Estimating the Deployment Pressure in Pumpkin Balloons

Abstract

A vexing conundrum that has hindered the development of a high-altitude heavy-lift pumpkin balloon system is the system’s ability to reliably deploy into the desired symmetric shape. With design strategies honed by empirical results from inflation tests in 2007 using 27-m-diam 200-gore pumpkin balloons, NASA’s Balloon Program Office executed successful pumpkin deployments in 2008: Flight 586-NT, a 2 million cubic foot 200-gore design, and Flight 591-NT, a 6 million ft 200-gore design. Earlier stability analysis of strained symmetric float shapes yielded partial information regarding deployment. Balloons that were found to have a large number of unstable modes at float conditions were found also to have deployment problems. The question remained, what was the minimum pressure at which a pumpkin balloon would deploy into a cyclically symmetric shape (not necessarily the fully developed one)? Careful examination of a family of axisymmetric ascent shapes revealed a correlation between differential pressure and the number of unstable modes. In this paper, criteria that can be used to estimate the deployment pressure is presented. To bound the problem, fixed-, constrained-, and free-boundary conditions are considered for the top endplate fitting. It was found that the stability results using the fixed and constrained conditions are nearly identical, and so the strategy for estimating the deployment pressure, called the deployment pathway portrait, is as follows. The deployment pressurePDep is defined as the smallest nadir pressurewhere the topfree boundary condition yields two unstable modes and the top-fixed boundary condition yields no unstable modes. The approach is validated by comparing analytical predictions of deployment pressures based on the deployment pathway portrait with the deployment pressures that were recorded during the inflation tests in 2007: Flight 586-NT and Flight 591-NT. Very good agreement between the observed deployment pressures and the analytical predictions was found. This is the first analytical-based approach for estimating the deployment pressure.