Local thermal sensation modeling-a review on the necessity and availability of local clothing properties and local metabolic heat production

Veselá, Stephanie; Kingma, Brm Boris; Frijns, Ajh Arjan

doi:10.1111/ina.12324

Cited by 24 publications

(12 citation statements)

References 45 publications

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“…To achieve a high predictability, these models require reliable input data of the local clothing thermal resistance and clothing area factor. However, Veselá et al ( 2017 ) show that the available data is limited for typical office clothing ensembles. Furthermore, few studies were performed on the local effect of increased air speeds and body movement on the dry thermal resistance.…”

Section: Introductionmentioning

confidence: 99%

Local clothing thermal properties of typical office ensembles under realistic static and dynamic conditions

2018

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An accurate local thermal sensation model is indispensable for the effective development of personalized conditioning systems in office environments. The output of such a model relies on the accurate prediction of local skin temperatures, which in turn depend on reliable input data of the local clothing thermal resistance and clothing area factor. However, for typical office clothing ensembles, only few local datasets are available in the literature. In this study, the dry thermal resistance was measured for 23 typical office clothing ensembles according to EN-ISO 15831 on a sweating agile manikin. For 6 ensembles, the effects of different air speeds and body movement were also included. Local clothing area factors were estimated based on 3D scans. Local differences can be found between the measured local insulation values and local area factors of this study and the data of other studies. These differences are likely due to the garment fit on the manikin and reveal the necessity of reporting clothing fit parameters (e.g., ease allowance) in the publications. The increased air speed and added body movement mostly decreased the local clothing insulation. However, the reduction is different for all body parts, and therefore cannot be generalized. This study also provides a correlation between the local clothing insulation and the ease allowance for body parts covered with a single layer of clothing. In conclusion, the need for well-documented measurements is emphasized to get reproducible results and to choose accurate clothing parameters for thermo-physiological and thermal sensation modeling.Electronic supplementary materialThe online version of this article (10.1007/s00484-018-1625-0) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Local clothing thermal properties of typical office ensembles under realistic static and dynamic conditions

2018

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“…Since the adopted approach fixes the local skin temperatures to a given temperature and the environmental conditions are given as well, changes in body heat storage will occur, influencing the internal body temperature. and 26°C, respectively) [63], which could possibly lead to a change in thermal sensation predictions of approximately 0.5 unit (estimation based on the sensitivity of DTS predictions for comparable clothing and metabolic rate in similar conditions reported in [12]). However, the influence of local clothing properties on the local skin temperatures is much stronger, resulting in a shift of predicted local thermal sensation (depending on the body part; e.g., 3 units for feet or 1 unit for upper arm [63]).…”

Section: Validation Examples Outcomementioning

confidence: 99%

“…and 26°C, respectively) [63], which could possibly lead to a change in thermal sensation predictions of approximately 0.5 unit (estimation based on the sensitivity of DTS predictions for comparable clothing and metabolic rate in similar conditions reported in [12]). However, the influence of local clothing properties on the local skin temperatures is much stronger, resulting in a shift of predicted local thermal sensation (depending on the body part; e.g., 3 units for feet or 1 unit for upper arm [63]). Such differences in local thermal sensation predictions can burden the evaluation of indoor conditions, e.g.…”

Section: Validation Examples Outcomementioning

confidence: 99%

“…However, these coefficients usually do not take into account the variety of possible activities engaging different muscles, or even different postures. By comparing the same scenario simulated with three different sets of local heat production coefficients, Veselá et al[63] found differences in predicted mean skin temperature up to 0.3°C, with local skin differences up to 1.3°C. Although the difference in mean skin temperature is not substantial, the local thermal sensation predictions could shift approximately 1 thermal sensation unit, impairing the evaluation of complex, heterogeneous environments (see Section 4.3)…”

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Human simulator – A tool for predicting thermal sensation in the built environment

Koelblen

Psikuta

Bogdan

et al. 2018

Building and Environment

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One of the challenges for engineers designing indoor environments is merging the need for energy savings with providing thermally comfortable conditions for the occupants. Since the best way to evaluate thermal comfort, i.e. direct enquiry, is at the same time the most costand time-consuming one, various modelling tools are widely used. However, in order to assess complex heterogeneous environments created by novel building systems, there is a need for more sophisticated and precise tools. In this paper, we present a new human simulator methodology for indoor environmental research, combining three tools to predict thermal sensation, namely, a thermal manikin, a thermoregulation model, and a thermal sensation model. Thanks to the thermoregulation model's control, the thermal manikin is capable of mimicking the thermo-physiological response of a human exposed to chosen environmental conditions, which provides reliable input data for advanced thermal sensation models. Along with presenting this concept, the performance of a commercially available human simulator was demonstrated on five validation examples representing office-like conditions for which thermal sensation was predicted with satisfactory accuracy. Based on the presented results, we discussed the capabilities and limitations of human simulators for indoor environment research such as the benefits of performing measurements directly in the assessed environment with real garments, and the challenges related to the manikin's accuracy. The presented human simulator approach is suitable to apply in the building's design process, as well as the development of new solutions for conditioning indoor spaces, and can support the evaluation of existing buildings.

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“…PHS) [9] and thermo-physiological prediction models, such as the Fiala model [10] or the FMTK model (an abbreviation in Czech for the Fiala-based thermal comfort model) [11], also contain clothing properties as some of their most important input parameters. This is why clothing parameters should be measured with the highest possible degree of precision to mitigate prediction errors [12][13][14]. As it is important to protect workers' health, the main purpose of these models is to calculate the maximum exposure to a given environment with a given activity level without endangering the subject.…”

Section: Introductionmentioning

confidence: 99%

Using a thermal manikin to determine evaporative resistance and thermal insulation – A comparison of methods

Toma

Kuklane²,

Fojtlín

et al. 2020

Journal of Industrial Textiles

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Heat transfer from the human body, especially through the evaporation of sweat from the skin, is often restricted when protective clothing is used, which may result in overheating. For this reason, it is important to consider the parameters of protective clothing as input data in physiological models, such as predicted heat strain. The two most important parameters are thermal insulation and evaporative resistance with clothing area factor strongly influencing both. These parameters were determined for two clothing ensembles using a (dry) non-sweating thermal manikin. First, the clothing area factor was determined using the photographic method. Second, thermal insulation was measured in both static and dynamic conditions, and multiple equations for predicting dynamic thermal insulation from static ones were evaluated. Third, methodology for measuring evaporative resistance based on pre-wetted skin was adopted and multiple corrections were assessed. Finally, sensitivity analyses were completed using PHS to determine the impact of different equations on the duration limited exposure. For the thermal insulation measurements, we found that predictive equation (32) from ISO 9920 was the most accurate, but choosing the correct equation for protective clothing proved challenging. Although a manikin’s surface temperature is widely used for calculating evaporative resistance, the skin temperature should be used instead, since it is correct from a physical point of view and there is a difference of up to 15% in the results. Because these measures are used in thermal risk analyses conditions, a high degree of accuracy and a knowledge of the inputs must be guaranteed.

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Local thermal sensation modeling-a review on the necessity and availability of local clothing properties and local metabolic heat production

Cited by 24 publications

References 45 publications

Local clothing thermal properties of typical office ensembles under realistic static and dynamic conditions

Local clothing thermal properties of typical office ensembles under realistic static and dynamic conditions

Human simulator – A tool for predicting thermal sensation in the built environment

Using a thermal manikin to determine evaporative resistance and thermal insulation – A comparison of methods

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