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Published November 1, 2020 | public
Journal Article

Global thermodynamic, transport-property and dynamic characteristics of the Venus lower atmosphere below the cloud layer

Abstract

This study investigates the global characteristics of the Venus lower atmosphere below the cloud layer. Starting from available data regarding species composition, temperature and pressure, the general thermodynamic and transport properties of the Venus lower atmosphere are first computed at altitudes below the cloud layer. The thermodynamic and transport properties are validated with known data. A thermodynamic stability analysis for a variety of potentially existing mixtures is conducted to highlight the sensitivity of the thermodynamic regime to the variation of the species molar fractions. The limits of thermodynamic stability are represented in the thermodynamic phase diagram of the mixture, and thus regions of phase stability and instability are determined. It is found that the Venus lower-atmosphere thermodynamic regime is located in the stable, single-phase regime, with supercritical conditions occurring in the lower few kilometers, thereby validating ad-hoc expectations which have never envisaged the existence of a two-phase regime at those altitudes. Using thermodynamic and transport-property information thus computed, several non-dimensional numbers are calculated. The Prandtl number is first evaluated. Then, the characteristic Reynolds number is estimated for different assumed length scales, showing that, independent of the length scale, the entire lower atmosphere below the cloud deck is in a fully-turbulent flow regime. Finally, the speed of sound and the Mach number are computed as function of altitude. The information obtained from all these calculations fills a scientific void, and may be crucial for the design of landers on the Venus surface.

Additional Information

© 2020 Published by Elsevier Inc. Received 5 December 2019, Revised 14 February 2020, Accepted 16 March 2020, Available online 9 April 2020. This work was conducted at the Jet Propulsion Laboratory (JPL) of the California Institute of Technology (Caltech) and sponsored by JPL internal development funds. This study is part of Stefano Morellina's (SM) Master thesis and was performed while he was an intern at JPL. The contributions of Dr. Aswin Gnanaskandan and Dr. Luca Sciacovelli are thankfully recognized. Stefano Morellina's Master thesis supervisor, Dr. Francesco Creta, and the co-supervisor, Dr. Pasquale E. Lapenna, are thanked for their advice. The Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO), the Sapienza University of Rome, and the Italian Space Agency are thanked for providing support to SM during his internship at JPL. The computational resources were provided by the NASA Advanced Supercomputing at Ames Research Center under the Science Mission Directorate program.

Additional details

Created:
August 22, 2023
Modified:
October 20, 2023