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In order to investigate the transport law of an unsteady wake in a downstream cascade channel in a turbine stage environment, this study was based on a self-designed unsteady wake generator, and a low-pressure turbine cascade was the research object. The research was carried out through a combination of experiments and numerical simulation. The results show that in the range of −50° to 20° inflow incidence, there is no separation on the blade suction surface, the total pressure loss coefficient is low, and the cascade has good adaptability to the inflow incidence. When the incoming flow is at a negative incidence, the transport of the unsteady upstream wake to the downstream unsteady wake is basically the same; the same holds for a non-negative incidence. When the upstream unsteady wake is transported downstream in the cascade channel, the wake near the cascade suction surface follows a detour and barely interacts with the mainstream fluid. The total pressure loss fluctuation value obtained via numerical calculation shows good periodicity; therefore, the unsteady cascade effect under the action of upstream wake sweeping becomes very obvious. [ABSTRACT FROM AUTHOR]

With the impact of size on low-pressure turbines (LPTs) increasing, the gap between the blades has shrunk, inevitably influencing the unsteady effects inside the turbine. In this study, the aerodynamic effect of the blade gap size is investigated using a compressible unsteady Reynolds-averaged Navier–Stokes (URANS) model on the basis of a four-stage LPT. Simulations are conducted in which the gap between the third-stage stator (S3) and rotor (R3) varies from 0.2 to 0.8 times the axial chord length of the R3 blade. The multi-stage environment reflects the complexity of real low-Reynolds flow fields. Computational fluid dynamics is used to analyze the flow field in detail. The results demonstrate that in the small-gap (AG-0.2) case, the turbulence kinetic energy (TKE) level of the S3 wake close to the R3 leading edge is four-thirds of that in the large-gap (AG-0.8) case. The higher intensity of the wake impacting on the blade results in a higher inverse pressure gradient in the rear part of the R3 suction surface, which increases the profile loss. However, the AG-0.2 case leads to fewer losses caused by the passage vortex in the hub area under the influence of the higher intensity of the wake. [ABSTRACT FROM AUTHOR]

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China's Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method., Author(s): Lianbo Deng [1]; Jing Xu [1]; Ningxin Zeng [2]; Xinlei Hu (corresponding author) [1] 1. Introduction In recent years, high-speed railway (HSR) in China has developed rapidly, but operating [...]