Abstract:
Indoor air quality research is becoming increasingly important as the affects of particulate matter on health [6] are understood. Experimentally, particle concentrations and flow velocities can be measured in specific locations, but this often does not give a clear picture of the global dynamics of the system. Numerical simulation allows the fluid velocities, pressures, temperatures, and particle concentrations to be determined everywhere in the environment. This then allows one to fully understand how particulates are transported. My research goal is to develop and apply a 3D high-order accurate finite element method (FEM) to an indoor environment in order to predict and understand indoor air quality.
I am developing a high order hp-FEM (h for mesh size and p for polynomial degree) that uses an adaptive unstructured tetrahedral mesh. This method allows complex geometries (such as a mannequin) to be meshed using a non-uniform resolution. Adaption can be used to locally refine regions of the mesh to increase accuracy (resolve the Kolmogorov scale) and to coarsen others for better performance. The adaption procedures are well developed and will be handled by coupling to MeshAdapt which is part of the open source project Gmsh [3]. I have also already implemented a particle modeling algorithm for tetrahedral meshes as part of a final project in a particle transport graduate course. The particles are assumed to be small and dilute and a one-way force coupling is used to determine the dynamics [4]. To validate this new method, I will simulate the conditions in the CARTI experiment. The simulations will be compared to data derived from detailed particle image velocimetry measurements near the mannequins face, above the head, and near the inlet, as well as to particle concentrations data. I hope to demonstrate that by combining these techniques I will be able to solve indoor air quality problems on complex geometries with more efficiency and accuracy then previously possible.
Indoor air quality research is becoming increasingly important as the affects of particulate matter on health [6] are understood. Experimentally, particle concentrations and flow velocities can be measured in specific locations, but this often does not give a clear picture of the global dynamics of the system. Numerical simulation allows the fluid velocities, pressures, temperatures, and particle concentrations to be determined everywhere in the environment. This then allows one to fully understand how particulates are transported. My research goal is to develop and apply a 3D high-order accurate finite element method (FEM) to an indoor environment in order to predict and understand indoor air quality.
I am developing a high order hp-FEM (h for mesh size and p for polynomial degree) that uses an adaptive unstructured tetrahedral mesh. This method allows complex geometries (such as a mannequin) to be meshed using a non-uniform resolution. Adaption can be used to locally refine regions of the mesh to increase accuracy (resolve the Kolmogorov scale) and to coarsen others for better performance. The adaption procedures are well developed and will be handled by coupling to MeshAdapt which is part of the open source project Gmsh [3]. I have also already implemented a particle modeling algorithm for tetrahedral meshes as part of a final project in a particle transport graduate course. The particles are assumed to be small and dilute and a one-way force coupling is used to determine the dynamics [4]. To validate this new method, I will simulate the conditions in the CARTI experiment. The simulations will be compared to data derived from detailed particle image velocimetry measurements near the mannequins face, above the head, and near the inlet, as well as to particle concentrations data. I hope to demonstrate that by combining these techniques I will be able to solve indoor air quality problems on complex geometries with more efficiency and accuracy then previously possible.