Aerosol Particle Diffusion Coefficient
Before pursuing a career in data science, I was an aerosol researcher (mainly computational research). Although I entered a new world of data science, I found that the concepts (such as random numbers, distributions, modeling) and the best practices of scientific computing are the same across two disciplines. I picked up Python because it is open source (free) and widely used in the data science industry. Thus I thought of compiling a set of Python code that might give a headstart to future aerosol researchers.
Below is a code of computing particle diffusion coefficient as a function of particle diameter. The theory is taken from my Ph.D. advisor’s book: “Smoke, Dust, and Haze: Fundamentals of Aerosol Dynamics” (SDH) by Sheldon K. Friedlander. The output of this code is comparable to SDH Table 2.1 on page 34.
In addition to the diffusion coefficient, other parameters that are calculated are:
- Slip correction factor
- Friction coefficient
- Relaxation time
Further Readings
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4398662/
- https://www.researchgate.net/publication/233274784_Online_Nanoparticle_Mass_Measurement_by_Combined_Aerosol_Particle_Mass_Analyzer_and_Differential_Mobility_Analyzer_Comparison_of_Theory_and_Measurements
Please free to reach out to me if you have any questions, suggestions, code requests, or if you found any errors in the above code. I will be happy to provide (free) consultation on aerosol computational research.
