This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
With the economic and social development of the country,vocational education is playing an increasingly significant role in cultivating highly skilled talents.However,the mechanical drawing courses in vocational colle...With the economic and social development of the country,vocational education is playing an increasingly significant role in cultivating highly skilled talents.However,the mechanical drawing courses in vocational colleges still face numerous challenges in the teaching process,such as outdated textbook content,inadequate practical resources,weak teaching staff,and low student interest.This paper aims to explore these issues and propose corresponding coping strategies.The findings of this study not only provide specific improvement suggestions for vocational colleges but also emphasize the importance of these strategies in enhancing students’comprehensive abilities and promoting the development of vocational education.By addressing these challenges,this paper contributes to the enhancement of teaching quality and the overall advancement of vocational skills education.展开更多
To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal draw...To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal drawing and explores automation and intelligent equipment solutions within the framework of the group coal drawing method.Numerical simulations were performed to investigate the impact of the Number of Drawing Openings(NDO)and rounds on top-coal recovery,coal draw-ing efficiency,and Top Coal Loss(TCL)mechanism.Subsequently,considering the recovery and coal drawing efficiency and by introducing the instantaneous gangue content and cumulative gangue content in simulations,the top-coal recovery,gangue content,and coal loss distribution when considering excessive coal drawing were analyzed.This established a foun-dation for determining the optimal NDO and shutdown timing.Finally,the key technical principle and automated control of a shock vibration and hyperspectral fusion recognition device were detailed,and an intelligent coal drawing control method based on this technology was developed.This technology enabled the precise control of the instantaneous gangue content(35%)during coal drawing.The top-coal recovery at the Tashan Mine 8222 working face increased by 14.78%,and the gangue content was controlled at~9%,consistent with the numerical simulation results.Thus,the reliability of the numerical simulation results was confirmed to a certain extent.Meanwhile,the single-group drawing method significantly enhanced the production capacity of the 8222 working face,achieving an annual output of 15 million tons.展开更多
With the development and popularization of computer application technology,the use of computer graphics and image processing technology has become the main means of modern engineering design and drawing.Learning and m...With the development and popularization of computer application technology,the use of computer graphics and image processing technology has become the main means of modern engineering design and drawing.Learning and mastering 3D modeling technology and mechanical information modeling technology have become an important goal of learning engineering drawing.To meet the teaching requirements of the“New Engineering”program,higher education should cultivate innovative talents with the ability to identify,express,analyze,and solve complex engineering problems;promote the transformation of teaching methods for the course of“Mechanical Drawing and Computer Drawing”from“teaching well”to“learning well.”This change is not only a change in course content,but also a change in training objectives.It introduces modern 3D design concepts into the drawing course,constructs a learning system with 3D modeling technology as the main line,solves the problem of imagination in traditional teaching,makes the learning process more in line with scientific cognitive laws,better meets the needs of modern manufacturing industry for new technologies,and improves students’drawing skills and ability to use modern tools(computer drawing).展开更多
The effects of drawing strain during intermediate annealing on the microstructure and properties of Cu-20 wt%Fe alloy wires while maintaining constant total deformation were investigated.Intermediate annealing effecti...The effects of drawing strain during intermediate annealing on the microstructure and properties of Cu-20 wt%Fe alloy wires while maintaining constant total deformation were investigated.Intermediate annealing effectively removes work hardening in both the Cu matrix and Fe fibers,restoring their plastic deformation capacity and preserving fiber continuity during subsequent redrawing.The process also refines the Fe phase,leading to a more uniform size distribution and straighter,better-aligned Cu/Fe phase interfaces,thereby enhancing the comprehensive properties of the alloy.The magnitude of drawing strain during intermediate annealing plays a critical role in balancing the mechanical strength and electrical conductivity of redrawn wires.A lower initial drawing strain requires greater redrawing strain,leading to excessive hardening of the Fe fibers,which negatively impacts the electrical conductivity and tensile plasticity.Conversely,a higher initial drawing strain can result in insufficient work hardening during the redrawing deformation process,yielding minimal strength improvements.Among the tested alloys,H/3.5 wires show a slight reduction in strength and hardness compared to W and H/4.5 wires but exhibit a significant increase in tensile elongation and electrical conductivity.The tensile strength was 755 MPa,and the electrical conductivity was 47%international-annealed copper standard(IACS).The optimal performance is attributed to the formation of a high-density,ultrafine Fe fiber structure-aligned parallel to the drawing direction,which is achieved through a suitable combination of the drawing process and intermediate annealing.展开更多
