Deposition of particle pollution in turbulent forced-air cooling

Rotating fans are the prevalent forced cooling method for heat generating equipment and buildings. As the concentration of atmospheric pollutants has increased, the accumulation of microscale and nanoscale particles on surfaces due to advection-diffusion has led to adverse mechanical, chemical and electrical effects that increase cooling demands and reduce the reliability of electronic equipment. Here, we uncover the mechanisms leading to enhanced deposition of particle matter (PM$_{10}$ and PM$_{2.5}$) on surfaces due to turbulent axial fan flows operating at Reynolds numbers, $Re \sim 10^5$. Qualitative observations of long-term particle deposition from the field were combined with \textit{in situ} particle image velocimetry on a telecommunications base station, revealing the dominant role of impingement velocity and angle. Near-wall momentum transport for $10 < y^+ < 50$ were explored using a quadrant analysis to uncover the contributions of turbulent events that promote particle deposition through turbulent diffusion and eddy impaction. By decomposing these events, the local transport behaviour of fine particles from the bulk flow to the surface has been categorised. The transition from deposition to clean surfaces was accompanied by a decrease in shear velocity, turbulent stresses, and particle sweep motions with lower flux in the wall-normal direction. Finally, using these insights, selective filtering of coarse particles was found to promote the conditions that enhance the deposition of fine particle matter

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multitype branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for hematopoiesis and transposable element evolution.Comment: 18 pages, 4 figures, 2 table