The project aims to help UC San Diego's Return-To-Learn program by using CFD to analyze the risk of Covid-19 transmission in a classroom of your choice.
The final project is conducted in groups of three. Each group has three deliverables:
1. a written report (60 pts),
2. a 15 min oral presentation during the time of the final (40 pts), and
3. all case files required to reproduce the results (10 pts).
CAD files in STEP-format of 7 rooms are provided below. As a group, carefully consider your choices: a large, complex lecture hall like Galbraith Hall is spectacular but computationally expansive to simulate and challenging to mesh. A simple room like Powell-Focht 106 may not seem
as exciting, but you might be able to run parameter studies, run transient simulations, or use computationally expensive physical models to simulate the spread of aerosol particles.
You are allowed to use the following resources:
⚫ all GPU workstations of your group members (in the Fluent start menu, set the number of CPUs to 8 for good performance),
⚫ all numerical models in your arsenal (if in doubt, apply the RTFM method to the Fluent manual),
⚫ all information you can find in the peer-reviewed literature and textbooks.
If you would like to use other resources at your disposal like private workstations or friends who are public health experts, you have to ask for our permission first, and then clearly document these resources in your final report.
The objective is an open research question and we are looking for the most creative and effective ways to contribute to this greater cause. There are many factors that contribute to the risk of viral transmission and many physical models that can help with the analysis. It is up to you to search the internet and scientific publications for inspiration. The "Inspirations & Resources" section in the problem overview is a good starting point. The minimum requirements for the project are:
• pick a room, generate a grid and simulate it using Fluent with the correct ACH value (you might have to edit the CAD file to create a watertight geometry with well-defined individual inlets and outlets)
⚫ document your calculation of the inlet velocities (or mass fluxes) from the ACH number
• plot the velocity field in the x-y, x-z, and y-z planes passing through the centroid of the room
⚫ conduct a grid refinement study that includes a table that summarizes the number of grid points and important grid quality measures for each at least 3 refinement levels (Note: you might not actually be able to reach gird convergence given the computational and time constraints. That is absolutely fine, but transparently state that your results are preliminary and potentially grid-independent)
Some reasonable assumptions for the flow physics, that you are by no means bound to, are:
⚫ incompressible
• steady-state
⚫ no buoyancy
⚫ kw (SST) turbulence model with default parameters (and correctly adjusted hydraulic diameter values for inlets)
•
Do not feel limited to these defaults. Just make sure that you reasonably justify your choices. Some note on CAD files:
• Do not expect them to work out-of-the-box. They never do. The SpaceClaim auto-repair features are really good at cleaning them up for you, though.
• You may at your own discretion alternate the CAD files, add features, etc. We ask you, however, to document these changes clearly.
Lastly, we ask you to go above and beyond. If you have taken a look at the linked resources, you will know we want to get some insight as to how infected particles circulate through classrooms. You will also get an idea of what numerical methods are relevant. In particular, we want to look for regions where the particles tend to stagnate or get trapped in recirculation regions. Some ideas to pursue are:
• Simulate particles in the room, and see where there might be large concentrations,
⚫ you can make particles enter through an inlet, or you can simulate a person sneezing.
• Conduct a parameter study on ACH. Do recirculation regions or stagnation zones significantly change?
• Identify recirculation regions using 3-D pathlines.
These are just a couple of ideas. Given that this is a current research topic, there are still many unknowns and avenues to explore. You are highly encouraged to try out any ideas you might have.
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