The ultrafine copper wire with a diameter of 18μm is prepared via cold drawing process from the single crystal downcast billet(Φ8 mm),taking a drawing strain to 12.19.In this paper,in-depth investigation of the micr...The ultrafine copper wire with a diameter of 18μm is prepared via cold drawing process from the single crystal downcast billet(Φ8 mm),taking a drawing strain to 12.19.In this paper,in-depth investigation of the microstructure feature,texture evolution,mechanical properties,and electrical conductivity of ultrafine wires ranging fromΦ361μm toΦ18μm is performed.Specially,the microstructure feature and texture type covering the whole longitudinal section of ultrafine wires are elaborately characterized.The results show that the average lamella thickness decreases from 1.63μm to 102 nm during the drawing process.Whereas,inhomogeneous texture evolution across different wire sections was observed.The main texture types of copper wires are comprised of<111>,<001>and<112>orientations.Specifically,the peripheral region is primarily dominated by<111>and<112>,while the central region is dominated by<001>and<111>.As the drawing strain increases,the volume fraction of hard orientation<111>with low Schmid factor increases,where notably higher fraction of<111>is resulted from the consumption of<112>and<001>for the wire ofΦ18μm.For drawn copper wire of 18μm,superior properties are obtained with a tensile strength of 729.8 MPa and an electrical conductivity of 86.9%IACS.Furthermore,it is found that grain strengthening,dislocation strengthening,and texture strengthening are three primary strengthening mechanisms of drawn copper wire,while the dislocation density is the main factor on the reducing of conductivity.展开更多
In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their ...In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.展开更多
Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as...Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask segmentation. Then, based on the obtained shape masks, a vector extraction network guided by heatmaps is designed to extract structural vectors by fusing the features from node heatmap and skeleton heatmap, respectively. Compared with the state-of-the-art methods, experiment results illustrate that the proposed semantic segmentation method can effectively eliminate the interference of many elements on drawings to segment the shape masks effectively, meanwhile, the model trained by the proposed vector extraction network can accurately extract the vectors such as nodes and line connections, avoiding redundant vector detection. The proposed method lays a solid foundation for automatic 3D model reconstruction and contributes to technological advancements in relevant fields.展开更多
Magnesium alloy,the lightest structural metal substance currently known,has garnered a great deal of interest in recent times.Magnesium alloys not only offer high specific strength,high specific stiffness,and low dens...Magnesium alloy,the lightest structural metal substance currently known,has garnered a great deal of interest in recent times.Magnesium alloys not only offer high specific strength,high specific stiffness,and low density,but they also have outstanding anti-electromagnetic interference properties,shock absorption,are easy to recycle,and are biocompatible.It has a wide range of uses,including automotive,aerospace,military,and biological.Magnesium alloy’s compact hexagonal structure creates few slip systems at room temperature,leading to low plasticity and limited applicability.Deep drawing of magnesium alloys is a major procedure in the aerospace and automotive sectors due to the high strength-to-weight ratio.This paper presents all the aspects of deep drawing of magnesium alloys,covering the innovative methods of deep drawing,factors influencing the performance of deep drawing,simulation and modeling,optimization of deep drawing,and the microstructural changes during deep drawing and its impact on mechanical properties.Finally,the challenges and scope for future research are explored.展开更多
In the present study,two-layered stainless steel-copper composites with a thickness of 50μm were initially subjected to annealing at 800,900 and 1000℃for 5 min,respectively,to achieve diverse microstructural feature...In the present study,two-layered stainless steel-copper composites with a thickness of 50μm were initially subjected to annealing at 800,900 and 1000℃for 5 min,respectively,to achieve diverse microstructural features.Then the influence of annealing temperature on the formability of stainless steel-copper composites and the quality of micro composite cups manufactured by micro deep drawing(MDD)were investigated,and the underlying mechanism was analyzed.Three finite element(FE)models,including basic FE model,Voronoi FE model and surface morphological FE model,were developed to analyze the forming performance of stainless steel-copper composites during MDD.The results show that the stainless steel-copper composites annealed at 900℃possess the best plasticity owing to the homogeneous and refined microstructure in both stainless steel and copper matrixes,and the micro composite cup with specimen annealed at 900℃exhibits a uniform wall thickness as well as high surface quality with the fewest wrinkles.The results obtained from the surface morphological FE model considering material inhomogeneity and surface morphology of the composites are the closest to the experimental results compared to the basic and Voronoi FE model.During MDD process,the drawing forces decrease with increasing annealing temperature as a consequence of the strength reduction.展开更多
In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In t...In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.展开更多
Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast res...Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of posit...In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process.To avoid these issues,we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices.In their full specifications,the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts.Moreover,the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation.An application to two crude oils,WTI and Brent,and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations.In addition,the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.展开更多
In this work,the{10–12}tensile twins are introduced to improve the drawability of the AZ31 Mg alloy sheet.Concretely,the drawing depth is increased by 32%compared with the as-received sheet at 200℃.This is because{1...In this work,the{10–12}tensile twins are introduced to improve the drawability of the AZ31 Mg alloy sheet.Concretely,the drawing depth is increased by 32%compared with the as-received sheet at 200℃.This is because{10–12}tensile twins promote the occurrences of many deformation mechanisms during warm deep drawing,such as slips,detwinning,dynamic recrystallization(DRX)behaviors,etc.Further,based on the different stress states during deep drawing,these mechanisms and their competition relationships,as well as texture evolutions,are systematically studied.Combined with critical resolved shear stress(CRSS)and microstructure evolution,the global Schmid factor(GSF)obtained by quantizing stress states by stress tensor(σ)can accurately predict the activation trend of deformation mechanisms.It is found that the stress states have a reverse influence on the activation trend of the{10–12}twinning and detwinning.The change of stress states affects the competitive relationships between detwinning and DRX,and then affects the process and degree of DRX.The{10–12}tensile twins and large plane strain promote the activation of prismatic slips,and the larger plane strain also deflected the{10–12}twinning lattice.The{10–12}tensile twins and their induced deformation mechanisms can prominently weaken the basal texture and improve the drawability.展开更多
Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in we...Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.展开更多
Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving mod...Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.展开更多
The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed wit...The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-sc...A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-scale(SGS)turbulent fluxes associated with convective clouds in idealized tropical cyclone(TC)simulations.To evaluate the capability of the modified scheme in simulating real TCs,two sets of simulations of TC Soudelor(2015),one with the modified scheme and the other with the original scheme,are conducted.Comparisons with observations and coarse-grained results from large eddy simulation benchmarks demonstrate that the modified scheme improves the forecasting of the intensity and structure,as well as the SGS turbulent fluxes of Soudelor.Using the modified turbulence scheme,a TC with stronger intensity,smaller size,a shallower but stronger inflow layer,and a more intense but less inclined convective updraft is simulated.The rapid intensification process and secondary eyewall features can also be captured better by the modified scheme.By analyzing the mechanism by which turbulent transport impacts the intensity and structure of TCs,it is shown that accurately representing the turbulent transport associated with convective clouds above the planetary boundary layer helps to initiate the TC spin-up process.展开更多
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金support from the Science and Technology Key Project of Beijing Polytechnic(Project Leader:Jinru Ma,No.2024X008-KXZ).
文摘With the economic and social development of the country,vocational education is playing an increasingly significant role in cultivating highly skilled talents.However,the mechanical drawing courses in vocational colleges still face numerous challenges in the teaching process,such as outdated textbook content,inadequate practical resources,weak teaching staff,and low student interest.This paper aims to explore these issues and propose corresponding coping strategies.The findings of this study not only provide specific improvement suggestions for vocational colleges but also emphasize the importance of these strategies in enhancing students’comprehensive abilities and promoting the development of vocational education.By addressing these challenges,this paper contributes to the enhancement of teaching quality and the overall advancement of vocational skills education.
