Off-design performance loss model for radial turbines with pivoting, variable-area stators

An off-design performance loss model was developed for variable stator (pivoted vane), radial turbines through analytical modeling and experimental data analysis. Stator loss is determined by a viscous loss model; stator vane end-clearance leakage effects are determined by a clearance flow model. Rotor loss coefficient were obtained by analyzing the experimental data from a turbine rotor previously tested with six stators having throat areas from 20 to 144 percent of design area and were correlated with stator-to-rotor throat area ratio. An incidence loss model was selected to obtain best agreement with experimental results. Predicted turbine performance is compared with experimental results for the design rotor as well as with results for extended and cutback versions of the rotor. Sample calculations were made to show the effects of stator vane end-clearance leakage

Loss model for off-design performance analysis of radial turbines with pivoting-vane, variable-area stators

An off-design performance loss model is developed for variable-area (pivoted vane) radial turbines. The variation in stator loss with stator area is determined by a viscous loss model while the variation in rotor loss due to stator area variation (for no stator end-clearance gap) is determined through analytical matching of experimental data. An incidence loss model is also based on matching of the experimental data. A stator vane end-clearance leakage model is developed and sample calculations are made to show the predicted effects of stator vane end-clearance leakage on performance

Computer code for off-design performance analysis of radial-inflow turbines with rotor blade sweep

The analysis procedure of an existing computer program was extended to include rotor blade sweep, to model the flow more accurately at the rotor exit, and to provide more detail to the loss model. The modeling changes are described and all analysis equations and procedures are presented. Program input and output are described and are illustrated by an example problem. Results obtained from this program and from a previous program are compared with experimental data

Use of similarity parameters for examination of geometry characteristics of high-expansion- ratio axial-flow turbines

Similarity parameters used for examining geometry characteristics of axial flow turbines with high expansion rati

Designing Interfaces for Human-Computer Communication: An On-Going Collection of Considerations

While we do not always use words, communicating what we want to an AI is a conversation -- with ourselves as well as with it, a recurring loop with optional steps depending on the complexity of the situation and our request. Any given conversation of this type may include: (a) the human forming an intent, (b) the human expressing that intent as a command or utterance, (c) the AI performing one or more rounds of inference on that command to resolve ambiguities and/or requesting clarifications from the human, (d) the AI showing the inferred meaning of the command and/or its execution on current and future situations or data, (e) the human hopefully correctly recognizing whether the AI's interpretation actually aligns with their intent. In the process, they may (f) update their model of the AI's capabilities and characteristics, (g) update their model of the situations in which the AI is executing its interpretation of their intent, (h) confirm or refine their intent, and (i) revise their expression of their intent to the AI, where the loop repeats until the human is satisfied. With these critical cognitive and computational steps within this back-and-forth laid out as a framework, it is easier to anticipate where communication can fail, and design algorithms and interfaces that ameliorate those failure points