In order to realize seedbed mechanization of whole plastic-film mulching on double ridges and to overcome the difficulty in crosswise belt type soil covering by whole plastic-film,a kind of crosswise belt type whole p...In order to realize seedbed mechanization of whole plastic-film mulching on double ridges and to overcome the difficulty in crosswise belt type soil covering by whole plastic-film,a kind of crosswise belt type whole plastic-film ridging-mulching corn seeder on double ridges was designed in this study.The key components of the sample machine was designed and its working parameters of seedbed soil covering device,crosswise-belt soil covering mechanism and profiling sowing depth adjustment device were determined.After numerical simulation on the film edge and crosswise soil covering by whole plastic-film on double ridges by discrete element method,the velocity and displacement of the oscillating plate,and the variation rule of amount of covered soil with time were explored.Field test results show that,when the advancing velocity of the machine was 0.50 m/s,the qualified rate of soil width covered on film edge of the seedbed reached 96.1%,qualified rate of crosswise soil belt width was 94.5%,qualified rate of soil thickness on seedbed was 95.3%,qualified rate of sowing depth was 89.3%,qualified rate of spacing between crosswise soil belts reached 93.6%,which all met related standards in China and satisfied design requirements,and could realize seedbed mechanization of whole plastic-film mulching on double ridges.Comparison tests on working performances of practical soil covering show a basic consistence with the seedbed soil covering simulation,and verified the effectiveness of the soil covering model built by using discrete element method.展开更多
In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most ...In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most complex components in aero-engine design.Therefore,it is interesting to adopt the existing AI technologies in the design of the cooling passages.Given that the application of AI relies on a large amount of data,the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning.It includes the parameterized three-dimensional(3-D)geometrical modeling,automatic meshing and computational fluid dynamics(CFD)batch automatic simulation of different film cooling structures through customized developments of UG,ICEM and Fluent.It is demonstrated that the trained artificial neural network(ANN)can predict the cooling effectiveness on the external surface of the turbine vane.The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model.Using this established method,a multi-output model is constructed based on which a simple tool can be developed.It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.展开更多
Residual films on the sowing layer produced after mulching in Xinjiang farmland,harm the sowing quality and root growth of crops.In this study,a sowing layer residual film recovery machine based on a radial plate arc-...Residual films on the sowing layer produced after mulching in Xinjiang farmland,harm the sowing quality and root growth of crops.In this study,a sowing layer residual film recovery machine based on a radial plate arc-shaped nail-tooth roller structure was designed.Meanwhile,the key device structures were designed and the main working parameters were analyzed.Then,taking the working depth,the forward speed of the machine and the rotation speed of the nail tooth roller as the test factors,and the film collection rate and film intertwining rate as the test indicators,the single factor tests and the Box-Behnken response surface tests were carried out to evaluate the performance of the sowing layer residual film recovery machine.Consequently,the results showed that the order of significant factors was the working depth,the forward speed of the machine,and the rotation speed of the nail tooth roller.Besides,the optimal working parameters were determined,which the working depth,the forward speed of the machine,and the rotation speed of the nail tooth roller were 100 mm,4.8 km/h,and 49.3 r/min,respectively.Moreover,the predicted value of the film collection rate was 69.20%.Finally,the verification test was taken with the optimal working parameter,and the results showed that the film collection rate was 66.84%,and the film intertwining rate was 1.39%.The relative error between the test value and the predicted value of the film collection rate was 3.40%.It indicated that the machine can perform the collection of sowing layer residual films.This study can provide a theoretical basis and reference for the design of new sowing layer residual film machines.展开更多
Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated(EHL)contacts.In this contribution,we demonstr...Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated(EHL)contacts.In this contribution,we demonstrate that machine learning(ML)and artificial intelligence(AI)approaches(support vector machines,Gaussian process regressions,and artificial neural networks)can predict relevant film parameters more efficiently and with higher accuracy and flexibility compared to sophisticated EHL simulations and analytically solvable proximity equations,respectively.For this purpose,we use data from EHL simulations based upon the full-system finite element(FE)solution and a Latin hypercube sampling.We verify that the original input data are required to train ML approaches to achieve coefficients of determination above 0.99.It is revealed that the architecture of artificial neural networks(neurons per layer and number of hidden layers)and activation functions influence the prediction accuracy.The impact of the number of training data is exemplified,and recommendations for a minimum database size are given.We ultimately demonstrate that artificial neural networks can predict the locally-resolved film thickness values over the contact domain 25-times faster than FE-based EHL simulations(R^(2) values above 0.999).We assume that this will boost the use of ML approaches to predict EHL parameters and traction losses in multibody system dynamics simulations.展开更多
基金The authors acknowledge that this work was financially supported by National Natural Science Foundation of China(Grant No.51775115No.51405086)China Agriculture Research System(CARS-14-1-28).
