In terms of multiple temporal and spatial scales, massive data from experiments, flow field measurements, and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics. Machine...In terms of multiple temporal and spatial scales, massive data from experiments, flow field measurements, and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics. Machine Learning(ML) provides a wealth of analysis methods to extract potential information from a large amount of data for in-depth understanding of the underlying flow mechanism or for further applications. Furthermore, machine learning algorithms can enhance flow information and automatically perform tasks that involve active flow control and optimization. This article provides an overview of the past history, current development, and promising prospects of machine learning in the field of fluid mechanics. In addition, to facilitate understanding, this article outlines the basic principles of machine learning methods and their applications in engineering practice, turbulence models, flow field representation problems, and active flow control. In short, machine learning provides a powerful and more intelligent data processing architecture, and may greatly enrich the existing research methods and industrial applications of fluid mechanics.展开更多
The recently developed data-driven approach can establish the material law for nonlinear elastic composite materials(especially newly developed materials)by the generated stress-strain data under different loading pat...The recently developed data-driven approach can establish the material law for nonlinear elastic composite materials(especially newly developed materials)by the generated stress-strain data under different loading paths(Computational Mechanics,2019).Generally,the displacement(or strain)fields can be obtained relatively easier using digital image correlation(DIC)technique experimentally,but the stress field is hard to be measured.This situation limits the applicability of the proposed data-driven approach.In this paper,a method based on artificial neural network(ANN)to identify stress fields and further obtain the material law of nonlinear elastic materials is presented,which can make the proposed data-driven approach more practical.A numerical example is given to prove the validity of the method.The limitations of the proposed approach are also discussed.展开更多
In this paper, the hydrodynamic characteristics of water flow in Chaohu Lake are studied by using the finite volume coastal ocean model（FVCOM）, which is verified by the observed data. The typical flow field and the ...In this paper, the hydrodynamic characteristics of water flow in Chaohu Lake are studied by using the finite volume coastal ocean model（FVCOM）, which is verified by the observed data. The typical flow field and the 3-D flow structure are obtained for the lake. The flow fields under extreme conditions are analyzed to provide a prospective knowledge of the water exchange and the transport process.The influence of the wind on the flow is determined by the cross spectrum method. The results show that the wind-driven flow dominates most area of the lake. Under prevailing winds in summer and winter, the water flows towards the downwind side at the upper layer while towards the upwind side at the lower layer in most area except that around the Chaohu Sluice. The extreme wind speed is not favorable for the water exchange while the sluice＇s releasing water accelerates the process. The water velocity in the lake is closely related with the wind speed.展开更多
The production-oriented approach （POA） has been developed over a decade. It is driven by the need to improve English classroom instruction for university students in China （Wen, 2016）. It is also motivated by the ...The production-oriented approach （POA） has been developed over a decade. It is driven by the need to improve English classroom instruction for university students in China （Wen, 2016）. It is also motivated by the aspiration to enhance the quality of foreign language education in other similar pedagogical contexts outside China. A volume of research has been done by Wen Qiufang and her research team, to formulate the theory of POA and to test its effectiveness in classroom pedagogy （e.g. Wen, 2016, 2015; Yang, 2015; Zhang, 2015）. At the moment, the POA is still at an early stage of theory building and almost all empirical research is done in the Chinese context. In order to improve the quality of this theory and to make it intelligible to the international academic community, a one-day symposium was held in Beijing Foreign Studies University on May 15, 2017. The symposium was entitled ＇The first international forum on innovative foreign language education in China： Appraisal of the POA＇. In the forum, leading experts in applied linguistics were invited to discuss the strengths and weaknesses of the POA and the directions for its future development. The symposium was the first attempt for the POA research team to discuss its latest work with international scholars. This Viewpoint section collects the responses of four experts who participated in the symposium, listed in alphabetical order. The collection of articles covers three topics related to the POA： its pedagogical application, its use for teacher training, and its research. Alister Cumming is Professor Emeritus and the former Head of the Centre for Educational Research on Languages and Literacies, University of Toronto, Canada. His article focuses primarily on POA research as an exemplary case of design-based research. Rod Ellis is Research Professor in the School of Education at Curtin University, Australia. He discusses POA in terms of pedagogy, teacher training and research, with both critiques and constructive suggestions. Paul Kei M展开更多
