Introducing the surface properties [initial vortex, ground temperature and surface momentum impact height （SMIH）] for the boundary conditions, dust-devil-scale large eddy simulations （LES） were carried out. Given ...Introducing the surface properties [initial vortex, ground temperature and surface momentum impact height （SMIH）] for the boundary conditions, dust-devil-scale large eddy simulations （LES） were carried out. Given three parameters of initial vortex, ground temperature and the SMIH based on Sinclair＇s observation, the dust devil physical characteristics, such as maximum tangential velocity, updraft velocity, pressure drop in the inner core region, and even reverse flow at the top of the core region, are predicted, and are found to be close to the observations, thus demonstrating the ability of the simulation. The physical characteristics of different modeled dust devils are reproduced and compared to the values predicted by Renno et al.＇ theory. Even for smaller temperature differences or weaker buoyancy, severe dust devils may be formed by strong incipient vortices. It is also indicated that SMIH substantially affects the near-surface shape of terrestrial dust devils.展开更多
The detection of dust devils on Mars poses significant challenges,primarily due to the substantial variability in target scales,the susceptibility of small-scale features to loss or distortion during feature extractio...The detection of dust devils on Mars poses significant challenges,primarily due to the substantial variability in target scales,the susceptibility of small-scale features to loss or distortion during feature extraction and fusion,and the interference from complex Martian backgrounds.To tackle these issues,we propose the Dynamic Triplet Fusion Attentive Net(DTFA-Net),a framework tailored for Martian dust devil detection.Within DTFA-Net,we design a Multi-Dimensional Dynamic Feature Pyramid Network(MDFPN),which is based on the Bi-directional Feature Pyramid Network(BiFPN)and enhances multi-level feature fusion by incorporating shallow-layer features and employing a cross-scale connection strategy.Additionally,we propose three innovative lightweight and plug-and-play modules:the Local-Channel Cross-Stage Module(LCCS)to boost feature diversity,the Progressive Feature Enhancement Module(PFE)to increase the focus on critical features,and the Triplet-Aware Cross-Stage Module(TACS)for capturing interactions across spatial and channel dimensions.Furthermore,the framework incorporates the Dynamic Head(DyHead),which uses multi-dimensional attention mechanisms to dynamically adjust to various scales,spatial positions,and detection challenges.Experimental results show that DTFA-Net achieved a detection Precision of 94.3%,a Recall of 92.8%,and mAP50 of 96.6%on the Amazonis Planitia dust devil dataset.Its overall performance significantly surpasses that of existing mainstream methods,while also demonstrating strong generalization capability on cross-regional datasets.Beyond detection,this framework was further applied to analyze the seasonal activity and spatial distribution patterns of Martian dust devils in Amazonis Planitia,and the prevailing winds of each season were examined to explore the mechanisms underlying the formation of activity hotspots.In addition,the model was extended to the ten core candidate landing sites of the Tianwen-3 mission to systematically assess dust devil distribution across these regions.Based on the detection results and previous studies,we suggest that four sites—Kasei Valles,Oxia Planum,McLaughlin Crater,and Mawrth Vallis—offer a more balanced trade-off among scientific value,dust-cleaning,and engineering safety,making them relatively ideal landing sites for the Tianwen-3 mission.Overall,this study provides important insights into the spatiotemporal distribution,activity patterns,and potential environmental risks of Martian dust devils,thereby offering valuable references for Mars exploration missions.Furthermore,it provides guidance for the optimized design and safe operation of spacecraft,and contributes scientific support for the planning and implementation of future Mars exploration endeavors.展开更多
The frequent attacks of strong and extraordinarily strong dust devil in the northern China in recent years are closely related to the serious destruction of the vegetation in the region in recent decades, the increasi...The frequent attacks of strong and extraordinarily strong dust devil in the northern China in recent years are closely related to the serious destruction of the vegetation in the region in recent decades, the increasing expansion of the desertification area, the increase of sand sources due to eco environmental deterioration, the warming up of climate and the weakening of the resisting capacity of our natural environment against dust devil. Measures for preventing and controlling dust devil are to improve the eco environment by increasing vegetation, establish a dust devil pre warning system and a dynamic monitoring & forecast system for dust dveil frequent areas, and enhance the enforcement of laws and administrative efforts so as to bring the eco environment construction of dust devil afflicted areas into the orbit of laws.展开更多
文摘Introducing the surface properties [initial vortex, ground temperature and surface momentum impact height （SMIH）] for the boundary conditions, dust-devil-scale large eddy simulations （LES） were carried out. Given three parameters of initial vortex, ground temperature and the SMIH based on Sinclair＇s observation, the dust devil physical characteristics, such as maximum tangential velocity, updraft velocity, pressure drop in the inner core region, and even reverse flow at the top of the core region, are predicted, and are found to be close to the observations, thus demonstrating the ability of the simulation. The physical characteristics of different modeled dust devils are reproduced and compared to the values predicted by Renno et al.＇ theory. Even for smaller temperature differences or weaker buoyancy, severe dust devils may be formed by strong incipient vortices. It is also indicated that SMIH substantially affects the near-surface shape of terrestrial dust devils.
