A passive simulation method based on the six degrees of freedom(6-DOF)model and dynamic mesh is proposed according to the working principle to study the dynamic characteristics of the turbine flow sensors.This simulat...A passive simulation method based on the six degrees of freedom(6-DOF)model and dynamic mesh is proposed according to the working principle to study the dynamic characteristics of the turbine flow sensors.This simulation method controls the six degrees of freedom of the impeller using the user-defined functions(UDF)program so that it can only rotate under the impact of fluid.The impeller speed can be calculated in real-time,and the inlet speed can be set with time to obtain the dynamic performance of the turbine flow sensors.Based on this simulation method,three turbine flow sensors with different diameters were simulated,and the reliability of the simulation method was verified by both steady-state and unsteady-state experiments.The results show that the trend of meter factor with flow rate acquired from the simulation is close to the experimental results.The deviation between the simulation and experiment results is low,with a maximum deviation of 2.88%.In the unsteady simulation study,the impeller speed changed with the inlet velocity of the turbine flow sensor,showing good tracking performance.The passive simulation method can be used to predict the dynamic performance of the turbine flow sensor.展开更多
The multi-dimensional time-domain computational fluid dynamics(CFD) approach is extended to calculate the acoustic attenuation performance of water-filled piping silencers. Transmission loss predictions from the time-...The multi-dimensional time-domain computational fluid dynamics(CFD) approach is extended to calculate the acoustic attenuation performance of water-filled piping silencers. Transmission loss predictions from the time-domain CFD approach and the frequency-domain finite element method(FEM) agree well with each other for the dual expansion chamber silencer, straight-through and cross-flow perforated tube silencers without flow. Then, the time-domain CFD approach is used to investigate the effect of flow on the acoustic attenuation characteristics of perforated tube silencers. The numerical predictions demonstrate that the mean flow increases the transmission loss, especially at higher frequencies, and shifts the transmission loss curve to lower frequencies.展开更多
The last half-century was transformed by the electronic revolution that essentially reproduced the human brain and its computing capacity on a chip. But over time, scientists have realized that something was missing t...The last half-century was transformed by the electronic revolution that essentially reproduced the human brain and its computing capacity on a chip. But over time, scientists have realized that something was missing to give life, so to speak, to the small chip with a brain： One needed to awaken its senses and develop its muscles! This challenge was solved through MEMS （micro electro mechanical systems）. Indeed, MEMS today are equipped with the sense of sight, smell, hearing, taste and touch through microsensors. They are also capable of physical exertion through small muscles called microactuators. These new capabilities open wide fields of imagination and important specific applications.展开更多
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage...Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.展开更多
基金The National Natural Science Foundation of China(No.62173122)the Hebei Key Project of Natural Science Foundation(No.F2021201031)。
文摘A passive simulation method based on the six degrees of freedom(6-DOF)model and dynamic mesh is proposed according to the working principle to study the dynamic characteristics of the turbine flow sensors.This simulation method controls the six degrees of freedom of the impeller using the user-defined functions(UDF)program so that it can only rotate under the impact of fluid.The impeller speed can be calculated in real-time,and the inlet speed can be set with time to obtain the dynamic performance of the turbine flow sensors.Based on this simulation method,three turbine flow sensors with different diameters were simulated,and the reliability of the simulation method was verified by both steady-state and unsteady-state experiments.The results show that the trend of meter factor with flow rate acquired from the simulation is close to the experimental results.The deviation between the simulation and experiment results is low,with a maximum deviation of 2.88%.In the unsteady simulation study,the impeller speed changed with the inlet velocity of the turbine flow sensor,showing good tracking performance.The passive simulation method can be used to predict the dynamic performance of the turbine flow sensor.
基金Project(11174065)supported by the National Natural Science Foundation of China
文摘The multi-dimensional time-domain computational fluid dynamics(CFD) approach is extended to calculate the acoustic attenuation performance of water-filled piping silencers. Transmission loss predictions from the time-domain CFD approach and the frequency-domain finite element method(FEM) agree well with each other for the dual expansion chamber silencer, straight-through and cross-flow perforated tube silencers without flow. Then, the time-domain CFD approach is used to investigate the effect of flow on the acoustic attenuation characteristics of perforated tube silencers. The numerical predictions demonstrate that the mean flow increases the transmission loss, especially at higher frequencies, and shifts the transmission loss curve to lower frequencies.
文摘The last half-century was transformed by the electronic revolution that essentially reproduced the human brain and its computing capacity on a chip. But over time, scientists have realized that something was missing to give life, so to speak, to the small chip with a brain： One needed to awaken its senses and develop its muscles! This challenge was solved through MEMS （micro electro mechanical systems）. Indeed, MEMS today are equipped with the sense of sight, smell, hearing, taste and touch through microsensors. They are also capable of physical exertion through small muscles called microactuators. These new capabilities open wide fields of imagination and important specific applications.
基金supported by the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81773634 to M.Z. and 81430084 to K.C.)the “Personalized Medicines-Molecular Signature-based Drug Discovery and Development”, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.)+1 种基金National Key Research & Development Plan (2016YFC1201003 to M.Z.)the National Basic Research Program (2015CB910304 to X.L.)
文摘Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.
