End-to-end data-driven prediction of urban airflow and pollutant dispersion

Abstract

Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This study targets to develop fast and accurate models of urban pollutant dispersion to support decision-makers, enabling them to implement mitigation measures in a timely and cost-effective manner. To reach this goal, an end-to-end data-driven approach is proposed to model and predict the airflow and pollutant dispersion in a street canyon in skimming flow regime. A series of time-resolved snapshots obtained from large eddy simulation (LES) serves as the database. The proposed framework is based on four fundamental steps. Firstly, a reduced basis is obtained by spectral proper orthogonal decomposition (SPOD) of the database. The projection of the time series snapshot data onto the SPOD modes (time-domain approach) provides the temporal coefficients of the dynamics. Secondly, a nonlinear compression of the temporal coefficients is performed by autoencoder to reduce further the dimensionality of the problem. Thirdly, a reduced-order model (ROM) is learned in the latent space using Long Short-Term Memory (LSTM) netowrks. Finally, the pollutant dispersion is estimated from the predicted velocity field through convolutional neural network that maps both fields. The results demonstrate the efficacy of the model in predicting the instantaneous as well as statistically stationary fields over long time horizon.

Figures (22)

• Figure 1: (a) Overview of the simulated street canyon geometry (not to scale). The bounds of the computational domain is indicated by dashed line along with the boundary conditions. The pollutant source is indicated by the red line which spans the center of the canyon base. The vertical sampling plane located mid-span of the canyon is indicated in green. (b) Mean velocity magnitude with streamlines on the $x$-$z$ plane. (c) Mean concentration of the pollutant emitted from source.
• Figure 2: Schematic of the neural network architectures used in the ROM framework: (a) Autoencoder, (b) Long Short-Term Memory (LSTM) cell, and (c) Convolutional Neural Network (CNN).
• Figure 3: Schematic of the data-driven ROM framework. The offline stage involves training the three independent neural network architectures -- AE, LSTM and CNN. The online stage utilizes the trained models to forecast the velocity and concentration fields from an initial condition.
• Figure 4: (a) Spectrum of the two leading SPOD eigenvalues ($\lambda_{f_k}^{(1)}$ -- solid, and $\lambda_{f_k}^{(2)}$ -- dashed) for five values of the block size $N_\mathrm{fft}$. The numbers at the end of the colored arrows indicate the resulting number of blocks $N_\mathrm{blk}$. (b) Eigenvalue spectrum showing the $95\%$ confidence intervals (shaded area) for three values of $N_\mathrm{fft}$.
• Figure 5: (a) Eigenvalue spectrum of SPOD modes for $N_\mathrm{fft}=4096$ and $N_\mathrm{blk}=34$. (b) Contour of nondimensional eigenvalues with respect to both the frequency and the spatial mode number. The summed eigenvalues along the frequency and the spatial mode number are also plotted in the top and right insets respectively. Here $\sum_k$ and $\sum_n$ denote summation of the eigenvalues over all the discrete frequencies and modes.