Abstract
New propulsive concepts, such as boundary layer ingestion, involve stronger interactions between the engine and its environment, and are thus more complex flows compared to classical architectures. Usual turbomachinery design tools are inadequate, and new numerical methodologies are needed to accurately predict the engine performance with affordable CPU resources. The present paper examines the relevance of a reduced-order modeling approach—the body force modeling (BFM) method—for a low-speed cooling fan with inflow distortion. The formulation itself accounts for the blade metal blockage and compressibility effects, and it relies on a semiempirical loss model, independent of computational fluid dynamics (CFD) calibration. The BFM results obtained in the present work are assessed against full-annulus unsteady Reynolds-averaged Navier-Stokes (URANS) results and experiments. The comparison shows that the BFM approach successfully quantifies the fan stage performance. Furthermore, the distortion transfer across the stage is examined and the flow patterns observed are found to be the same as in the URANS results and in the measurements. Hence, this methodology, coming at a low CPU cost, is well-adapted to the early design phase of an innovative propulsion system.
Highlights
Further reducing the environmental impact of aviation requires the investigation of new aircraft architectures, among which embedded propulsion is a promising solution
Full-annulus 3D unsteady computational fluid dynamics (CFD) simulations can be seen as a reference method to capture the aerodynamics of such flows, but the cost of this approach still remains prohibitive for early design phases
We examine the accuracy of a reduced-order modeling approach: the body force modeling (BFM) method
Summary
Further reducing the environmental impact of aviation requires the investigation of new aircraft architectures, among which embedded propulsion is a promising solution. With BLI, the fan constantly operates under severely distorted inflow conditions, with large variations in the upstream total pressure and swirl angle, among other quantities. This can have a strong impact on the fan performance, and correctly capturing the mass flow and work spatial redistributions across the blade row currently represents a challenge. Distortion transfer predictions across the stage are important with respect to the performance of downstream components. This emphasizes the need to accurately estimate the performance of the different engine components as early as possible in a pre-design cycle. Because of time and CPU cost, even with current conventional architectures, the coupling between air intake and fan designs still
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