Understanding the interface effect in dielectric nanocomposites is crucial to the enhancement of their performance.In this work,a data-driven interface design strategy based on high-throughput phase-field simulations ...Understanding the interface effect in dielectric nanocomposites is crucial to the enhancement of their performance.In this work,a data-driven interface design strategy based on high-throughput phase-field simulations is developed to study the interface effect and then optimize the permittivity and breakdown strength of nanocomposites.Here,we use two microscopic features that are closely related to the macroscopic dielectric properties,the thickness and permittivity of the interface phases,to evaluate the role of interfaces in experimental configuration,and thus provide quantitative design schemes for the interfacial phases.Taking the polyvinyl difluoride(PVDF)-BaTiO_(3) nanocomposite as an example,the calculation results demonstrate that the interfacial polarization could account for up to 83.6% of the increase in the experimentally measured effective permittivity of the nanocomposite.Based on the interface optimized strategy,a maximum enhancement of ~156% in the energy density could be achieved by introducing an interface phase with d/r=0.55 and ε_(interface)/ε_(filler)=0:036,compared to the pristine nanocomposite.Overall,the present work not only provides fundamental understanding of the interface effect in dielectric nanocomposites,but also establishes a powerful data-driven interface design framework for such materials that could also be easily generalized and applied to study interface issues in other functional nanocomposites,such as solid electrolytes and thermoelectrics.展开更多
Accurate measurements of physical parameters in a scramjet isolator are very important to promote the design and optimization of the isolator and even the scramjet.In a ground experiment,limited by the inherent charac...Accurate measurements of physical parameters in a scramjet isolator are very important to promote the design and optimization of the isolator and even the scramjet.In a ground experiment,limited by the inherent characteristics of measurement technology and equipment,it is a big challenge to obtain the velocity field inside an isolator.In this study,a deep learning approach was introduced to combine data obtained from ground experiments and numerical simulations,and a velocity field prediction model was developed for obtaining the velocity field inside an isolator based on experimental Schlieren images.The velocity field prediction model was designed with convolutional neural networks as the main structure.Ground experiments of a scramjet isolator under continuous Mach number variation were carried out,and Schlieren images of the flow field inside the isolator were collected.Numerical simulations of the isolator were also carried out,and the velocity fields inside the isolator under various Mach numbers were obtained.The velocity field prediction model was trained using flow field datasets containing experimental Schlieren images and velocity field,and the mapping relationship between the experimental Schlieren images and the predicted velocity field was successfully established.展开更多
With ificreasing demand for large cylindrical forgings, a new technology--electroslag remelting （ESR） for direct manufacture of hollow ingots rather than solid ingots has been developed. The main features of the pro...With ificreasing demand for large cylindrical forgings, a new technology--electroslag remelting （ESR） for direct manufacture of hollow ingots rather than solid ingots has been developed. The main features of the process include a T-shaped current supplying mould （CSM）, double power supply, an ingot withdrawing system, a metal level automatic control system based on a level sensor using the electromagnetic eddy current method, and the exchange of a consumable multi-electrode. ANSYS software was used to calculate the fluid flow and heat transfer in the slag bath 1 and metal pool of this ESR hollow ingot process with its T-shaped CSM. The mathematmal model was Verified by measuring the geometry of the liquid metal pool as observed in the macrostructure of 4650 mm （external diameter）/ 4450 mm （internal diameter） hollow ingots by sulphur print method： the. observed shape and depth of the s!ag bath were consistent with the simulated results. Simulation of the ESR process can improve understanding of the process and allow better operating parameters to be selected.展开更多
The hydrothermal wave was investigated numerically for large-Prandtl-number fluid (Pr = 105.6) in a shallow cavity with different heated sidewalls. The traveling wave appears and propagates in the direction opposite t...The hydrothermal wave was investigated numerically for large-Prandtl-number fluid (Pr = 105.6) in a shallow cavity with different heated sidewalls. The traveling wave appears and propagates in the direction opposite to the surface flow (upstream) in the case of zero gravity when the applied temperature difference grows and over the critical value. The phase relationships of the disturbed velocity,temperature and pressure demonstrate that the traveling wave is driven by the disturbed tem-perature,which is named hydrothermal wave. The hydrothermal wave is so weak that the oscillatory flow field and temperature distribution can hardly be observed in the liquid layer. The exciting mechanism of hydrothermal wave is analyzed and discussed in the present paper.展开更多
基金supported by the National Natural Science Foundation of China(No.11972139)。
文摘In terms of multiple temporal and spatial scales, massive data from experiments, flow field measurements, and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics. Machine Learning(ML) provides a wealth of analysis methods to extract potential information from a large amount of data for in-depth understanding of the underlying flow mechanism or for further applications. Furthermore, machine learning algorithms can enhance flow information and automatically perform tasks that involve active flow control and optimization. This article provides an overview of the past history, current development, and promising prospects of machine learning in the field of fluid mechanics. In addition, to facilitate understanding, this article outlines the basic principles of machine learning methods and their applications in engineering practice, turbulence models, flow field representation problems, and active flow control. In short, machine learning provides a powerful and more intelligent data processing architecture, and may greatly enrich the existing research methods and industrial applications of fluid mechanics.
