No abstract

Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and SpaceAdministration and Deutsches Zentrum für Luft-und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments. Nomenclature à = REA average B i = bias of model i b = vortex spacing b 0 = initial vortex pair separation D i = absolute distance to ensemble mean of model i f  = ensemble forecast uncertainty e = ensemble  = dimensional eddy dissipation rate i f = forecast of ith model f~ = forecast average  f = upper ensemble uncertainty limit  f = lower ensemble uncertainty limit g = gravitational acceleration  = vortex circulation 0  = initial vortex circulation *  = vortex circulation strength normalized by initial vortex strength,  0 m = weighting factor for R B,i nv = natural variability n = weighting factor for R D,i 2 N = dimensional Brunt-Väisälä frequency p = parameter  = potential temperature R D,i = weighting factor for model i (considering model convergence) R B,i = weighting factor for model i (considering model bias) R i = total weighting factor for model i  = standard deviation obs  = variability calculated from measurements err  = lidar measurement error nv  = natural variability of vortices t = vortex age t 0 = normalized vortex age T = non-dimensional time u = crosswind V 0 = initial vortex descent velocity w = vortex descent speed y = lateral position of the vortex core y* = vortex lateral transport normalized by b 0 z = vertical position of the vortex core z* = vortex height normalized by b 0

Mechanical Dynamics Inc. has entered its twenty-sixth year of producing ADAMS® (Automatic Dynamic Analysis of Mechanical Systems), the world’s leading commercial mechanical analysis software for kinematics and dynamics of multibody systems. During the past twenty-five years, use of mechanical analysis software has grown dramatically. No longer the province of a fringe group of highly trained specialists, it now has a presence on nearly every engineer’s desktop. The software comes in many forms—embedded in Computer Aided Design (CAD) software, or as stand-alone packages such as ADAMS. Use of the software even has a new name: Functional Virtual Prototyping refers to the ability to use mechanical analysis software to complete the engineer’s understanding of the physics of a design without the need for prototype parts. Due to large numbers of mechanical systems, a high cost of prototypes, and relentless pressure on cost and safety, the automotive industry has always been at the vanguard of the use of Functional Virtual Prototyping technology. A recent popular trend in the automotive industry is called the Six Sigma process—intended to get as close as possible to zero defects—in part by assuring robust quality and performance of components, subsystems, and systems. To satisfy the demands of the Six Sigma trend, the automotive industry now uses Functional Virtual Prototyping software such as ADAMS as an important tool in Robust Design. This paper describes new software for Robust Design, called ADAMS/Insight, and illustrates how the behavior of a mechanical system model in ADAMS can now be understood more completely over a range of system parameters and tolerances.