A review of flow control techniques and optimisation in S-shaped ducts

This paper is a review of significant studies in the complex flow physics in diffusive, s-shaped ducts, focusing on flow control methods employed to counteract the onset of separation, swirl formation, and non-uniformity of pressure at the duct exit plane. Passive, active, and hybrid flow control, along with optimisation techniques used to control the dominant flow features are discussed. According to the literature, tapered fin vortex generators and submerged vortex generators improve pressure loss and distortion by double digit percentages, and three-dimensional synthetic jets and pulsed micro-jets show greatest promise amongst active flow control devices. Plasma flow control methods have only sparsely been used in s-ducts with one study performing experiments with alternating-current dielectric-barrier-discharge plasma actuators. The importance of flow unsteadiness has been identified in the literature, with peak values as high as one order of magnitude different from the time-averaged properties. Despite this, very few flow control studies have used time-dependent solution methods to quantify the effect of flow control methods on the unsteadiness of the flow

Block Topology Generation for Structured Multi-block Meshing with Hierarchical Geometry Handling

AbstractMulti-block structured mesh generation remains one of the most popular meshing techniques because of its superior simulation quality but it is difficult to apply when dealing with complex three dimensional (3D) domains. To this end, a hybrid blocking approach, combining the medial axis based method with level set isosurface is presented and applied to mesh complex 3D external flow domains. Secondly, a hierarchical geometry handling approach is demonstrated which makes use of the lower order modeling, overset meshes and zonal blocking to reduce the meshing and modeling effort. Typical external aerodynamics cases have been showcased to describe how such techniques can be used for efficiently addressing modern industrial meshing challenges

Gradient-enhanced least-square polynomial chaos expansions for uncertainty quantification and robust optimization

Regression-based Polynomial Chaos expansions offer several advantages over projection-based approaches, including their lower computation cost and greater flexibility. In the presence of expensive function evaluations, such as with computational fluid dynamics and finite element analysis, the availability of gradient information, coming from adjoint solvers, can be used to reduce the cost of least-square estimation. Particular attention needs to be payed to the accuracy of gradient information, as adjoint solvers are often more noisy than their primal counterparts. This paper compares different approaches for gradient-enhanced least-square Polynomial Chaos expansion, both for algebraic test cases, and for real-world test cases, i.e. a transonic compressor and a modern jet engine fan

Extending highly loaded axial fan operability range through novel blade design

The tip clearance size has historically been considered to be the main factor affecting stability range in axial fan and compressors. This paper reveals that the stall characteristics are defined by the axial momentum flux of the tip leakage flow and that tip clearance is primarily a strong driver for this metric. A bespoke methodology for carefully tailoring the axial momentum via three-dimensional design is presented, which enables a higher degree of control over the stability range for cases where the tip clearance responds to other considerations and cannot be defined for this purpose. The effect of the axial momentum on efficiency is also addressed and the trade-off between operability range and design point performance is derived. The results show that the conditions for optimal stability differ from those for optimal efficiency and that control over the axial momentum enables tuning the design for a desired exchange. Numerical simulations have been employed to drive the analysis through a high-fidelity computational model whose behavior is supported by rich set of experimental data. Contrary to current belief, results further indicate that an accurate characterization of stall, including onset mechanism, can be achieved through steady-state simulations, minimizing the need for expensive time-accurate computations during the design phase

Using shock control bumps to improve transonic fan/compressor blade performance

Shock control bumps can help to delay and weaken shocks, reducing loss generation and shock-induced separation and delaying stall inception for transonic turbomachinery components. The use of shock control bumps on turbomachinery blades is investigated here for the first time using 3D analysis. The aerodynamic optimisation of a modern research fan blade and a highly loaded compressor blade are carried out using shock control bumps to improve their performance. Both the efficiency and stall margin of transonic fan and compressor blades may be increased through the addition of shock control bumps to the geometry. It is shown how shock induced separation can be delayed and reduced for both cases. A significant efficiency improvement is shown for the compressor blade across its characteristic, and the stall margin of the fan blade is increased by designing bumps that reduce shock-induced separation near to stall. Adjoint surface sensitivities are used to highlight the critical regions of the blade geometries, and it is shown how adding bumps in these regions improves blade performance. Finally, the performance of the optimised geometries at conditions away from where they are designed is analysed in detail

Affordable uncertainty quantification for industrial problems: application to aero-engine fans

Uncertainty quantification (UQ) is an increasingly important area of research. As components and systems become more efficient and optimized, the impact of uncertain parameters is likely to become critical. It is fundamental to consider the impact of these uncertainties as early as possible during the design process, with the aim of producing more robust designs (less sensitive to the presence of uncertainties). The cost of UQ with high-fidelity simulations becomes therefore of fundamental importance. This work makes use of least-squares approximations in the context of appropriately selected polynomial chaos (PC) bases. An efficient technique based on QR column pivoting has been employed to reduce the number of evaluations required to construct the approximation, demonstrating the superiority of the method with respect to full-tensor quadrature (FTQ) and sparse-grid quadrature (SGQ). Orthonormal polynomials used for the PC expansion are calculated numerically based on the given uncertainty distribution, making the approach optimal for any type of input uncertainty. The approach is used to quantify the variability in the performance of two large bypass-ratio jet engine fans in the presence of shape uncertainty due to possible manufacturing processes. The impacts of shape uncertainty on the two geometries are compared, and sensitivities to the location of the blade shape variability are extracted. The mechanisms at the origin of the change in performance are analyzed in detail, as well as the differences between the two configurations

Affordable Uncertainty Quantification for Industrial Problems: Application to Aero-Engine Fans

Uncertainty quantification (UQ) is an increasingly important area of research. As components and systems become more efficient and optimized, the impact of uncertain parameters is likely to become critical. It is fundamental to consider the impact of these uncertainties as early as possible during the design process, with the aim of producing more robust designs (less sensitive to the presence of uncertainties). The cost of UQ with high-fidelity simulations becomes therefore of fundamental importance. This work makes use of least-squares approximations in the context of appropriately selected polynomial chaos (PC) bases. An efficient technique based on QR column pivoting has been employed to reduce the number of evaluations required to construct the approximation, demonstrating the superiority of the method with respect to full-tensor quadrature (FTQ) and sparse-grid quadrature (SGQ). Orthonormal polynomials used for the PC expansion are calculated numerically based on the given uncertainty distribution, making the approach optimal for any type of input uncertainty. The approach is used to quantify the variability in the performance of two large bypass-ratio jet engine fans in the presence of shape uncertainty due to possible manufacturing processes. The impacts of shape uncertainty on the two geometries are compared, and sensitivities to the location of the blade shape variability are extracted. The mechanisms at the origin of the change in performance are analyzed in detail, as well as the differences between the two configurations