Heat transfer in the tip region of a rotor blade simulator

In gas turbines, the blades of axial turbine stages rotate in close proximity to a stationary peripheral wall. Differential expansion of the turbine wheel, blades, and the shroud causes variations in the size of the clearance gap between blade tip and stationary shroud. The necessity to tolerate this differential thermal expansion dictates that the clearance gap cannot be eliminated altogether, despite accurate engine machining. Pressure differences between the pressure and suction sides of a blade drives a flow through the clearance gap. This flow, the tip leakage flow, is detrimental to engine performance. The primary detrimental effect of tip leakage flow is the reduction of turbine stage efficiency, and a second is the convective heat transfer associated with the flow. The surface area at the blade tip in contact with the hot working gas represents an additional thermal loading on the blade which, together with heat transfer to the suction and pressure side surface area, must be removed by the blade internal cooling flows. Experimental results concerned with the local heat transfer characteristics on all surfaces of shrouded, rectangular cavities are reported. A brief discussion of the mass transfer system used is given

Heat transfer in the tip region of a rotor blade simulator

The objective of this study of heat transfer in the tip region of a rotor blade simulator is to acquire, through experimental and computational approaches, improved understanding of the nature of the flow and convective heat transfer in the blade tip region. Such information should enable designers to make more accurate predictions of performance and durability, and should support the future development of improved blade tip cooling schemes

Bag Formation in Quantum Hall Ferromagnets

Charged skyrmions or spin-textures in the quantum Hall ferromagnet at filling factor nu=1 are reinvestigated using the Hartree-Fock method in the lowest Landau level approximation. It is shown that the single Slater determinant with the minimum energy in the unit charge sector is always of the hedgehog form. It is observed that the magnetization vector's length deviates locally from unity, i.e. a bag is formed which accommodates the excess charge. In terms of a gradient expansion for extended spin-textures a novel O(3) type of effective action is presented, which takes bag formation into account.Comment: 13 pages, 3 figure

Reentrant Melting of Soliton Lattice Phase in Bilayer Quantum Hall System

At large parallel magnetic field $B_\parallel$, the ground state of bilayer quantum Hall system forms uniform soliton lattice phase. The soliton lattice will melt due to the proliferation of unbound dislocations at certain finite temperature leading to the Kosterlitz-Thouless (KT) melting. We calculate the KT phase boundary by numerically solving the newly developed set of Bethe ansatz equations, which fully take into account the thermal fluctuations of soliton walls. We predict that within certain ranges of $B_\parallel$, the soliton lattice will melt at $T_{\rm KT}$. Interestingly enough, as temperature decreases, it melts at certain temperature lower than $T_{\rm KT}$ exhibiting the reentrant behaviour of the soliton liquid phase.Comment: 11 pages, 2 figure

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation