This paper presents a simplified design tool based on semi-analytical formulations to investigate the dynamic response of an immersed composite cylinder subjected to a far-field underwater explosion.The cylinder is si...This paper presents a simplified design tool based on semi-analytical formulations to investigate the dynamic response of an immersed composite cylinder subjected to a far-field underwater explosion.The cylinder is simply supported,fully submerged and filled with air inside.A classical shell theory using a Double Fourier series solution combined with the first-order Doubly Asymptotic Approximation(DAA1)formulation is adapted to model the fluid-structure interaction.An explicit non-standard finite difference scheme is applied to solve the coupled differential equations in time domain.The validity of DAA1 model is established by comparing the LS-DYNA/USA finite element results with existing experimental data from the literature.Then the proposed semi-analytical solutions are compared to the LS-DYNA/USA results,showing good correlation with a discrepancy of 7%for peak deflections and±9%for maximum stresses at the stand-off point for cylinders with relatively small length over radius ratios.Parametric studies examining the effect of different loading conditions,areal masses,and material configurations reveal that a large charge mass located far from the composite panel turns out to be more damaging than a small mass located nearby due to a broader pressure-time profile.Finally,the proposed model demonstrates a significant reduction in computation time,being approximately 30 times faster than its numerical counterpart,LS-DYNA/USA,making it a valuable tool for the preliminary design stages.展开更多
Mercury(Hg)is a global pollutant that is subject to strict regulations to reduce anthropogenic emissions.The production of energy represents an important activity that leads to Hg emissions into the atmosphere.Of all ...Mercury(Hg)is a global pollutant that is subject to strict regulations to reduce anthropogenic emissions.The production of energy represents an important activity that leads to Hg emissions into the atmosphere.Of all the systems used,IGCC plants are the most promising for reducing Hg emissions,since it is possible to remove Hg from syngas prior to combustion.The aim of the present work was to evaluate the presence of Hg in the main streams of an experimental IGCC plant(ELCOGAS,Puertollano)in order to quantify Hg emissions and investigate the possibility of reducing them.The main streams of the system were sampled for three consecutive days and both the solids,i.e.,raw material(coal and petroleum coke),fine and coarse slags,fly ash,sulphur,and the liquids,i.e.,slag system,Venturi scrubber and saturator,were studied.The results show that an average of 12.9%of the Hg that enters the IGCC power plant is eliminated with solid waste and only 0.08%with liquid waste.There is still an average of 87.12%of Hg that is not accounted for in the mass balance and that could remain in the system and/or be eliminated in the streams that were not analysed.Although it is impossible to offer an explanation for the final fate of the Hg lost in the system based on the obtained results,the data suggest that sulphur byproducts could be primarily responsible for the elimination of Hg from the syngas,and that a major proportion of Hg should be emitted via the chimney after the syngas combustion process.展开更多
Yield control in the integrated circuit manufacturing process is very important,and defects are one of the main factors affecting chip yield.As the process control becomes more and more critical and the critical dimen...Yield control in the integrated circuit manufacturing process is very important,and defects are one of the main factors affecting chip yield.As the process control becomes more and more critical and the critical dimension becomes smaller and smaller,the identification and location of defects is particularly important.This paper uses a machine learning algorithm based on transfer learning and two fine-tuned neural network models to realize the autonomous recognition and classification of defects even the data set is small,which achieves 94.6%and 91.7%classification accuracy.The influence of network complexity on classification result is studied at the same time.This paper also establishes a visual display algorithm of defects,shows the process of extracting the deep-level features of the defective image by the network,and then analyze the defect features.Finally,the Gradient-weighted Class Activation Mapping technology is used to generate defect heat maps,which locate the defect positions and probability intensity effects.This paper greatly expands the application of transfer learning in the field of integrated circuit lithography defect recognition,and greatly improves the friendliness of defect display.展开更多
基金supported by French Defense Innovation Agency(AID-DGA)(Grant No.ANR-21-ASM2-0002-02)in the framework of the Astrid Maturation SUCCESS+project,a collaborative French research project.
