Thermal sensation and percentage of dissatisfied in thermal environments with positive and negative vertical air temperature differences

Considering the percentage of dissatisfied due to local thermal sensation(PD LTSV),a vertical air temperature difference(ΔT_(d))threshold of about 3°C was recommended in standards.However,some novel air distribution methods might create large positive(which means the head warmer than the feet,vice versa)or negativeΔT_(d),with no suitable proved criteria to be used.In this study,sixteen subjects were seated in a climatic box placed in a climate chamber to evaluate thermal sensation and percentage of dissatisfied with negative and positiveΔT_(d) in different whole-body thermal conditions.Air temperatures were controlled independently at the upper and lower body parts,with 13 different air temperature sets combined with 22°C,25°C,28°C,and 31°C(i.e.-9°C≤ΔT_(d)≤9°C).Results showed that subjects were more thermally sensitive at the upper body and with positiveΔT_(d) than at the lower body or with negativeΔT_(d).The 80%acceptableΔT_(d) range is about-8 to 7°C in overall neutral(TSV=0),-7°C to 6°C in slightly cool(TSV=-0.5)conditions,which is wider than-3 to 3°C in slightly warm conditions(TSV=+0.5).By considering the factors of both TSV andΔT_(d),a new overall percentage of dissatisfied index(OPD P)was proposed.Case studies show that the new OPD P index is more precise and suitable for the evaluations of different air distributions to predict overall percentage of dissatisfied in thermal environments with vertical air temperature difference.

Climate chamber investigation of the effect of indoor thermal histories on thermal adaptation in different seasons

The indoor thermal history of residents in the hot summer and cold winter climate zone in China have undergone a significant change in recent years,which also changes their seasonal thermal adaptations and this has formed feed-back effects to the increasing usage of air conditioning units in this area.To study the seasonal variations of thermal adaptation,the thermal comfort experiments were conducted on two groups of participants.Each groups included 20 participants who had indoor history mainly with natural ventilation(NV group)and air-conditioning(AC group),respectively.The results demonstrated that the thermal sensation vote(TSV)in warm environments did not differ between AC and NV subjects in summer.However,the TSV of AC subjects were much lower than that of NV subjects in the same standard effective temperature and skin temperature in a cold environment in winter.Overall,the participants who spent most of their time in AC space in winter showed a low level of thermal adaptation with a narrower acceptable skin temperature range of 32.6-33.5℃.Thus,this study presents the basic information regarding the seasonal effects on human thermal adaptation due to different long-term indoor thermal histories.

Characteristics and comfort evaluation of sinusoidal airflows by regulating motor rotating frequency of a floor fan

Increasing air movement by utilizing electric fans is among the common approaches for comfort and energy savings in buildings in summer;however,the use of electric fans is usually the forced constant airflow.This study reformed the one-chip computer program of a floor fan motor and simulated dynamic airflow through controlling the rotating frequency only.The flow field characteristics of constant,oscillated,sinusoidal airflows with periods of 10 s,30 s,60 s,100 s were measured.The comfort performance was evaluated by chamber experiments,with 20 subjects exposed to six airflow patterns under 30℃,70%RH.The results showed that the simulated sinusoidal airflows had relatively higher turbulence intensity(32%–37%)andβvalues(>0.4).While subjects’thermal sensations were not statistically significant among six airflows,their reported discomfort symptoms during 60 min exposure were reduced under sinusoidal airflows.The calculated convective heat transfer shared similar variations to instantaneous air velocity and skin temperature.A large fluctuation of 10–50 W/m^(2)and higher total convective heat loss(3000–3500 W/m^(2))were found for sinusoidal period 30 s.This study develops a new method to simulate varying air velocities through conveniently controlling the fan motor amplitude and frequency,and verifies the comfortable feelings to dynamic sinusoidal airflows.The work benefits to improve the performance of the current electric fans with lower costs and promote the applications of personal ventilation devices in buildings,thus optimizing human thermal comfort,reducing dependences on air conditionings and achieving building energy efficiency.

Transient particle transport prediction based on lattice Boltzmann method-based large eddy simulation and Markov chain model

Fast and accurate prediction of particle transport is essential for the determination of as-needed mitigation strategies to improve indoor air quality.Several methods have been proposed to achieve this goal.However,they mainly based on the Reynolds-averaged Navier-Stokes(RANS)approach,which may affect the accuracy of particle calculations.Considering the lattice Boltzmann method(LBM)can execute high-speed large eddy simulation(LES)while Markov chain model performs well for particle calculations.This study proposed an LBM-LES-Markov-chain framework for indoor transient particle transport simulation.The performance of the proposed framework was investigated in a two-zone ventilated chamber,and compared to the CFD-LES based models.Results show that the proposed framework is as accurate as but faster than the CFD-LES based models.The mean normalized root-mean-square deviations of the proposed model is 12%,similar to the CFD-LES-Lagrangian(15%)and CFD-LES-Eulerian(13%)models.The computing time of the proposed model is 5.66 h,shorter than the CFD-LES-Lagrangian(153 h)and CFD-LES-Eulerian(15.03 h)models.Furthermore,we further compared the framework with CFD-RNG based Markov chain model,CFD-RANS based models,and FFD-Markov-chain model and found that it is an alternative for the fast prediction of indoor particle concentration.

Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter

Individual thermal comfort models based on physiological parameters could improve the efficiency of the personal thermal comfort control system.However,the effect of thermal history has not been fully addressed in these models.In this study,climate chamber experiments were conducted in winter using 32 subjects who have different indoor and outdoor thermal histories.Two kinds of thermal conditions were investigated:the temperature dropping(24-16℃)and severe cold(12℃)conditions.A simplified method using historical air temperature to quantify the thermal history was proposed and used to predict thermal comfort and thermal demand from physical or physiological parameters.Results show the accuracies of individual thermal sensation prediction was low to about 30%by using the PMV index in cold environments of this study.Base on the sensitivity and reliability of physiological responses,five local skin temperatures(at hand,calf,head,arm and thigh)and the heart rate are optimal input parameters for the individual thermal comfort model.With the proposed historical air temperature as an additional input,the general accuracies using classification tree model C5.0 were increased up by 15.5%for thermal comfort prediction and up by 29.8%for thermal demand prediction.Thus,when predicting thermal demands in winter,the factor of thermal history should be considered.