Turbine energy recovery is a process energy saving technology, and understanding turbine efficiency has important operational and economic benefits for the operator of a power plant. There are three main areas of research into turbine energy efficiency: the structural performance of the turbine itself, the configuration of the recovery device and the regulation of operating conditions. This paper summarizes recent research advances in hydraulic turbine energy efficiency improvement, focusing on the design factors that can affect the overall efficiency of a hydraulic turbine. To quantify the impact of these factors, this paper investigates the effects of surface roughness, flow rate, head and impeller speed on overall efficiency. Methods for optimizing improvements based on these design factors are reviewed, and two methods, the Box–Behnken Design method and the NSGA-II genetic algorithm, are described with practical examples to provide ideas for future research.

The numerical simulation is similar to the experimental research, which uses the computer. 3D printing is an experimental study, which is compared with simulation. 3D printing and precision casting of impellers are frequently used in industrial scenarios. To address the problem of low casting yield of aluminium alloy impellers in gypsum‐type casting, three‐dimensional modelling by magics, casting simulation, and parameter optimization method by applying ProCAST software are used to improve the casting yield of impellers. The results show that the optimized structure of the casting system is more reasonable, so that the shrinkage and shrinkage defects are concentrated in the location of the riser; the maximum stress value of the casting and the casing during the solidification stage is 2.71 MPa, which is much smaller than the stress limit of the casing; the maximum horizontal displacement of the casting along the X and Y axes after solidification is completed at 0.025 mm and 0.022 mm, respectively, and the maximum displacement along the Z axis due to the presence of the riser is 0.009 mm, which greatly improves casting accuracy and molding quality.

Dynamic response of flexible multi-body large wind turbines has been quickly growing in recent years. With the new normal economic policy, the economy of China is developing innovatively and stably. New energy development and utilization is an important strategy for people’s lives and economic development around the world. It is feasible to analyze from a broad perspective. In particular, the development and application of wind power is affecting the economic development of industry to a certain extent. Persistent and significant large wind turbines have cast concern over the prospects of wind power technology, and a comprehensive development potential of wind power technology has been analyzed its potential use in the future. The multi-body dynamics method can better analyze and describe the impact of flexible blade elastic deformation on motion characteristics and provides a practical analysis method for the aeroelastic stability analysis and control system design of wind turbines.

Due to the rapid development of today's industry, it not only greatly accelerates the consumption of energy, but also causes serious pollution to the environment. Therefore, reducing energy waste and recovering energy has become an important research direction today. This paper introduces the future development trend of renewable energy, discusses the progress of recovery of nonpolluting energy such as marine, bioenergy and thermal energy, and analyzes the efficiency and social benefits of various types of energy recovery. Energy recovery methods for natural gas pipelines are briefly analyzed, and current research advances in energy pressure recovery devices are provided, leading to a summary of methods to improve the efficiency of fluid energy recovery. Finally, it is concluded that although the current energy sources are diversified, the most valued energy sources at present and in the future should be solar energy and ocean, which are infinitely recyclable. This paper will benefit researchers to further understand and study the various aspects of energy recovery.

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s² to −1.2646 × 10⁻⁷ m/s², while the variance decreases from 0.0133 m/s² to 1.0975 × 10⁻⁵ m/s². In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

In recent decades, the additive manufacturing technology has made great progress in software and methods in various fields, and gradually explored in a deeper and broader manner. It has changed from the mature homogenized lattice type and model design to a non-uniform direction. It has also started to improve from the aspects of material innovation, additive manufacturing printing technology, etc., to change the additive manufacturing technology and control parameters in the manufacturing process, Furthermore, the model or part can be improved to have better mechanical properties, such as stiffness, strength and wear resistance, which provides an important research methodology for the better development of this direction. These aspects include the software used, the type of structural analysis, the software used and verification, as well as the methods applied in the study of variable density lattices and the application and verification of improved research methods. In addition, there are density design optimization, variable density lattice design and lattice geometric characteristics’ design in geometric topology optimization design. The expected design of the model or part at the design level has reached the ideal model or part, which provides both a framework and ideas for the future research direction of non-uniform lattice design and a broader field of application, and will promote the future research and development prospects of variable density lattices.