Many tasks in fluids engineering require knowledge of the turbulence in jets. There is a strong, although fragmented, literature base for low order statistics, such as jet spread and other meanvelocity field characteristics. Some sources, particularly for low speed cold jets, also provide turbulence intensities that are required for validating Reynolds-averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) codes. There are far fewer sources for jet spectra and for space-time correlations of turbulent velocity required for aeroacoustics applications, although there have been many singular publications with various unique statistics, such as Proper Orthogonal Decomposition, designed to uncover an underlying low-order dynamical description of turbulent jet flow. As the complexity of the statistic increases, the number of flows for which the data has been categorized and assembled decreases, making it difficult to systematically validate prediction codes that require high-level statistics over a broad range of jet flow conditions. For several years, researchers at NASA have worked on developing and validating jet noise prediction codes. One such class of codes, loosely called CFD-based or statistical methods, uses RANS CFD to predict jet mean and turbulent intensities in velocity and temperature. These flow quantities serve as the input to the acoustic source models and flow-sound interaction calculations that yield predictions of far-field jet noise. To develop this capability, a catalog of turbulent jet flows has been created with statistics ranging from mean velocity to space-time correlations of Reynolds stresses. The present document aims to document this catalog and to assess the accuracies of the data, e.g. establish uncertainties for the data. This paper covers the following five tasks:• Document acquisition and processing procedures used to create the particle image velocimetry (PIV) datasets.• Compare PIV data with hotwire and laser Doppler velocimetry (LDV) data published in the open literature.• Compare different datasets acquired at roughly the same flow conditions to establish uncertainties.• Create a 'consensus' dataset for a range of hot jet flows, including uncertainty bands.• Analyze this consensus dataset for self-consistency and compare jet characteristics to those of the open literature.One final objective fulfilled by this work was the demonstration of a universal scaling for the jet flow fields, at least within the region of interest to aeroacoustics. The potential core length and the spread rate of the half-velocity radius were used to collapse of the mean and turbulent velocity fields over the first 20 jet diameters in a highly satisfying manner. Nomenclature ! "shear layer thickness, U j /|dU/dr| max # shear layer coordinate, = (r -r 0.5 )/! " , # $ shear layer growth rate coordinate, = (r -r 0·5

During the ballistic phase of a cryogenic upper stage, attitude and orbit control are usually performed by a cold gas thruster system. Since the thruster inlet pressure is driven by the propellant tank ullage conditions, a sufficient pressure regeneration rate has to be assured to compensate the pressure decrease during thruster activation. The coupling between the thruster activation sequence, stage kinematics, and thermal environment is essential for the performance prediction of cold gas systems. An iteration loop using a statistical model considering the thruster activation sequence is applied. For detailed final analysis, an EcosimPro model is used, including three-dimensional (3D) CFD (computational fluid dynamics) sloshing analysis and all relevant boundary conditions.