文摘In order to realize seedbed mechanization of whole plastic-film mulching on double ridges and to overcome the difficulty in crosswise belt type soil covering by whole plastic-film,a kind of crosswise belt type whole plastic-film ridging-mulching corn seeder on double ridges was designed in this study.The key components of the sample machine was designed and its working parameters of seedbed soil covering device,crosswise-belt soil covering mechanism and profiling sowing depth adjustment device were determined.After numerical simulation on the film edge and crosswise soil covering by whole plastic-film on double ridges by discrete element method,the velocity and displacement of the oscillating plate,and the variation rule of amount of covered soil with time were explored.Field test results show that,when the advancing velocity of the machine was 0.50 m/s,the qualified rate of soil width covered on film edge of the seedbed reached 96.1%,qualified rate of crosswise soil belt width was 94.5%,qualified rate of soil thickness on seedbed was 95.3%,qualified rate of sowing depth was 89.3%,qualified rate of spacing between crosswise soil belts reached 93.6%,which all met related standards in China and satisfied design requirements,and could realize seedbed mechanization of whole plastic-film mulching on double ridges.Comparison tests on working performances of practical soil covering show a basic consistence with the seedbed soil covering simulation,and verified the effectiveness of the soil covering model built by using discrete element method.
基金the Program for National Natural Science Foundation of China(51876005).
文摘In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most complex components in aero-engine design.Therefore,it is interesting to adopt the existing AI technologies in the design of the cooling passages.Given that the application of AI relies on a large amount of data,the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning.It includes the parameterized three-dimensional(3-D)geometrical modeling,automatic meshing and computational fluid dynamics(CFD)batch automatic simulation of different film cooling structures through customized developments of UG,ICEM and Fluent.It is demonstrated that the trained artificial neural network(ANN)can predict the cooling effectiveness on the external surface of the turbine vane.The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model.Using this established method,a multi-output model is constructed based on which a simple tool can be developed.It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.
基金the National Natural Science Foundation of China(Grant No.52175240)the Major Scientific and Technological Projects of Xinjiang Production and Construction Corps(2018AA001/03)the Graduate Education Innovation Project of Xinjiang Uygur Autonomous Region(Grant No.XJ2022G083).
文摘Residual films on the sowing layer produced after mulching in Xinjiang farmland,harm the sowing quality and root growth of crops.In this study,a sowing layer residual film recovery machine based on a radial plate arc-shaped nail-tooth roller structure was designed.Meanwhile,the key device structures were designed and the main working parameters were analyzed.Then,taking the working depth,the forward speed of the machine and the rotation speed of the nail tooth roller as the test factors,and the film collection rate and film intertwining rate as the test indicators,the single factor tests and the Box-Behnken response surface tests were carried out to evaluate the performance of the sowing layer residual film recovery machine.Consequently,the results showed that the order of significant factors was the working depth,the forward speed of the machine,and the rotation speed of the nail tooth roller.Besides,the optimal working parameters were determined,which the working depth,the forward speed of the machine,and the rotation speed of the nail tooth roller were 100 mm,4.8 km/h,and 49.3 r/min,respectively.Moreover,the predicted value of the film collection rate was 69.20%.Finally,the verification test was taken with the optimal working parameter,and the results showed that the film collection rate was 66.84%,and the film intertwining rate was 1.39%.The relative error between the test value and the predicted value of the film collection rate was 3.40%.It indicated that the machine can perform the collection of sowing layer residual films.This study can provide a theoretical basis and reference for the design of new sowing layer residual film machines.
基金support from Pontificia Universidad Católica de Chile.A.Rosenkranz gratefully acknowledges the financial support given by ANID(Chile)in the framework of the Fondecyt projects(Nos.11180121 and EQM190057)Additionally,A.Rosenkranz acknowledges the financial support given by the VID of the University of Chile within the project U-Moderniza(No.UM-04/19).
文摘Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated(EHL)contacts.In this contribution,we demonstrate that machine learning(ML)and artificial intelligence(AI)approaches(support vector machines,Gaussian process regressions,and artificial neural networks)can predict relevant film parameters more efficiently and with higher accuracy and flexibility compared to sophisticated EHL simulations and analytically solvable proximity equations,respectively.For this purpose,we use data from EHL simulations based upon the full-system finite element(FE)solution and a Latin hypercube sampling.We verify that the original input data are required to train ML approaches to achieve coefficients of determination above 0.99.It is revealed that the architecture of artificial neural networks(neurons per layer and number of hidden layers)and activation functions influence the prediction accuracy.The impact of the number of training data is exemplified,and recommendations for a minimum database size are given.We ultimately demonstrate that artificial neural networks can predict the locally-resolved film thickness values over the contact domain 25-times faster than FE-based EHL simulations(R^(2) values above 0.999).We assume that this will boost the use of ML approaches to predict EHL parameters and traction losses in multibody system dynamics simulations.
