Multi-objective heat transfer optimization of 2D helical micro-fins using NSGA-II

dc.citationMann, G. W., & Eckels, S. (2019). Multi-objective heat transfer optimization of 2D helical micro-fins using NSGA-II. International Journal of Heat and Mass Transfer, 132, 1250–1261. https://doi.org/10.1016/j.ijheatmasstransfer.2018.12.078
dc.citation.doi10.1016/j.ijheatmasstransfer.2018.12.078
dc.citation.issn0017-9310
dc.citation.jtitleInternational Journal of Heat and Mass Transfer
dc.citation.volume132
dc.contributor.authorMann, Garrett W.
dc.contributor.authorEckels, Steven
dc.contributor.authoreideckels
dc.date.accessioned2019-12-23T22:47:25Z
dc.date.available2019-12-23T22:47:25Z
dc.date.issued2019-04-01
dc.date.published2019
dc.descriptionCitation: Mann, G. W., & Eckels, S. (2019). Multi-objective heat transfer optimization of 2D helical micro-fins using NSGA-II. International Journal of Heat and Mass Transfer, 132, 1250–1261. https://doi.org/10.1016/j.ijheatmasstransfer.2018.12.078
dc.description.abstractA numerical simulation of helical micro-fins is implemented in ANSYS Fluent 15. The model is scripted to automatically set up and execute given three input parameters: fin height, helix angle, and number of starts. The simulation results reasonably predict experimental pressure drop and heat transfer for multiple micro-fin geometries. A multi-objective parameter optimization is implemented based on the NSGA-II algorithm to estimate the optimal trade-off (Pareto front) between Nusselt number and friction factor of a micro-fin tube for 0.0006 < e/D < 0.045, 10 < Ns < 70, at Reynolds number of 49,013. The resulting Pareto front is analyzed and compared with several experimental data points. From the optimal results, a distinct difference in flow characteristics was identified between geometries above and below a helix angle of approximately 45°. How the Pareto front can be used to choose micro-fin geometries for different performance evaluation criterion scenarios is demonstrated. Optimal results from various existing correlations are also compared to the optimization results.
dc.description.embargoVersion of Record (VoR)
dc.identifier.urihttp://hdl.handle.net/2097/40333
dc.relation.urihttps://doi.org/10.1016/j.ijheatmasstransfer.2018.12.078
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleMulti-objective heat transfer optimization of 2D helical micro-fins using NSGA-II
dc.typeText

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