System dynamics of human body thermal regulation in outdoor environments
Thermal comfort of people in outdoor urban spaces is a growing concern in cities due to climate change and urbanization. In outdoor settings the climate and behavior of people are more dynamic than in indoor situations, therefore a steady state of the thermoregulatory system is rarely reached. Under...
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Main Authors: | , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/105589 http://hdl.handle.net/10220/50157 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Thermal comfort of people in outdoor urban spaces is a growing concern in cities due to climate change and urbanization. In outdoor settings the climate and behavior of people are more dynamic than in indoor situations, therefore a steady state of the thermoregulatory system is rarely reached. Understanding the dynamics of outdoor thermal comfort requires accurate predictive models. In this paper we extend a classical two-node model of human body thermal regulation. We give a detailed description and interpretation of all the components and parameter values and test the dynamics of the model against experimental data. We propose a modification of the skin blood flow model which, while keeping realistic values and responsiveness, improves skin temperature prediction nearly fourfold. We further analyze the sensitivity of the model with respect to climatic and personal parameters. This analysis reveals the relative importance of, for instance, air temperature, wind speed and clothing, in thermoregulatory processes of the human body in various climatic settings. We conclude, that our model realistically reproduces the dynamics of aggregate measures of human body thermal regulation. Validated for cool, warm and hot environments, the model is shown to be accurate in terms of its dynamics and it is conceptually and computationally far more efficient than any existing multi-node and multi-part model. |
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