The focus of this study is to compare the aerothermodynamic cycle design space of a gas turbine engine generated using two on-design approaches. The traditional approach uses a single design point (SDP) for on-design cycle analysis, where off-design cycle analysis must be performed at other operating conditions of interest. A multi-design point (MDP) method performs on-design cycle analysis at all operating conditions where performance requirements are specified. Effects on the topography of the cycle design space as well as the feasibility of the space are examined. The impacts that performance requirements and cycle assumptions have on the bounds and topography of the feasible space are investigated. The deficiencies of a SDP method in determining an optimum gas turbine engine will be shown for a given set of requirements. Analysis will demonstrate that the MDP method, unlike the SDP method, always obtains a properly sized engine for a set of given requirements and cycle design variables, resulting in an increased feasible region of the aerothermodynamic cycle design space from which the optimum performance engine can be obtained.

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The development of a clean sheet gas turbine engine program can be a multi-billion dollar undertaking. The decision to take on such a program can place the company at great risk. In order to distribute this capital risk among a large quantity of products, the engine core should be utilized across a family of products. However, common core engine variant designs must also achieve performance levels that are competitive and economically viable options for likely customers. Common engine core design decisions should be made with knowledge of how a candidate core definition will impact initial and future product applications. Implications must be drawn to estimate the impacts on performance, weight, and cost when employing a single core definition across a variety of likely product applications. This introduces an immense computational challenge. If commonality were enforced via post processing of simulation data, a large portion of the design space would not represent common core applications, making the associated data useless to the designer. Therefore, engine commonality should be implicitly imposed across the various product applications being considered. To further reduce the computational burden of simulating multiple applications, design space exploration of the core and corresponding variant applications must also be done in an efficient manner. This research aims to develop and demonstrate a computationally efficient method for modeling and simulating a variety of common core engine variant applications simultaneously. The modeling approach to enforce commonality will first be shown. Additionally, the method will be shown to enable design space exploration of multiple common core engine applications simultaneously. Through the use of surrogate models, the relationship between a common engine core definition and corresponding variant application will be captured in mathematical form. This mathematical relationship will then be duplicated for each product application, tying all applications to a single baseline engine core definition. The approach allows core design implications to be drawn instantaneously to each product application considered. After establishing the unique modeling and simulation approach, the method will be demonstrated for a multiple application common core design problem. The process will be used to arrive at an engine core definition that can be employed across multiple high bypass turbofan applications. In order to enumerate the amount of compromise made by employing a single baseline core definition across multiple applications, each resultant common core variant design will be compared to corresponding clean sheet designs selected for each individual application. The knowledge gained from this modeling and simulation approach allows the designer to make performance, weight, and cost trades efficiently across a family of products earlier in the development process.

Designing propulsion system architectures to meet next generation requirements requires many tradeoffs be made. These trades are often between performance, risk, and cost. For example, the core of an engine is the most expensive and highest risk area of a propulsion system design. However, a new core design provides the greatest flexibility in meeting future performance requirements. The decision to upgrade or redesign the core must be justified by comparison with other lower risk options. Furthermore, for turboshaft applications, the choice of compressor, whether axial or centrifugal, is a major decision and trade with the choice being heavily driven by both current and projected weight and performance requirements. This problem is confounded by uncertainty in potential benefits of technologies or future performance of components. To address these issues this research proposes the use of a Bayesian belief network (BBN) to extend the more traditional robust engine design process. This is done by leveraging forward and backward inference to identify engine upgrade paths that are robust to uncertainty in requirements performance. Prior beliefs on the different scenarios and technology uncertainty can be used to quantify risk. Forward inference can be used to compare different scenarios. The problem will be demonstrated using a two-spool turboshaft architecture modeled using the Numerical Propulsion System Simulation (NPSS) program. Upgrade options will include off the shelf, derivative engine (fixed core) with no technologies, derivative engine with new technologies, a new engine with no technologies, and a new engine with new technologies. The robust design process with a BBN will be used to identify which engine cycle and upgrade scenario is needed to meet performance requirements while minimizing cost and risk. To demonstrate how the choice of upgrade and cycle change with changes in requirements, studies are performed at different horsepower, ESFC, and power density requirements.