Mist cooling is a widely known and applied heat mitigation technology, especially in urban settings. Despite this, conceiving the right installation is no trivial matter as scattered and unstandardized is the knowledge on the multiple interrelations with the local microclimate. This paper investigates how the cooling efficiency of a dry mist system depends on the local meteorological trends. An experimental system of 24 overhead nozzles constantly operating at 0.7 MPa, was installed in Italy and monitored for a week in summertime. Temperature and relative humidity underneath the mist were mapped in five locations with a time step of 10 s, together with the main meteorological parameters, measured at an undisturbed location, for reference. Cooling and humidification capacity were characterized as probability density, key summary statistics and relevant confidence intervals with minimal redundancy and minimal distortion. A supervised learning algorithm was used to disclose the sensitivity of the recorded temperature drop to the contextual microclimatic evolution. It was demonstrated that the cooling capacity of the tested system was largely a function of the local wet bulb depression, as instantaneous reading as well as short-term trend. Additionally, solar irradiation and wind speed were found to be negatively and positively correlated, respectively.

Mist cooling in urban spaces: Understanding the key factors behind the mitigation potential

Zinzi M.
2020

Abstract

Mist cooling is a widely known and applied heat mitigation technology, especially in urban settings. Despite this, conceiving the right installation is no trivial matter as scattered and unstandardized is the knowledge on the multiple interrelations with the local microclimate. This paper investigates how the cooling efficiency of a dry mist system depends on the local meteorological trends. An experimental system of 24 overhead nozzles constantly operating at 0.7 MPa, was installed in Italy and monitored for a week in summertime. Temperature and relative humidity underneath the mist were mapped in five locations with a time step of 10 s, together with the main meteorological parameters, measured at an undisturbed location, for reference. Cooling and humidification capacity were characterized as probability density, key summary statistics and relevant confidence intervals with minimal redundancy and minimal distortion. A supervised learning algorithm was used to disclose the sensitivity of the recorded temperature drop to the contextual microclimatic evolution. It was demonstrated that the cooling capacity of the tested system was largely a function of the local wet bulb depression, as instantaneous reading as well as short-term trend. Additionally, solar irradiation and wind speed were found to be negatively and positively correlated, respectively.
Artificial intelligence
Evaporative cooling
Experimental monitoring
Sensitivity analysis
Urban climate
Water misting
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12079/57145
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
social impact