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dc.contributor.authorLiang, Xing
dc.contributor.authorXie, Yongqi
dc.contributor.authorDay, Rodney
dc.contributor.authorMeng, Xianhai
dc.contributor.authorWu, Hongwei
dc.date.accessioned2020-12-09T00:06:58Z
dc.date.available2020-12-09T00:06:58Z
dc.date.issued2021-02-01
dc.identifier.citationLiang , X , Xie , Y , Day , R , Meng , X & Wu , H 2021 , ' A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity ' , International Journal of Heat and Mass Transfer , vol. 166 , 120743 . https://doi.org/10.1016/j.ijheatmasstransfer.2020.120743
dc.identifier.issn0017-9310
dc.identifier.otherPURE: 23035212
dc.identifier.otherPURE UUID: cf17100c-4cae-42e0-8ddd-3ad80a6b03f2
dc.identifier.otherScopus: 85097414850
dc.identifier.urihttp://hdl.handle.net/2299/23563
dc.description© 2020 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.ijheatmasstransfer.2020.120743
dc.description.abstractIn this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (R 2, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, and especially achieved excellent performance in predicting outputs that have a very large range of value differences.en
dc.format.extent16
dc.language.isoeng
dc.relation.ispartofInternational Journal of Heat and Mass Transfer
dc.subjectBoiling
dc.subjectdeep neural network
dc.subjectheat transfer
dc.subjecthelical coils
dc.subjecthigh gravity
dc.subjectCondensed Matter Physics
dc.subjectMechanical Engineering
dc.subjectFluid Flow and Transfer Processes
dc.titleA data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravityen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionEnergy and Sustainable Design
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionCentre for Climate Change Research
dc.description.statusPeer reviewed
dc.date.embargoedUntil2021-12-04
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097414850&partnerID=8YFLogxK
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.ijheatmasstransfer.2020.120743
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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