<p class="MsoListParagraphCxSpFirst" style="margin-top: 0cm; margin-right: -33.75pt; margin-bottom: 10.0pt; margin-left: 1.0cm">When modelling future turbofan engines, critical design parameters such as fan pressure ratio, bypass ratio, and overall pressure ratio will have a major impact on the predicted performance. Most often these parameters are obtained by means of optimization. Because of the multidisciplinary character of these types of problems, the resulting design will therefore depend on the scope of the study as well as the objectives of the optimization. A too narrow scope will lead to suboptimal solutions while conflicting objectives will result in ambiguity. Uncertainties in these approaches brings about the need to verify the methods against actual data on state-of-the-art engines in use today.&nbsp; Unfortunately, due to the proprietary nature of such data, one will be forced to model these engines as well. However, if one would be able to do so with enough precision, this would - to the author’s opinion - be a valuable complement to studies of a more exploratory nature predicting future technology trends.</p><p class="MsoListParagraphCxSpLast" style="margin-top: 0cm; margin-right: -33.75pt; margin-bottom: 10.0pt; margin-left: 1.0cm">The present paper is focused on estimating the performance metrics, and their interrelationship, of today’s civil turbofans, such as fan pressure ratio, fan-face Mach number and high-low pressure system power split. &nbsp;This is achieved by applying an inverse model-matching approach on one of the most prominent turbofan engines in use today, namely, the GEnx-1B. This approach makes use of efficiency correlations for turbomachinery components together with publicly available data obtained from the aircraft engine emission databank, type certificate data sheets and data publicly provided by the engine manufacturer. The study is supported by a Monte Carlo simulation where uncertainties are estimated for the publicly available data and the correlations. The outcome is an estimation of the design parameters and performance metrics of interest as well as their influence on each other in terms of statistical measures for this particular inverse design problem. This work will thus shed additional light on what can be obtained from publicly available sources with data on contemporary engines.</p>

Estimation of Design Parameters and Performance for a State-of-the-Art Turbofan

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

Turbo Expo 2021

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