Building Energy & Environment Laboratory
Let the dream set sail

2019

Lai, D.*, Chen, C.* (2019). Comparison of the linear regression, multinomial logit, and ordered probability models for predicting the distribution of thermal sensation. Energy and Buildings, 188–189, 269–277.


This study compares the linear regression model, ordered probability model, and multinomial logit model for prediction of the individual thermal sensation votes (TSVs) and TSV distributions under given conditions. Two thermal comfort datasets were used to develop and evaluate the models. One dataset was taken from an indoor thermal comfort survey conducted in Pakistan, and the other was taken from an outdoor thermal comfort survey conducted in Tianjin, China. The data were divided into training and validation datasets. The training datasets were used for model development. The developed models were then used to predict new cases in the validation dataset. The predictive capability of the three models were systematically evaluated and compared to examine how well the developed models predicted individual TSVs and TSV distributions for the validation dataset. The results showed that the ordered probability model and the multinomial logit model correctly predicted around 50% of the individual TSVs, whereas the accuracy of the linear regression model was only around 20 to 40%. In addition, the chi-square statistics show that the ordered probability model and the multinomial logit model better predicted the TSV distributions than the linear regression model.

2018

Lai, D., Chen, C., Liu, W., Shi, Y., Chen, C.* (2018). An ordered probability model for predicting outdoor thermal comfort. Energy and Buildings, 168, 261-271.


Outdoor thermal comfort in urban spaces is gaining increasing research attention because it is associated with the quality of life in cities. This paper presents an ordered probability model for predicting the probability distribution of thermal sensation votes (TSVs) based on 1549 observations obtained from a large-scale field survey conducted at a park in Tianjin, China. With a given set of inputs, the developed model can predict the probability that people will feel cold, cool, slightly cool, neutral, slightly warm, warm, or hot. The predictive capability of the ordered probability model was systematically assessed by comparing it with the survey data and a traditional multivariate linear model. Both models had a similar accuracy in predicting single-value TSVs. However, the ordered probability model performed much better than the multivariate linear model in predicting the probability distribution of TSVs. A sensitivity analysis of the ordered probability model revealed that outdoor air temperature was the most important influencing factor. The impacts of global radiation, relative humidity, and activity level on predicted thermal sensation depended on the outdoor air temperature. The developed ordered probability model was used to predict suitable time periods for holding outdoor activities in Tianjin across a whole year. This new model is a more informative tool for predicting outdoor thermal comfort. 


Bian, Y., Zhang, L.*, Chen, C.* (2018). Experimental and modeling study of pressure drop across electrospun nanofiber air filters. Building and Environment, 142,244-251.


Electrospun nanofiber air filters can achieve high PM2.5 removal efficiency with a relatively low pressure drop because of the slip effect. They may therefore be applied in buildings to reduce indoor exposure to PM2.5 with lower energy consumption. This study first fabricated 25 nylon nanofiber filters with different filter parameters of fiber diameter, filter thickness, and packing density. The pressure drop across each nanofiber filter was measured under five different face velocities. This study then developed a method for modeling the pressure drop across electrospun nanofiber air filters using the filter parameters. 125 sets of experimental data were obtained for the model development, and a semi-empirical model was developed to predict the pressure drop across nylon electrospun nanofiber filters. The results showed that the pressure drop was proportional to the face velocity and filter thickness. The product of drag coefficient and Reynolds number was a function of both packing density and Knudsen number. The semi-empirical model reasonably predicted the pressure drop across the nylon electrospun nanofiber filters with a median relative error of 4.3%. 


Xia, T., Bian, Y., Zhang, L., Chen, C.* (2018). Relationship between pressure drop and face velocity for electrospun nanofiber filters. Energy and Buildings, 158, 987-999.


Nanofiber filters are typically fabricated using the electrospinning technique, which can reach high particle removal efficiency with relatively low air resistance because of the gas slip effect. They have a great potential for applications in the filtration units of heating, ventilation, and air-conditioning (HVAC) systems to reduce the fan energy consumption. This study systematically examined the relationship between pressure drop and face velocity for electrospun nanofiber filters. Experimental data of 122 nanofiber filters were collected from the literature to validate the theory regarding pressure drop for nanofiber filters proposed in previous studies. The linear regressions between pressure drop and face velocity showed that 89% of the tested nanofiber filters had an R2 value greater than 0.9. Therefore, the pressure drop can be confidently regarded as being proportional to the face velocity for nanofiber filters. This conclusion was further confirmed by additional experimental measurements conducted in this study. After confirming the theory, the air resistance coefficients of nanofiber filters were calculated and compared with that of commercial HVAC filters. The comparison showed that the air resistances of nanofiber filters with careful design could be lower than that of the commercial filters with similar particle removal efficiency. 


2016

Chen, C., Zhang, X., Groll, E., McKibben, A., Long, N., Dexter, M., Chen, Q. (2016). A method of assessing the energy cost saving from using an effective door closer. Energy and Buildings, 118, 329-338.


Door closers are widely used for doors in commercial buildings, not only for safety purposes but also for reducing the airflow through door openings. This study aimed to develop a method for quickly assessing, in the design phase, the heating and cooling energy cost saving from using an effective door closer. The method developed in this study consists of a stop angle model, airflow model, and energy cost calculation. This investigation also conducted experimental measurements in a full-scale test facility to validate the models. This study then used the proposed method to assess the heating and cooling energy cost saving from using an effective door closer in the cities of Minneapolis, Boston, San Francisco, and Phoenix. It was found that, under a greater indoor-outdoor pressure differential, using an effective door closer would save more energy cost. When using a closer with a larger size, the energy cost lost would decrease, but a large closing torque may significantly reduce ease of use and accessibility and potentially violate building codes related to the Americans with Disabilities Act (ADA). Furthermore, the energy cost saving from using an effective door closer in San Francisco would be lower than that in Minneapolis, Boston, and Phoenix.