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We can use the concept of exergy to analyze a human body as a heat emitter: while generating heat continuously, the body remains at roughly the same temperature through physiological responses such as shivering, sweating, breathing thus raising/decreasing the core and/or skin temperature to maintain effective heat dissipation. Existing literature provides an estimated exergy consumption rate of the human body ranging from 2 to 5 W / m 2 , while nearly unanimously agreeing on a local exergy consumption minima points to potential individual thermal comfort. To clarify the underlying assumptions used in the existing human body exergy models, we analytically and numerically reviewed the terms used for assessing metabolism, radiation, evaporation, and convection exergy changes of the human body in this paper. We observed overestimations of exergy from metabolism, underestimations of exergy change through radiation, and some caveats in the signage of convective exergy losses in the results we obtained. We were also able to propose an improved expression to estimate human body radiation exergy exchanges as well as selecting reference temperatures that are more process-specific. Future studies that provide experimental verification of these models were also deemed necessary. • Term-by-term comparison of existing human body exergy model expressions; • Showed T op/E cons minima combination for male/female occupants across age span; • Compared linear radiant heat transfer coefficient against actual radiation heat transfer; • Showed how reference temperature affects the sign of evaporation exergy loss. [ABSTRACT FROM AUTHOR]

Determining variations in air temperature and mean radiant temperature (MRT), two primary environmental parameters directly linked to thermal comfort, can be challenging. The ease and effectiveness of both measurement and simulation are quite different for determining air temperature and MRT. We review these challenges and present an experiment demonstrating how these differences in complexity and resolution can lead to challenges coupling these variables, whose effect on thermal comfort is interdependent. Bearing these challenges in mind, we measured and simulated the air temperatures and MRTs inside a radiantly heated high bay laboratory space. We found the spatial variation of the MRTs to vary by 5 ° C at the elevation of 1.3 m, which is much larger than the variations observed in air temperature. The variations in measured air temperature were up to 2 ° C. This calls for improvement to current tools and methodologies for measuring and simulating the radiant environment, particularly at locations closer to the exterior envelope where hot and cold surfaces affect both air temperature and MRT. It is important to improve the evaluation techniques for MRT, as we observe its spatial variation to be much greater than that of air temperature, while its measurement is limited and usually ignored in practice. • Simulated and modeled air and mean radiant temperatures distributions. • Used angle-factor-based MRT characterization when modeling 3D MRT. • Demonstrated rarely-captured spatial MRT variation in an indoor environment. • Discussed existing limitations in using Ta to evaluate indoor thermal environment. • Showed importance of integral indoor environment sensing and monitoring. [ABSTRACT FROM AUTHOR]

Mean radiant temperature is central to our understanding of the radiant heat exchange between the human body and surrounding environment. This paper will present a review of the concept's evolution including its qualitative definition, methods of quantitative evaluation and corresponding challenges. In the process, this review suggests that more effort needs to be invested in addressing the geometric complexities of radiant heat transfer in research into MRT; the ASHRAE definition is broad and is liable to simplification, and research which uses the definition relies on a variety of simplifications, often without acknowledging the degree of geometric complexity which exists in reality. Existing means of obtaining an estimate of mean radiant temperature range from direct measurements using globe thermometers or net radiometers, to computational simulations, and are widely used for studies within indoor and outdoor environments. Previous literature studying the correlation between air temperature and MRT has found equivalence ratios, the relative importance of convection to radiation, ranging from 0.71 to 1.4, however, it is often assumed to be 1.0 in current research practices. We also identified a rapid increase in the usage of MRT in biometeorological studies during the last ten years on top of the increased usage in indoor environment sensing and modeling in light of recent developments in heating and cooling systems. Recent efforts to include the short-wave component in indoor MRT characterization have shown an increase in cooling capacity of radiant floors from 32 to 110 W/m2; significantly decreasing peak energy demand. • We have expanded the conclusion section with both numerical conclusions and expanded discussions on the limitations of existing MRT usages. • Included new citations that covers the latest development of MRT research and standardization effort (ASHRAE Standard 55-2017, for example). • Adding new illustration of the MRT as a concept in relation to the human body geometry. • Expanded review on how indoor MRT variations due to shortwave radiation are characterized [ABSTRACT FROM AUTHOR]

The indoor environment directly affects health and comfort as humans spend most of the day indoors. However, improperly controlled ventilation systems can expend unnecessary energy and increase health risks, while improved thermal and air quality can often result in higher energy consumption. One way to approach this dilemma is by understanding the effectiveness of the variables influencing indoor air quality (IAQ) related health and comfort. The objective of this paper is to highlight evidence and variables from empirical and deterministic models, which are combined in analytical models that current machine learning techniques often overlook. This paper reviews the analytical models and identifies the corresponding input variables, discussing their application in models based on artificial neural networks (ANNs) and reinforcement learning (RL). ANN and RL models have accurately described non-linear systems with uncertain dynamics and provided predictive and adaptive control strategies. The first part of this study focuses on the most common thermal comfort models and their variables, mainly related to steady-state and adaptive models. The second part reviews typical models of determining indoor air pollutants and their relationship with ventilation requirements and health effects. Forty-five works closely related to the field are summarized in multiple tables. The last part identifies the factors needed to predict thermal comfort and IAQ. • Analytical models and its variables of IAQ related thermal comfort are discussed. • Analytical models and its variables of IAQ related health are investigated. • ANNs have emerged as the most used technique to define building sensing systems. • RL is an efficient one for improving human-building interaction. • Improving thermal comfort and health while lessening energy use is feasible. [ABSTRACT FROM AUTHOR]