A model for prediction of STOVL ejector dynamics
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A model for prediction of STOVL ejector dynamics

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Published by National Aeronautics and Space Administration, For sale by the National Technical Information Service in [Washington, DC], [Springfield, Va .
Written in English


  • Vertically rising aircraft -- Jet propulsion -- Ejectors.

Book details:

Edition Notes

StatementColin K. Drummond.
SeriesNASA technical memorandum -- 102098
ContributionsUnited States. National Aeronautics and Space Administration.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL17100899M

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In this paper, a simple yet effective ejector model for a real time control and optimization of an ejector system is proposed. Firstly, a fundamental model for calculation of ejector entrainment ratio at critical working conditions is derived by one-dimensional analysis and the shock circle grupo-operativo-gei-porcino.com by: One-dimensional Model of an Optimal Ejector and Parametric Study of Ejector Efficiency Ronan K. McGoverna, Kartik V. Bulusub, Mohammed A. Antarc and John H. Lienhard Vd a Massachusetts Institute of Technology, Cambridge, MA, U.S.A., [email protected] b The George Washington University, Washington D.C., U.S.A., [email protected] c King Fahd University of Petroleum . The present study describes a thermodynamic ejector model which is based on experimental observations and CFD numerical results. It clearly distinguishes between the assumptions and conditions used for the design of the ejector and those used for the prediction of its performance at off-design combinations of inlet and outlet grupo-operativo-gei-porcino.com by: Gas Ejector Modeling for Design and Analysis. (December ) Chaqing Liao, B.S., Zhejiang University; M.E., Texas A&M University Chair of Advisory Committee: Dr. Frederick R. Best A generalized ejector model was successfully developed for gas ejector design and performance analysis. Previous 1-D analytical models can be derived from this new.

In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. A model for prediction of STOVL ejector dynamics [microform] / Colin K. Drummond A theoretical study of two stage thrust augmenting ejectors / by A.M. Abdel-Fattah A computational study of thrust augmenting ejectors based on a viscous-inviscid approach [microform] / T. A generalized ejector model was successfully developed for gas ejector design and performance analysis. Previous 1-D analytical models can be derived from this new comprehensive model as. N NASA Technical Memorandum 9 Supersonic STOVL Ejector Aircraft from a Propulsion Point of View R. Luidens, R. Plencner, W. Haller, and A. blassman Lewis Research Center Cleveland, Ohio Prepared for the Twentieth Joint Propulsion Coiiference cosponsored by the AIAA, SAE, and ASME i Cincinnati, Ohio, June , I.

Mar 22,  · In this respect, computational fluid dynamics may represent a tool to overcome these difficulties and analyze the flow details of arbitrary ejector geometries. In this chapter, we will try to overview some of the main features that should be considered in order to set up a reliable CFD scheme for ejector flow grupo-operativo-gei-porcino.com: Giuseppe Grazzini, Adriano Milazzo, Federico Mazzelli. Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the s. In recent years it has also been used in power system balancing models and in power grupo-operativo-gei-porcino.com predictive controllers rely on dynamic models of. Comparison of ejector predicted performance by thermodynamic and CFD models. Results obtained with the CFD model for Dp2 = m, Pp0/Ps0 = and Tp0/Ts0 = (Ts0 = K. Prediction means projecting the model response k steps ahead into the future using the current and past values of measured input and output values. k is called the prediction horizon, and corresponds to predicting output at time kT s, where T s is the sample time.