Evaluation of a Coupled Model to Predict the Impact of Adaptive Behaviour in the Thermal Sensation of Occupants of Naturally Ventilated Buildings in Warm-Humid Regions

: Improving indoor environment quality and making urban centres in tropical regions more sustainable has become a challenge for which computational models for the prediction of thermal sensation for naturally ventilated buildings (NVBs) have major role to play. This work performed analysis on thermal sensation for non-residential NVBs located in Brazilian tropical warm-humid climate and tested the effectiveness of suggested adaptive behaviours to mitigate warm thermal sensation. The research method utilized transient computational ﬂuid dynamics models coupled with a dynamic model for human thermophysiology to predict thermal sensation. The calculated results were validated with comparison with benchmark values from questionnaires and from ﬁeld measurements. The calculated results for dynamic thermal sensation (DTS) seven-point scale showed higher agreement with the thermal sensation vote than with the predicted mean vote. The test for the suggested adaptive behaviours considered reducing clothing insulation values from 0.18 to 0.32 clo (reducing DTS from 0.1 to 0.9), increasing the air speed in 0.9 m/s (reducing DTS from 0.1 to 0.9), and applying both suggestions together (reducing DTS from 0.1 to 1.3) for ﬁve scenarios with operative temperatures spanning 34.5–24.0 ◦ C. Results quantiﬁed the tested adaptive behaviours’ efﬁciency showing applicability to improve thermal sensation from slightly-warm to neutral.


Introduction
Seven out of ten of the most populated mega-cities [1] are in both developing countries [2] and hot-dry or warm-humid regions based on Köppen-Geiger world climate classification [3]. An unprecedented growth in the number of room air-conditioning (RAC) systems installed in cellular office's and apartment's buildings has been observed in those cities and countries [4]. This has been happening due to a combination of rise in income and reduction of RAC acquisition price but also due to increasing expectations for thermal comfort and low thermal performance under hot weather of most of the constructions in these locations. The tendency of growth in the RAC systems may be accentuated by the rising number of companies and individuals opting for home office activities during and after the COVID-19 pandemic [5].
Improving the quality of the indoor environment and living conditions and making buildings more sustainable in established urban centres have become challenges which demand the attention of today's architects, engineers, and urban planners. In that sense, natural ventilation has a major role to play, being not only restricted to indoor air quality by supplying fresh air to dilute pollutants but also by delivering thermal comfort and curbing unnecessary energy consumption with RAC [6,7].
Thermal comfort is defined in the ASHRAE Standard 55: Thermal Environmental Conditions for Human Occupancy [8] (p. 3) as "a condition of mind that expresses satisfaction with the thermal environment and is assessed by subjective evaluation". Although

Objectives
The main objective of this work is to evaluate the effectiveness of a coupled model to predict thermal comfort sensation for occupants of naturally ventilated buildings located in Brazilian tropical warm-humid climatic regions. A secondary objective of this work comprises to test additional parameters to simulate adaptive behaviours with the coupled models and perform an analysis on the impact of each parameter on the calculated results.
To meet the intended objectives, simulations were carried out with transient CFD models coupled with a model of human thermophysiology for the prediction of thermal sensation. The computational coupled models were validated and calibrated with the comparison of calculated results with benchmark values reported in the literature for thermal sensation indices obtained with field measurement and with questionnaires answered by volunteers.
This paper is structured as follows: Section 1 provides an introduction to the research problem; Section 2 states the main objective; Section 3 describes the research method; Section 4 shows the results for the validation exercise and for the potential improvements with adaptive behaviour, and presents a discussion highlighting aspects and practical applications, and the conclusions are presented in Section 5.

Methodology
The methodological approach utilized in this work consisted of two stages: a validation exercise (Stage 1) and an analysis for testing adaptive behaviour (Stage 2). A description of each stage and of the parameters analysed for each simulated scenario is shown in Figure 1 (the lists of the scenarios for each stage with detailed setup parameters are provided later in this section). For Stage 1 the validation exercise comprised the comparison of calculated results for dynamic thermal sensation (DTS) and PPD with benchmark values from experimental data for thermal sensation votes (TSV) and unacceptable votes (UV), from surveys with volunteers, and for PMV and PPD, from field measurements, informed Sustainability 2021, 13, 255 4 of 23 in Lamberts and Andreasi [35] and Andreasi, Lamberts and Cândido [36]. The calculated results were obtained with transient CFD simulations coupled with the IESD/Fiala model of human thermophysiology for the prediction of thermal sensation. The experimental results were reported in the referenced literature [35,36] to have been obtained with measured data and questionnaires answered by volunteers from three cities located in Brazilian hot-humid climatic region. For Stage 2, parameters to simulate adaptive behaviour were added to the coupled models to be tested and to identify their efficacy to improve thermal sensation under warm-humid climatic conditions. Before presenting the scenarios considered for each stage, some considerations are shown for the overall setup and quality check for the CFD models, for the thermal sensation model, and for the modelling of virtual ceiling fan.
Sustainability 2021, 12, x FOR PEER REVIEW 4 of 23 of calculated results for dynamic thermal sensation (DTS) and PPD with benchmark values from experimental data for thermal sensation votes (TSV) and unacceptable votes (UV), from surveys with volunteers, and for PMV and PPD, from field measurements, informed in Lamberts and Andreasi [35] and Andreasi, Lamberts and Cândido [36]. The calculated results were obtained with transient CFD simulations coupled with the IESD/Fiala model of human thermophysiology for the prediction of thermal sensation. The experimental results were reported in the referenced literature [35,36] to have been obtained with measured data and questionnaires answered by volunteers from three cities located in Brazilian hot-humid climatic region. For Stage 2, parameters to simulate adaptive behaviour were added to the coupled models to be tested and to identify their efficacy to improve thermal sensation under warm-humid climatic conditions. Before presenting the scenarios considered for each stage, some considerations are shown for the overall setup and quality check for the CFD models, for the thermal sensation model, and for the modelling of virtual ceiling fan.