基金the support from the National Natural Science Foundation of China (Grant 11872139)the support from the National Natural Science Foundation of China (Grants 11732004 and 11821202)Program for Changjiang Scholars, Innovative Research Team in University (PCSIRT)
文摘The recently developed data-driven approach can establish the material law for nonlinear elastic composite materials(especially newly developed materials)by the generated stress-strain data under different loading paths(Computational Mechanics,2019).Generally,the displacement(or strain)fields can be obtained relatively easier using digital image correlation(DIC)technique experimentally,but the stress field is hard to be measured.This situation limits the applicability of the proposed data-driven approach.In this paper,a method based on artificial neural network(ANN)to identify stress fields and further obtain the material law of nonlinear elastic materials is presented,which can make the proposed data-driven approach more practical.A numerical example is given to prove the validity of the method.The limitations of the proposed approach are also discussed.
基金supported by the Special Foundation (Class D) of "Hundred Talents Program" of Chinese Academy of Sciences
文摘In this paper, the hydrodynamic characteristics of water flow in Chaohu Lake are studied by using the finite volume coastal ocean model（FVCOM）, which is verified by the observed data. The typical flow field and the 3-D flow structure are obtained for the lake. The flow fields under extreme conditions are analyzed to provide a prospective knowledge of the water exchange and the transport process.The influence of the wind on the flow is determined by the cross spectrum method. The results show that the wind-driven flow dominates most area of the lake. Under prevailing winds in summer and winter, the water flows towards the downwind side at the upper layer while towards the upwind side at the lower layer in most area except that around the Chaohu Sluice. The extreme wind speed is not favorable for the water exchange while the sluice＇s releasing water accelerates the process. The water velocity in the lake is closely related with the wind speed.
文摘The production-oriented approach （POA） has been developed over a decade. It is driven by the need to improve English classroom instruction for university students in China （Wen, 2016）. It is also motivated by the aspiration to enhance the quality of foreign language education in other similar pedagogical contexts outside China. A volume of research has been done by Wen Qiufang and her research team, to formulate the theory of POA and to test its effectiveness in classroom pedagogy （e.g. Wen, 2016, 2015; Yang, 2015; Zhang, 2015）. At the moment, the POA is still at an early stage of theory building and almost all empirical research is done in the Chinese context. In order to improve the quality of this theory and to make it intelligible to the international academic community, a one-day symposium was held in Beijing Foreign Studies University on May 15, 2017. The symposium was entitled ＇The first international forum on innovative foreign language education in China： Appraisal of the POA＇. In the forum, leading experts in applied linguistics were invited to discuss the strengths and weaknesses of the POA and the directions for its future development. The symposium was the first attempt for the POA research team to discuss its latest work with international scholars. This Viewpoint section collects the responses of four experts who participated in the symposium, listed in alphabetical order. The collection of articles covers three topics related to the POA： its pedagogical application, its use for teacher training, and its research. Alister Cumming is Professor Emeritus and the former Head of the Centre for Educational Research on Languages and Literacies, University of Toronto, Canada. His article focuses primarily on POA research as an exemplary case of design-based research. Rod Ellis is Research Professor in the School of Education at Curtin University, Australia. He discusses POA in terms of pedagogy, teacher training and research, with both critiques and constructive suggestions. Paul Kei M
基金supported by Basic Science Center Program of NSFC(Grant No.51788104)the NSF of China(Grant No.51625202,and 51572141)the National Key Research and Development Program(Grant No.2017YFB0701603).
