محبوبترین نویسندگان :
نویسندگان مقالات پربازدید :
ساختمان و محیط زیست
جزئیات رویداد
اینستاگرام تی پی بین
همایش ، رویداد ، ژورنال
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  • local and overall thermal comfort in an aircraft cabin and their interrelations

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    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1390/01/01
    • تاریخ انتشار در تی پی بین: 1390/01/01
    • تعداد بازدید: 480
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     in this study the interrelation between local and overall thermal comfort of passengers in aircraft cabins was investigated by thirteen simulated flights. for each of the tests forty test persons filled out questionnaires concerning their perceived overall and local thermal comfort at temperatures of 20 °c–25 °c, which were measured at every second seat. with these physical and subjective data pmv (predicted mean vote) and tsmv (thermal sensation mean vote) of test persons as well as ppd (predicted percentage of dissatisfied) and pd (percentage of dissatisfied) were compared. the pmv was consistently similar to the tsmv, while the thermal dissatisfaction in tests was always higher than ppd. the hypothesis at the beginning of this study was that the high ratio of thermal dissatisfaction in the aircraft cabin reported in literature might be caused by local discomfort. therefore statistical analyses about the interrelations between local and overall thermal comfort were performed and models indicating such interrelations were developed. some local perceptions are significantly different from overall thermal perception and these body segments alter in dependence of the overall thermal environment. also body segments rated similarly were detected and these segments were pooled to distinct body regions using principal component analysis. under the same overall thermal sensation the local thermal perception on a certain body region predominantly influenced the overall thermal comfort. therefore weighting factors of local body regions on the overall thermal comfort were determined in dependence of the overall thermal sensation by means of multiple linear regression models.

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