基金the Fundamental Research Funds for the Central Universities(2023YQTD02)National Key R&D Program of China(2023YFC2907501)。
文摘To address issues such as inefficient top-coal drawing,challenges in simultaneously mining and drawing,and the need for intelligent control in extra-thick coal seams,this study examines the principles of top-coal drawing and explores automation and intelligent equipment solutions within the framework of the group coal drawing method.Numerical simulations were performed to investigate the impact of the Number of Drawing Openings(NDO)and rounds on top-coal recovery,coal draw-ing efficiency,and Top Coal Loss(TCL)mechanism.Subsequently,considering the recovery and coal drawing efficiency and by introducing the instantaneous gangue content and cumulative gangue content in simulations,the top-coal recovery,gangue content,and coal loss distribution when considering excessive coal drawing were analyzed.This established a foun-dation for determining the optimal NDO and shutdown timing.Finally,the key technical principle and automated control of a shock vibration and hyperspectral fusion recognition device were detailed,and an intelligent coal drawing control method based on this technology was developed.This technology enabled the precise control of the instantaneous gangue content(35%)during coal drawing.The top-coal recovery at the Tashan Mine 8222 working face increased by 14.78%,and the gangue content was controlled at~9%,consistent with the numerical simulation results.Thus,the reliability of the numerical simulation results was confirmed to a certain extent.Meanwhile,the single-group drawing method significantly enhanced the production capacity of the 8222 working face,achieving an annual output of 15 million tons.
文摘With the development and popularization of computer application technology,the use of computer graphics and image processing technology has become the main means of modern engineering design and drawing.Learning and mastering 3D modeling technology and mechanical information modeling technology have become an important goal of learning engineering drawing.To meet the teaching requirements of the“New Engineering”program,higher education should cultivate innovative talents with the ability to identify,express,analyze,and solve complex engineering problems;promote the transformation of teaching methods for the course of“Mechanical Drawing and Computer Drawing”from“teaching well”to“learning well.”This change is not only a change in course content,but also a change in training objectives.It introduces modern 3D design concepts into the drawing course,constructs a learning system with 3D modeling technology as the main line,solves the problem of imagination in traditional teaching,makes the learning process more in line with scientific cognitive laws,better meets the needs of modern manufacturing industry for new technologies,and improves students’drawing skills and ability to use modern tools(computer drawing).
基金support provided by the National Natural Science Foundation of China(Nos.52405364,and 52171110)the Jiangsu Funding Program for Excellent Postdoctoral Talent.W.Huo acknowledges the support from the European Union Horizon 2020 Research and Innovation Program(No.857470)+1 种基金from the European Regional Development Fund via the Foundation for Polish Science International Research Agenda PLUS Program(No.MAB PLUS/2018/8)The publication was partly created within the framework of the project of the Minister of Science and Higher Education"Support for the activities of Centers of Excellence established in Poland under Horizon 2020"(No.MEiN/2023/DIR/3795).
文摘The effects of drawing strain during intermediate annealing on the microstructure and properties of Cu-20 wt%Fe alloy wires while maintaining constant total deformation were investigated.Intermediate annealing effectively removes work hardening in both the Cu matrix and Fe fibers,restoring their plastic deformation capacity and preserving fiber continuity during subsequent redrawing.The process also refines the Fe phase,leading to a more uniform size distribution and straighter,better-aligned Cu/Fe phase interfaces,thereby enhancing the comprehensive properties of the alloy.The magnitude of drawing strain during intermediate annealing plays a critical role in balancing the mechanical strength and electrical conductivity of redrawn wires.A lower initial drawing strain requires greater redrawing strain,leading to excessive hardening of the Fe fibers,which negatively impacts the electrical conductivity and tensile plasticity.Conversely,a higher initial drawing strain can result in insufficient work hardening during the redrawing deformation process,yielding minimal strength improvements.Among the tested alloys,H/3.5 wires show a slight reduction in strength and hardness compared to W and H/4.5 wires but exhibit a significant increase in tensile elongation and electrical conductivity.The tensile strength was 755 MPa,and the electrical conductivity was 47%international-annealed copper standard(IACS).The optimal performance is attributed to the formation of a high-density,ultrafine Fe fiber structure-aligned parallel to the drawing direction,which is achieved through a suitable combination of the drawing process and intermediate annealing.
