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A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches.

Author(s) : SOHANI A., ZABIHIGIVI M., MORADI M. H., et al.

Type of article: Article

Summary

Developing the soft computing and statistical tools (SCST) for predicting the behavior pattern of the performance features of a cellulose evaporative cooling pad system was studied. Three soft computing and statistical tools- artificial neural network (ANN), genetic programming (GP), and multiple linear regression (MLR)- were used to predict the supply air temperature and pad pressure drop. The prediction abilities of obtained models were analyzed and compared with analytical models, and a comprehensive error analysis was conducted. It was found that the MLR and ANN models perform better than the other approaches for predicting the supply air temperature and the pad pressure drop, respectively. The obtained models had the accuracy of numerical models as well as the simplicity of analytical methods. Effects of inlet air conditions and pad characteristics on nine different system performance parameters like thermal comfort indices were also studied, comprehensively. It was found that the best values for pad thickness and specific contact area are the minimum values of them, which provide thermal comfort conditions (7 cm and 420 m2 m-3 for the investigated case respectively). Utilizing the direct evaporative cooling system with recirculation of a part of the cooled air in very hot and dry weather conditions was investigated and suggested as an alternative for conventional systems.

Details

  • Original title: A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches.
  • Record ID : 30020689
  • Languages: English
  • Source: Applied Thermal Engineering - vol. 110
  • Publication date: 2017/01/05
  • DOI: http://dx.doi.org/10.1016/j.applthermaleng.2016.08.216

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