Quantitative Evaluation of Effects of Airflow from Cross-flow Fans on Passengers' Thermal Comfort in Commuter Vehicles

Almost all commuter trains in Japan are equipped with cross-flow fans in order to circulate the air in a cabin and provide beneficial cooling to passengers, especially during the hot and humid summer seasons. The purpose of this study is to propose a method for predicting the passengers’ thermal com...

Full description

Bibliographic Details
Main Authors: Hiroharu ENDOH, Shota ENAMI, Fumitoshi KIKUCHI, Sachiko YOSHIE, Yasuhiko IZUMI, Jun NOGUCHI
Format: Article
Language:Japanese
Published: The Japan Society of Mechanical Engineers 2022-12-01
Series:Nihon Kikai Gakkai ronbunshu
Subjects:
Online Access:https://www.jstage.jst.go.jp/article/transjsme/88/916/88_22-00171/_pdf/-char/en
Description
Summary:Almost all commuter trains in Japan are equipped with cross-flow fans in order to circulate the air in a cabin and provide beneficial cooling to passengers, especially during the hot and humid summer seasons. The purpose of this study is to propose a method for predicting the passengers’ thermal comfort in non-steady state thermal environments with airflow from cross-flow fans in commuter trains in summer. The proposed method is composed of two calculation parts: a part for calculating sensory temperature based on a human thermoregulation model applicable to non-steady state thermal environments, and a part for calculating the percentage of passengers dissatisfied with the thermal environment based on a statistical model derived from the results of experiments conducted in commuter trains in summer. In order to evaluate the thermal comfort with a cyclic wind from cross-flow fans, the proposed method converts the cyclic wind to a constant wind speed equal to the total amount of heat loss from the whole-body calculated by human thermoregulation model. Applying the proposed method to our previous research where fan-off/fan-on conditions and the congestion rates of below 100%, 120% and 180% had been performed, we examined the prediction accuracy of it. As a result, the mean absolute prediction errors of the proposed method in the congestion rates of below 100%, 120% and 180% under fan-off condition were 9.4pt, 8.2pt and 7.3pt, respectively, and those under fan-on condition were 4.5pt, 10.0pt and 13.2pt, respectively.
ISSN:2187-9761