In the last decade wind energy has been exensinly used over the world. It is necessary to have long period wind speed data and their statistical characteristics for determined wind energy generated, using of Wind Energy Eonversion Systems and decreasing the damage of wind erosion. In this study, Weibull distribution, Markov chain and time series methods were used to generate hundred 100 years tree hourly wind speed values at Zabol and Yazd stations. In Markov chain first, transition probability matrix were developed from the real wind data, cumulative probability matrix were determined and then wind speed generated. For time series method one model was developed for each month and in most months AR2 and AR5 were used. Result showed that time series and Weibull distribution have better performance in average and standard deviation criteria and for Autocorrelation and Skewness parameter time series and Markov chain had better application. Regarding to serial correlation in wind speed series time series model had better application and suggested for wind speed generation.

Turbulent wind flow over buildings occurs due to the complexity like sharp corners, ground effect and different vortexes is one of the best choices to evaluate turbulence methods. DES and DDES are hybrid RANS-LES models for simulating turbulent flow which for their characteristic treat near wall as RANS and farther the wall act as LES model. Consequently computational time will decrease compared to traditional LES models. In this article to evaluate DES and DDES models, turbulent incompressible flow in Re=22000 over 3D building is simulated using parallel processing facilities. For verification purpose other investigators experiment results are used. Also the mentioned models are compared with classic RANS and LES models, like k-e and LES-Smagorinsky to depict their performance. Our results illustrate DES model with fine grid has good precision for simulating turbulent incompressible wind flow over building and decline of 26 percentage of computational time compared to LES-Smagorinsky model.

Iran is a country completely dependent on fossil fuel resources. In order to obtain a diversity of energy sources, it requires other resources, especially renewable energy. Utilization of wind energy appears to be one of the most efficient ways of achieving sustainable development. The quantification of wind potential is a pivotal and essential initial step while developing strategies for the development of wind energy. This study presents an investigation of the potential of wind power, using two methods—Weibull and Rayleigh—at Karaj, the center of Alborz province of Iran. The wind speed data for a three-hour time interval measured over a 10-year period (2004–2015) was used to calculate and estimate the wind power generation potential. After calculating the factors related to power density and wind energy, it was concluded that data fitting via Weibull distribution was partly better than the Rayleigh distribution function. The RMSE values of Weibull and Rayleigh were respectively 0.018 and 0.013, and R2 values of Weibull and Rayleigh were 0.95 and 0.97 in Karaj for the years 2004–2015. The wind rose charts of Karaj for the 2004–2015 period show that the most prevalent wind direction is NW (North-West). The wind power density obtained indicates the region is not completely suitable for large on-grid wind farms and related investments. But the region can be suitable for off-grid applications such as water pumping and irrigation, lighting, electric fan, battery charging, and, as hybrid, with other power sources.

This work regards the extension of the Miles’ and Jeffreys’ theories of growth of wind-waves in water of finite depth. It is divided in two major sections. The first one corresponds to the surface water waves in a linear regimes and the second one to the surface water waver considered in a weak nonlinear, dispersive and antidissipative regime. In the linear regime, we extend the Miles’ theory of wind wave amplification to finite depth. The dispersion relation provides a wave growth rate depending to depth. A dimensionless water depth parameter depending to depth and a characteristic wind speed, induces a family of curves representing the wave growth as a function of the wave phase velocity and the wind speed. We obtain a good agreement between our theoretical results and the data from the Australian Shallow Water Experiment as well as the data from the Lake George experiment. In a weakly nonlinear regime the evolution of wind waves in finite depth is reduced to an anti-dissipative Kortewegde Vries-Burgers equation and its solitary wave solution is exhibited. Anti-dissipation phenomenon accelerates the solitary wave and increases its amplitude which leads to its blow-up and breaking. Blow-up is a nonlinear, dispersive and anti-dissipative phenomenon which occurs in finite time. A consequence of anti-dissipation is that any solitary waves’ adjacent planes of constants phases acquire different velocities and accelerations and ends to breaking which occurs in finite space and in a finite time prior to the blow-up. It worth remarking that the theoretical amplitude growth breaking time are both testable in the usual experimental facilities. At the end, in the context of wind forced waves in finite depth, the nonlinear Schrö dinger equation is derived and for weak wind inputs, the Akhmediev, Peregrine and Kuznetsov-Ma breather solutions are obtained.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.