Computational Analysis of Water Condensation for Passive Dew Condensers to Optimize Water Condensation

Abstract

This thesis generated a mathematical and computational framework for simulating water condensation on Passive Dew Condensers (PDC) using meteorological data using transient analysis. Climate change will require finding new sources of water. A PDC has the potential to condense water from the atmosphere without the use of electricity. This thesis models water condensation using a PDC. A numerical analysis of the water condensation process affected by thermodynamical phenomena of convection, radiation, condensation, and evaporation and its interaction with a fluid (humid air) using the Navier-Stokes-Equation was investigated. Finite element analysis was used to compare the numerical results for simple geometries and analyze more complex solution domains. The obtained results generate an accurate description of water condensation as a function of time and the position on a condensing surface. The simulation results were compared against experimental data from different locations and time frames to validate the model accuracy. The simulation results matched the observed data accurately. To understand how variables affect water condensation, additional results for different tilt angles were analyzed to find the optimal configuration for given conditions and idealized meteorological conditions. This thesis also addressed how PDC characteristics and climatological conditions condense water. Understanding the interaction between PDC characteristics can help maximize PDC water collection. A tool was created to estimate the dew yield for a PDC as a function of its construction parameters such as the tilt angle, length, and PDC materials, and external imposed meteorological parameters such as air temperature, relative humidity, cloud cover, and wind speed. The only limitation of the model is the assumption of laminar airflow that ignores turbulence above the PDC surface. It is also limited to two-dimensional analysis and ignores some edge effects. The analysis was simplified to a two-dimensional PDC to reduce the complexity of the solution domain. A three-dimensional analysis was carried out to compare air velocity results. A three-dimensional analysis would include edge effects that are not taken into consideration. The computational intensity was reduced by optimizing the study area and limiting it to the top of the PDC. New linearization techniques were used to simplify the computational solution and generalize the results. The results obtained can accurately predict water condensation over a PDC surface for different locations around the world using meteorological data. This will allow us to estimate the water condensation potential. This tool can also calculate how water condensation will look in the future with a set of evolving meteorological conditions in the context of global warming.