基金supported by the National Natural Science Foundation of China(Grant Nos.12103020&12363009)the Science and Technology Development Fund(FDCT)of Macao(Grant Nos.0021/2024/RIA1,0158/2024/AFJ and 0034/2024/AMJ)+4 种基金the Open Project Program of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology)(Grant No.002/2024/SKL)the Key Technology Research Project of TW-3(Grant No.TW3004)the Youth Talent Project of the Science and Technology Plan of Ganzhou(Grant No.2023CYZ26970)the Graduate Innovation Special Fund Project of Jiangxi University of Science and Technology(Grant No.XY2024-S213)the Key Laboratory of Low Dimensional Quantum Materials and Sensor Devices of Jiangxi Education Institutes(Grant No.Gan Jiao Ke Zi-20241301)。
文摘The detection of dust devils on Mars poses significant challenges,primarily due to the substantial variability in target scales,the susceptibility of small-scale features to loss or distortion during feature extraction and fusion,and the interference from complex Martian backgrounds.To tackle these issues,we propose the Dynamic Triplet Fusion Attentive Net(DTFA-Net),a framework tailored for Martian dust devil detection.Within DTFA-Net,we design a Multi-Dimensional Dynamic Feature Pyramid Network(MDFPN),which is based on the Bi-directional Feature Pyramid Network(BiFPN)and enhances multi-level feature fusion by incorporating shallow-layer features and employing a cross-scale connection strategy.Additionally,we propose three innovative lightweight and plug-and-play modules:the Local-Channel Cross-Stage Module(LCCS)to boost feature diversity,the Progressive Feature Enhancement Module(PFE)to increase the focus on critical features,and the Triplet-Aware Cross-Stage Module(TACS)for capturing interactions across spatial and channel dimensions.Furthermore,the framework incorporates the Dynamic Head(DyHead),which uses multi-dimensional attention mechanisms to dynamically adjust to various scales,spatial positions,and detection challenges.Experimental results show that DTFA-Net achieved a detection Precision of 94.3%,a Recall of 92.8%,and mAP50 of 96.6%on the Amazonis Planitia dust devil dataset.Its overall performance significantly surpasses that of existing mainstream methods,while also demonstrating strong generalization capability on cross-regional datasets.Beyond detection,this framework was further applied to analyze the seasonal activity and spatial distribution patterns of Martian dust devils in Amazonis Planitia,and the prevailing winds of each season were examined to explore the mechanisms underlying the formation of activity hotspots.In addition,the model was extended to the ten core candidate landing sites of the Tianwen-3 mission to systematically assess dust devil distribution across these regions.Based on the detection results and previous studies,we suggest that four sites—Kasei Valles,Oxia Planum,McLaughlin Crater,and Mawrth Vallis—offer a more balanced trade-off among scientific value,dust-cleaning,and engineering safety,making them relatively ideal landing sites for the Tianwen-3 mission.Overall,this study provides important insights into the spatiotemporal distribution,activity patterns,and potential environmental risks of Martian dust devils,thereby offering valuable references for Mars exploration missions.Furthermore,it provides guidance for the optimized design and safe operation of spacecraft,and contributes scientific support for the planning and implementation of future Mars exploration endeavors.
文摘The frequent attacks of strong and extraordinarily strong dust devil in the northern China in recent years are closely related to the serious destruction of the vegetation in the region in recent decades, the increasing expansion of the desertification area, the increase of sand sources due to eco environmental deterioration, the warming up of climate and the weakening of the resisting capacity of our natural environment against dust devil. Measures for preventing and controlling dust devil are to improve the eco environment by increasing vegetation, establish a dust devil pre warning system and a dynamic monitoring & forecast system for dust dveil frequent areas, and enhance the enforcement of laws and administrative efforts so as to bring the eco environment construction of dust devil afflicted areas into the orbit of laws.