基金Project supported by“Unveiled the List of Commanders”Key Core Common Technology Projects of Ji’an,ChinaProject(LJKMZ20220591)supported by the Basic Scientific Research Project of the Education Department of Liaoning Province,ChinaProject(CSTB2023NSCQ-LZX0116)supported by the Natural Science Foundation Joint Fund for Innovation and Development Projects of Chongqing,China。
文摘The ultrafine copper wire with a diameter of 18μm is prepared via cold drawing process from the single crystal downcast billet(Φ8 mm),taking a drawing strain to 12.19.In this paper,in-depth investigation of the microstructure feature,texture evolution,mechanical properties,and electrical conductivity of ultrafine wires ranging fromΦ361μm toΦ18μm is performed.Specially,the microstructure feature and texture type covering the whole longitudinal section of ultrafine wires are elaborately characterized.The results show that the average lamella thickness decreases from 1.63μm to 102 nm during the drawing process.Whereas,inhomogeneous texture evolution across different wire sections was observed.The main texture types of copper wires are comprised of<111>,<001>and<112>orientations.Specifically,the peripheral region is primarily dominated by<111>and<112>,while the central region is dominated by<001>and<111>.As the drawing strain increases,the volume fraction of hard orientation<111>with low Schmid factor increases,where notably higher fraction of<111>is resulted from the consumption of<112>and<001>for the wire ofΦ18μm.For drawn copper wire of 18μm,superior properties are obtained with a tensile strength of 729.8 MPa and an electrical conductivity of 86.9%IACS.Furthermore,it is found that grain strengthening,dislocation strengthening,and texture strengthening are three primary strengthening mechanisms of drawn copper wire,while the dislocation density is the main factor on the reducing of conductivity.
基金supported by the National Natural Science Foundation of China(No.52374264)the National Key Technologies Research and Development Program of China(No.2024YFC2909600)the Major Science and Technology Projects in Yunnan Province(No.202402AB080010).
文摘In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.
基金funded by the Chinese State Grid Jiangsu Electric Power Co.,Ltd.Science and Technology Project Funding,Grant Number J2023031.
文摘Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask segmentation. Then, based on the obtained shape masks, a vector extraction network guided by heatmaps is designed to extract structural vectors by fusing the features from node heatmap and skeleton heatmap, respectively. Compared with the state-of-the-art methods, experiment results illustrate that the proposed semantic segmentation method can effectively eliminate the interference of many elements on drawings to segment the shape masks effectively, meanwhile, the model trained by the proposed vector extraction network can accurately extract the vectors such as nodes and line connections, avoiding redundant vector detection. The proposed method lays a solid foundation for automatic 3D model reconstruction and contributes to technological advancements in relevant fields.
文摘Magnesium alloy,the lightest structural metal substance currently known,has garnered a great deal of interest in recent times.Magnesium alloys not only offer high specific strength,high specific stiffness,and low density,but they also have outstanding anti-electromagnetic interference properties,shock absorption,are easy to recycle,and are biocompatible.It has a wide range of uses,including automotive,aerospace,military,and biological.Magnesium alloy’s compact hexagonal structure creates few slip systems at room temperature,leading to low plasticity and limited applicability.Deep drawing of magnesium alloys is a major procedure in the aerospace and automotive sectors due to the high strength-to-weight ratio.This paper presents all the aspects of deep drawing of magnesium alloys,covering the innovative methods of deep drawing,factors influencing the performance of deep drawing,simulation and modeling,optimization of deep drawing,and the microstructural changes during deep drawing and its impact on mechanical properties.Finally,the challenges and scope for future research are explored.
基金Projects(51975398,52105392)supported by the National Natural Science Foundation of ChinaProject(YDZJSX2021A006)supported by the Central Government Guided Local Science and Technology Development Fund Project,China+1 种基金Project(20210035)supported by the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province,ChinaProject(2020-037)supported by the Fund Program for the Research Project Supported by Shanxi Scholarship Council,China。
文摘In the present study,two-layered stainless steel-copper composites with a thickness of 50μm were initially subjected to annealing at 800,900 and 1000℃for 5 min,respectively,to achieve diverse microstructural features.Then the influence of annealing temperature on the formability of stainless steel-copper composites and the quality of micro composite cups manufactured by micro deep drawing(MDD)were investigated,and the underlying mechanism was analyzed.Three finite element(FE)models,including basic FE model,Voronoi FE model and surface morphological FE model,were developed to analyze the forming performance of stainless steel-copper composites during MDD.The results show that the stainless steel-copper composites annealed at 900℃possess the best plasticity owing to the homogeneous and refined microstructure in both stainless steel and copper matrixes,and the micro composite cup with specimen annealed at 900℃exhibits a uniform wall thickness as well as high surface quality with the fewest wrinkles.The results obtained from the surface morphological FE model considering material inhomogeneity and surface morphology of the composites are the closest to the experimental results compared to the basic and Voronoi FE model.During MDD process,the drawing forces decrease with increasing annealing temperature as a consequence of the strength reduction.
