Unsteady Behavior of Leading-edge Vortex and Diffuser Stall in a Centrifugal Compressor with Vaned Diffuser

The characteristics of a rotating stall of an impeller and diffuser and the evolution of a vortex generated at the diffuser leading-edge(i.e., the leading-edge vortex(LEV)) in a centrifugal compressor were investigated by experiments and numerical analysis. The results of the experiments revealed that both the impeller and diffuser rotating stalls occurred at 55 and 25 Hz during off-design flow operation. For both, stall cells existed only on the shroud side of the flow passages, which is very close to the source location of the LEV. According to the CFD results, the LEV is made up of multiple vortices. The LEV is a combination of a separated vortex near the leading-edge and a longitudinal vortex generated by the extended tip-leakage flow from the impeller. Therefore, the LEV is generated by the accumulation of vorticity caused by the velocity gradient of the impeller discharge flow. In partial-flow operation, the spanwise extent and the position of the LEV origin are temporarily transmuted. The LEV develops with a drop in the velocity in the diffuser passage and forms a significant blockage within the diffuser passage. Therefore, the LEV may be regarded as being one of the causes of a diffuser stall in a centrifugal compressor.

Correlation between microstructures and macroscopic properties of nickel/ yttria-stabilized zirconia (Ni-YSZ) anodes: Meso-scale modeling and deep learning with convolutional neural networks

A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttriastabilized zirconia anodes in solid oxide fuel cells(SOFCs)to their effective macroscopic properties.A variety of microstructures are generated by the discrete element method and the meso‑scale kinetic Monte Carlo method.Then,the finite element method and the homogenization theory are used to calculate the effective elastic modulus(E),Poisson’s ratio(υ),shear modulus(G)and coefficient of thermal expansion(CTE)of representative volume elements.In addition,the triple-phase boundary length density(LTPB)is also calculated.The convolutional neural network(CNN)based deep learning model is trained to find the potential relationship between the microstructures and the five effective macroscopic properties.The comparison between the ground truth and the predicted values of the new samples proves that the CNN model has an excellent predictive performance.This indicates that the CNN model could be used as an effective alternative to numerical simulations and homogenization because of its accurate and rapid prediction performance.Hence the deep learning-based homogenization framework could potentially accelerate the continuum modeling of SOFCs for microstructure optimization.