Overall Setup and Quality Check for the CFD Simulations
The CFD models utilized in both Stage 1 and Stage 2 were built and meshed with ANSYS ICEM R16.0 [37] and solved with ANSYS CFX R19.1 [38] using a high-performance computing facility. The CFX solves the conservation law equations using the finite volume method from Versteeg and Malalasekera [39]. Simulations were performed in transient mode using small time-steps of 1s for periods of 15 min. The iterative convergence values were set as 1e-05 root mean square (RMS) for residual targets. Satisfactory results are reported for convergence values ranging from three to four orders of magnitude decrease for normalized residuals [34,40,41]. The turbulence model utilized was the Shear Stress Transport (SST). The SST is based on a two-equation for eddy-viscosity (k-ω) solution with automatic near-wall function. When compared with other models, such as the Reynolds-Averaged Navier-Stokes (RANS), the Renormalization Group (RGN), and the Reynolds Stress Model (RSM), several authors report the superior capacity of the SST turbulence model to solve with accuracy and robustness complex fluid dynamics problems, such as the downward vortex produced by a ceiling fan [32,[42][43][44][45][46].
The three-dimensional model utilized in this work represents a room with respective dimensions for width, length, and height of 4.0 × 6.0 × 3.0 m, totalizing 24.0 m 2 of floor area and 72.0 m 3 of internal volume. Meshes were built using unstructured elements combining tetrahedra, pyramids, and wedges with ten layers of prisms added adjacent to all boundaries to accurately model the near-wall heat and mass transfer and to accommodate

Overall Setup and Quality Check for the CFD Simulations
The CFD models utilized in both Stage 1 and Stage 2 were built and meshed with ANSYS ICEM R16.0 [37] and solved with ANSYS CFX R19.1 [38] using a high-performance computing facility. The CFX solves the conservation law equations using the finite volume method from Versteeg and Malalasekera [39]. Simulations were performed in transient mode using small time-steps of 1s for periods of 15 min. The iterative convergence values were set as 1e-05 root mean square (RMS) for residual targets. Satisfactory results are reported for convergence values ranging from three to four orders of magnitude decrease for normalized residuals [34,40,41]. The turbulence model utilized was the Shear Stress Transport (SST). The SST is based on a two-equation for eddy-viscosity (k-ω) solution with automatic near-wall function. When compared with other models, such as the Reynolds-Averaged Navier-Stokes (RANS), the Renormalization Group (RGN), and the Reynolds Stress Model (RSM), several authors report the superior capacity of the SST turbulence model to solve with accuracy and robustness complex fluid dynamics problems, such as the downward vortex produced by a ceiling fan [32,[42][43][44][45][46].
The three-dimensional model utilized in this work represents a room with respective dimensions for width, length, and height of 4.0 × 6.0 × 3.0 m, totalizing 24.0 m 2 of floor area and 72.0 m 3 of internal volume. Meshes were built using unstructured elements combining tetrahedra, pyramids, and wedges with ten layers of prisms added adjacent to all boundaries to accurately model the near-wall heat and mass transfer and to accommodate wall functions. A procedure to verify the grid independence of the solutions and to estimate the averaged numerical uncertainty due to discretization errors was performed: the Fine Grid Convergence Index (GCI fine ) method, proposed by Celik et al. [34] and reviewed in Hadjukiewicz et al. [47]. The GCI fine is based on Richardson extrapolation to determine the level of grid independence and its impact on the spatial convergence of a CFD solution [48]. The procedure consists of a statistical comparison of averaged values for key variables (points monitored during the simulations) between three grid solutions, and an example for grid independence check is presented here ( Table 1). The results for the calculated GCI fine (Table 2) show that the averaged numerical uncertainty between the coarse Grid 1 and the medium Grid 2 is 1.70%, while between the medium Grid 2 and the fine Grid 3 is 0.4%. This means that the solution for Grid 2 shows considerable reduction on the numerical uncertainty due to discretization errors when compared to Grid 1, but there is no significant improvement when compared with Grid 3. Conversely, the computing time for Grid 2 solutions was 34% smaller than the required for Grid 3 solutions. For this reason, the criteria to create the mesh utilized for Grid 2 was adopted for all CFD models utilized in this work. Further mesh quality check was performed for the Grid 2 utilizing quality criteria described in ANSYS [40,41], and the calculated values were within the required ranges (Table 3). An example of mesh created with the Grid 2 solution and applied to the three-dimensional models utilized in this work is shown in Figure 2. The cross-section of the room illustrates the virtual manikin in a seated position and the ceiling fan (Figure 2a), and the amplified figure shows details of the mesh surrounding the head the virtual manikin ( Figure 2b).  Table 2. Grid refinement factor and sample calculations of discretization errors and GCI fine [34].

The Thermal Sensation Model
The computational model for the prediction of thermal comfort sensation utilized in this work is the IESD/Fiala version 1.4.0, a Linux based solver which models the human thermophysiology for the prediction of thermal sensation [16,[29][30][31]. This model is named here as "thermal sensation model". This model runs coupled with the transient CFD simulation and considers the heat transfer throughout the body and with the virtual environment to predict thermoregulatory responses for the passive and the active systems ( Figure  3).
The interaction between the thermal sensation model and the CFD model happens via a virtual manikin and is described in Cropper et al. [32]. During the coupled simulation data is continuously exchanged between both models for pre-established time-steps until the convergence criteria is achieved ( Figure 4). The manikin is multi-segmented in 59 parts (for the standing or seated positions) or 50 parts (lying on a horizontal surface). In this work, the manikin on the seated position is utilized for the coupled simulations. Further description for this generic human body considers Dubois-area of 1.86 m 2 , weight of 73.5 kg with 15% of fat content, basal metabolism of 87 W with basal evaporation from the skin of 18 W, and circulatory capacity of 4.9 L/min [32]. As output, the thermal sensation model informs, in addition to DTS and PPD indices, results for hypothalamus temperature, mean skin temperature, mean radiant temperature, body metabolic rate, sweat moisture production, wetted skin area, evaporation due to skin and respiration, and blood flow rates.