基金support of the National Natural Science Foundation of China(No.12372221)is acknowledged.
文摘In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130608 and 42075142)the National Key Research and Development Program of China(Grant No.2020YFA0608000)the CUIT Science and Technology Innovation Capacity Enhancement Program Project(Grant No.KYTD202330)。
文摘Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
文摘In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process.To avoid these issues,we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices.In their full specifications,the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts.Moreover,the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation.An application to two crude oils,WTI and Brent,and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations.In addition,the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.
基金supported by the National Natural Science Foundations of China[No.52374395,52474419]Natural Science Foundation of Chongqing[CSTB2024NSCQMSX0267]+6 种基金the Natural Science Foundation of Shanxi province[No.20210302123135,20210302123163]the China Postdoctoral Science Foundation[No.2022M710541]the Research Project Supported by Shanxi Scholarship Council of China[No.2022-038]Scientific and Technological Achievements Transformation Guidance Special Project of Shanxi Province[202104021301022,202204021301009]the Ministry of Science and Higher Education of the Russian Federation for financial support under the Megagrant[no.075-15-2022-1133]he National Research Foundation(NRF)grant funded by the Ministry of Science and ICT[2015R1A2A1A01006795]Korea through the Research Institute of Advanced Materials.
文摘In this work,the{10–12}tensile twins are introduced to improve the drawability of the AZ31 Mg alloy sheet.Concretely,the drawing depth is increased by 32%compared with the as-received sheet at 200℃.This is because{10–12}tensile twins promote the occurrences of many deformation mechanisms during warm deep drawing,such as slips,detwinning,dynamic recrystallization(DRX)behaviors,etc.Further,based on the different stress states during deep drawing,these mechanisms and their competition relationships,as well as texture evolutions,are systematically studied.Combined with critical resolved shear stress(CRSS)and microstructure evolution,the global Schmid factor(GSF)obtained by quantizing stress states by stress tensor(σ)can accurately predict the activation trend of deformation mechanisms.It is found that the stress states have a reverse influence on the activation trend of the{10–12}twinning and detwinning.The change of stress states affects the competitive relationships between detwinning and DRX,and then affects the process and degree of DRX.The{10–12}tensile twins and large plane strain promote the activation of prismatic slips,and the larger plane strain also deflected the{10–12}twinning lattice.The{10–12}tensile twins and their induced deformation mechanisms can prominently weaken the basal texture and improve the drawability.
基金supported by the National Natural Science Foundations of China(Grant Nos.42305163 and U22A20577)the Construction Project of Weather Modification Ability in Central China(Grant No.ZQC-H22256)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760300)the Projects of the Earth System Numerical Simulation Facility(Grant Nos.2024-EL-PT-000707,2023-ELPT-000482,2023-EL-ZD-00026,and 2022-EL-PT-00083)the STS Program of the Inner Mongolia Meteorological Service,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,and Institute of Atmospheric Physics,Chinese Academy of Sciences(Grant No.2021CG0047)。
文摘Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.
基金The National Key Research and Development Program of China under contract No.2021YFC3101602the National Natural Science Foundation of China under contract Nos 42176017 and 41976019.
文摘Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.
基金Supported by the National Natural Science Foundation of China(No.42276019)the Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Waters(No.GSTOEW)。
文摘The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFC3000803)the National Natural Science Foundation of China(Grant Nos.42375149,41975133 and 42205070)the Shanghai Pujiang Program(Grant No.22PJ1415900)。
文摘A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-scale(SGS)turbulent fluxes associated with convective clouds in idealized tropical cyclone(TC)simulations.To evaluate the capability of the modified scheme in simulating real TCs,two sets of simulations of TC Soudelor(2015),one with the modified scheme and the other with the original scheme,are conducted.Comparisons with observations and coarse-grained results from large eddy simulation benchmarks demonstrate that the modified scheme improves the forecasting of the intensity and structure,as well as the SGS turbulent fluxes of Soudelor.Using the modified turbulence scheme,a TC with stronger intensity,smaller size,a shallower but stronger inflow layer,and a more intense but less inclined convective updraft is simulated.The rapid intensification process and secondary eyewall features can also be captured better by the modified scheme.By analyzing the mechanism by which turbulent transport impacts the intensity and structure of TCs,it is shown that accurately representing the turbulent transport associated with convective clouds above the planetary boundary layer helps to initiate the TC spin-up process.
