Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims ...Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims to mitigate far-field noise.Among these cases,a reduction in the wavelength is found to be advantageous for noise suppression,with the smallest wavelength case achieving a maximum noise reduction of 1.9 dB.Furthermore,the noise radiation induced by WLEs is suppressed mainly at medium frequencies.The theory of multiprocess aeroacoustics is applied to reveal their underlying mechanisms.The dominant factor is the source cutoff effect,which significantly decreases the source strength on hills.Additionally,spanwise decoherence with phase interference serves as another crucial mechanism,particularly for reducing mid-frequency noise.展开更多
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
基金supported by the National Natural Science Foundation of China(12322210,12172351,92252202,and 12388101)the Fundamental Research Funds for the Central Universities.
文摘Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims to mitigate far-field noise.Among these cases,a reduction in the wavelength is found to be advantageous for noise suppression,with the smallest wavelength case achieving a maximum noise reduction of 1.9 dB.Furthermore,the noise radiation induced by WLEs is suppressed mainly at medium frequencies.The theory of multiprocess aeroacoustics is applied to reveal their underlying mechanisms.The dominant factor is the source cutoff effect,which significantly decreases the source strength on hills.Additionally,spanwise decoherence with phase interference serves as another crucial mechanism,particularly for reducing mid-frequency noise.
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