The Thermal Sensation Model
The computational model for the prediction of thermal comfort sensation utilized in this work is the IESD/Fiala version 1.4.0, a Linux based solver which models the human thermophysiology for the prediction of thermal sensation [16,[29][30][31]. This model is named here as "thermal sensation model". This model runs coupled with the transient CFD simulation and considers the heat transfer throughout the body and with the virtual environment to predict thermoregulatory responses for the passive and the active systems ( Figure 3).
The interaction between the thermal sensation model and the CFD model happens via a virtual manikin and is described in Cropper et al. [32]. During the coupled simulation data is continuously exchanged between both models for pre-established time-steps until the convergence criteria is achieved ( Figure 4). The manikin is multi-segmented in 59 parts (for the standing or seated positions) or 50 parts (lying on a horizontal surface). In this work, the manikin on the seated position is utilized for the coupled simulations. Further description for this generic human body considers Dubois-area of 1.86 m 2 , weight of 73.5 kg with 15% of fat content, basal metabolism of 87 W with basal evaporation from the skin of 18 W, and circulatory capacity of 4.9 L/min [32]. As output, the thermal sensation model informs, in addition to DTS and PPD indices, results for hypothalamus temperature, mean skin temperature, mean radiant temperature, body metabolic rate, sweat moisture production, wetted skin area, evaporation due to skin and respiration, and blood flow rates.  To start the coupled simulation, the user selects from a list of options the garment ensembles and enters initial values for activity level, air relative humidity (RH, in %), air temperature inside (Tinside, in °C), air temperature outside (Toutside, in °C ), mean radiant temperature (Tradmean, in °C ), and air speed (Vair, in m/s). The options for garment ensembles, and their corresponding estimated clothing insulation values, are the following: nude (0.00 clo), briefs (0.04 clo), summer (0.23 clo), casual (0.43 clo), casual with thin sweater (0.63 clo), casual with thick sweater (0.76 clo), suit (1.00 clo), and winter (1.40 clo). The estimated clothing insulation values were based on a list available in the standard ASHRAE-55 [8] (Table 5   To start the coupled simulation, the user selects from a list of options the garment ensembles and enters initial values for activity level, air relative humidity (RH, in %), air temperature inside (Tinside, in °C), air temperature outside (Toutside, in °C ), mean radiant temperature (Tradmean, in °C ), and air speed (Vair, in m/s). The options for garment ensembles, and their corresponding estimated clothing insulation values, are the following: nude (0.00 clo), briefs (0.04 clo), summer (0.23 clo), casual (0.43 clo), casual with thin sweater (0.63 clo), casual with thick sweater (0.76 clo), suit (1.00 clo), and winter (1.40 clo). The estimated clothing insulation values were based on a list available in the standard ASHRAE-55 [8] (Table 5  To start the coupled simulation, the user selects from a list of options the garment ensembles and enters initial values for activity level, air relative humidity (RH, in %), air temperature inside (T inside , in • C), air temperature outside (T outside , in • C), mean radiant temperature (T radmean , in • C), and air speed (V air , in m/s). The options for garment ensembles, and their corresponding estimated clothing insulation values, are the following: nude (0.00 clo), briefs (0.04 clo), summer (0.23 clo), casual (0.43 clo), casual with thin sweater (0.63 clo), casual with thick sweater (0.76 clo), suit (1.00 clo), and winter (1.40 clo). The estimated clothing insulation values were based on a list available in the standard ASHRAE-55 [8] (Table 5.2.2.2B and Table 5.2.2.2C), and no insulation value was added for the chair. The clothing insulation values (I cl , in clo) for each simulated scenario were calculated with the calculated results for mean skin temperature (T skin,m , in • C), mean clothing surface temperature (T cl,m , in • C), and overall dry heat loss (H DRY , in W/m 2 ) with the ambient, utilizing Equations (1) and (2) from Havenith et al. [49].
where R is radiant heat loss, C is convective heat loss, M is metabolic rate, W is effective mechanical power, E res is evaporative respiratory heat loss, C res -convective respiratory heat loss, E is evaporative heat loss, and S is body heat loss (all terms in W/m 2 ). The DTS calculated by the IESD/Fiala model is based on the rate of change of the mean skin temperature (dT skin,m /dt, in • C), the body core temperature (G, in • C), and the hypothalamus temperature (T hy , in • C), to drive the body thermoregulation system and define the overall thermal sensation (Equations (3) and (4)) [16].
Although the mechanisms of heat transfer over the body and within the environment are one, the calculation for the PMV and the PPD thermal sensation indices follow a different approach. These indices are calculated utilizing parameters for the individual characteristics for clothing level and physical activity level and for the environment for air temperature, mean radiant temperature, air speed, and air relative humidity (Equations (5) and (6)) [8,11].
where p a is the water vapour partial pressure (in Pa), ƒ cl is cloting surface area factor, and h c is convective heat transfer coefficient (in W/m 2 K).
In addition to the DTS informed by the coupled simulation, this work utilizes two methods to improve and adjust the calculated PMV indices: the extended model (here identified as DTSe), proposed by Fanger and Toftum [20], and the adjustment model (here identified as DTSa), proposed by Humphreys and Nicol [21]. While the former model utilizes an expectancy factor (e) ranging from 0.5-1.0 according to the climatic region (continuous hot weather or hot season) and type of building (air conditioned, mostly air conditioned, mostly naturally ventilated, or naturally ventilated) (Equation (7)), the latter utilizes environmental and occupant parameters to reduce bias related to discretization, which may result in overestimation of PMV (Equations (8) and (9)).

Modelling the Virtual Ceiling Fan
Stage 2 comprised the simulation of transient CFD models to reproduce the air speed and the flow field created by actual mechanical ceiling fan devices. The modelling parameters utilized are described in Babich et al. [44]. In the models utilized for this work, the virtual ceiling fan was modelled with the shape of a flat, hollow cylinder with height 5.0 cm, radius 60.0 cm and centre hole with radius 5.0 cm assigned as an individual body interfacing with the air from the outer domain ( Figure 5).
predicted flow rate is comparable to the flow rate produced by typical three-or fourblades ceiling fans operating at low-speed mode (with electric power of 25 W and efficiency of 0.045 (m 3 /s)/W) informed in Procel [50], a catalogue for energy consumption efficiency label for ceiling fans available in the Brazilian market. The air movement created by a ceiling fan contributes to reduce the temperature stratification in a room [51] and acts directly on the skin causing cooling thermal sensation with enhanced convection [52]. According to ASHRAE-55 [8], the cooling effect produced with an air speed of 0.90 m/s could maintain a thermal sensation of PMV +0.5 whilst the operative temperature is risen in up to 3.40 °C, for individuals with some local control over the air speed, wearing clothing insulation value of 0.50 clo, and performing an activity level of 1.1 met, as in ASHRAE-55 [8] (

