PurposeThe purpose of this paper is to investigate the overall flow and temperature field of the air in the whole power plant, especially around the air‐cooled heat exchanger (ACHE) to evaluate the feasibility of the thermal plant project.Design/methodology/approachThe commercial computational fluid dynamics code FLUENT with standard k‐ε turbulent model was used. The buoyancy of the air was also considered.FindingsIt is concluded that plume recirculation occurs in each case due to the wind effect and the suction of the fan. Installing a side board below or above the fan platform (side board I or side board II) is an effective method of avoiding the plume recirculation and, the higher the board, the better the effect. When the height of the side board I H1≥10 m or the height of the side board II H2≥12 m, the temperature distributions of the fan platform will be sufficient to meet the requirement.Research limitations/implicationsA proper distance between the adjacent high buildings and the ACHE should be found with further investigation.Practical implicationsThe paper presents a very useful numerical method for the prediction of the flow and temperature field around ACHE or in a large space.Originality/valueThe paper provides the numerical simulation of the flow and heat transfer inside the whole thermal power plant. Suggestions which can effectively avoid the unfavorable influence and ensure the whole system in safe conditions are offered. The study gives some useful information to the design of a thermal power plant with an ACHE system.
Ionic wind, an induced phenomenon during corona discharge, possessing the features of silent operation and no moving parts, has a wide range of applications. Ionic wind generation is accompanied by complex physical processes, involving gas ionization, ion recombination, flow, and various chemical reactions, as well as mutual couplings between some of them. Therefore, understanding the corona discharge process and ionic wind generation is crucial for researchers and engineers to better utilize this phenomenon in practical applications. In this review, the principles of corona discharge and its induced ionic wind are presented. Subsequently, ionic wind generators (IWGs) are discussed according to their applications, and the corresponding advances based on experimental studies and numerical simulations are also reviewed. Moreover, the challenges of transitioning the ionic wind technology from laboratory studies to practical applications are discussed. These challenges include the excessively high onset voltage of the corona, ozone emission, and influence of environmental conditions. Furthermore, the mechanisms of these barriers and several effective approaches for mitigating them are provided. Finally, some future research prospects and the conclusions are presented.
Purpose
The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time.
Design/methodology/approach
To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms.
Findings
The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment.
Originality/value
This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.
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