The nozzle inner-flow characteristic of the“spray G”injector was studied by the computational fluid dynamics(CFD)simulation,and the sensitivity of cycle fuel mass to the conicity and entrance radius of the nozzle ho...The nozzle inner-flow characteristic of the“spray G”injector was studied by the computational fluid dynamics(CFD)simulation,and the sensitivity of cycle fuel mass to the conicity and entrance radius of the nozzle hole were analyzed.Results show that the inner conicity of nozzle hole inhibits the development of cavitation phenomena,and increases the injection rate.While the outer conicity of nozzle hole promotes the diffusion of cavita-tion,leading to reductions of the liquid volume fraction of the nozzle outlet and the local flow resistance of the nozzle hole.The sensitivity of cycle fuel mass to inner-cone nozzle hole is stronger than that of the outer-cone noz-zle,especially at the smaller hole conicity.The increase of injection pressure enhances the sensitivity of the injection characteristics to the nozzle hole structure,in which inner-cone nozzle has higher sensitivity coefficient than the outer-cone nozzle hole.However,the increase of injection pressure aggravates the offset of liquid jet to the nozzle axis of the outer-cone nozzle hole.With the increase of the inner conicity of nozzle,the sensitivity of the injection characteristics to the entrance radius of the hole decreases.With the increase of the outer conicity of nozzle hole,the sensitivity of the injection characteristics to the entrance radius of the hole increases.展开更多
Ice-induced structural vibration generally decreases with an increase in structural width at the waterline. Definitions of wide/narrow ice-resistant conical structures, according to ice-induced vibration, are directly...Ice-induced structural vibration generally decreases with an increase in structural width at the waterline. Definitions of wide/narrow ice-resistant conical structures, according to ice-induced vibration, are directly related to structure width, sea ice parameters, and clearing modes of broken ice. This paper proposes three clearing modes for broken ice acting on conical structures: complete clearing, temporary ice pile up, and ice pile up. In this paper, sea ice clearing modes and the formation requirements of dynamic ice force are analyzed to explore criteria determining wide/narrow ice-resistant conical structures. According to the direct measurement data of typical prototype structures, quantitative criteria of the ratio of a cone width at waterline(D) to sea ice thickness(h) is proposed. If the ratio is less than 30(narrow conical structure), broken ice is completely cleared and a dynamic ice force is produced; however, if the ratio is larger than 50(wide conical structure), the front stacking of broken ice or dynamic ice force will not occur.展开更多
Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle networks.The transmission of information through in-vehicle networks needs to fo...Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle networks.The transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols regulations.Typically,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal data.However,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network attacks.In this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection method.In consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is tested.In addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal data.This paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data packets.The experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.展开更多
文摘The nozzle inner-flow characteristic of the“spray G”injector was studied by the computational fluid dynamics(CFD)simulation,and the sensitivity of cycle fuel mass to the conicity and entrance radius of the nozzle hole were analyzed.Results show that the inner conicity of nozzle hole inhibits the development of cavitation phenomena,and increases the injection rate.While the outer conicity of nozzle hole promotes the diffusion of cavita-tion,leading to reductions of the liquid volume fraction of the nozzle outlet and the local flow resistance of the nozzle hole.The sensitivity of cycle fuel mass to inner-cone nozzle hole is stronger than that of the outer-cone noz-zle,especially at the smaller hole conicity.The increase of injection pressure enhances the sensitivity of the injection characteristics to the nozzle hole structure,in which inner-cone nozzle has higher sensitivity coefficient than the outer-cone nozzle hole.However,the increase of injection pressure aggravates the offset of liquid jet to the nozzle axis of the outer-cone nozzle hole.With the increase of the inner conicity of nozzle,the sensitivity of the injection characteristics to the entrance radius of the hole decreases.With the increase of the outer conicity of nozzle hole,the sensitivity of the injection characteristics to the entrance radius of the hole increases.
基金Foundation item： Supported by the National Natural Science Foundation of China （Grant No. 41306087）, Public Science and Technology Research Funds Projects of Ocean （Grant No. 201505019）
文摘Ice-induced structural vibration generally decreases with an increase in structural width at the waterline. Definitions of wide/narrow ice-resistant conical structures, according to ice-induced vibration, are directly related to structure width, sea ice parameters, and clearing modes of broken ice. This paper proposes three clearing modes for broken ice acting on conical structures: complete clearing, temporary ice pile up, and ice pile up. In this paper, sea ice clearing modes and the formation requirements of dynamic ice force are analyzed to explore criteria determining wide/narrow ice-resistant conical structures. According to the direct measurement data of typical prototype structures, quantitative criteria of the ratio of a cone width at waterline(D) to sea ice thickness(h) is proposed. If the ratio is less than 30(narrow conical structure), broken ice is completely cleared and a dynamic ice force is produced; however, if the ratio is larger than 50(wide conical structure), the front stacking of broken ice or dynamic ice force will not occur.
基金supported by the the Young Scientists Fund of the National Natural Science Foundation of China under Grant 52102447by the Research Fund Project of Beijing Information Science&Technology University under Grant 2023XJJ33.
文摘Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle networks.The transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols regulations.Typically,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal data.However,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network attacks.In this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection method.In consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is tested.In addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal data.This paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data packets.The experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.
