Accurate simulation of the airflow and heat transfer in cleanrooms is highly dependent on the diffuser, yet diffusers have traditionally been very difficult to model.

The airflow in and around diffusers is particularly important in cleanrooms because diffusers play a vital role in the isolation and flushing strategies that are commonly used to meet performance requirements. An isolation strategy may involve the use of air curtains at entrance or to isolate specific areas of the cleanroom. On the other hand, a flushing strategy might involve the use of a raised floor arrayed with diffusers while the returns are located in the ceiling. In either case, a precision tool is required to deliver the airflow in the right locations and avoid generating flow where it could havea negative impact.

Simulation is required in nearly every cleanroom project to meet today’s challenging cleanroom performance requirements. However, diffuser modeling has posed difficulties in the past. The key information that is normally required is the cross-sectional velocity profile of the diffuser at its outlet for use as a boundary condition in a computational fluid dynamics (CFD) simulation. However, the CFD software used to model airflow in cleanrooms is generally not easy for modeling diffusers because of their small physical size and high flow velocities. The traditional way to model diffusers is to build a CFD model that matches the physical tests performed by the diffuser manufacturer. Then the diffuser boundary conditions are adjusted through a trial and error process until the simulation results match the diffuser manufacturer’s test results. This is a long and tedious process that substantially drives up the amount of time required to simulate airflow.

While working in the Building Technology Program for the Massachusetts Instituteof Technology’s (MIT) Department of Architecture, we identified a method for modeling diffuser airflow that eliminates the need for this trial and error process.¹ We used the momentum method to develop a model that predicts the diffuserboundary conditions based on a few parameters such as the type of diffuser, dimensions, flow rate, supply temperature, deflection angle, and effective area. We performed extensive physical testing that demonstrates that this model accurately predicts the boundary conditions over a wide range of applications. CFD software suppliers have developed web macros that enable users to simply type in a few parameters and then the macro generates the correct grid geometry and constraints required to accurately model the diffuser.

CHALLENGE OF MODELING DIFFUSERS
CFD modeling provides the ability to accurately model air flow and heat transfer within a building, enabling heating and cooling systems to be optimized in the form of a software prototype without the expense and time involved in making changes to the actual building. The greatest challenge in CFD is often modeling the diffusers because they have a major effect on the airflow in the building yet they are difficult to model with conventional CFD software. The difficulty with modeling diffusers arises from the fact that they are small compared to the size of a building and have very high flow velocities. To model detailed diffuser geometry would require millions of grid cells which would require large amounts of computer capacity. On the other hand, to model the diffuser at the same grid density as the room would ignore details that could introduce errors into the numerical simulation.

What needs to be known for an accurate CFD simulation is generally the boundary conditions consisting of the velocity and temperature cross-sectional profile at the diffuser outlet. Manufacturers of diffusers typically perform tests on their products, installing the diffuser in a room and measuring airflow in the room. Until recently, the most accurate way for CFD users to model diffusers has been to build a model that duplicates the tests performed by the diffuser manufacturer, then adjust the diffuser boundary conditions until the airflow in the room matches the test results. There are several weaknesses to this approach. One is that a considerable amount of time is required to create the room model and the other is that the boundary conditions determined by this method are only completely accurate for rooms that match the room used in the diffuser manufacturer’s test.

Several years ago, while at MIT, we set out to find a faster and easier method to determine the boundary conditions for common diffusers. We looked at several possible methods of determining boundary conditions for different types of diffusers. We identified the momentum method, which de-couples the momentum and mass flow in the CFD simulations of room airflow, as the most promising.