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Research Papers

Impact of Dynamics on the Accuracies of Different Experimental Data-Processing Methods for Steady-State Heat Transfer Rate Measurement

[+] Author and Article Information
Howard Cheung

Department of Building Services Engineering,
The Hong Kong Polytechnic University,
Room ZS 824, Hung Hom,
Kowloon, Hong Kong
e-mail: hcheun@polyu.edu.hk

Shengwei Wang

Professor
Chair Professor of Building Services Engineering
Department of Building Services Engineering,
The Hong Kong Polytechnic University,
Room ZS 859, Hung Hom,
Kowloon, Hong Kong
e-mail: shengwei.wang@polyu.edu.hk

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received June 26, 2016; final manuscript received June 4, 2017; published online September 13, 2017. Assoc. Editor: John C. Chai.

J. Thermal Sci. Eng. Appl 10(2), 021008 (Sep 13, 2017) (10 pages) Paper No: TSEA-16-1180; doi: 10.1115/1.4037543 History: Received June 26, 2016; Revised June 04, 2017

It is becoming often to measure steady-state heat transfer rate from thermal systems with variable speed and volume equipment and hence with fluctuating properties and mass flow rates. However, it is unclear if the conventional heat transfer rate measurement based on averages of temperature and pressure measurement is representative enough to represent the effect of system dynamics and measure their heat transfer rates accurately. This paper studied the issue by comparing its accuracy and uncertainty to that of alternative data-processing methods with theoretically less systematic bias. The comparison was conducted with steady-state data from a variable-speed ductless heat pump (DHP) system with occasional fluctuation of refrigerant flow and properties. The results show that the accuracy improvement brought by one alternative method is statistically significant albeit small in magnitude, and the other method may reduce uncertainty of the heat transfer rate measurement in tests with large periodic changes of measured variables. Nonetheless, both alternative methods are about 100 times more computationally expensive than the conventional averaging method, and averages of temperature and pressure measurement are still appropriate when computational resources are limited.

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References

Figures

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Fig. 1

Schematic of experimental setup around the DHP condenser

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Fig. 2

Example plot of changes of refrigerant mass flow rates during the experiments when the compressor is changing its speed constantly

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Fig. 3

Relative uncertainties of case 1 (data-processing method 1)

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Fig. 4

Relative uncertainties of case 2 (data-processing method 2)

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Fig. 5

Relative uncertainties of case 3 (data-processing method 3 with Δtf at 60 s)

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Fig. 6

Change of expanded uncertainty of heat transfer rate and random expanded uncertainty with integration time interval in experiment 1 in Table 7

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Fig. 7

Change of expanded uncertainty of heat transfer rate and random expanded uncertainty with integration time interval in experiment 2 in Table 7

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Fig. 8

Change of expanded uncertainty of heat transfer rate and random expanded uncertainty with integration time interval in experiment 3 in Table 7

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Fig. 9

Change of expanded uncertainty of heat transfer rate and random expanded uncertainty with integration time interval in experiment 4 in Table 7

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Fig. 10

Change of difference between heat transfer rates of cases 1 and 2 with relative random expanded uncertainty in case study 1

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Fig. 11

Refrigerant flow in the ductless heat pump system in the psychrometric chamber for its experiments of its heating operation

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