Search

Wind energy is anticipated to play a vital role to fulfill the worldwide energy requirements, whereas existing bare wind turbines can convert only a fraction of flow energy into electricity. To bridge the gap between the escalating demand and the generation capability, we propose a shrouded-twin-rotor turbine design, whose power coefficient exceeds the Betz-Joukowsky limit. We analyze the flow through the wind turbine assembly, using the actuator disk theory, and estimate the power output for a pair of rotor-loading coefficients. We find the existence of two regimes in which the proposed design performs better than the single-rotor configuration: (a) a turbine-turbine mode where both rotors work as turbines and (b) a turbine-fan mode where one rotor is a turbine while the other is a fan- an idea proposed by Betz (A. Betz, Wind-Energie und ihre Ausnutzung durch Windmuhlen, Vandenhoeck, 1926). Both modes enable achieving maximum power for multiple combinations of the loading coefficient pair. Moreover, power output depends solely on a single parameter, defined using the area weighted sum of the loading coefficients. We derive the optimum performance criterion and present the effects of the shroud geometry, back pressure, and flow-efficiency parameters on the power output and the performance envelope.

New techniques for more accurate segmentation of a 3D cerebrovascular system from time-of-flight (TOF) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe TOF-MRA images and desired maps of regions (blood vessels and the other brain tissues) by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modify a conventional Expectation-Maximization (EM) algorithm to deal with the LCDG and develop a sequential EM-based technique to get an initial LCDG-approximation for the modified EM algorithm. The initial segmentation based on the LCDG-models is then iteratively refined using a GMRF model with analytically estimated potentials. To validate the accuracy of our algorithm, a special 3D geometrical phantom motivated by statistical analysis of the MRA-TOF data is designed. Experiments with both the phantom and 50 real data sets confirm high accuracy of the proposed approach.

New techniques for more accurate segmentation of a 3D cerebrovascular system from phase contrast (PC) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe PC---MRA images and desired maps of regions by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modified the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments with both the phantoms and real data sets confirm high accuracy of the proposed approach.