A Thermal Comfort CFD simulation is a powerful engineering tool used to predict how comfortable people will feel inside a building. The goal of any HVAC (Heating, Ventilation, and Air Conditioning) system is to create a healthy and productive indoor environment. A Thermal Comfort Fluent analysis helps engineers see how air temperature, airflow, and heat from sources like sunlight will affect occupants. This report details a PMV CFD simulation using ANSYS Fluent. By calculating important comfort indices like PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied), we can go beyond simple temperature predictions. This PPD Fluent analysis allows us to design and optimize HVAC systems to ensure the majority of people will be comfortable, directly linking the engineering design to the human experience.

For more HVAC CFD simulation tutorials and projects, visit: https://cfdland.com/product-category/application/hvac-cfd-simulation/

Figure 1: A standard chart showing the relationship between the Predicted Mean Vote (PMV) scale and the Predicted Percentage of Dissatisfied (PPD).

Simulation Process: Fluent-CFD Setup for PMV & PPD Thermal Comfort Analysis

The simulation process began with a simple but realistic geometry: a cubic room with a single large glass window on one wall. This model was meshed using ICEM CFD to create a high-quality structured hexahedral grid. Inside ANSYS Fluent, the physics were configured to capture all the key elements of thermal comfort. To account for the heating effect of the sun, the Discrete Ordinates (DO) radiation model was activated. This model accurately simulates the solar energy passing through the glass window and warming the room’s interior surfaces and air.

The most critical part of this setup was the use of two custom User-Defined Functions (UDFs). One UDF was written to calculate the PMV index. This function takes the simulation’s results for air temperature, air velocity, humidity, and radiant temperature and uses them to predict the average thermal sensation on a scale from cold (-3) to hot (+3). A second UDF was then used to calculate the PPD index, which uses the PMV value to predict what percentage of people would be unhappy with the thermal conditions. This complete setup provides a direct and comprehensive thermal comfort analysis.

Figure 2: The 3D geometry of the cubic room with a glass window used for the Thermal Comfort CFD simulation.

Post-processing: Correlating Flow Dynamics with Occupant Thermal Comfort

The simulation results provide a complete engineering analysis, successfully linking the environmental factors of solar radiation and natural air movement to the direct human experience of thermal comfort. The analysis starts with the cause of thermal variation: the sun. The radiation temperature contours in Figure 5 clearly show a hot spot on the floor where the sunlight hits, with radiant temperatures reaching 313.96K (40.8°C). This solar energy heats the air near the floor and window. As shown by the velocity streamlines in Figure 6, this warm, lighter air rises, while the cooler, denser air from the other side of the room sinks. This creates a large, slow-moving natural convection loop with a maximum air speed of only 0.38 m/s, which gently circulates the air and heat throughout the space.

The PMV contours in Figure 3 show the direct effect of this on human comfort. The PMV scale ranges from -2.12 (cool) to +1.12 (slightly warm). The engineering goal for comfort is a PMV value between -0.5 and +0.5. The analysis shows that a large central area of the room successfully meets this comfort target. However, the simulation also identifies problem areas: it is too cool near the floor away from the window (negative PMV) and too warm near the ceiling and the sun-lit patch (positive PMV).

Figure 3: Contours of Predicted Mean Vote (PMV) from the fluent simulation, showing the distribution of thermal sensation throughout the room.

Figure 4: Contours of Predicted Percentage of Dissatisfied (PPD), directly indicating the areas of high and low occupant comfort.

The PPD contours in Figure 4 translate this into a simple success metric. The PPD value, which ranges from 0% to 79.46%, tells engineers what percentage of people would be dissatisfied. The analysis confirms that in the large central zone, the PPD is well below the 10% target for excellent comfort. It also quantifies the problem near the window, where the PPD rises sharply, indicating a high level of occupant dissatisfaction due to the solar heating. The most important achievement of this simulation is the successful use of custom PMV and PPD models to directly quantify human comfort zones within the room. By linking the solar radiation model to the natural convection patterns and then to the comfort indices, this Thermal Comfort CFD analysis provides precise, actionable data. It proves that while the general room condition is good, a specific design solution (like blinds or better window glazing) is needed to solve the localized discomfort caused by the sun.

Figure 5: Contours of radiation temperature, clearly showing the heating effect of solar energy passing through the glass window.

Figure 6: Air velocity streamlines illustrating the natural convection loop that circulates air and heat within the room.