A Masonry walls CFD simulation is a computer model used to study how heat moves through walls in buildings. This Building Energy Simulation is very important for designing energy-efficient homes and offices. Using a Masonry walls Fluent model, engineers can analyze the Building Envelope Performance to improve Thermal Comfort. The analysis involves Conjugate Heat Transfer (CHT) CFD, which means the computer calculates heat flow through solid parts (like concrete) and fluid parts (like air and water) at the same time. This is especially useful for modern designs like Radiant Cooling Fluent systems, where pipes inside the wall help control the building’s temperature. This type of Structural Thermal Modeling helps us create buildings that use less energy for heating and cooling.

Figure 1: The 2D computational mesh of the masonry wall system, showing the all-quadrilateral grid with 963,205 cells used for the CHT CFD analysis.

Simulation Process: Fluent CHT Setup, 2D Modeling of a Multi-Material Masonry Wall

To perform this Masonry walls CFD study, we created a 2D model to represent a cross-section of the wall. This 2D approach is very efficient and saves computer time, but it is still accurate because heat transfer through a uniform wall is mainly in one direction. We then used ANSYS Meshing to build a high-quality, all-quadrilateral grid with 963,205 cells. This type of structured mesh provides better accuracy and stability for heat transfer calculations. The simulation includes four different materials to create a realistic model: concrete for the wall, copper for the pipes, liquid water flowing inside the pipes, and air on the outside. The most important part of the setup in ANSYS Fluent was activating the Conjugate Heat Transfer (CHT) model. This powerful model is essential because it solves for heat conduction in the solid materials (concrete and copper) and heat convection in the fluids (water and air) all at the same time. This ensures the heat transfer between the solids and fluids is calculated perfectly. For the boundary conditions, we set the liquid water to enter the copper pipes at a cold temperature of 6°C (279.15 K) to simulate a radiant cooling system.

Figure 2: quadrilateral cells generated over all domains

Post-processing: CFD Analysis, Correlating Flow Dynamics with Radiant Cooling Performance

The simulation results provide a complete and clear picture of the radiant cooling system’s effectiveness. From an engineering standpoint, the primary goal is to remove heat from the wall, and the temperature contours in Figure 3 provide direct evidence of this success. The water enters the system at a cold 6°C and exits at 22°C. This 16°C temperature rise in the water is the most important result, as it is a direct measurement of the heat being successfully pulled out of the concrete wall. The contour shows the concrete wall remains at a high temperature of around 297 K (24°C), while the water effectively acts as a heat sink. This confirms that the Conjugate Heat Transfer model has accurately predicted the heat exchange.

Figure 3: Temperature Distribution Revealing Heat Transfer Through Masonry Wall

The velocity and streamline contours in Figures 4 and 5 explain the physics that makes this heat transfer so effective. The water flows through the channels at speeds up to 0.42 m/s. More importantly, the velocity streamlines in Figure 5 reveal complex circulation patterns and recirculation zones inside the wall cavities. This is a key finding. This turbulent mixing is not a problem; it is a benefit. It forces the water to tumble and mix, which breaks up the insulating thermal boundary layers on the pipe walls and brings more of the water into direct contact with the hot surfaces. This enhanced mixing dramatically improves the convective heat transfer rate from the wall to the water, which is why the system is so effective at cooling. The most important achievement of this simulation is proving the effectiveness of the radiant cooling design. By showing a 16°C temperature rise in the water, the model quantifies the system’s heat removal capacity. Furthermore, the analysis reveals that this high performance is driven by complex flow mixing inside the channels, which enhances heat transfer. This validated model is a powerful tool for designing energy-efficient building thermal management systems.

Figure 4: Velocity contours showing the water flow patterns inside the cooling channels, with a maximum velocity of 0.42 m/s.

Figure 5: Velocity streamlines from the Fluent simulation, displaying the complex circulation and mixing patterns that enhance Radiant Cooling performance.