Normalized performance parameters for a residential heat pump in the cooling mode with single faults imposed

Cho, Jin Min; Heo, Jaehyeok; Payne, William V.; Domanski, Piotr A.

doi:10.1016/j.applthermaleng.2014.03.010

Cited by 31 publications

(21 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This performance deviation is expressed non-dimensionally by Eq. 3 [29].

Y = \frac{X_{italicfault}}{X_{fault - free}}

where Y is the figure of merit and X denotes the operational parameter for the faulty and fault-free operation, as indicated by the subscripts.…”

Section: Performance Models For Vapor Compression Systemsmentioning

confidence: 99%

“…

Y = \frac{X_{italicfault}}{X_{fault - free}}

where Y is the figure of merit and X denotes the operational parameter for the faulty and fault-free operation, as indicated by the subscripts. These deviations depend on the type of fault, fault intensity, and operating conditions, and can be correlated using the following 2 nd order polynomial [29]:

Y = \frac{X_{italicfault}}{X_{fault - free}} = 1 + false(b_{0} + b_{1} T_{O D} + b_{2} T_{I D} + b_{3} F false) F

…”

Section: Performance Models For Vapor Compression Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Effect of common faults on the performance of different types of vapor compression systems

Domanski

Payne

2016

Applied Thermal Engineering

Self Cite

View full text Add to dashboard Cite

The effect of faults on the cooling capacity, coefficient of performance, and sensible heat ratio, was analyzed and compared for five split and rooftop systems, which use different types of expansion devices, compressors and refrigerants. The study applied multivariable polynomial and normalized performance models, which were developed for the studied systems for both fault-free and faulty conditions based on measurements obtained in a laboratory under controlled conditions. The analysis indicated differences in responses and trends between the studied systems, which underscores the challenge to devise a universal FDD algorithm for all vapor compression systems and the difficulty to develop a methodology for rating the performance of different FDD algorithms.

show abstract

“…This performance deviation is expressed non-dimensionally by Eq. 3 [29].

Y = \frac{X_{italicfault}}{X_{fault - free}}

where Y is the figure of merit and X denotes the operational parameter for the faulty and fault-free operation, as indicated by the subscripts.…”

Section: Performance Models For Vapor Compression Systemsmentioning

confidence: 99%

“…

Y = \frac{X_{italicfault}}{X_{fault - free}}

Y = \frac{X_{italicfault}}{X_{fault - free}} = 1 + false(b_{0} + b_{1} T_{O D} + b_{2} T_{I D} + b_{3} F false) F

…”

Section: Performance Models For Vapor Compression Systemsmentioning

confidence: 99%

Effect of common faults on the performance of different types of vapor compression systems

Domanski

Payne

2016

Applied Thermal Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…For determining the performance of a faulty heat pump, we applied dimensionless multipliers to the fault-free heat pump performance parameters (capacity, COP, etc.) [21,22]. The reader may refer to Domanski at al.…”

Section: Heat Pump Specifications and Modelingmentioning

confidence: 96%

Effect of heat pump commissioning faults on energy use in a slab-on-grade residential house

Domanski

Henderson

Payne

2015

Applied Thermal Engineering

Self Cite

View full text Add to dashboard Cite

“…AFDD tools are used in many heating, ventilation, and air-conditioning (HVAC) applications, and their usage is becoming more widespread [3][4][5][6][7][8]. An important question that naturally arises is this: how well do these tools perform?…”

Section: Introductionmentioning

confidence: 99%

Effect of the distribution of faults and operating conditions on AFDD performance evaluations

Yuill

Braun

2016

Applied Thermal Engineering

View full text Add to dashboard Cite

Automated fault detection and diagnosis (AFDD) tools are used to identify degradation faults that reduce the performance and life of airconditioning equipment. A recent methodology has been developed to evaluate the performance of AFDD tools. The methodology involves feeding a library of input data to an AFDD protocol and categorizing the results. The current paper describes a study that has been conducted to assess the effect of using various input data sets in the evaluations. These input data sets include different distributions of fault type, fault intensity, and operating temperatures. Case study evaluations of three AFDD protocols in current widespread use are used to demonstrate the effects. The paper shows that evaluation results are sensitive to input data sets, and argues that data sets used in previously published studies should be improved to give higher fidelity evaluations. It concludes that for AFDD performance evaluation to be meaningful, the fault and operating conditions need to be controlled so that they connect to the anticipated deployment conditions. A related conclusion is that it is necessary to use simulation data, rather than laboratory measurement data, to conduct performance evaluation of AFDD.

show abstract

Normalized performance parameters for a residential heat pump in the cooling mode with single faults imposed

Cited by 31 publications

References 11 publications

Effect of common faults on the performance of different types of vapor compression systems

Effect of common faults on the performance of different types of vapor compression systems

Effect of heat pump commissioning faults on energy use in a slab-on-grade residential house

Effect of the distribution of faults and operating conditions on AFDD performance evaluations

Contact Info

Product

Resources

About