文摘Understanding the interface effect in dielectric nanocomposites is crucial to the enhancement of their performance.In this work,a data-driven interface design strategy based on high-throughput phase-field simulations is developed to study the interface effect and then optimize the permittivity and breakdown strength of nanocomposites.Here,we use two microscopic features that are closely related to the macroscopic dielectric properties,the thickness and permittivity of the interface phases,to evaluate the role of interfaces in experimental configuration,and thus provide quantitative design schemes for the interfacial phases.Taking the polyvinyl difluoride(PVDF)-BaTiO_(3) nanocomposite as an example,the calculation results demonstrate that the interfacial polarization could account for up to 83.6% of the increase in the experimentally measured effective permittivity of the nanocomposite.Based on the interface optimized strategy,a maximum enhancement of ~156% in the energy density could be achieved by introducing an interface phase with d/r=0.55 and ε_(interface)/ε_(filler)=0:036,compared to the pristine nanocomposite.Overall,the present work not only provides fundamental understanding of the interface effect in dielectric nanocomposites,but also establishes a powerful data-driven interface design framework for such materials that could also be easily generalized and applied to study interface issues in other functional nanocomposites,such as solid electrolytes and thermoelectrics.
基金supported by the National Natural Science Foundation of China(No.52125603).
文摘Accurate measurements of physical parameters in a scramjet isolator are very important to promote the design and optimization of the isolator and even the scramjet.In a ground experiment,limited by the inherent characteristics of measurement technology and equipment,it is a big challenge to obtain the velocity field inside an isolator.In this study,a deep learning approach was introduced to combine data obtained from ground experiments and numerical simulations,and a velocity field prediction model was developed for obtaining the velocity field inside an isolator based on experimental Schlieren images.The velocity field prediction model was designed with convolutional neural networks as the main structure.Ground experiments of a scramjet isolator under continuous Mach number variation were carried out,and Schlieren images of the flow field inside the isolator were collected.Numerical simulations of the isolator were also carried out,and the velocity fields inside the isolator under various Mach numbers were obtained.The velocity field prediction model was trained using flow field datasets containing experimental Schlieren images and velocity field,and the mapping relationship between the experimental Schlieren images and the predicted velocity field was successfully established.
基金Item Sponsored by National Natural Science Foundation of China(51204041)National High Technology Research and Development Program(863 Program) of China(2012AA03A502)+1 种基金Fundamental Research Funds for the Central Universities of China(N130402016)Program for Liaoning's Innovative Research Team in University of China(LT20120008)
文摘With ificreasing demand for large cylindrical forgings, a new technology--electroslag remelting （ESR） for direct manufacture of hollow ingots rather than solid ingots has been developed. The main features of the process include a T-shaped current supplying mould （CSM）, double power supply, an ingot withdrawing system, a metal level automatic control system based on a level sensor using the electromagnetic eddy current method, and the exchange of a consumable multi-electrode. ANSYS software was used to calculate the fluid flow and heat transfer in the slag bath 1 and metal pool of this ESR hollow ingot process with its T-shaped CSM. The mathematmal model was Verified by measuring the geometry of the liquid metal pool as observed in the macrostructure of 4650 mm （external diameter）/ 4450 mm （internal diameter） hollow ingots by sulphur print method： the. observed shape and depth of the s!ag bath were consistent with the simulated results. Simulation of the ESR process can improve understanding of the process and allow better operating parameters to be selected.
基金Supported by the National Natural Science Foundation of China (Grant No. 10432060)
文摘The hydrothermal wave was investigated numerically for large-Prandtl-number fluid (Pr = 105.6) in a shallow cavity with different heated sidewalls. The traveling wave appears and propagates in the direction opposite to the surface flow (upstream) in the case of zero gravity when the applied temperature difference grows and over the critical value. The phase relationships of the disturbed velocity,temperature and pressure demonstrate that the traveling wave is driven by the disturbed tem-perature,which is named hydrothermal wave. The hydrothermal wave is so weak that the oscillatory flow field and temperature distribution can hardly be observed in the liquid layer. The exciting mechanism of hydrothermal wave is analyzed and discussed in the present paper.
