arXiv subjects

Gwenore F. Pokrifka

Publications and source records attributed to Gwenore F. Pokrifka.

3 recordsLinked to original sources

arXiv2026

Tracking water vapor homogeneous nucleation and droplet growth with spectroscopy and holography in a free expansion cloud chamber

We use a newly commissioned rapid expansion aerosol chamber (REACh) facility to study the homogeneous nucleation of water vapor to form liquid droplets. We perform high-speed measurements to track the partitioning of water into vapor and droplets throughout the expansion process, including tunable diode laser absorption spectroscopy (TDLAS) to access the vapor concentration and in-line holography to track the size and concentration of nucleating droplets. We retrieve the peak saturation ratio achieved in each expansion from the TDLAS measurements in combination with adjusted thermocouple temperature readout. We monitor the number of nucleated droplets and their subsequent growth as a function of saturation ratio, and observe the onset of homogeneous nucleation of water vapor occurring at a threshold saturation ratio near $S=5$, in agreement with prior literature and classical nucleation theory. The trends we observe in average diameter and droplet concentration suggest that warm air pockets near the chamber walls inhomogeneously mix with cold air at the center of the chamber following expansion. Active forced mixing with fans yields more spatially uniform temperature readings across the chamber, but also significantly broadens the droplet size distribution. Our results demonstrate the capability of TDLAS and holography techniques to track both water vapor and liquid water in the high saturation ratio environments necessary for the homogeneous nucleation of droplets. Our findings also reveal that droplet nucleation and growth dynamics are highly sensitive to turbulence.

physics.flu-dyn↗

arXiv2025

Discovering How Ice Crystals Grow Using Neural ODE's and Symbolic Regression

Depositional ice growth is an important process for cirrus cloud evolution, but the physics of ice growth in atmospheric conditions is still poorly understood. One major challenge in constraining depositional ice growth models against observations is that the early growth rates of ice crystals cannot be directly observed, and proposed models require assumptions about the functional dependence of physical processes that are still highly uncertain. Neural ordinary differential equations (NODE's) are a recently developed machine learning method that can be used to learn the derivative of an unknown function. Here we use NODE's to learn the functional dependence of unknown physics in the depositional ice growth model by optimizing against experimental measurements of ice crystal mass. We find a functional form for the depositional ice growth model that best fits 290 mass time series of ice crystals grown in a levitation diffusion chamber. We use symbolic regression to derive an equation for the function learned by the NODE model, which includes additional terms proportional to ice crystal mass in the capacitance growth model. We evaluate this functional form against experimental data sets from the AIDA Aerosol and Cloud Chamber, finding that our new proposed model for depositional ice growth accurately reproduces experimental results in the early stages of ice crystal growth.

physics.chem-ph↗

arXiv2024

Droplet Nucleation In a Rapid Expansion Aerosol Chamber

We present a new experimental facility to investigate the nucleation and growth of liquid droplets and ice particles under controlled conditions and characterize processes relevant to cloud microphysics: the rapid expansion aerosol chamber (REACh). REACh is an intermediate size chamber (~0.14 m$^3$) combining the principle of an expansion chamber with the ability to probe the influence of turbulent flows. Nucleation is achieved via a sudden pressure drop accompanied by a temperature drop, which can cause humid air to condense into a cloud of droplets under the appropriate thermodynamic conditions. REACh features tight control and monitoring of the initial saturation ratio of water vapor, identity and concentration of seeding aerosol particles, temperature, pressure, and air flow mixing, together with high speed real time measurements of aerosol and droplet size and number. Here, we demonstrate that the minimum temperature reached during each expansion can be reasonably described by the thermodynamics of dry or moist adiabats, for a wide range of initial relative humidity. The size and number of droplets formed, and the overall lifetime of the cloud, are characterized as a function of the aerosol concentration and initial water vapor saturation ratio. The total droplet concentration scales linearly with the seeding aerosol concentration, suggesting that all injected aerosol particles serve as condensation nuclei. While the total number of droplets formed increases with aerosol concentration, the mean droplet size decreases with the concentration of seeding aerosols as a result of competition for the available water vapor. Theoretical considerations provide a quantitative prediction for the mean droplet size for a wide range of conditions.

physics.flu-dyn↗