The Scenarios for the Validation Exercise (Stage 1)
To perform the validation exercise proposed for Stage 1, the boundary conditions for the CFD models reproduced both the ambient conditions (operative temperature, air temperature inside and outside, air relative humidity inside, and air speed) and the occupant conditions (metabolic rate for informed activity levels and clothing insulation levels) for a total of five scenarios for NVB. The scenarios were organized in descending order for operative temperature. The monitored data were reported in the referenced literature [35,36] to have been measured during spring and autumn seasons in three non-residential indoor environments consisting of open-plan rooms occupied by the Brazilian Armed Forces and located in the Brazilian State of Mato Grosso do Sul, characterised by hot-humid climate. The authors reported a comprehensive analysis on thermal sensation. Values for TSV and for occupant's unacceptable votes (UV), obtained with 1.301 questionnaires, and for PMV and PPD indices, calculated according to ISO standards [10,11,53], were used To predict the airflow field produced by a ceiling fan with rotational speed equivalent to 290 RPM (rotation per minute of the blades), the respective momentum sources were assigned for the axial, radial and theta components of the flat cylinder: 5.5 kg/m 2 s 2 , 0.0 kg/m 2 s 2 and 0.8 kg/m 2 s 2 . A test with the selected values shows that the virtual fan predicts an airflow rate of 1.13 m 3 /s and an air speed of approximately 0.90 m/s monitored in the jet core zone at the vertical axis of the ceiling fan and at 1.0m above the floor. The predicted flow rate is comparable to the flow rate produced by typical three-or four-blades ceiling fans operating at low-speed mode (with electric power of 25 W and efficiency of 0.045 (m 3 /s)/W) informed in Procel [50], a catalogue for energy consumption efficiency label for ceiling fans available in the Brazilian market. The air movement created by a ceiling fan contributes to reduce the temperature stratification in a room [51] and acts directly on the skin causing cooling thermal sensation with enhanced convection [52]. According to ASHRAE-55 [8], the cooling effect produced with an air speed of 0.90 m/s could maintain a thermal sensation of PMV +0.5 whilst the operative temperature is risen in up to 3.40 • C, for individuals with some local control over the air speed, wearing clothing insulation value of 0.50 clo, and performing an activity level of 1.1 met, as in ASHRAE-55 [8] (

The Scenarios for the Validation Exercise (Stage 1)
To perform the validation exercise proposed for Stage 1, the boundary conditions for the CFD models reproduced both the ambient conditions (operative temperature, air temperature inside and outside, air relative humidity inside, and air speed) and the occupant conditions (metabolic rate for informed activity levels and clothing insulation levels) for a total of five scenarios for NVB. The scenarios were organized in descending order for operative temperature. The monitored data were reported in the referenced literature [35,36] to have been measured during spring and autumn seasons in three non-residential indoor environments consisting of open-plan rooms occupied by the Brazilian Armed Forces and located in the Brazilian State of Mato Grosso do Sul, characterised by hot-humid climate. The authors reported a comprehensive analysis on thermal sensation. Values for TSV and for occupant's unacceptable votes (UV), obtained with 1.301 questionnaires, and for PMV and PPD indices, calculated according to ISO standards [10,11,53], were used as benchmark for comparison with the results calculated in this work. The list of the scenarios with the ambient and the occupant setup parameters utilized for the coupled simulations for Stage 1 is given in Table 4. Table 4. List of the scenarios with setup parameters for the ambient and for the occupants from the reference [35,36] utilized for the coupled simulations for Stage 1.

Metabolic
Clothing Parameters for the Environment Conditions Ratio Insulation T air outside T operative T air inside T radmean RH V air Garment (met) (clo) The virtual domain for the CFD models reproduced the ambient conditions and the occupants clothing and activity levels. Conversely, the virtual environment was discretized and did not reproduce the actual shapes and physical dimensions of the several rooms from the reported surveys, the ventilation systems, other occupants, furniture, and heat sources which may have happened during the field measurements. An assumption made in this work is that, although the actual thermal sensation is influenced by the whole characteristics of the environment, the PMV and PPD indices for thermal sensation are calculated by analytical models based on the interaction between four ambient conditions and two individual characteristics (refer to Equations (5) and (6)). For this reason, by reproducing the reported ambient conditions and the occupant characteristics in the coupled simulations, it is expected that the comparison of the calculated results for the prediction of thermal sensation will show agreement with the values reported in the referenced literature [35,36].

The Scenarios for the Application and Test of Adaptive Behaviour (Stage 2)
To simulate and test the effectiveness of the application of suggested adaptive behaviour, the scenarios from Stage 1 had some parameters modified for the simulations performed for Stage 2. Specific sets of simulation were carried out considering, first, the impact with the reduction for the clothing insulation value; second, the impact with the controlled increase of the local air speed; and finally, the impact of both suggested adaptive behaviours applied together. While the first application for adaptive behaviour was based on the selection of garment ensembles with lower clothing insulation level than the utilized for the previous stage, the second application was carried out with the introduction in the model of a virtual ceiling fan to produce controlled air movement and increase forced convective heat loss. Changes for the metabolic rate were not considered since the values utilized for the previous stage were informed in the referenced literature as indicated for the activity level performed by the actual occupants. The list of the scenarios with the ambient and the occupant setup parameters utilized for the coupled simulations for Stage 2 is given in Table 5.

Results, Analysis, and Discussion
In this section, the results obtained for the validation exercise (Stage 1) and for the application and test for adaptive behaviour (Stage 2) are presented and analysed and are followed by a discussion about the application of the models to adjust the thermal sensation indices.

