Influence of various volute designs on volute overall performance

This paper discusses the influence of various volute designs on volute overall performance for a certain centrifu- gal compressor with both vaned and vaneless diffuser. Firstly, based on a free vortex flow pattern and the assumption of a circumferentially uniform flow at the design condition, a corrected method for volute design is adopted. By means of this method, corresponding to five geometric parameters affecting the volute overall performance, ten volute cases are designed. Secondly, the numerical simulation is employed and the detailed flow field and losses in different volutes with different geometric parameters are analyzed. The numerical investigation reveals that in all of the five geometric parameters, the radial location of the cross-section has the strongest influence on the performance of the volute. The non-uniform volute inlet formed by the upward vaned diffuser outlet flow is another important factor. Finally a relatively better value of D1/D2 is concluded.

Receptivity and structural sensitivity study of the wide vaneless diffuser flow with adjoint method

The stability of the flow in the vaneless diffuser of a centrifugal compressor is studied with the linear theory. The characteristics of direct and adjoint perturbation modes are investigated,and the receptivity of the instability mode to momentum forcing or mass injection is identified based on the adjoint modes. Analysis shows that the perturbation with the largest amplitude is located at the outlet of the vaneless diffuser, while the highest-receptivity region is located in the middle of the vaneless diffuser along the radial direction. The large difference between the direct and adjoint modes indicates that the instability mechanism cannot be identified from a study of either eigenmode separately. Therefore, the structural sensitivity analysis is adopted to study the feedback of the instability mode. The structural sensitivity of the eigenvalue which is proportional to the perturbation pressure and velocity is used to explain the mechanism of flow control for the vaneless diffuser.

Multiple fault separation and detection by joint subspace learning for the health assessment of wind turbine gearboxes

The gearbox of a wind turbine （WT） has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection （JSLMFD） technique to construct different subspaces adaptively for different fault pattems. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.