Distortion of passive scalar structure during suction-based plume sampling

Studies of plume dynamics often rely on photoionization detectors (PID) to quantify spatiotemporal distributions of passive scalars (gases, vapors, odors). However, the potential for PID suction to distort filaments and to modify sensed time records remains unclear. We used computational fluid dynamics to model a common PID to quantify and parameterize suction distortion by considering how sensed time records compare to those registered by an ideal probe. Models cover a range of realistic plumes, and we show that PID can significantly modify the peak concentration and pulse shape of sensed records. We quantified distortion variations in three nondimensional parameters describing PID geometry and sampling conditions: relative suction rate, relative filament size, and ambient flow Reynolds number. We used analytical models, dimensional analysis, and scaling arguments to interpret results and discuss when distortion is likely and what drives it. We built dimensionless distortion prediction regressions, and our results enable PID users to estimate distortion levels and to employ mitigation strategies through suction velocity tuning. These findings can inform distortion-mitigating design principles and best sampling practices for other suction-based passive scalar sensing schemes.