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Research Papers

A Self-Adjusting Method for Real-Time Calculation of Thermal Loads in HVAC-R Applications

[+] Author and Article Information
M. A. Fayazbakhsh, F. Bagheri

Laboratory for Alternative Energy Conversion,
School of Mechatronic Systems Engineering,
Simon Fraser University,
250-13450 102 Avenue,
Surrey, BC V3T 0A3, Canada

M. Bahrami

Laboratory for Alternative Energy Conversion,
School of Mechatronic Systems Engineering,
Simon Fraser University,
250-13450 102 Avenue,
Surrey, BC V3T 0A3, Canada
e-mail: mbahrami@sfu.ca

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received March 20, 2015; final manuscript received June 8, 2015; published online July 28, 2015. Assoc. Editor: Zahid Ayub.

J. Thermal Sci. Eng. Appl 7(4), 041012 (Jul 28, 2015) (9 pages) Paper No: TSEA-15-1090; doi: 10.1115/1.4031018 History: Received March 20, 2015

A significant step in the design of heating, ventilating, air conditioning, and refrigeration (HVAC-R) systems is to calculate room thermal loads. The heating/cooling loads encountered by the room often vary dynamically while the common practice in HVAC-R engineering is to calculate the loads for peak conditions and then select the refrigeration system accordingly. In this study, a self-adjusting method is proposed for real-time calculation of thermal loads. The method is based on the heat balance method (HBM) and a data-driven approach is followed. Live temperature measurements and a gradient descent optimization technique are incorporated in the model to adjust the calculations for higher accuracy. Using experimental results, it is shown that the proposed method can estimate the thermal loads with higher accuracy compared to using sheer physical properties of the room in the heat balance calculations, as is often done in design processes. Using the adjusted real-time load estimations in new and existing applications, the system performance can be optimized to provide thermal comfort while consuming less overall energy.

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Figures

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Fig. 1

Schematic of the HBM [13]. Each wall is represented by a conductive resistance for steady-state conditions. Ti and To represent the temperatures on the inside and outside surfaces of the wall.

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Fig. 2

Flowchart of the self-adjusting algorithm for real-time calculation of thermal loads by automatic estimation of conduction coefficients

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Fig. 3

A picture of the testbed used for implementing the present model. Six thermocouple pairs are attached on the testbed walls. Each pair consists of two thermocouples attached on the opposite sides (inside and outside) of the wall.

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Fig. 4

Computer model of the testbed showing its overall dimensions and components: (a) full chamber model and (b) cut view showing the chamber's cross section

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Fig. 5

Average inside and outside temperatures (left axis) and temperature difference between inside and outside wall surfaces (Ti-To) (right axis) for various levels of controlled internal heat gain in the testbed

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Fig. 6

Progressive training of weight factors and calculated heat gain for an arbitrary heat gain of Q·I = 0.334 kW. The weight factors are adjusted based on real-time temperature measurements and the known heat gain.

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Fig. 7

Calculated steady-state heat gain for different heater power levels based on measured physical values as well as adjusted values of the weight factors

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