文摘This paper presents a simplified design tool based on semi-analytical formulations to investigate the dynamic response of an immersed composite cylinder subjected to a far-field underwater explosion.The cylinder is simply supported,fully submerged and filled with air inside.A classical shell theory using a Double Fourier series solution combined with the first-order Doubly Asymptotic Approximation(DAA1)formulation is adapted to model the fluid-structure interaction.An explicit non-standard finite difference scheme is applied to solve the coupled differential equations in time domain.The validity of DAA1 model is established by comparing the LS-DYNA/USA finite element results with existing experimental data from the literature.Then the proposed semi-analytical solutions are compared to the LS-DYNA/USA results,showing good correlation with a discrepancy of 7%for peak deflections and±9%for maximum stresses at the stand-off point for cylinders with relatively small length over radius ratios.Parametric studies examining the effect of different loading conditions,areal masses,and material configurations reveal that a large charge mass located far from the composite panel turns out to be more damaging than a small mass located nearby due to a broader pressure-time profile.Finally,the proposed model demonstrates a significant reduction in computation time,being approximately 30 times faster than its numerical counterpart,LS-DYNA/USA,making it a valuable tool for the preliminary design stages.
基金funded by Spanish Ministry of Economy and Competitiveness(Project CTM2012-33918)ICEX España Exportación e Inversiones(Project 2014/03076).
文摘Mercury(Hg)is a global pollutant that is subject to strict regulations to reduce anthropogenic emissions.The production of energy represents an important activity that leads to Hg emissions into the atmosphere.Of all the systems used,IGCC plants are the most promising for reducing Hg emissions,since it is possible to remove Hg from syngas prior to combustion.The aim of the present work was to evaluate the presence of Hg in the main streams of an experimental IGCC plant(ELCOGAS,Puertollano)in order to quantify Hg emissions and investigate the possibility of reducing them.The main streams of the system were sampled for three consecutive days and both the solids,i.e.,raw material(coal and petroleum coke),fine and coarse slags,fly ash,sulphur,and the liquids,i.e.,slag system,Venturi scrubber and saturator,were studied.The results show that an average of 12.9%of the Hg that enters the IGCC power plant is eliminated with solid waste and only 0.08%with liquid waste.There is still an average of 87.12%of Hg that is not accounted for in the mass balance and that could remain in the system and/or be eliminated in the streams that were not analysed.Although it is impossible to offer an explanation for the final fate of the Hg lost in the system based on the obtained results,the data suggest that sulphur byproducts could be primarily responsible for the elimination of Hg from the syngas,and that a major proportion of Hg should be emitted via the chimney after the syngas combustion process.
基金Thanks for the support of the National Natural Science Foundation of China(61604172,61874002)and the Key Laboratory of Microelectronic Devices and Integration Technology.The authors would like to thank Y.B.Feng from YMTC for the helpful discussions.
文摘Yield control in the integrated circuit manufacturing process is very important,and defects are one of the main factors affecting chip yield.As the process control becomes more and more critical and the critical dimension becomes smaller and smaller,the identification and location of defects is particularly important.This paper uses a machine learning algorithm based on transfer learning and two fine-tuned neural network models to realize the autonomous recognition and classification of defects even the data set is small,which achieves 94.6%and 91.7%classification accuracy.The influence of network complexity on classification result is studied at the same time.This paper also establishes a visual display algorithm of defects,shows the process of extracting the deep-level features of the defective image by the network,and then analyze the defect features.Finally,the Gradient-weighted Class Activation Mapping technology is used to generate defect heat maps,which locate the defect positions and probability intensity effects.This paper greatly expands the application of transfer learning in the field of integrated circuit lithography defect recognition,and greatly improves the friendliness of defect display.
