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Evaporator innovation - Miniature helical coil evaporators


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Evaporator innovation - Miniature helical coil evaporators

Ahmed Elsayed won the Ted Perry Award for student research for work carried out at the School of Mechanical Engineering of the University of Birmingham as part of his PhD “Heat transfer inside helically coiled small diameter tubes for miniature cooling systems”. He successfully developed a sophisticated experimental facility for measuring the heat transfer coefficient, a mathematical thermal model (using Matlab) that predicts the performance of the miniature cooling system and used artificial neural networks to develop correlations for R134a boiling inside helical coils. He also modelled the heat transfer performance of water/AL2O3 nanofluid flow in straight and helical coiled tubes.


This theoretical research will provide an important practical tool for designers of refrigeration evaporators and heat exchangers. The modelling should provide a useful basis for further developing focused cooling applications, particularly in electronics and medical fields. There is significant growth in cooling requirements in, for example, data centres and this work could allow more effective localised cooling applications to be considered.

Flow boiling through helical coils is an effective heat transfer enhancement technique where the centripetal force distributes the liquid film on the wall resulting in thinner liquid film and higher critical heat flux. Although there are several empirical correlations in the literature, most of these correlations are applicable for specific operating conditions. Recently, Artificial Neural Networks (ANNs) technique has been used for performance prediction in various thermal engineering topics. This paper presents the application of feed forward neural network with Levenberg-Marquardt training algorithm and Genetic Algorithm (GA) for optimizing the weight initialization process to predict the heat transfer coefficients of flow boiling inside helically coiled tubes. The technique has been used to predict both conventional scale and small scale helical coils boiling heat transfer coefficients. For conventional scale coils, the normalized Prandtl, Dean, Convective and Boiling numbers were utilized as network input while the two-phase to liquid heat transfer coefficients ratio was used as output. For small scale coils, normalized confinement number was used as an additional parameter in the network inputs. Results showed that the developed network predicted wide range of experimental results within ±15% which is better than available empirical correlations.