Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.

In this article, an ultrahigh step-up quadratic boost converter is proposed for photovoltaic systems. By integrating with a coupled inductor and a voltage multiplier, the voltage gain can be improved significantly without high turns ratio and the voltage stress across the power switches are alleviated. Thus, low on-resistance of the power switches is available to enhance efficiency. Common ground structure of quadratic boost and synchronous operation of the two switches facilitate the control. Also, the continuous input current promotes the maximum power point tracking (MPPT). Detailed description of the operation principles and steady-state analysis of this converter in continuous conduction mode and discontinuous conduction mode are presented. A 250-W rated laboratory prototype is fabricated and verified, demonstrating a maximum efficiency of 96.5%. Moreover, the dynamic response is tested to prove the stability of the proposed converter. Finally, a simulation is built to evaluate the feasibility of MPPT function.

Remarkable effectiveness has been demonstrated by deep neural networks in the despeckling task for Synthetic Aperture Radar (SAR) images. However, blurring and loss of fine details can result from many despeckling models due to up-sampling and mean square error (MSE) loss. Additionally, existing degradation models and prior information are ignored by existing despeckling models, which directly learn the mapping from degraded to clear images. To address these issues, an optimization algorithm for the SAR despeckling task based on the integral-Newton method is proposed in this article. Then, a prior-driven despeckling network is proposed, which can automatically capture the implicit priors in SAR images to replace traditional manually-made priors. Furthermore, to make the network focus more on learning the structural prior information of images, a structure-preserving loss function based on MSE and the Canny edge detection operator is designed, which improves the detail of the network retention ability and speeds up convergence. Outstanding results on both simulated datasets and real SAR images are achieved by the proposed method, as shown by a large number of experimental results. Moreover, significant advantages of the proposed method both visually and quantitatively are revealed by comparison with classical and state-of-the-art despeckling algorithms.

A multi‐microgrid economic dispatching strategy based on adaptive mutation genetic algorithm is proposed for multi‐microgrid systems with different load types and power demands. Based on the analysis of industrial, residential and commercial loads, considering the synergy and complementarity between multi‐microgrids, an optimal dispatching model of multi‐microgrids based on the minimum operation cost and environmental protection cost of multi‐microgrids is established from the point of view of environmental protection and economy. At the same time, an adaptive mutation genetic algorithm is proposed to optimize the system model and find the optimal economic dispatching scheme.

A dam breaking is a major flood catastrophe. The shape, depth, and wave Doppler effect of initial water flow are all modified as a result of the interaction of the water body with downstream structures after a dam breach, forming a diffraction and reflection flow field. This study investigates the dam breaking problem of a single liquid, by creating a two-dimensional simplified numerical model using the VOF approach, analysing the interaction and effect between barriers of various forms and the dam failure flow, and explains the problem of a complex flow mechanism involving significant deformation of the free surface of a medium. According to the findings, obstacles of varying forms could obstruct the dam break’s water flow to various degrees, and the viscous dissipation characteristic of the water body at the edge of the obstacle is closely related to the slope of the site. The numerical simulation presented in this study is validated, demonstrating its accuracy for both the gate-pulling and downstream wet-bed scenarios.

Wind and wave energy have gained significant attention in recent years as high-quality renewable energy sources. Commercial applications of these technologies are still in their infancy and do not offer significant benefits to the general public due to their low economic efficiency. The main objective of this paper is to contribute to the commercialization of wind and wave energy. The first step toward achieving this goal is to review equation models related to the economic benefits of wind and wave energy. A case study approach is then used to examine several successful offshore wind and wave energy conversion devices. As a result of this examination, we identify limitations and difficulties in commercializing and developing wind and wave energy. Finally, we propose various measures to address these challenges, including technological innovation, policy support, and market regulation. Research and decision-makers interested in the promotion of renewable energy sources will gain valuable insights from this study, which will ultimately lead to the adoption of sustainable energy practices for the benefit of society and the environment.