Water Mist Spray Nozzle CFD Simulation: Evaporative Analysis Validation in ANSYS Fluent
Water Mist Spray Nozzle CFD Simulation: Evaporative Analysis Validation in ANSYS Fluent
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Air-cooled chillers are very common in industrial buildings. However, they consume a lot of electricity. To make them more efficient, engineers use water mist spray systems. These systems spray very small water droplets onto the condenser coils. When the water evaporates, it absorbs heat from the air. This cools down the coils and lowers the energy needed for the compressor. This process is called evaporative cooling.
In this Water Mist Spray Nozzle CFD simulation, we perform a validation study. We recreate the work of a research paper by Heidarinejad et al. (2019). We use ANSYS Fluent to simulate the evaporation of water droplets in an air channel. This ANSYS Fluent DPM validation proves that we can accurately predict the cooling effect and the Coefficient of Performance (COP) of the system. For more particle tracking projects, please visit our DPM & Particle Tracking tutorials.
- Reference [1]: Heidarinejad, Ghassem, Mohammad Reza As’ adi Moghaddam, and Hadi Pasdarshahri. “Enhancing COP of an air-cooled chiller with integrating a water mist system to its condenser: Investigating the effect of spray nozzle orientation.” International Journal of Thermal Sciences137 (2019): 508-525.

Figure 1- Conical spray nozzle injection showing droplet distribution.
Simulation Process: 2-Way DPM and Species Transport Setup in ANSYS Fluent
For this Water Mist Spray Nozzle analysis, we modeled a specific section of a 20-ton Trane air-cooled chiller. We created the geometry using ANSYS Design Modeler. It is a square channel with a cross-section of 0.7 m by 0.7 m. The spray nozzle is located 0.7 m away from the air inlet. The geometry is simple, so we generated a structured mesh with hexagonal cells. In total, we used 686,000 cells in ANSYS Meshing. A structured grid is excellent for this type of flow because it aligns with the main air direction and provides accurate results with fewer cells.
In the ANSYS Fluent solver, the physics setup is complex because it involves two phases: air and water. We used the Eulerian-Lagrangian approach. The air is the continuous phase (Eulerian), and the water droplets are the discrete phase (Lagrangian). To do this, we activated the Discrete Phase Model (DPM). We selected 2-way coupling, which means the air moves the droplets, and the droplets also affect the air temperature and momentum. To model the evaporation, we enabled the Species Transport model. This allows the liquid water droplets to turn into water vapor (gas) and mix with the air. We simulated the spray process in unsteady (transient) mode to track the droplets over time accurately.

Figure 2– Water mist system setup in the condenser channel.
Post-processing: Water Mist Spray Nozzle Evaporation and Validation Analysis
In this section, we analyze the results of the Water Mist Spray Nozzle CFD simulation. We focus on the evaporation patterns and compare the numerical data with the reference paper to validate the work. First, we look at the Evaporation and Mass Fraction. The contours of H2O vapor show clearly where the evaporation happens. The conical shape of the spray distributes the mist uniformly. We can see the highest concentration of water vapor near the nozzle injection point. As the droplets move down the channel, they change phase from liquid to gas. This process absorbs latent heat from the surrounding air, which causes the temperature to drop. The DPM simulation successfully captures this interaction. The peak evaporation rates occur near the entrance region where the difference between the water temperature and air temperature is the highest.
Second, we validate the Coefficient of Performance (COP). The main goal is to see if the simulation predicts the correct outlet temperature (Tout). We used the formula provided in the paper:
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The comparison shows a strong agreement.
- Reference Paper: The measured outlet temperature was 38.87°C, resulting in a COP of 2.99.
- CFD Simulation: Our simulation predicted an outlet temperature of 37.92°C, resulting in a COP of 3.05.
The relative error is only 2%. This is a very small difference. The simulation predicted a slightly lower temperature (better cooling) than the experiment. This might be because the simulation assumes ideal mixing, while real experiments have some inefficiencies. However, this close match validates that using 2-way DPM with Species Transport is a reliable method for simulating evaporative cooling systems.


Figure 3- Mass fraction of water vapor showing evaporation after injection.
| Air Temperature (°C) | COP | Error | |
| Paper Data | 38.87 | 2.99 | 2% |
| CFD Simulation | 37.92 | 3.05 |
Key Takeaways & FAQ
- Q: What is Water Mist Spray?
- A: It is a system that sprays very fine water droplets into the air. It is widely used for fire fighting and for cooling (evaporative cooling) because the small droplets evaporate very quickly and absorb heat.
- Q: What is Evaporative Cooling?
- A: Evaporative cooling is a natural process where water evaporates into the air. To change from liquid to gas, water needs energy (heat). It takes this heat from the surrounding air, which lowers the air temperature.
- Q: How do you model Water Mist Spray in CFD?
- A: To model water mist, we typically use the Discrete Phase Model (DPM). We treat the air as a continuous fluid and the water droplets as individual particles that move through the air.
- Q: Which ANSYS Fluent modules are needed for water mist modeling?
- A: You need three main modules:
- DPM (Discrete Phase Model): To track the droplets.
- Species Transport: To simulate the water turning into vapor.
- Energy Equation: To calculate the temperature change.
- A: You need three main modules:
We pride ourselves on presenting unique products at CFDLAND. We stand out for our scientific rigor and validity. Our products are not based on guesswork or theoretical assumptions like many others. Instead, most of our products are validated using experimental or numerical data from valued scientific journals. Even if direct validation isn’t possible, we build our models and assumptions on the latest research, typically using reference articles to approximate reality.
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