Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

This paper presents a study of a wind speed time series from La Venta, Oaxaca, Mexico. The time series consists of anemometric measurements taken by the Federal Electricity Commission of Mexico throughout a little over 6 years. The study was conducted to calculate the Hurst correlation coefficient using: box counting, rescaled range, power spectrum, detrended fluctuation analysis, and multifractal detrended fluctuation analysis techniques. The main objective of this research is to know the correlation among wind speed data to obtain a better description of real conditions of the time series, which is not always available, and to define the structure of its behavior. In this way, more suitable wind speed prediction models can be achieved. Results obtained from techniques above were used to generate fractals time series for a typical month, using the Hurst coefficient and a self‐affine trace generator, which produces fractals time series whose probability distribution is always normal. These time series were compared against time series generated by using random numbers with Gaussian behavior and the characteristics of a typical month. Fractals time series highlight in the qualitative part regarding the modeling of wind speed variability and the descriptive statistics (average, standard deviation, and coefficient of variation), which is similar to the real series. Discordance tests were applied to the datasets to detect deviated values, and so ensure the normal behavior of the samples. These tests showed the existence of different populations with normal behavior in the samples that had bimodal characteristics. By separating the samples, it was possible to apply the self‐affine trace generator to each population found, to generate the fractal time series. An additional objective was to find the level of change in the structure of the original series concerning its statistical and fractal characteristics at different window widths of the time series (daily, monthly, seasonal, and annual) to identify either a specific tendency or dynamic behavior. The results showed a wind speed time series with a negative correlation (antipersistent), a high degree of scale invariance (homothetic), and a fractal dimension very close to 2, thus indicating that the time series is more irregular than a random process.

This paper presents three-dimensional numerical simulations and experimental investigations of cavitating flow through an axial inducer. Particularly, this work focuses on the influence of radial tip clearance on cavitation behavior. Numerical analysis was carried out on two different configurations: first, the inducer was modeled without taking tip clearance into consideration. Later, the inducer was modeled with nominal tip clearance and some modifications of this. It was found that radial tip clearance has a significant influence on the overall inducer performance in the non-cavitating regime because of the small size of the inducer. Moreover, the effects of radial tip clearance are strong in inducer cavitation behavior. Numerical results and experimental data with nominal tip clearance were compared in cavitating and non-cavitating regimes and these were discussed. The cavitation model used for calculation is based on a single-fluid multiphase flow method, assuming thermal equilibrium between phases. It is based on the classical conservation equations of the vapor phase and a mixture phase, with mass transfer due to cavitation appearing as a source and a sink term in the vapor mass fraction equation. Mass transfer rates are derived from the Rayleigh–Plesset model for bubble dynamics.