Literature DB >> 9230523

Evaluation of evaporative heat transfer characteristics of helmets.

X Liu1, I Holmér.   

Abstract

The prime purpose of a safety helmet is to protect against occupational hazards. However, thermal comfort is one important ergonomics requirement for a helmet to be accepted by its wearer. To design and manufacture a thermally comfortable helmet, a method for testing and evaluating the thermal properties is essential. Research has long focused on the evaluation of dry heat transfer (conduction, convection and radiation). Evaporative heat transfer was not much addressed. In order to analyze the wet heat transfer (evaporation) component, a sweating thermal head manikin has been used. In this study the method has been further improved by constructing a new sweating head manikin. The surface of the head manikin is divided into five zones which can provide more detailed information about the environmental effects on the heat transfer from the head when a helmet is worn. Water supply (simulated sweating) is also improved by use of an electronic pumping system which provides a steady and adjustable flow rate of water to the head manikin. Experiments were conducted within a climatic chamber with this improved method under different test conditions: the ambient temperature and the head manikin surface temperature are set at the same level: 34 +/- 0.5 degrees C; two levels of head surface wettedness: 0.44 and 1.0; two levels of ambient humidity: 30% and 60%; and two levels of wind speed: 0.4 m/s and 1.0 m/s. Seven different helmets were used in the experiments. The results showed that the improved method revealed more detailed information about the evaporative heat transfer; it is easier to use and control; less error is involved with the measurement.

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Year:  1997        PMID: 9230523     DOI: 10.2114/jpa.16.107

Source DB:  PubMed          Journal:  Appl Human Sci        ISSN: 1341-3473


  2 in total

1.  Spatial differences in sensible and latent heat losses under a bicycle helmet.

Authors:  Guido De Bruyne; Jean-Marie Aerts; Georges Van der Perre; Jan Goffin; Ignace Verpoest; Daniel Berckmans
Journal:  Eur J Appl Physiol       Date:  2008-07-23       Impact factor: 3.078

2.  Application of Machine Learning Algorithm on MEMS-Based Sensors for Determination of Helmet Wearing for Workplace Safety.

Authors:  Yan Hao Tan; Agarwal Hitesh; King Ho Holden Li
Journal:  Micromachines (Basel)       Date:  2021-04-16       Impact factor: 2.891

  2 in total

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