Article Contents
Article Contents

# Self-adaptive algorithm based on a posteriori analysis of the error applied to air quality forecasting using the finite volume method

This work was partially supported by the project PICS Mairoc with a corporation with the Jean Leray Laboratory of Nantes university (France) and the National Meteorological Direction (DMN) of Morocco

• In this work, we present a self-adaptive algorithm based on the techniques of the a posteriori estimates for the transport equation modeling the dispersion of pollutants in the atmospheric boundary layer at the local scale (industrial accident, urban air quality). The goal is to provide a powerful model for forecasting pollutants concentrations with better manipulation of available computing resources.

This analysis is based on a vertex-centered Finite Volume Method in space and an implicit Euler scheme in time. We apply and validate our model, using a self-adaptive algorithm, with real atmospheric data of the Grand Casablanca area (Morocco).

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

 Citation:

• Figure 1.  Vertex-centred cell in 2D

Figure 2.  The measurement stations on the Grand Casablanca area

Figure 3.  Numerical simulations obtained using a uniform mesh with 443 elements at two times $t = 50$, (A), and $t = 1400$, (B)

Figure 4.  Numerical simulations obtained using a uniform mesh with 22887 elements at two times $t = 50$, (A), and $t = 1400$, (B)

Figure 5.  (A) and (B) are the approximate solution using a adaptive mesh at $t = 50$ and $t = 1400$ respectively in level 2

Figure 6.  (A), (B) and (C) are the adaptively refined meshes at $t = 50$ in level 1, 2 and 3 respectively

Figure 7.  (A), (B) and (C) are the adaptively refined meshes at $t = 1400$ in level 1, 2 and 3 respectively

Figure 8.  Forecasted concentration of ozone before and after the implementation of our algorithm in two positions: Jahid station, (A) and Khansae station, (B)

Table 1.  The calculated parameters for the numerical model

 Parameter Signification Value $D$ Diffusion coefficient $95.3\ m^2.s^{-1}$ $V$ Velocity vector $[3.9\ m.s^{-1}, 1.58\ m.s^{-1}]$ $r$ Reaction balance $3.9$ $f$ Second member $2.95\ \mu g.m^{-3}$ $c_0$ Initial concentration $64\ \mu g.m^{-3}$

Table 2.  The self-adaptive algorithm at an iteration

 Step 1: Mesh generating and data input. Step 2: Solve the discrete problem. Step 3: Calculate the local error indicators. Step 4: Mark mesh cells and adapt mesh. Step 5: Solve the discrete problem in the new adapted mesh. Step 6: if the stopping test is satisfied, go to the step 7 else, go to the step 3. Step 7: Interpolate solution and visualization.

Table 3.  The results obtained before and after the application of the self-adaptive algorithm of the mesh at $t = 50$

 Level Number of jump residual CPU elements indicator indicator time primal mesh $443$ $5.0132e-03$ $1.6088e-04$ $1.4053\ s$ $t=50$ $1$ $196$ $9.8885e-05$ $9.4178e-04$ $6.0905\ s$ $2$ $278$ $3.1335e-05$ $2.6348e-04$ $7.2734\ s$ $3$ $629$ $7.8928e-06$ $3.1045e-06$ $8.2617\ s$ uniform mesh $22887$ $1.3548e-07$ $1.9964e-06$ $53.4408\ s$

Table 4.  The results obtained before and after the application of the self-adaptive algorithm of the mesh at $t = 1400$

 Level Number of Jump Residual CPU elements indicator indicator time Primal mesh $443$ $1.6483e-05$ $1.9601e-05$ $1.4053\ s$ Adaptive mesh $1$ $828$ $1.3212e-07$ $1.9516e-06$ $18.1240\ s$ $2$ $1374$ $7.2050e-08$ $1.5499e-07$ $25.0560\ s$ uniform mesh $22887$ $1.1405e-08$ $1.2477e-07$ $95.6868\ s$
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