Intercooler Flow Path for Gas Turbines: CFD Design and Experiments

The Advanced Turbine Systems (ATS) program was created by the U.S. Department of Energy to develop ultra-high efficiency, environmentally superior, and cost competitive gas turbine systems for generating electricity. Intercooling or cooling of air between compressor stages is a feature under consideration in advanced cycles for the ATS. Intercooling entails cooling of air between the low pressure (LP) and high pressure (HP) compressor sections of the gas turbine. Lower air temperature entering the HP compressor decreases the air volume flow rate and hence, the compression work. Intercooling also lowers temperature at the HP discharge, thus allowing for more effective use of cooling air in the hot gas flow path

Lift-Off Characteristics and Flame Base Structure of Coal Seeded Gas Jet Flames

An experimental study of the burner rim stability characteristics and the flame base structure of flames co-fired with pulverized coal and propane gas is presented. Lift-off and reattachment characteristics are examined as functions of propane concentration in the jet stream for lignite, bituminous and anthracite coals. The effects on flame base structure are studied in terms of temperature, product species concentration and radiation profiles. The addition of lignite and anthracite coals favours the lift-off transitions. Bituminous coal, on the other hand, makes the flame more stable. The peak values of temperature and concentrations of major combustion product species in the flame stabilization region strongly depend upon the rank of coal. Among the coals tested, bituminous coal produces the highest peak temperature and its flame emits maximum radiation from the stabilization region. Anthracite and lignite coals produce somewhat comparable stability characteristics and structure of the flame base. The effects of coal rank are explained by the differences in volatile matter, moisture and pyrolysis characteristics of coals.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

IMECE2008-66225 ATOMIZATION CHARACTERISTICS, GLOBAL EMISSIONS, AND TEMPERATURE IN BIOFUEL AND PETROLEUM FUEL SPRAY FLAMES

ABSTRACT Spray flame characteristics of canola methyl ester biofuel (CME) and petroleum fuel (No. 2D) are described. An enclosed spray flame in a heated co-flow air environment at ambient pressure was studied. A single nozzle, swirl-type, air-blast atomizer with a nozzle diameter of 300 microns was used to create the spray. The spray droplet size and velocity distributions were measured using a two-component phase Doppler particle analyzer. In-flame temperature profiles were measured using a type-R thermocouple. Global emission indices of NO and CO were derived from concentration measurements in the combustion products. The overall equivalence ratio was kept at 0.75 to simulate lean burning conditions. The changes in atomization air flow rate produced similar changes in atomization characteristics of both fuels. Emission indices of NO and CO for petroleum fuel were higher than those of the CME fuel. In-flame temperature levels were lower for the CME fuel than for the petroleum fuel at corresponding flame locations. NOMENCLATURE CME-Canola methyl ester D 32 -Sauter mean diameter NDIR-Nondispersive infrared PDPA-Phase Doppler particle analyzer T cf = Air co-flow temperature V f = Volumetric flow rate of fuel V aa = Volumetric flow rate of atomization air V cf = Volumetric flow rate of air co-flo

From von Neumann architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications

Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time. Making use of suitable models that can capture the “hidden” interactions and interrelationship among multivariate data, is vital in SSTD investigation. This chapter describes a number of prominent applications built using the Kasabov’s NeuCube-based Spiking Neural Network (SNN) architecture for mapping, learning, visualization, classification/regression and better understanding and interpretation of SSTD