No abstract

This paper investigates the use of a passive control device, namely, a tuned mass damper (TMD), for the mitigation of vibrations due to the along-wind forced vibration response of a simplified wind turbine.The wind turbine assembly consists of three rotating uniform rotor blades connected to the top of a flexible uniform annular tower, constituting a multi-body dynamic system.First,the free vibration properties of the tower and rotating blades are each obtained separately using a discrete parameter approach, with those of the tower including the presence of a rigid mass at the top, representing the nacelle, and those of the blade including the effects of centrifugal stiffening due to blade rotation and self-weight. Dragbased loading is assumed to act on the rotating blades, in which the phenomenon of rotationally sampled wind turbulence is included. Blade response time histories are obtained using the mode acceleration method, allowing base shear forces due to flapping motion for the three blades to be calculated. The resultant base shear is imparted into the top of the tower. Wind drag loading on the tower is also considered, and includes Davenport-type spatial coherence information. The tower/nacelle is then coupled with the rotating blades by combining their equations of motion. A TMD is placed at the top of the tower, and when added to the formulation, a Fourier transform approach allows for the solution of the displacement at the top of the tower under compatibility of response conditions. An inverse Fourier transform of this frequency domain response yields the response time history of the coupled blades/tower/damper system. A numerical example is included to qualitatively investigate the influence of the damper. Figure 4. Transfer function for the coupled tower/nacelle and rotating blades model (Ω = 15 rev min -1 ) including and excluding the TMD 314 P. J. Murtagh et al.Figure 7. Simulated displacement response at the top of the tower coupled tower/nacelle and rotating blades model (Ω = 30 rev min -1 ) including and excluding the TMD 316 P. J. Murtagh et al.

No abstract

No abstract

This paper presents a model for producing real time air quality forecasts with both high accuracy and high computational efficiency. Temporal variations in nitrogen dioxide (NO2) levels and historical correlations between meteorology and NO2 levels are used to estimate air quality 48 h in advance. Non--parametric kernel regression is used to produce linearized factors describing variations in concentrations with wind speed and direction and, furthermore, to produce seasonal and diurnal factors. The basis for the model is a multiple linear regression which uses these factors together with meteorological parameters and persistence as predictors. The model was calibrated at three urban sites and one rural site and the final fitted model achieved R values of between 0.62 and 0.79 for hourly forecasts and between 0.67 and 0.84 for daily maximum forecasts. Model validation using four model evaluation parameters, an index of agreement (IA), the correlation coefficient (R), the fraction of values within a factor of 2 (FAC2) and the fractional bias (FB), yielded good results. The IA for 24 hr forecasts of hourly NO2 was between 0.77 and 0.90 at urban sites and 0.74 at the rural site, while for daily maximum forecasts it was between 0.89 and 0.94 for urban sites and 0.78 for the rural site. R values of up to 0.79 and 0.81 and FAC2 values of 0.84 and 0.96 were observed for hourly and daily maximum predictions, respectively. The model requires only simple input data and very low computational resources. It found to be an accurate and efficient means of producing real time air quality forecasts.