INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING
Year:2013 | Volume:4 | Issue:4|Papers Count:4

A general mathematical model is developed to specify the performance of an irreversible gas turbine Brayton cycle incorporating two-stage compressor, two-stage gas turbine, intercooler, reheater, and regenerator with irreversibilities due to finite heat transfer rates and pressure drops. Ranges of operating parameters resulting in optimum performance (i.e., hI³38³hII³60%, ECOP³1.65, xloss£0.150 MJ/kg, BWR£0.525, wnet³0.300 MJ/kg, and qadd£0.470 MJ/kg) are determined and discussed using the Monte Carlo method. These operating ranges are minimum cycle temperature ranges between 302 and 315 K, maximum cycle temperature ranges between 1,320 and 1,360 K, maximum cycle pressure ranges between 1.449 and 2.830 MPa, and conductance of the heat exchanger ranges between 20.7 and 29.6 kW/K. Exclusive effect of each of the operating parameters on each of the performance parameters is mathematically given in a general formulation that is applicable regardless of the values of the rest of the operating parameters and under any condition of operation of the cycle.

Wind turbines operate mostly in yaw conditions that give rise to cyclic variations in aerodynamic forces applied on the blade. This induced load fluctuation is closely related to the upstream velocity field of the rotor and can be a significant source of fatigue and vibration. An accurate prediction of blade loading is considered the key in designing reliable and efficient wind turbines. The related calculation remains a complicated task to perform and requires enormous computing time. In this context, a numerical method is presented, aimed at evaluating the azimuthal fluctuation of the normal force. This method is obtained by coupling the Beddoes near-wake model and the free-wake method: the near-wake-induced velocities are calculated using Beddoes near-wake model with the far-wake contribution evaluated using the free-wake method. In addition, the unsteady effects on the aerodynamic coefficients are taken into account using the Beddoes-Leishman dynamic stall model. A computer code was developed, and numerical values were obtained in acceptable computational time. Results are compared with measurements performed in the NASA Ames wind tunnel.

Lignite has emerged as an additional important fuel source for thermal power generation in India. Circulating fluid bed combustion technology is applied considering the impurities, moisture, ash and sulfur content, and wide variations for large units. Lignite mineralogy greatly influences combustion behavior. Agglomeration and clogging/blockage are experienced due to sintering of lignite at lower temperature regime in which circulating fluid bed boilers operate (800oC to 900oC). At this low temperature range, the extensive knowledge built for pulverized coal combustion with respect to slagging, fouling, and high-temperature corrosion is not useful. Sintering studies using the heating microscope's potential are applied for understanding this phenomenon. The gray clay, which occurs as intrusions/thin bands in the lignite mine, is sampled at the mine site and taken up for analysis. Morphology of sintered deposits in the bottom ash is matched with the properties of clay which is very close to halloysite mineral (kaolinite group). Sintering is avoided by selecting the operating temperature range of combustion on either side of peak sintering temperature in the sample case. With higher ashing temperature, the lignite ash loses part of its sintering tendency. This indicates to a new hypothesis that once the lignite ash undergoes transformations resulting in sintering, its sintering tendency is lowered. Conventional slagging indices are also analyzed, and correlation was derived for sintering behavior of lignite.

Nowadays, different sectors of the economy are being significantly affected by the weather vagaries. Electricity market is one of the most sensitive sectors, due to the fact that electricity demand is connected to the numerous climatic variables, especially the atmospheric temperature. In this paper we have deduced the link between electricity consumption and mean monthly maximum temperature index in Pakistan, as a case study. ARIMA time series forecast model is developed for the temperature index. The forecast values of mean monthly maximum temperature shows an increasing trend. Linear trend model for electricity consumption is also developed as a function of temperature. Electricity consumption reveals a significant trend due to increase in temperature and socio- economic factors. The monthly behavior of our forecast values depicts that the electricity consumption is more for summer season, and this demand will be highest (6785.6 GWh) in July 2020, due to rise in temperature. Forecast model reveals that the electricity consumption (EC) and mean monthly maximum temperature are increasing with the passage of time.