Multivariable optimization of PCM-enhanced radiant floor of a highly glazed study room in cold climates


Hydronic radiant floor systems enhanced with phase change materials (PCMs) could achieve significant energy savings while improving the thermal comfort of occupants in lightweight buildings. However, successful integration of PCMs typically requires comprehensive numerical analysis due to their complex nature. This study aims to investigate two scenarios for optimal integration of PCM into the hydronic floor heating system of a highly glazed study room exposed to cold weather conditions. Scenario 1 includes optimization of two design variables, including PCM melting temperature and thickness. Scenario 2 encompasses optimization of seven design variables, including PCM melting temperature and thickness, insulation thickness and thermal conductivity, floor thickness, thermal conductivity, and solar absorbance. Both scenarios are optimized for the six supply water temperatures, ranging from 35.6 °C to 45.6 °C with a 2 °C step. The overall findings suggest that successful integration of PCMs into the hydronic heating system requires a comprehensive solution tailored for the specific application. Thus, scenario 1 and scenario 2 achieved the highest total energy savings of approximately 17.7% and 20.5% for the lowest supply water temperature of 35.6 °C, whereas under both scenarios the supply water temperature of 43.6 °C provided the best thermal comfort. Furthermore, scenario 1 achieved more substantial cooling energy savings over the wider temperature range (41.6-35.6 °C) compared to scenario 2 (39.6-35.6 °C). The findings also suggest that the addition of the insulation layer in the second scenario reduced the thickness of PCM and payback period for more than 70% compared to the first scenario.

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C p :

specific heat capacity of the material (J/(g·°C))

F k :

floor conductivity (W/(m·K))

F T :

floor thickness (cm)

F α :

floor solar absorbance

I k :

insulation conductivity (W/(m·K))

I T :

insulation thickness (cm)

K :

thermal conductivity (W/(m·K))


PCM melting temperature (°C)


PCM thickness (cm)

S :

normalized sensitivity coefficient

T :

temperature of the material (°C)

Δx :

variation in model variable

x :

model variable

Φ :

sensitivity coefficients

i :

output variable

j :

input parameters


reference model




energy use intensity


genetic algorithm


heating, ventilation, and air-conditioning


indices of energy savings for cooling energy


indices of energy savings for heating energy


indices of energy savings for total energy


Leadership in Energy and Environmental Design


phase change materials


predicted mean vote


Stanley Pauley Engineering Building


static payback period


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This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Program. The authors would like to acknowledge Compute Canada for providing computational facilities. The authors would also like to thank ‘PureTemp’ for providing the information required for PCM modeling.

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Correspondence to Miroslava Kavgic.

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Mohammadzadeh, A., Kavgic, M. Multivariable optimization of PCM-enhanced radiant floor of a highly glazed study room in cold climates. Build. Simul. 13, 559–574 (2020).

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  • phase change materials
  • simulation-based optimization
  • genetic algorithm
  • hysteresis
  • curtain wall
  • thermal comfort