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To comprehensively address the underlying phenomena that govern the particular issue at hand, the following set of equations are introduced[13] :
$ {V}^{*}=\dfrac{V}{\sqrt{g\overline{H}}}$ (1) $ {Q}^{*}=\dfrac{Q}{\sqrt{{\rho }_{0}{C}_{P}{T}_{0}\sqrt{g{\overline{H}}^{5}}}} $ (2) $ {V}^{*}=0.4{\left[0.20\right]}^{-\frac{1}{3}}{\left[{Q}^{*}\right]}^{\frac{1}{3}};\;for\;{Q}^{*}\le 0.2 $ (3) $ {V}^{*}=0.4;\;for\;{Q}^{*} \lt 0.20 $ (4) Where, V is the ventilation velocity, m/s; Q is the fire convective heat release rate, kW; V* is the dimensionless critical velocity based on hydraulic tunnel height; Q* is dimensionless heat release rate based on hydraulic tunnel height; g is gravitational force, m/s2;
is ambient air density, kg/m3; CP is specific heat capacity of air, KJ/kg.k; T0 is ambient temperature, °C. To determine the Critical Ventilation Velocity and average temperature of fire gases, Kennedy et al.[20] proposed Eqns (5) and (6) to calculate the critical velocity, which assumes Froude Number (Eqn 7). These equations were also quoted in NFPA 502[21].$ {\rho }_{0} $ $ {V}_{c}={K}_{1}{K}_{g}{\left(\dfrac{g.H.Q}{\rho .{C}_{p}.A.{T}_{ f}}\right)}^{1/3} $ (5) $ {T}_{ f}=\dfrac{Q}{\rho .{C}_{p}.A.{V}_{c}}+T $ (6) The Froude number is defined as:
$ F r=\dfrac{{V}^{2}}{gD}=\dfrac{Inertia\;f orces}{Gravity\;f orces} $ (7) Where, Vc is the critical velocity, m/s; the Froude number factor (Fr−1/3) K1 is equal to 0.606 (7); Kg is the grade factor; g is gravitational force, m/s2; H is height of tunnel, m; Q is heat release rate, KW; ρ is average density of approach (upstream air), kg/m3; CP is specific heat capacity of air, KJ/kg.k; A is area perpendicular to the flow, m2; Tf is average temperature of the fire site gases, K; T is temperature of the approach air, K. In our research endeavor, our primary goal is to meticulously emulate real-world scenarios by generating a tunnel model characterized by specific dimensions measuring 300 m in length, 7.22 m wide, and 10.5 m in height, employing the sophisticated ANSYS Spaceclaim design tool. This innovative approach has empowered us to intricately capture the nuanced intricacies of the tunnel's geometry, thereby securing the utmost applicability of our findings within real-world practical settings. Illustrated in Fig. 1, this figure offers an exhaustive portrayal of the model, affording a visual examination of its multifaceted dimensions and intricate structural composition. This comprehensive level of detail and precision has consequently facilitated our comprehensive comprehension of the multitude of factors that exert influence upon tunnel ventilation.
In this configuration (Table 1), meticulous attention has been dedicated to ensuring the comfort and safety of users. To achieve this, the maximum height of the sinusoid is intentionally set at 1/3 of the tunnel entrance height (H), thereby adhering to the prescribed maximum slope. This precautionary measure is implemented to guarantee that the tunnel's gradient remains below a specific threshold, thereby enhancing the overall experience for users. It's imperative to highlight that this constraint is derived from a comprehensive evaluation of various comfort and safety criteria, including factors such as travel velocity and field of vision. In this specific setup, as outlined in Table 1, a deliberate effort has been made to prioritize the comfort of tunnel users. By establishing a maximum sinusoidal height equivalent to 1/3 of the tunnel entrance height (H), it is the aim to ensure that the tunnel's gradient stays within acceptable limits, thereby contributing to a more enjoyable experience for those traversing the tunnel.
Table 1. Dimensional characteristics of the tunnel.
