varying solar irradiance, wind velocities, etc.

Research activities

  • Carry out CFD-DEM modelling and simulations
  • Deploy machine learning (ML) approaches to optimise PV farm layouts
  • Evaluate the influence of wind loads, adhesive dust deposition, solar irradiance, etc on PV performance
  • Develop fundamental understanding of the performance of PV arrays under a range of conditions in the agrivoltaic industry.

This project is a collaboration between QUT and CSIRO. It is part of a long term vision/endeavour to tackle the dual energy-food nexus thereby aligning with CSIRO's initiatives, primarily on Sustainable Energy & Resources (Towards Net Zero & Renewable Energy) under the "Ultra Low Cost Solar (ULCS)" program at CSIRO Energy. It also directly aligns with the Australian Government's national priority two areas: renewables and low emission technologies  and value-adding in agriculture.

Outcomes

  • validated CFD-DEM model for particle-laden flows
  • further optimise solar PV fields to maximize PV solar thermal performance, crop yields using ML techniques
  • minimise dust deposition and cleaning/maintenance costs thereby maximising the use of the limited fertile lands in Australia.

Skills and experience

Some experience in fluid mechanics/dynamics, and computational modelling and simulation is required. Strong knowledge in math, mechanical and process engineering are preferable.

Scholarships

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Keywords

Contact

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