Robust design methodology for common core gas turbine engines

Robust design;Robust design simulation;Propulsion system;Gas turbine engine core;Product family;Common core;Design;Systems design and optimization;Design variant;Probabilistic design;Design under uncertainty;Multiple design point

Sands, Jonathan Stephen ; Mavris, Dimitri N. Aerospace Engineering German, Brian Schutte, Jeffrey Jagoda, Jechiel Karl, Alexander ; Mavris, Dimitri N.

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Robust design;    Robust design simulation;    Propulsion system;    Gas turbine engine core;    Product family;    Common core;    Design;    Systems design and optimization;    Design variant;    Probabilistic design;    Design under uncertainty;    Multiple design point;   

Others  :  https://smartech.gatech.edu/bitstream/1853/53520/1/SANDS-DISSERTATION-2015.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

A gas turbine engine design process was developed for the design of a common core engine family. The process considers initial and projected variant engine applications, likely technology maturation, and various sources of uncertainty when making initial core design considerations. A physics based modeling and simulation environment was developed to enforce geometric core commonality between the core defining design engine and a common core variant engine. The environment also allows for upgrade options and technology to be infused into the variant engine design. The relationships established in the model enable commonality to be implicitly enforced when performing simultaneous design space explorations of the common core design and all corresponding variant engine designs. A robust design simulation process was also developed, enabling probabilistic surrogate model representations of the common core engine family design space to be produced. The probabilistic models provide confidence interval performance estimates with a single function call for an inputted set of core and variant design settings and the uncertainty distribution shape parameter settings representative of an uncertainty scenario of interest. The unique form of the probabilistic surrogate models enables large numbers of common core engine family applications to be considered simultaneously, each being simulated under a unique uncertainty scenario. Implications of core design options can be instantaneously predicted for all engine applications considered, allowing for favorable common core design regions to be identified in a highly efficient manner.