Results for the Validation Exercise (Stage 1)
The results obtained for the validation exercise are presented and analysed here. First, a qualitative analysis for the flow field over the manikin is shown for one scenario, followed by a quantitative analysis for the five scenarios simulated. Then, the comparison between the benchmark values for TSV, PMV, UV, and PPD, from the referenced literature [35,36], with the calculated results for DTS and PPD, from the CFD simulations coupled with thermal sensation model, is provided. Further, results for the adjusted thermal sensation (DTSa) and from the extended thermal sensation (DTSe), calculated with the DTS, are also brought to the comparison in this section.
The qualitative analysis for the selected scenario 2 ( Figure 6) reveals that the flow field around the manikin resulted in low mean air speed (0.12 m/s) which is similar to the value reported for this scenario (0.15 m/s). A vertical plume of hot air developed on the vertical axis above the seated manikin, being slightly disturbed by local flow near the ceiling. The flow throughout the window clearly indicates a buoyancy-driven pattern. The calculated operative temperature was equal to the informed: 33.3 • C. The clothing insulation value calculated with the resulting environmental conditions, the manikin mean skin temperature and moisture production and based on the selected option of garment ensemble (casual) was 0.46 clo, while the value informed for this scenario was 0.34 clo. As shown later in this section, the respective thermal sensation indices from the referenced literature (TSV) and from the coupled simulation (DTS) for this scenario were identical (+2) or very close, if showed with one decimal place (+1.9 and +1.8), defining warm thermal sensation. The calculated operative temperature, air speed, and clothing insulation values for the five scenarios simulated for Stage 1, and the respective measured values, are shown in Table 6. The comparison for the values for operative temperature showed an average variation of ±0.30 °C and a maximum variation of 0.60 °C (scenario 5). The values for air speed showed an average variation of ±0.07 m/s and a maximum variation of 0.14 m/s (scenario 3), and the updated values for clothing insulation were close to the ones in the reference, with an average variation of ±0.09 clo and a maximum variation of 0.13 clo (scenario 5). In terms of percentual variation between the calculated results and the reference values, the respective averaged percentages for operative temperature, air speed, and clothing insulation were: ±1%, ±111% and ±15%. While for operative temperature and clothing insulation the percentages were low, the high percentual variation for air speed could reside in two facts: the overall speeds informed were all lower than 0.22 m/s, and a difference of 0.10 m/s could imply twofold results; the values informed in the referenced literature suggested one measurement with undisclosed position, while the results from the CFD simulation refer to the averaged values based on the global range of speed for the environment. The benchmark values for TSV and PMV [35,36] and the calculated values for DTS, DTSa, and DTSe, from the CFD simulations, are shown in Table 7. The results for DTSa The calculated operative temperature, air speed, and clothing insulation values for the five scenarios simulated for Stage 1, and the respective measured values, are shown in Table 6. The comparison for the values for operative temperature showed an average variation of ±0.30 • C and a maximum variation of 0.60 • C (scenario 5). The values for air speed showed an average variation of ±0.07 m/s and a maximum variation of 0.14 m/s (scenario 3), and the updated values for clothing insulation were close to the ones in the reference, with an average variation of ±0.09 clo and a maximum variation of 0.13 clo (scenario 5). In terms of percentual variation between the calculated results and the reference values, the respective averaged percentages for operative temperature, air speed, and clothing insulation were: ±1%, ±111% and ±15%. While for operative temperature and clothing insulation the percentages were low, the high percentual variation for air speed could reside in two facts: the overall speeds informed were all lower than 0.22 m/s, and a difference of 0.10 m/s could imply twofold results; the values informed in the referenced literature suggested one measurement with undisclosed position, while the results from the CFD simulation refer to the averaged values based on the global range of speed for the environment. The benchmark values for TSV and PMV [35,36] and the calculated values for DTS, DTSa, and DTSe, from the CFD simulations, are shown in Table 7. The results for DTSa were calculated based on the DTS utilizing adjustment equations [21] (refer to Equations (7) and (8)), and the results for DTSe were calculated utilizing expectancy factors [20] (refer to Equation (9)). In this work, the results for thermal sensation indices are shown with one decimal place to give more precision to the analysis. As described in Lamberts and Andreasi [35] and Andreasi, Lamberts, and Cândido [36], divergences were observed between the TSV and the PMV indices, with the former index overestimating hot sensation. For scenario 1, a difference of 1.6 was informed between the TSV and the PMV indices. For this same scenario, the divergence between the TSV and the calculated DTS was 0.6, and this was the highest difference found between these two indices. Further, all the calculated results for DTS felt between the values for the voted TSV and the measured PMV, being closer to the latter ones. On average, the difference found between the TSV and the DTS values was of ±0.3. Identical values, or the nearest values, were found between the voted TSV and the calculated dynamic thermal sensation with DTSe = 0.7 for scenario 1, DTS for scenario 2, DTSe = 0.9 for scenario 3, DTSe = 0.6 for scenario 4, and DTSa for scenario 5. The extended moderate expectancy factors for the DTSe ranging from 0.9-0.7 were suggested for NVBs located in regions with warm summer season [20], which agrees with the description of the locations of the buildings mentioned in the referenced literature. Conversely, there were no identical values between the informed PMV and the calculated DTS. On average, the difference found between the PMV and the DTS values was of 0.7. Table 7. Thermal sensation for the five scenarios for Stage 1: benchmark values for TSV and PMV from the reference [35,36], calculated results (CFD) for DTS, adjusted results (DTSa) [21], and expanded results (DTSe) utilizing five expectancy factors [20]. The comparison of the PPD DTS (based on the DTS) with the benchmark values for unsatisfied votes (UV) and for the PPD PMV (based on the PMV) [35,36] showed the same trend described for the thermal sensation (Figure 7). The PPD PMV were overestimated (reaching almost 100% of dissatisfied for two scenarios) and greater than both the percentages informed for UV and for PPD DTS in four out of five scenarios analysed: The exception was the scenario 5, for the lowest operative temperature. The PPD DTS were close to the UV in four out of five scenarios, showing an average variation of 9%. A statistical analysis was carried out to quantify the correlation strength between the benchmark values and the calculated results, utilizing the correlation coefficient (r) and the coefficient of determination (R 2 ) measurements. These measurements identify the statistical strenght based on the linear association between either two series of data with the same metric or a serie of data compared to an independent variable [54]. The  A statistical analysis was carried out to quantify the correlation strength between the benchmark values and the calculated results, utilizing the correlation coefficient (r) and the coefficient of determination (R 2 ) measurements. These measurements identify the statistical strenght based on the linear association between either two series of data with the same metric or a serie of data compared to an independent variable [54]. The significance value is provided in a scale ranging from +1.00 (perfect correlation) to −1.00 (reverse correlation), on which zero means absence of correlation [55]. The comparison of the benchmark values for TSV with the informed PMV and with the calculated results for DTS, DTSa, and DTSe for five expectancy factors (Figure 8) shows that the highest correlation values were found for the DTSa (R 2 = 0.95 and r = 0.97), while the lowest correlation values were found for the PMV (R 2 = 0.88 and r = 0.94). The comparison of the values for thermal sensation (benchmark values for TSV and PMV and calculated values for DTS, DTSa, and DTSe) with the respective operative temperatures utilized as an independent variable (Figure 9) shows that the correlation values for DTS (R 2 = 0.989 and r = 0.994) were marginally higher than those for TSV (R 2 = 0.917 and r = 0.958), and the lowest correlation was found for the PMV (R 2 = 0.961 and r = 0.980). Further statistical analysis was carried out to identify which of the calculated values for the predicted thermal sensation (DTS, DTSa, and DTSe for five expectancy factors) shows the strongest statistical agreement with the benchmark value for the voted thermal sensation (TSV) ( Table 8). In addition to the standard deviation, the correlation coefficient and the coefficients of determination, other five methods were utilized: the Mean Bias Error (MBE), the Normalized Mean Bias Error (NMBE), the Root Mean Square Error (RMSE), and the Coefficient of Variation of the Root Mean Square Error (CV RMSE ). The results were also compared with calibration criteria from ASHRAE Guideline 14 [56] and from the International Performance Measurement and Verification Protocol (IPMVP) [57]. The analysis of the results showed that, when compared with the benchmark values for TSV, the prediction for the dynamic thermal sensation utilizing the extended expectancy factor of 0.8 (DTSe = 0.8) presented the highest statistical strength for five out of six criteria, and this demonstrates that the best adjustment for the calculated thermal sensation was achieved for a moderate expectancy factor [20].   [35,36], and with the calculated results for DTS, DTSa, and DTSe, from the coupled simulations for Stage 1. Figure 9. Comparison of the values for operative temperature with the benchmark values for TSV and PMV, from the reference [35,36], and with calculated results for DTS, DTSa, and DTSe, from the coupled simulations for Stage 1.  Figure 9. Comparison of the values for operative temperature with the benchmark values for TSV and PMV, from the reference [35,36], and with calculated results for DTS, DTSa, and DTSe, from the coupled simulations for Stage 1. To conclude, the calculated results for Stage 1 for dynamic thermal sensation and for predicted percentage of dissatisfied showed good agreement with the voted thermal sensation indices from the referenced literature, demonstrating that the thermal sensation model predicts well thermal sensation. The level of agreement was improved after extending the DTS values with a moderate expectancy factor ranging from 0.9-0.7, category suggested for NVBs located in regions with warm summer season [20].

