A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches. (5th January 2017)
- Record Type:
- Journal Article
- Title:
- A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches. (5th January 2017)
- Main Title:
- A comprehensive performance investigation of cellulose evaporative cooling pad systems using predictive approaches
- Authors:
- Sohani, Ali
Zabihigivi, Mitra
Moradi, Mohammad Hossein
Sayyaadi, Hoseyn
Hasani Balyani, Hamidreza - Abstract:
- Highlights: Performance of evaporative pad was modeled using SCST approaches. SCST models were used to predict the supply temperature and pressure drop. It was shown that SCST are more accurate than analytical models. Sensitivity analysis of evaporative pad was studied using the SCST model. The effect of conditioned air recirculation was studied in hot areas. Abstract: 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 comfortHighlights: Performance of evaporative pad was modeled using SCST approaches. SCST models were used to predict the supply temperature and pressure drop. It was shown that SCST are more accurate than analytical models. Sensitivity analysis of evaporative pad was studied using the SCST model. The effect of conditioned air recirculation was studied in hot areas. Abstract: 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 m 2 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. … (more)
- Is Part Of:
- Applied thermal engineering. Volume 110(2017:Jan.)
- Journal:
- Applied thermal engineering
- Issue:
- Volume 110(2017:Jan.)
- Issue Display:
- Volume 110 (2017)
- Year:
- 2017
- Volume:
- 110
- Issue Sort Value:
- 2017-0110-0000-0000
- Page Start:
- 1589
- Page End:
- 1608
- Publication Date:
- 2017-01-05
- Subjects:
- Artificial neural network -- Cellulose pad -- Evaporative cooling -- Genetic programming -- Inlet air pre-cooling -- Multiple linear regression
Heat engineering -- Periodicals
Heating -- Equipment and supplies -- Periodicals
Periodicals
621.40205 - Journal URLs:
- http://www.sciencedirect.com/science/journal/13594311 ↗
http://www.elsevier.com/homepage/elecserv.htt ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.applthermaleng.2016.08.216 ↗
- Languages:
- English
- ISSNs:
- 1359-4311
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1580.101000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 2787.xml