**2.3 Simulation study**

This work is carried out to track contaminant movement in a horizontal direction and is analyzed using a 2D model of pollutant transport in a porous medium with regulated discharge. COMSOL Multiphysics generated a soil matrix for water recharging of treated household sewage. With a 2D analysis, the diffusion is positioned in the center of the recharge as it flows downhill and horizontally through the soil matrix. The soil column dimensions are 1.5 × 1.2 m. The flow pattern in lateral direction was investigated in COMSOL to study the transport of recharge water as well as its pollutant constituents such as completely dissolved solids with adsorption


#### **Table 1.**

*Characteristics of aquifer recharge for different [4].*

*Study on the Impact of Artificial Recharge on Treated Domestic Sewage DOI: http://dx.doi.org/10.5772/intechopen.109868*

**Figure 4.** *Flow diagram of pollutant transport modeling.*

and desorption characteristics in soil. This research was carried out by developing and running domestic wastewater subsurface infiltration systems. It was discovered that this work discusses the significance of purifying substrates and the types of structures for optimizing diverse operation modes such as HLR, PLR, intermittent operation, aeration, and shunting distribution operation [5]. Metabolomics analysis of a subsurface wastewater infiltration system subjected to organic load fluctuations and current microbiology [6]. Depending on the size of the pores involved, the transport of pollutants in porous media equation was utilized for saturated porous medium. The primary goal of the research is to comprehend the concentration distribution and find the impact after infiltration in the porous medium. Variations in concentration are seen at the outflow based on the study time from the physical model study (**Figure 4**).

Governing equation for the contaminant transport in porous media

$$\frac{\partial \left(\boldsymbol{\varepsilon}\_{p}\boldsymbol{\varepsilon}\_{i}\right)}{\partial t} + \frac{\partial \left(\rho \boldsymbol{\varepsilon}\_{p\_{j}}\right)}{\partial t} + \nabla \cdot \mathbf{J}\_{i} + \mathbf{u} \cdot \nabla \boldsymbol{\varepsilon}\_{i} = \mathbf{R}\_{i} + \mathbf{S}\_{i} \tag{1}$$

$$\mathbf{J}\_i = -\left(\mathbf{D}\_{\mathrm{Dj}} + \mathbf{D}\_{\mathrm{e}\dagger}\right)\nabla \mathbf{c}\_i \tag{2}$$

θ ε= <sup>p</sup> Under no flux condition with the associated boundary conditions.

$$
\theta = \mathfrak{e}\_{\mathfrak{p}} \tag{3}
$$

$$-\mathbf{n} \cdot \left(\mathbf{J}\_i + \mathbf{u}c\_i\right) = \mathbf{0} \tag{4}$$

The model for the best results, we must partition the entire model into discrete portions. Depending on the intended effects, this meshing can be further split in a variety of ways. Because the emphasis is on the model's inlet and outflow, a small mesh was chosen based on the geometry of the model. Table 4 shows the statistics used to solve the mesh (**Figure 5**).

#### **Figure 5.**

*3D and 2D boundary layer meshing and mesh extrusion make it possible to efficiently discretize.*

