Plasma assisted combustion: Dynamics and chemistry

http://engine.princeton.edu/Publications/Ombrello_2010-2.pdf

Flame propagation enhancement by plasma excitation of oxygen. Part I: Effects of O3

conferencia sobre "Particle Image Velocimetry" de les 13:00-14:00 a la Sala de Juntes de l'FNB impartida pel professor Peter Bueken da l'Antwerp Maritime Acedemy (Belgica)

Particle Image Velocimetry

A review of livestock monitoring and the need for integrated systems

This paper presents a method aimed at recognizing environmental sounds for surveillance and security applications. We propose to apply one-class support vector machines (1-SVMs) together with a sophisticated dissimilarity measure in order to address audio classification, and more specifically, sound recognition. We illustrate the performance of this method on an audio database, which consists of 1015 sounds belonging to nine classes. The database used presents high intraclass diversity in temps of signal properties and some kind of interclass similarities. A large discrepancy in the number of items in each class implies nonuniform probability of sound appearances. The method proceeds as follows: first, the use of a set of state-of-the-art audio features is studied. Then, we introduce a set of novel features obtained by combining elementary features. Experiments conducted on a nine-class classification problem show the superiority of this novel sound recognition method. The best recognition accuracy (96.89%) is obtained when combining wavelet-based features, MFCCs, and individual temporal and frequency features. Our 1-SVM-based multiclass classification approach overperforms the conventional hidden Markov model-based system in the experiments conducted, the improvement in the error rate can reach 50%. Besides, we provide empirical results showing that the single-class SVM outperforms a combination of binary SVMs. Additional experiments demonstrate our method is robust to environmental noise.

Using One-Class SVMs and Wavelets for Audio Surveillance

Active recovery from a rotating stall operating condition in an axial flow compression system is accomplished by altering the temperature and/or density of the working fluid flowing in the inlet of the compression system once rotating stall is detected. A burner is used to increase the compressor inlet flow temperature and concomitantly reduce the inlet flow density. Alternatively, a low density gas such as helium is injected into the compressor inlet. The ingestion of high temperature gases or other low density gases into the compressor energizes a dynamic response from the compression system that induces rotating stall recovery.

Process and apparatus for recovery from rotating stall in axial flow fans and compressors

Preliminary experiments on active control of fan noise from a turbofan engine

The StreamVane Method: A New Way to Generate Swirl Distortion for Jet Engine Research

There is an increasing interest in design methods and performance prediction for aircraft engine turbines operating at low Reynolds numbers. In this regime, boundary layer separation may be more likely to occur in the turbine flow passages. For accurate computational fluid dynamics (CFD) predictions of the flow, correct modeling of laminar-turbulent boundary layer transition is essential to capture the details of the flow. To investigate possible improvements in model fidelity, CFD models were created for the flow over two low pressure turbine blade designs. A new three-equation eddy-viscosity type turbulent transitional flow model, originally developed by Walters and Leylek (2004, "A New Model for Boundary Layer Transition Using a Single Point RANS Approach, " ASME J. Turbomach., 126(1), pp. 193―202), was employed for the current Reynolds averaged Navier―Stokes (RANS) CFD calculations. Previous studies demonstrated the ability of this model to accurately predict separation and boundary layer transition characteristics of low Reynolds number flows. The present research tested the capability of CFD with the Walters and Leylek turbulent transitional flow model to predict the boundary layer behavior and performance of two different turbine cascade configurations. Flows over low pressure turbine (LPT) blade airfoils with different blade loading characteristics were simulated over a Reynolds number range of 15,000―100,000 and predictions were compared with experimental cascade results. Part I of this paper discusses the prediction methodology that was developed and its validation using a lightly loaded LPT blade airfoil design. The turbulent transitional flow model sensitivity to turbulent flow parameters was investigated and showed a strong dependence on freestream turbulence intensity with a second-order effect of turbulent length scale. Focusing on the calculation of the total pressure loss coefficients to judge performance, the CFD simulation incorporating Walters and Leylek's turbulent transitional flow model produced adequate prediction of the Reynolds number performance for the lightly loaded LPT blade cascade geometry. Significant improvements in performance were shown over predictions of conventional RANS turbulence models. Historically, these models cannot adequately predict boundary layer transition.

Walter F. O'Brien

Papers

Process and apparatus for recovery from rotating stall in axial flow fans and compressors

Preliminary experiments on active control of fan noise from a turbofan engine

The StreamVane Method: A New Way to Generate Swirl Distortion for Jet Engine Research

Predicting Separation and Transitional Flow in Turbine Blades at Low Reynolds Numbers—Part I: Development of Prediction Methodology

An improved aerodynamic ramp injector in supersonic flow