Average-passage flow model development

A 3-D model was developed for simulating multistage turbomachinery flows using supercomputers. This average passage flow model described the time averaged flow field within a typical passage of a bladed wheel within a multistage configuration. To date, a number of inviscid simulations were executed to assess the resolution capabilities of the model. Recently, the viscous terms associated with the average passage model were incorporated into the inviscid computer code along with an algebraic turbulence model. A simulation of a stage-and-one-half, low speed turbine was executed. The results of this simulation, including a comparison with experimental data, is discussed

Crescentic ramp turbine stage

A turbine stage includes a row of airfoils joined to corresponding platforms to define flow passages therebetween. Each airfoil includes opposite pressure and suction sides and extends in chord between opposite leading and trailing edges. Each platform includes a crescentic ramp increasing in height from the leading and trailing edges toward the midchord of the airfoil along the pressure side thereof

How Much DC Power Is Necessary?

Many proposals for future power systems for warships are extant. Anticipated improvements in capability, operating economy, and signature reduction may not be uniquely associated with these power systems. Alternatives are available for constructing variable speed drives and prime movers for ships with electric drives. These alternatives may open new design possibilities

Comparison of Temperature Profile and Heat Transfer Predictions With Statistically Modeled Data From a Cooled 1 1 2 Stage High Pressure Transonic Turbine

This paper compares predictions from a 3-D Reynolds-Averaged Navier-Stokes code and a statistical representation of measurements from a cooled 1-1/2 stage high-pressure transonic turbine to quantify predictive process sensitivity. A multivariable regression technique was applied to both the inlet temperature measurements obtained at the inlet rake, and the wall temperature and heat transfer measurements obtained via heat-flux gauges on the blade airfoil surfaces. By using the statistically-modeled temperature profiles to generate the inlet boundary conditions for the Computational Fluid Dynamics (CFD) analysis, the sensitivity of blade heat transfer predictions due to the variation in the inlet temperature profile and uncertainty in wall temperature measurements and surface roughness is calculated. All predictions are performed with and without cooling. Heat transfer predictions match reasonably well with the statistical representation of the data, both with and without cooling. Predictive precision for this study is driven primarily by inlet profile uncertainty followed by surface roughness and gauge position uncertainty

Uncertainty Analysis of Heat Transfer Predictions Using Statistically Modeled Data From a Cooled 1-1/2 Stage High-Pressure Transonic Turbine

This paper compares predictions from a 3D Reynolds-averaged Navier-Stokes code and a statistical representation of measurements from a cooled 1-1/2 stage high-pressure transonic turbine to quantify predictive process sensitivity. A multivariable regression technique was applied to both the inlet temperature measurements obtained at the inlet rake, the wall temperature, and heat transfer measurements obtained via heat-flux gauges on the blade airfoil surfaces. By using the statistically modeled temperature profiles to generate the inlet boundary conditions for the computational fluid dynamics analysis, the sensitivity of blade heat transfer predictions due to the variation in the inlet temperature profile and uncertainty in wall temperature measurements and surface roughness is calculated. All predictions are performed with and without cooling. Heat transfer predictions match reasonably well with the statistical representation of the data, both with and without cooling. Predictive precision for this study is driven primarily by inlet profile uncertainty followed by surface roughness and gauge position uncertainty

Pitfalls of modeling wind power using Markov chains

An increased penetration of wind turbines have given rise to a need for wind speed/power models that generate realistic synthetic data. Such data, for example, might be used in simulations to size energy storage or spinning reserve. In much literature, Markov chains have been proposed as an acceptable method to generate synthetic wind data, but we have observed that the autocorrelation plots of wind speeds generated by Markov chains are often inaccurate. This paper describes when using Markov chains is appropriate and demonstrates the gross underestimation of storage requirements that occurs at short time steps. We found that Markov chains should not be used for time steps shorter than 15 to 40 minutes, depending on the order of the Markov chain and the number of wind power states. This result implies that Markov chains are of limited use as synthetic data generators for small microgrid models and other applications requiring short simulation time steps. New algorithms for generating synthetic wind data at shorter time steps must be developed.MIT-Portugal Progra