2022
Zhou, Y., An, Y., Huang, W., Chen, C., You, R.* 2022. A combined deep learning and physical modelling method for estimating air pollutants' source location and emission profile in street canyons. Building and Environment, 219: 109246.
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Abstract: Roadside air pollution monitoring stations have become frequently available for street canyons. To efficiently estimate source location and emission profile in street canyons, this study developed a combined deep learning and physical modelling method using the monitoring data as inputs. First, a deep neural network (DNN) was constructed for locating the source. The training datasets were generated from numerical simulations by the computational fluid dynamics (CFD)-Markov chain model. An inverse method based on Tikhonov regularization was then used to estimate the emission profile. Finally, the Markov chain model was used to calculate the air pollutant distribution in the whole street canyon. Case studies were conducted to demonstrate the performance of the proposed method. For the unit impulse source in the 2-D ventilated chamber of 27 m2, the source in 83% of the cases were accurately identified, and in another 13% of the cases, the identified source was within 0.4 m to the true location. For the continuous pollutant source with varying emission profile in the 3-D street canyon with an area of 25,600 m2, the source in 36% of the cases were accurately located, and in another 52% of the cases, it was within 10 m from the true location.
2021
Zhou, Y., An, Y., Chen, C., You, R.* 2021. Exploring the feasibility of predicting contaminant transport using a stand-alone Markov chain solver based on measured airflow in enclosed environments. Building and Environment, 202: 108027.
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Abstract: Correctly predicting contaminant transport in enclosed environments is crucial for improving interior layouts to reduce infection risks. Using the measured airflow field as input to predict the contaminant transport may overcome the challenges of measuring complex boundary conditions and inaccurate turbulence modeling in the existing methods. Therefore, this study numerically explored the feasibility of predicting contaminant transport from the measured airflow field. A stand-alone Markov chain solver was developed so that the calculations need not rely on commercial software. Airflow information from CFD simulation results, including the three-dimensional velocity components and turbulence kinetic energy, was used as surrogate for experimental measurement based on the spatial resolution of ultrasonic anemometers. Three cases were used to assess the feasibility of the proposed method, and the calculation results were compared with the benchmark calculated by the commercial CFD software. The results show that, when the airflow was simple, such as that in an isothermal ventilated chamber, the stand-alone Markov chain solver based on the measured airflow field predicted the trend of contaminant transport and peak concentrations reasonably well. However, for complex airflow, such as that in non-isothermal chambers with heat sources or occupants, the solver can reasonably predict only the general trend of contaminant transport.
2012
You, R., Zhao, B., Chen, C. 2012. Developing an empirical equation for modeling particle deposition velocity onto inclined surfaces in indoor environments. Aerosol Science and Technology, 46: 1090-1099.
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Abstract: For the purpose of modelling indoor particle dispersion with an Eulerian drift flux model or analyzing indoor particle deposition onto various surfaces accurately, it may take considerable time to calculate the deposition velocity for each surface as numerical integration or calculation is usually needed. In this paper, a modified three-layer model is presented to calculate indoor particle deposition velocities for surfaces with different inclinations and for different friction velocities. Then, 1020 cases, covering the common indoor scenarios, were modelled to obtain a database of indoor particle deposition velocities. Based on the results of the 1020 cases, an empirical equation was generated to determine indoor particle deposition velocities. The empirical equation was divided into four parts, named the Fine zone, Coarse zone, Zero zone, and Transition zone. In the Fine zone, the friction velocity decides the particle deposition velocity, while in the Coarse zone, the inclination angle of the surface is the decisive parameter for the deposition velocity. The results show that the average error of the empirical equation to the database was 1.53%, 1.50% and 21.93% in the Fine zone, Coarse zone and Transition zone, respectively. The deposition velocities in the Zero zone can all be deemed as zero. Empirical equation predictions agree well with experimental data for a spherical chamber (Cheng, 1997). The empirical equation generated in this study is therefore applicable for easily calculating the boundary conditions for Eulerian drift flux model or analyzing indoor particle deposition onto smooth surfaces with varying inclinations with reasonable accuracy.
