Sediment and cavitation erosion of the hydroelectric power turbine components are the fundamental problems in the rivers of Himalayas and Andes. In the present work, the latest research conducted in both the fields by various investigators and researchers are discussed and critically analyzed at different turbine components. Analysis shows that both types of erosion depends on flow characteristics, surface, and erodent material properties. Design optimization tools, coalesced effect (CE) of sediment and cavitation erosion and well conducted experiments will yield results that are beneficial for erosion identification and reduction. Although some researchers have done experimental work on the coalesced effect (CE) of sediment and cavitation erosion, very limited Computational Fluid Dynamics (CFD) work is available in literature. The present research work will be beneficial for practitioners and researchers in the future to address the erosion problem successfully.

Centrifugal pumps are being widely used in various industries for moving fluids that carry solids through pipelines where the need of head and flow rate is not high. Slurry erosion and cavitation are an extremely complex and not yet fully understood phenomenon that occur in centrifugal pumps; however, these undesirable phenomena can be reduced to a certain extent. Appropriate design and development of experiments is required to reasonably predict slurry erosion and cavitation. However, CFD methodology complements analytical solutions and experiments whenever testing of equipment has limitations. The current paper highlights the various slurry erosion and cavitation reduction techniques utilized by different researchers. Economic analysis conducted for a case study relevant to centrifugal pump (CP) usage in Pakistan shows that an 8% enhancement in pump efficiency can reduce the life cycle cost to about 17.6%, which could save up to USD 4281 for a single pump annually in Pakistan.

Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine’s service life by improving the turbine blades’ fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade’s fatigue life is calculated using Morrow’s equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade’s fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation.

The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study’s findings can be used in various fields, e.g., wind energy forecasting and wind farm planning.

The current study's objective is to evaluate wind and solar energy and select the finest kind of energy systems having minimum levelized of energy systems by using Home Pro software for Jeju Island, the largest island in South Korea. Using Weibull and mean wind speed diagrams, the wind conditions in the initial section of this study are explained. To assess the wind potential of every area, size and form criteria are offered. It was found that two main wind directions were southeast or southwest, with southwest being more common. Finally, based on the wind conditions at each site and IEC recommendations, a set of five wind turbines that were best suited for each location were shortlisted. Each wind turbine's performance was assessed using technical criteria, that is, monthly energy production, annual energy production, and capacity factors. Similar to this, economic considerations such as the levelized cost of electricity, internal rate of return, payback period, and net present value were also made. A Hanjin model HJWT 87/2000 is best suitable for Jeju Island in the event of an off-shore wind farm, according to the findings. To explore the implications of modifying the hub height of the turbines on the economics of wind generating, a thorough economic sensitivity analysis is also included. The second section explains the solar potential of the Island. The average solar energy potential of Jeju Island is estimated to be 13.775 Mj·m ² ·day ⁻¹ . To cover the energy demand and supply gap for Jaju Island, various types of local electricity generators and batteries have been studied for finding the optimal energy system for the island.