A prognostic analysis on experimental evaluation of thermosyphon using refrigerant R134a and water based on machine lear
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ORIGINAL
A prognostic analysis on experimental evaluation of thermosyphon using refrigerant R134a and water based on machine learning and optimization techniques Anand R S 1 & Shibin David 1 Received: 3 May 2020 / Accepted: 15 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Thermosyphon is an effective heat transfer device which is widely used all over the world for its ease of use, feasible with different environmental challenges. In this research article, the experimentation with different boiling point of working fluids water and R134a has been used in modified thermosyphon. The modified thermosyphon comprises of the cone frustum attached between the condenser and adiabatic section to hold up for high heat inputs. It is noted from the experiment that the working fluids have unique heat transfer capability with regard to thermal properties for applied heat input. The thermal resistance for different fill ratios with water and R134a in thermosyphon was experimented and the optimality in the fill ratio is identified. Due to the modification in the condenser, R134a condenser performs better for both low and high heat inputs but water works well only in high heat inputs. To predict and compare the temperature outputs at the evaporator, adiabatic and condenser sections of the thermosyphon, machine learning algorithm and optimization technique has been deployed and the output is measures for its accuracy, false positive, predictive positive value and effective performance. It is noted both from the experimental and algorithmic approach that the experiment produces less false positive rate which is ≤ 2% and true positive rate which is ≥ 98%, accuracy of the outputs which are ≥ 98%. The optimized outcome also stabilizes the experimental setup strongly and generates an effective performance rate which is ≥ 95%. Keywords Thermosyphon . Optimization . Cone frustum . Machine learning . High heat inputs
AbbreviationsNomenclature Cp Specific heat capacity (J/kgK) I Ammeter (A) m Mass flow rate (kg/s) Q Heat (W) R Resistance (oC/W) T Temperature (oC) V Voltmeter kg.m2/s3.A Subscripts con Condenser eve Evaporator in Input out Output ther Thermal symbols µ Dynamic viscosity (kg/ms) * Shibin David [email protected] 1
Karunya Institute of Technology and Sciences, Coimbatore, India
ƞ Efficiency Δ Change Abbreviations HTC Heat Transfer Coefficient IF Internal fin IFIC Internal fin with internal cut NR-IFIC Nanorefrigerant in internal fin with internal cut TPCT Two Phase Closed Thermosyphon
1 Introduction Two phase closed thermosyphon (TPCT) is designed for the transfer of liquids, and unstable gas exists through the air temperature gradients in heating and air conditioning applications such as heat pumps, furnaces and the cycle [1, 2]. TPCT usually has a functioning fluid that remains at the portion of the evaporator. The operational fluid gains energy as the heat is introduced in the evaporator, which converts the state to gas which enters the condenser. Because of the condensation effec
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