Validation of the RANS-simulation of laminar separation bubbles on airfoils

This paper presents RANS simulations of the low-Reynolds-number flow past an SD7003 airfoil at $\mathit{Re}=6\times {10}^4$, where transition takes place across a laminar separation bubble. The transition prediction procedure using an approximate envelope method as well as a linear stability solver is discussed. The numerical results are validated against PIV- and force measurements obtained in several wind- and watertunnels and are also compared to XFOIL results. Good agreement is found within the operational range of the airfoil.     

Effect of rotor-mounted protrusion on sealing performance and flow structure in rotor-stator cavity

This paper presents an experimental investigation on the effect of protrusion radial position and height on the sealing performance and flow structure in the rotor-stator cavity. The rotor-mounted protrusions are assembled at three radial positions and are set to three heights. The cavity is equipped with three rim seals: a radial seal, an axial seal and a seal with double fins on the stator. The annulus Reynolds number is set at $4.39\times {10}^5$ and the rotational Reynolds number ranges from $7.51\times {10}^5$ to $1.20\times {10}^6$. Heat and mass transfer analogy is applied. Pressure and ${\mathrm{C}\mathrm{O}}_2$ concentration are measured. The experimental results show that in cavities with different rim seals, radial distributions of the sealing efficiency, pressure and swirl ratio are basically the same. The sealing performance is improved by protrusions compared with the cavity without protrusion and improves with the increase of protrusion radial position and height. The effect of protrusion increases with the increase of the rotational Reynolds number. The windage loss and the flow resistance introduced by protrusions are investigated. It is found that induced windage loss and flow resistance decrease with the increase of protrusion radial position but increase with the protrusion height.     

Intrinsic component filtering for fault diagnosis of rotating machinery

Fault diagnosis of rotating machinery has always drawn wide attention. In this paper, Intrinsic Component Filtering (ICF), which achieves population sparsity and lifetime consistency using two constraints: $l_{1/2}$ norm of column features and $l_{3/2}$ -norm of row features, is proposed for the machinery fault diagnosis. ICF can be used as a feature learning algorithm, and the learned features can be fed into the classification to achieve the automatic fault classification. ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience. Simulated and experimental signals of bearing fault are used to validate the performance of ICF. The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis, weak signature detection and compound fault separation.