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Dive into the research topics where Jagdev Singh is active.

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Featured researches published by Jagdev Singh.


International Journal of Air-conditioning and Refrigeration | 2017

Experimental Analysis of R134a/LPG as Replacement of R134a in a Vapor-Compression Refrigeration System

Jatinder Gill; Jagdev Singh

This paper presents an experimental analysis of a vapor compression refrigeration system (VCRS) using the mixture of R134a and LPG with mass fractions of 28:72 as an alternative to R134a. In this work, we compare the energy performance of both refrigerants, R134a/LPG (28:72) and R134a, in a monitored vapor compression refrigeration system under a wide range of experimental conditions. So, the System with R134a/LPG (28:72) was tested by varying the capillary tube length and refrigerant charge under experimental conditions. Performance comparisons of both the systems are made taking refrigerant R134a as baseline, and the results show that the compressor power consumption, compressor discharge temperature and pull down time obtained with R134a/LPG (28:72) of 118g and capillary tube length of 5.1 m in vapor compression refrigeration system are about 4.4% 2.4% and 5.3%, respectively, lower than that obtained with R134a in the studied range. Also, when using R134a/LPG (28:72), the system shows values of refrige...


Journal of Thermal Analysis and Calorimetry | 2018

Exergy analysis of vapor compression refrigeration system using R450A as a replacement of R134a

Jatinder Gill; Jagdev Singh; Olayinka S. Ohunakin; Damola S. Adelekan

This paper experimentally investigated exergetic performance analysis of vapor compression refrigeration system using R450a as a replacement for R134a at different evaporator and condenser temperatures within controlled environmental conditions. The exergetic performance analysis of the vapor compression refrigeration system with test parameters including efficiency defects in the components, total irreversibility, and exergy efficiency of the refrigeration system was performed. Findings showed that the total irreversibility and exergy efficiency of the vapor compression refrigeration system using R450A refrigerant were lower and higher than R134a by about 15.25–27.32% and 10.07–130.93%, respectively. However, the efficiency defect in the condenser, compressor, and evaporator of the R450A refrigeration system was lower than R134a by about 16.99–26.08%, 5.03–20.11%, and 1.85–15.85%, respectively. Conversely, efficiency defect in the capillary tube of the R450A refrigeration system was higher than R134a by about 14.66–78.97% under similar operating conditions. Overall, it was found that the most efficient component was the evaporator, and the least efficient component was the compressor for both refrigerants.


Journal of Thermal Analysis and Calorimetry | 2018

ANN approach for irreversibility analysis of vapor compression refrigeration system using R134a/LPG blend as replacement of R134a

Jatinder Gill; Jagdev Singh; Olayinka S. Ohunakin; Damola S. Adelekan

This paper experimentally evaluated the irreversibility in the components (compressor, condenser, capillary tube, and evaporator) of the vapor compression refrigeration system (VCRS) using R134a/LPG refrigerant as a replacement for R134a. For this aim, different tests were conducted for various evaporator and condenser temperatures under controlled surrounding conditions. The results reported that the irreversibilities in the components of VCRS using R134a/LPG blend were found lesser than irreversibilities in the components of VCRS using R134a under similar experimental conditions. Artificial neural network (ANN) models were developed to predict the second law of efficiency and total irreversibility of the refrigeration system. ANN and ANFIS model predictions were also compared with experimental results and an absolute fraction of variance in range of 0.980–0.994 and 0.951–0.977, root-mean-square error in the range of 0.1636–0.2387 and 0.2501–0.4542 and mean absolute percentage error in the range of 0.159–0.572 and 0.308–0.931%, respectively, were estimated. The outcomes suggested that ANN model shows better statistical prediction than ANFIS model.


International Journal of Refrigeration-revue Internationale Du Froid | 2017

Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system

Jatinder Gill; Jagdev Singh


International Journal of Refrigeration-revue Internationale Du Froid | 2017

Performance analysis of vapor compression refrigeration system using an adaptive neuro-fuzzy inference system

Jatinder Gill; Jagdev Singh


Experimental Thermal and Fluid Science | 2017

Energetic and exergetic performance analysis of the vapor compression refrigeration system using adaptive neuro-fuzzy inference system approach

Jatinder Gill; Jagdev Singh


International Journal of Refrigeration-revue Internationale Du Froid | 2017

Energy analysis of vapor compression refrigeration system using mixture of R134a and LPG as refrigerant

Jatinder Gill; Jagdev Singh


Heat and Mass Transfer | 2015

An experimental study of the flow of LPG as refrigerant inside an adiabatic helical coiled capillary tube in vapour compression refrigeration system

Sanjeev Singh Punia; Jagdev Singh


International Journal of Refrigeration-revue Internationale Du Froid | 2018

An applicability of ANFIS approach for depicting energetic performance of VCRS using mixture of R134a and LPG as refrigerant

Jatinder Gill; Jagdev Singh


International Journal of Refrigeration-revue Internationale Du Froid | 2018

Use of Artificial Neural Network approach for depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system

Jatinder Gill; Jagdev Singh

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Ravinder Kumar

Chandigarh Engineering College

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