Results for the Application and Test for Adaptive Behaviour (Stage 2)
In this section, the results obtained for the testing of adaptive behaviours with the coupled simulation models are presented and analysed. The objective was to identify and quantify potential reduction on the thermal sensation indices calculated in the previous stage. The coupled simulations performed for Stage 2 considered the reduction in the clothing insulation value, the increase in the air speed, and both actions combined. The calculated results for Stage 2 are presented for DTS ( Figure 10) and the respective PPD (Figure 11), and for DTSe utilizing an expectancy factor of 0.8 (Figure 12), to best fit to indoor environments for NVBs located in regions with warm summer season, and the respective PPD ( Figure 13). Further, an analysis is provided comparing ambient temperature, body temperature, and body moisture production with the tested adaptive behaviours for the five scenarios covered in Stage 2 ( Figure 14). 3) for scenario 4, whose respective operative temperatures were 34.5 and 28.9 °C . For the extended DTSe values, the respective decreases were smaller: 0.1 (from +1.6 to +1.5) for scenario 1 and 1.2 (from +1.0 to −0.2) for scenario 4. For scenario 5, the slightly cold thermal sensation was increased from −0.2 to −1.1 for DTS and from −0.2 to −0.9 for DTSe. For the PPD based on the DTS, the smallest and greatest decreases found were of 11% (scenario 1) and of 31% (scenario 3), and for scenario 5 the PPD increased from 6% to 30%. For the PPD index based on the extended DTSe, the respective smallest and greatest decreases found were 1% (scenario 1) and 20% (scenario 3), while an increase of dissatisfied occupants of 15% was noticed for scenario 5.

DTS-Dynamic Thermal Sensation Index (7 Points Scale) Scenarios investigated with responses for the tested adaptive behaviours and respective operative temperatures
Dynamic thermal sensation DTS   3) for scenario 4, whose respective operative temperatures were 34.5 and 28.9 °C . For the extended DTSe values, the respective decreases were smaller: 0.1 (from +1.6 to +1.5) for scenario 1 and 1.2 (from +1.0 to −0.2) for scenario 4. For scenario 5, the slightly cold thermal sensation was increased from −0.2 to −1.1 for DTS and from −0.2 to −0.9 for DTSe. For the PPD based on the DTS, the smallest and greatest decreases found were of 11% (scenario 1) and of 31% (scenario 3), and for scenario 5 the PPD increased from 6% to 30%. For the PPD index based on the extended DTSe, the respective smallest and greatest decreases found were 1% (scenario 1) and 20% (scenario 3), while an increase of dissatisfied occupants of 15% was noticed for scenario 5.

DTS-Dynamic Thermal Sensation Index (7 Points Scale) Scenarios investigated with responses for the tested adaptive behaviours and respective operative temperatures
Dynamic thermal sensation DTS   Results for the extended DTSe with moderate expectancy factor of 0.8 [20] for three suggested adaptive behaviours: reduced clothing value, increased air speed, and both actions. Figure 13. Results for PPD based on the extended DTSe with moderate expectancy factor of 0.8 [20] for three tested adaptive behaviours: reduced clothing value, increased air speed, and both actions.
The variation for the body hypothalamus temperature, mean skin temperature, the mean radiant temperature, and the body moisture production, with the tested adaptive behaviours and for the operative temperatures utilized in each scenario covered for Stage 2 is shown in Figure 13. Variations on the hypothalamus temperature, responsible for triggering the human body active thermoregulation responses for sweating, ranged between 36.92-36.88 °C. Conversely, a wide range was found for the perimetral body temperatures among the investigated scenarios: 35.5-32.7 °C for the mean skin temperature and 34.8-24.5 °C for the mean radiant temperature. The decrease noticed for the body moisture production with sweat was of up to threefold and in the moisture evaporated by the skin was of up to fivefold. A trendline characterized by a descending diagonal shows how the combination of the progressive reduction in the operative temperature and adoption of the suggested adaptive behaviours act on the switch from warm to slightly cool thermal sensation throughout the five scenarios investigated. The values for scenario 1 showed the highest mean skin temperature and mean radiant temperature, with the highest moisture production by sweating and evaporation via skin and respiration, with little variation in the body temperature and moisture production for any of the tested adaptive behaviours. The decrease in the clothing level (0.25 clo) and the increase in the air speed (0.9 m/s,

