A Computational Sensitivity Study To Refine Photoacoustic Absorption Signals Through Geometry Optimization

PRABHAV UPADHYAY, Benjamin Sumlin, Rajan K. Chakrabarty, Washington University in Saint Louis

     Abstract Number: 103
     Working Group: Instrumentation and Methods

Abstract
Aerosols can absorb and scatter solar radiation, leading to changes in the radiative balance of the atmosphere and impacts on climate and air quality. Specifically, the absorption of sunlight by aerosols can lead to atmospheric warming. Measuring aerosol light absorption is essential to understand these effects, and photoacoustic spectroscopy (PAS) is a widely used technique for this purpose. However, the sensitivity of PAS measurements is limited by several factors, including background noise, electrical interference, and other sources.

Improving the signal-to-noise ratio of PAS measurements is an important goal for accurate and precise measurements of aerosol light absorption. This limitation can be addressed by increasing incident laser power or optimizing the cell geometry. Higher laser power beyond a certain point can lead to vaporization of volatile compounds. Rather, we seek to optimize the geometry by increasing the quality factor (Q_c) of the photoacoustic cell and optimizing the transfer function of the acoustic notches, or Helmholtz resonators, which define the acoustic boundary conditions. Q_c is affected by various factors like loss of energy by fluid viscosity, heat conduction through the instrument walls, and the laser modulation frequency relative to that of cell resonance.

We propose a computational fluid dynamics model to simulate the PAS instrument and carry out a sensitivity study on the geometry of the Helmholtz resonators and the photoacoustic cell. The modeling is done in COMSOL Multiphysics and includes the acoustics module. This model involves solving the photoacoustic wave equation in the frequency domain and simulating the behavior of a PAS system under different conditions to identify the optimal design parameters for maximizing the signal-to-noise ratio and reducing measurement uncertainties.