Influence of the state of the inlet endwall boundary layer on the interaction between pressure surface separation and endwall flows

This paper describes the effect of the state of the inlet boundary layer (laminar or turbulent) on the structure of the endwall flow on two different profiles of low-pressure (LP) turbine blades (solid thin and hollow thick). At present the state of the endwall boundary layer at the inlet of a real LP turbine is not known. The intention of this paper is to show that, for different designs of LP turbine, the state of the inlet boundary layer affects the performance of the blade in very different ways. The testing was completed at low speed in a linear cascade using area traversing, flow visualization and static pressure measurements. The paper shows that, for a laminar inlet boundary layer, the two profiles have a similar loss distribution and structure of endwall flow. However, for a turbulent inlet boundary layer the two profiles are shown to differ significantly in both the total loss and endwall flow structure. The pressure side separation bubble on the solid thin profile is shown to interact with the passage vortex, causing a higher endwall loss than that measured on the hollow thick profile.

Real Time Concrete Damage Visual Assessment for First Responders

First responders are in danger when they perform tasks in damaged buildings after earthquakes. Structural collapse due to the failure of critical load bearing structural members (e.g. columns) during a post-earthquake event such as an aftershock can make first responders victims, considering they are unable to assess the impact of the damage inflicted in load bearing members. The writers here propose a method that can provide first responders with a crude but quick estimate of the damage inflicted in load bearing members. Under the proposed method, critical structural members (reinforced concrete columns in this study) are identified from digital visual data and the damage superimposed on these structural members is detected with the help of Visual Pattern Recognition techniques. The correlation of the two (e.g. the position, orientation and size of a crack on the surface of a column) is used to query a case-based reasoning knowledge base, which contains apriori classified states of columns according to the damage inflicted on them. When query results indicate the column's damage state is severe, the method assumes that a structural collapse is likely and first responders are warned to evacuate.