Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by ...Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.展开更多
Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studi...Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.展开更多
Although conventional coal mine designs are conservative regarding pillar strength,local failures such as roof-falls and pillar bursts still affect mine safety and operations.Previous studies have identified that disc...Although conventional coal mine designs are conservative regarding pillar strength,local failures such as roof-falls and pillar bursts still affect mine safety and operations.Previous studies have identified that discontinuous,layered roof materials have some self-supporting capacity.This research is a preliminary step towards understanding these mechanics in coal-measure rocks.Although others have considered broad conceptual models and simplified analogs for mine roof behavior,this study presents a unique numerical model that more completely represents in-situ roof conditions.The discrete element method(DEM)is utilized to conduct a parametric analysis considering a range of in-situ stress ratios,material properties,and joint networks to determine the parameters controlling the stability of single-entries modeled in two-dimensions.Model results are compared to empirical observations of roof-support effectiveness(ARBS)in the context of the coal mine roof rating(CMRR)system.Results such as immediate roof displacement,overall stability,and statistical relationships between model parameters and outcomes are presented herein.Potential practical applications of this line of research include:(1)roof-support optimization for a range of coal-measure rocks,(2)establishment of a relationship between roof stability and pillar stress,and(3)determination of which parameters are most critical to roof stability and therefore require concentrated evaluation.展开更多
In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using...In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using the technique of matrix decomposition, a group of analytical solutions for the network equation are obtained. With the analytical solutions, a calculation for heavy element abundance of the solar system is carried out and the results are in good agreement with the astrophysical measurements.展开更多
Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has bee...Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has been an excellent supplement,but the computation is time-consuming.Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model.A large number of finite element models were developed;the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples.Study focused on steel 4340tpolyurea laminates with various configurations.Four different 3D shapes of the projectiles were modeled and used in the training.The trained neural network and decision tree model was tested using independently generated test samples using finite element models.The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models.Additionally,both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples.Performance of both the models was evaluated and compared.Models trained with Finite element simulation data samples were found capable to give more accurate predication,compared to the models trained with experimental data,because finite element modeling can generate much larger training set,and thus finite element solvers can serve as an excellent teacher.This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.展开更多
为解决SONiC(software for open networking in the cloud)交换机操作系统对多模态网络(polymor phic network,PINet)中模态适配及模态管控问题,提出了一个基于P4Runtime的SONiC网元控制通道容器p4runtime-pins,使多模态网元设备可以支...为解决SONiC(software for open networking in the cloud)交换机操作系统对多模态网络(polymor phic network,PINet)中模态适配及模态管控问题,提出了一个基于P4Runtime的SONiC网元控制通道容器p4runtime-pins,使多模态网元设备可以支持多种网络模态流表的配置。p4runtime-pins容器通过gRPC服务模块实现与控制器的连接,使用邻近网元发现算法实现控制器对链路的发现。设计了网元端口更新算法解决了网元设备在实际应用环境中存在的端口变更问题。同时,针对SONiC网元交换机中硬件转发处理单元存在的流表支持性差异问题,设计了内部流表转存和gRPC网元代理功能,实现了不同网络模态流表的部署。实验结果表明,p4runtime-pins容器资源消耗低,仅占用了1.70%的CPU资源和0.45%的内存资源。同时,部署p4runtime-pins容器的SONiC网元设备能够准确地接收并配置控制器下发的流表规则,流表配置延迟仅为0.027~0.037 s。展开更多
基金funded in part by the Advanced Research Projects AgencyEnergy (ARPA-E), U.S. Department of Energy, under award number DE-AR0001471。
文摘Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.
基金Otokar Otomotiv ve Savunma Sanayi A.S. for the financial support
文摘Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods(FEM) in this research field.The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort,therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time.This study aims to apply a hybrid method using FEM simulation and artificial neural network(ANN) analysis to approximate ballistic limit thickness for armor steels.To achieve this objective,a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition.In this methodology,the FEM simulations are used to create training cases for Multilayer Perceptron(MLP) three layer networks.In order to validate FE simulation methodology,ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569.Afterwards,the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor.Results show that even with limited number of data,FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.
基金sponsored by the Alpha Foundation for the Improvement of Mine Safety and Health, Inc. (Alpha Foundation)the funding provided for this project by the Alpha Foundationpartially funded by the National Institute of Occupational Health and Science (NIOSH) under Grant Number 200-2016-90154.
文摘Although conventional coal mine designs are conservative regarding pillar strength,local failures such as roof-falls and pillar bursts still affect mine safety and operations.Previous studies have identified that discontinuous,layered roof materials have some self-supporting capacity.This research is a preliminary step towards understanding these mechanics in coal-measure rocks.Although others have considered broad conceptual models and simplified analogs for mine roof behavior,this study presents a unique numerical model that more completely represents in-situ roof conditions.The discrete element method(DEM)is utilized to conduct a parametric analysis considering a range of in-situ stress ratios,material properties,and joint networks to determine the parameters controlling the stability of single-entries modeled in two-dimensions.Model results are compared to empirical observations of roof-support effectiveness(ARBS)in the context of the coal mine roof rating(CMRR)system.Results such as immediate roof displacement,overall stability,and statistical relationships between model parameters and outcomes are presented herein.Potential practical applications of this line of research include:(1)roof-support optimization for a range of coal-measure rocks,(2)establishment of a relationship between roof stability and pillar stress,and(3)determination of which parameters are most critical to roof stability and therefore require concentrated evaluation.
基金supported by the National Natural Science Foundation of China (Grant No 10447141)the Youth Foundation of Beijing University of Chemical Technology,China (Grant No QN0622)
文摘In this paper, the network equation for the slow neutron capture process （s-process） of heavy element nucleosynthesis is investigated. Dividing the s-process network reaction chains into two standard forms and using the technique of matrix decomposition, a group of analytical solutions for the network equation are obtained. With the analytical solutions, a calculation for heavy element abundance of the solar system is carried out and the results are in good agreement with the astrophysical measurements.
文摘Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has been an excellent supplement,but the computation is time-consuming.Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model.A large number of finite element models were developed;the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples.Study focused on steel 4340tpolyurea laminates with various configurations.Four different 3D shapes of the projectiles were modeled and used in the training.The trained neural network and decision tree model was tested using independently generated test samples using finite element models.The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models.Additionally,both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples.Performance of both the models was evaluated and compared.Models trained with Finite element simulation data samples were found capable to give more accurate predication,compared to the models trained with experimental data,because finite element modeling can generate much larger training set,and thus finite element solvers can serve as an excellent teacher.This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.
文摘为解决SONiC(software for open networking in the cloud)交换机操作系统对多模态网络(polymor phic network,PINet)中模态适配及模态管控问题,提出了一个基于P4Runtime的SONiC网元控制通道容器p4runtime-pins,使多模态网元设备可以支持多种网络模态流表的配置。p4runtime-pins容器通过gRPC服务模块实现与控制器的连接,使用邻近网元发现算法实现控制器对链路的发现。设计了网元端口更新算法解决了网元设备在实际应用环境中存在的端口变更问题。同时,针对SONiC网元交换机中硬件转发处理单元存在的流表支持性差异问题,设计了内部流表转存和gRPC网元代理功能,实现了不同网络模态流表的部署。实验结果表明,p4runtime-pins容器资源消耗低,仅占用了1.70%的CPU资源和0.45%的内存资源。同时,部署p4runtime-pins容器的SONiC网元设备能够准确地接收并配置控制器下发的流表规则,流表配置延迟仅为0.027~0.037 s。