Scenarios investigated with responses for the tested adaptive behaviours and respective operative temperatures
Dynamic thermal sensation DTS   [20] for three suggested adaptive behaviours: reduced clothing value, increased air speed, and both actions. Figure 13. Results for PPD based on the extended DTSe with moderate expectancy factor of 0.8 [20] for three tested adaptive behaviours: reduced clothing value, increased air speed, and both actions.
The variation for the body hypothalamus temperature, mean skin temperature, the mean radiant temperature, and the body moisture production, with the tested adaptive behaviours and for the operative temperatures utilized in each scenario covered for Stage 2 is shown in Figure 13. Variations on the hypothalamus temperature, responsible for triggering the human body active thermoregulation responses for sweating, ranged between 36.92-36.88 °C. Conversely, a wide range was found for the perimetral body temperatures among the investigated scenarios: 35.5-32.7 °C for the mean skin temperature and 34.8-24.5 °C for the mean radiant temperature. The decrease noticed for the body moisture production with sweat was of up to threefold and in the moisture evaporated by the skin was of up to fivefold. A trendline characterized by a descending diagonal shows how the combination of the progressive reduction in the operative temperature and adoption of the suggested adaptive behaviours act on the switch from warm to slightly cool thermal sensation throughout the five scenarios investigated. The values for scenario 1 showed the highest mean skin temperature and mean radiant temperature, with the highest moisture production by sweating and evaporation via skin and respiration, with little variation in the body temperature and moisture production for any of the tested adaptive behaviours. The decrease in the clothing level (0.25 clo) and the increase in the air speed (0.9 m/s,  Figure 13. Results for PPD based on the extended DTSe with moderate expectancy factor of 0.8 [20] for three tested adaptive behaviours: reduced clothing value, increased air speed, and both actions. The reduction in the clothing insulation level was achieved selecting different garment ensembles from the available options (refer to Section 3.2-The thermal sensation modelfor details). The change from casual (with an averaged value of 0.46 clo) to summer (with an averaged value of 0.21 clo), resulted on an averaged decrease in the clothing insulation level of 0.25 clo, which was applied to scenarios 1, 2 and 3 (with respective operative temperatures of 34.5, 33.3, and 30.1 • C). The respective reductions on the DTS for these three scenarios were nil, −0.1, and −0.3, with similar reductions observed for DTSe. The respective reductions in terms of PPD were 3% for scenario 1, 7% for scenario 2, and 12% for scenario 3, based on DTS results, and 2% for scenario 1, 5% for scenario 2, and 8% for scenario 3, based on the extended DTSe results. The change from "casual with thin sweater" (with an averaged value of 0.64 clo) to "casual" resulted in an averaged decrease of 0.18 clo and was applied to scenario 4 (operative temperature of 28.9 • C), decreasing the DTS in 0.9 and the PPD in 23%. For the DTSe, the respective decreases were 0.7 and 15%. The change from casual with thick sweater (with an averaged value of 0.94 clo) to casual with thin sweater reduced the clo in 0.32. When applied to scenario 5 (operative temperature of 24.0 • C), the reduction in the clothing value decreased the DTS in 0.2 and increased the PPD in 3%, while for the DTSe the respective values were decrease of 0.1 and increase of 1%.
production with sweat ceased. For these same scenarios, the resulting DTS was reduced from slightly warm to neutral thermal sensation. When compared to scenario 4, moisture production evaporated by respiration and evaporated by the skin increased for scenario 5, whilst no sweat was perceived and body skin and radiant temperatures and ambient operative temperatures were respectively reduced by 1.0, 4.5, and 4.9 °C . The respective operative temperature and reduction in the clothing lever for scenario 5 were 24.0 °C and 0.32 clo. This may have happened because the body thermoregulation active system increased skin moisture to compensate faster evaporation due to both forced convection and moisture adjustment for an environment delivering slightly cool thermal sensation [58]. In conclusion, the results presented in this section demonstrate that the actions taken for the application of adaptive behaviours allowed to quantify the potential and the effectiveness for each suggested adaptation. Figure 14. Initial results for five scenarios analysed and the corresponding results based on three suggested adaptive behaviours (reduced clothing value, increased air speed, and both actions) for operative temperature, hypothalamus temperature, mean skin temperature, mean radiant temperature, sweat, and moisture evaporated by respiration and by the skin.

Discussion about the Application of the Models to Adjust Thermal Sensation Indices
The results for DTS calculated with the coupled simulation for both Stage 1 and Stage 2 were adjusted utilizing two methods from the literature: the expectancy factor (DTSe), proposed by Fanger and Toftum [20], and the adjustment model (DTSa) proposed by Humphreys and Nicol [21]. While the latter model utilizes expectancy factors to extend the thermal sensation based on the climate (hot weather seasonal or constant) and type of building operation (predominantly NVB or HVAC), the former model proposes adjustments to reduce the effect of discretization which could imply in overestimation of hot thermal sensation (for further detail refer to Section 3.2-The thermal sensation model). Therefore, the application of one model would not exclude the other since their theories are grounded and approach different subjects to enhance the predicted thermal sensation. Conversely, if both models were applied together, a given thermal sensation index could be reduced to 30-10% of its initial value. For example, the calculated DTS with the coupled simulation for scenario 1 was of +2. 1 Figure 14. Initial results for five scenarios analysed and the corresponding results based on three suggested adaptive behaviours (reduced clothing value, increased air speed, and both actions) for operative temperature, hypothalamus temperature, mean skin temperature, mean radiant temperature, sweat, and moisture evaporated by respiration and by the skin.
The combined test for the reduction in the clothing insulation level and the increase in the air speed decreased the predicted DTS indices from a minimum of 0.3 (from +2.1 to +1.9) for scenario 1 to a maximum of 1.3 (from +1.0 to −0.3) for scenario 4, whose respective operative temperatures were 34.5 and 28.9 • C. For the extended DTSe values, the respective decreases were smaller: 0.1 (from +1.6 to +1.5) for scenario 1 and 1.2 (from +1.0 to −0.2) for scenario 4. For scenario 5, the slightly cold thermal sensation was increased from −0.2 to −1.1 for DTS and from −0.2 to −0.9 for DTSe. For the PPD based on the DTS, the smallest and greatest decreases found were of 11% (scenario 1) and of 31% (scenario 3), and for scenario 5 the PPD increased from 6% to 30%. For the PPD index based on the extended DTSe, the respective smallest and greatest decreases found were 1% (scenario 1) and 20% (scenario 3), while an increase of dissatisfied occupants of 15% was noticed for scenario 5.
The variation for the body hypothalamus temperature, mean skin temperature, the mean radiant temperature, and the body moisture production, with the tested adaptive behaviours and for the operative temperatures utilized in each scenario covered for Stage 2 is shown in Figure 13. Variations on the hypothalamus temperature, responsible for triggering the human body active thermoregulation responses for sweating, ranged between 36.92-36.88 • C. Conversely, a wide range was found for the perimetral body temperatures among the investigated scenarios: 35.5-32.7 • C for the mean skin temperature and 34.8-24.5 • C for the mean radiant temperature. The decrease noticed for the body moisture production with sweat was of up to threefold and in the moisture evaporated by the skin was of up to fivefold. A trendline characterized by a descending diagonal shows how the combination of the progressive reduction in the operative temperature and adoption of the suggested adaptive behaviours act on the switch from warm to slightly cool thermal sensation throughout the five scenarios investigated. The values for scenario 1 showed the highest mean skin temperature and mean radiant temperature, with the highest moisture production by sweating and evaporation via skin and respiration, with little variation in the body temperature and moisture production for any of the tested adaptive behaviours. The decrease in the clothing level (0.25 clo) and the increase in the air speed (0.9 m/s, applied for the five scenarios) did little to alleviate the warm thermal sensation predicted for this scenario, with operative temperature of 34.5 • C. For scenarios 3 and 4, the respective operative temperatures and reduction in the clothing level were 30.1 and 28.9 • C, and 0.25 and 0.18 clo. The mean skin temperature and mean radiant temperature were both reduced in 0.5 • C for scenario 3, and respectively in 1.0 and 0.6 • C, for scenario 4. The moisture production evaporated via the skin reached the lowest level and the moisture production with sweat ceased. For these same scenarios, the resulting DTS was reduced from slightly warm to neutral thermal sensation. When compared to scenario 4, moisture production evaporated by respiration and evaporated by the skin increased for scenario 5, whilst no sweat was perceived and body skin and radiant temperatures and ambient operative temperatures were respectively reduced by 1.0, 4.5, and 4.9 • C. The respective operative temperature and reduction in the clothing lever for scenario 5 were 24.0 • C and 0.32 clo. This may have happened because the body thermoregulation active system increased skin moisture to compensate faster evaporation due to both forced convection and moisture adjustment for an environment delivering slightly cool thermal sensation [58]. In conclusion, the results presented in this section demonstrate that the actions taken for the application of adaptive behaviours allowed to quantify the potential and the effectiveness for each suggested adaptation.