Characteristics Dimensions Tunnel length L (m) 300 Tunnel height H (m) 7.22 Tunnel width W (m) 10.5 Amplitude of the sinusoid α (m) 2.4 Size of fire pit (m3) 2 × 2 × 2 To tackle this intricate problem comprehensively, the finite volume method, a numerical approach that discretizes the domain into control volumes was used to solve the governing equations. Specifically, the equations corresponding to unsteady, incompressible fluid flow were addressed using the Boussinesq approximation. This approximation is particularly suited for modeling buoyancy-driven flows, which are prevalent in tunnel ventilation scenarios. Simulation efforts were conducted through the utilization of Computational Fluid Dynamics (CFD), a powerful tool for numerically solving fluid flow problems. The CFD approach allowed analysis of the complex interactions of fluid dynamics within the tunnel environment. To capture the effects of turbulence in the present analysis, the K-ω SST (Shear-Stress Transport) turbulence model was employed. This model is well-suited for confined flows, offering a combination of the K-ω model near the walls, where turbulence is strongly influenced by viscous effects and the K-ε model in the core of the flow, where turbulence characteristics are different. One of the critical aspects in CFD simulations is the mesh, as it discretizes the computational domain into discrete elements. In the current study, with the presence of a sinusoidal geometry, it was essential to carefully select an appropriate mesh density. To achieve this, a mesh sensitivity analysis was conducted, systematically varying the mesh size and assessing its impact on the results. After thorough examination it was determined that a mesh with 3,960,850 nodes, as illustrated in Fig. 2, provides the most accurate representation of the flow physics, capturing all relevant phenomena while still maintaining computational efficiency. By employing these advanced numerical techniques and fine-tuning the mesh density to the optimal level, the complex problem at hand was effectively addressed and the reliability and accuracy of the simulation results ensured. This comprehensive approach allowed a deep insight into the intricate dynamics of tunnel ventilation.
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This work is the result of collaboration between: Fluid Mechanics and Applications Laboratory, Department of Physics, Faculty of Science and Technology, Cheikh Anta Diop University (UCAD), the Water, Energy, Environment, and Industrial Processes Laboratory (LE3PI) at the Polytechnic School of Dakar, and the Department of Physics, College of Sciences, Qassim University, Buraidah, Saudi Arabia. This collaboration was made possible thanks to the logistical support of ANSYS, who provided a teaching license of Ansys Fluent version 2024R1 through their representative, Analytics Systems Africa.
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About this article
Cite this article
Drame O, Yahya Z, Sarr A, Mbow C. 2024. Soumbedjioune tunnel in dakar: a case study for CFD modeling of fire-smoke extraction in a sinusoidal floor tunnel. Emergency Management Science and Technology 4: e011 doi: 10.48130/emst-0024-0011
Soumbedjioune tunnel in dakar: a case study for CFD modeling of fire-smoke extraction in a sinusoidal floor tunnel
- Received: 17 February 2024
- Accepted: 17 April 2024
- Published online: 21 May 2024
Abstract: Despite numerous studies on fires, each simulation faces several limitations. Fire is a complex phenomenon, and some of its processes remain poorly understood. Furthermore, the progression of flames and smoke is influenced by turbulent flow phenomena. In this context, this article delves into the analysis of smoke flow behavior using numerical fluid dynamics simulations (CFD) with the assistance of ANSYS FLUENT. The primary focus lies in describing the longitudinal ventilation system, a critical element for fire management. The chosen study area is the Soumbédjioune tunnel located in Dakar, Senegal, characterized by a sinusoidal floor with an amplitude equivalent to one-third of the entrance height. The tunnel has dimensions of 300 m in length (L), 10.5 m in width (W), and 7.22 m in height (H). To simulate the source of heat and smoke, a 2-cubic meter fire was placed in the center of the tunnel, modeling the combustion of N2, H2O, and O2 gases at a high temperature of 926.85 °C. The simulations yielded a critical ventilation velocity of 5.8 m/s, a crucial piece of information for fire management in this specific context. Furthermore, this article extends its analysis by examining the evolution of temperature and velocity contours in both ventilated and non-ventilated conditions, providing a deeper understanding of the underlying mechanisms at play.
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Key words:
- Computer simulation /
- Environmental pollutants /
- Air pollution control /
- Pollutant removal /
- Efficiency /
- Optimization