Mon, 14 August, 2023
A new research paper on bubble analysis has been published by the peer-reviewed, 'Case Studies in Thermal Engineering' journal.
The paper, ‘Deep Learning-Based Approach to R-134a Bubble Detection and Analysis for Geothermal Applications,’ investigates the impact of bubble behaviour on heat exchanger plates using deep learning techniques.
This research sought to better understand the reduced conversion efficiency of geothermal power as a result of lower reinjection temperature limits to avoid silica scaling and the relatively low temperatures of geothermal heat sources when compared to fuel combustion.
The study lays the foundation for future research into the challenges of bubble dynamics and heat transfer performance in both coated and uncoated heat exchanger plates.
This will, in turn, help advance geothermal energy’s potential for power generation, geo-exchange and direct thermal applications, all while offering a renewable resource with a lower carbon impact than fossil fuels to help mitigate global warming.
Surface modification techniques were tested to improve nucleate boiling in evaporators to enhance heat transfer performance in geothermal operations. Surface roughness enhances heat transfer by increasing the surface area and thereby the number of nucleation sites, creating a greater number of bubbles.
A high-speed camera caught images of the bubbles in order to assess parameters such as bubble departure diameter, departure frequency, and active nucleation site density (NSD). The number of nucleation sites per unit area of a heater’s surface, measured as NSD, is one of the critical parameters used in modelling and predicting nucleate boiling heat transfer.
The nucleation site density and other bubble parameters evaluated through this research work will be used in the simulation of bubble dynamics which is being developed by a GEOHEX project partner. The deep learning algorithm developed through this work can also analyse other bubbles, such as water and thermal oil bubbles.
You can see the paper, in full, here.
The GEOHEX project has received funding from the European Union's Horizon 2020 research and innovation programme. Grant agreement 851917.