Discussion about the Application of the Models to Adjust Thermal Sensation Indices
The results for DTS calculated with the coupled simulation for both Stage 1 and Stage 2 were adjusted utilizing two methods from the literature: the expectancy factor (DTSe), proposed by Fanger and Toftum [20], and the adjustment model (DTSa) proposed by Humphreys and Nicol [21]. While the latter model utilizes expectancy factors to extend the thermal sensation based on the climate (hot weather seasonal or constant) and type of building operation (predominantly NVB or HVAC), the former model proposes adjustments to reduce the effect of discretization which could imply in overestimation of hot thermal sensation (for further detail refer to Section 3.2-The thermal sensation model). Therefore, the application of one model would not exclude the other since their theories are grounded and approach different subjects to enhance the predicted thermal sensation. Conversely, if both models were applied together, a given thermal sensation index could be reduced to 30-10% of its initial value. For example, the calculated DTS with the coupled simulation for scenario 1 was of +2.1. The benchmark value from the referenced literature for this scenario was TSV= +1.4. With the application of these models individually, the calculated thermal sensation would become: +0.8, for DTSa, and +1.4, for DTSe (for low expectancy factor of 0.7). For the combined application of these models, the resulting thermal sensation would be +0.5, representing 26% of initial value for the DTS.

Conclusions
The objectives of this work were (i) to perform an analysis on thermal sensation for occupants of non-residential naturally ventilated buildings located in Brazilian tropical warm-humid climatic region utilizing as research method transient computational fluid dynamics (CFD) simulation coupled with a dynamic human thermophysiology model for the prediction of thermal sensation, and (ii) to test additional parameters to simulate adaptive behaviours and analyse their impact on the results and their effectiveness to mitigate warm and slightly-warm thermal sensations.
Computational simulations were validated with the comparison of calculated results for dynamic thermal sensation (DTS) and predicted percentage of dissatisfied (PPD) with benchmark results from literature [35,36] for thermal sensation votes (TSV) and unacceptable votes (UV), from surveys, and for predicted mean vote (PMV) and PPD, from field measurements and calculated according to ISO standards [10][11][12].
The comparison of the calculated results with the benchmark values showed that the calculated DTS agreed more with the TSV than with the PMV. The same value with one decimal place for TSV and DTS was observed for one scenario out of five analysed. The same values were also found between the TSV and the adjusted dynamic thermal sensation (DTSa) for one scenario and for three scenarios for TSV and the extended dynamic thermal sensation (DTSe) when utilizing a moderate expectancy factor ranging from 0.9-0.7. No similar values were found between the measured PMV and the calculated DTS, the adjusted DTSa, or the extended DTSe and the measured PMV values.
The application and test of adaptive behaviours for the five validated scenarios allowed to quantify the effectiveness of each solution in the reductions of warm thermal sensation. The simulated adaptive behaviours consisted in the reduction the clothing insulation value range of 0.18-0.32 clo, the increase in the air speed of 0.9m/s introducing a virtual ceiling fan in the 3D models, and the combined application of both suggestions. Operative temperatures spanned 34.5-24.0 • C.
The single test for the reduction of the clothing values resulted in decreases for the DTS ranging from 0.1 to 0.9 and for the PPD from 3% to 23%. The single test for the increase in the air speed resulted in decreases for the DTS ranging from 0.1 to 1.0 and for the PPD from 7% to 24%. The combined application of both tested adaptive behaviours resulted in decreases for the DTS from 0.1 to 1.3 and the PPD from 9% to 31%.
The most efficient results happened for the scenario with operative temperature of 28.9 • C, for which the thermal sensation changed from slightly warm to neutral. The reduction in the clothing values, the increase in the air speed, and the combination of both resulted in the respective reductions in the DTS of 0.9, 1.0, and 1.3, and in the PPD of 23%, 23%, and 21%, which show that the effect on the thermal sensation observed with the tests individually was not summed when they were tested together. Further, the PPD increased in 2% for the tests combined. For the scenario with operative temperature of 24.0 • C, the tested adaptive behaviours increased the PPD from 6% to 30% and changed the thermal sensation from neutral to slightly cool. The least efficient results happened for the scenario with operative temperature of 34.5 • C, for which the thermal sensation was maintained as warm regardless the tested adaptive behaviours.
The combined application for the tested adaptive behaviours resulted in a wide range on the perimetral body temperatures and affected the body moisture production: The mean skin temperature range was 35.5-32.7 • C; the mean radiant temperature range was 34.8-24.5 • C; the body sweat production was reduced in up to three times, and the evaporation by the skin by up to five times.
The discussion raised aspects related to the selection of existing models to improve the calculated DTS either by utilizing adjustment equations to reduce bias on the values or by extending the values with factors which are more suitable to specific building operation and climatic condition.
In conclusion, this work evaluated the capacity of a computational coupled model to predict thermal sensation for occupants of naturally ventilated buildings in warm-humid regions and tested the effectiveness of tested adaptive behaviours in the calculated indices. This method could be particularly suitable for investigations related to thermal sensation comprising varied design solutions and multiple locations in continental-size countries which have large territories located in tropical zones and for which field measurement would be unfeasible.