In this paper,the field synergy principle is firstly performed on the viscoelastic fluid-based nanofluid and other relevant fluid in channel at turbulent flow state to scrutinize their heat transfer performance based ...In this paper,the field synergy principle is firstly performed on the viscoelastic fluid-based nanofluid and other relevant fluid in channel at turbulent flow state to scrutinize their heat transfer performance based on our direct numerical simulation database.The cosine values of intersection angle between velocity vector and temperature gradient vector are calculated for different simulated cases with varying nanoparticle volume fraction,nanoparticle diameter,Reynolds number and Weissenberg number.It is found that the filed synergy effect is enhanced when the nanoparticle volume fraction is increased,nanoparticle diameter is decreased and Weissenberg number is decreased,i.e.the heat transfer is also enhanced.However,the filed synergy effect is weakened with the increase of Reynolds number which may be the possible reason for the power function relationship in empirical correlation of heat transfer between heat transfer performance and Reynolds number with the constant power exponent lower than 1.Finally,it is also observed that the field synergy principle can be used to analyze the heat transfer process of viscoelastic fluid-based nanofluid at the turbulent flow state even if some negative cosine values of intersection angle exist in the flow field.展开更多
High volume conveyor systems in distribution centers have very large footprint and can handlelarge volumes and hold thousands of items.Traditional discrete-event cell-based approach to simulatesuch networks becomes co...High volume conveyor systems in distribution centers have very large footprint and can handlelarge volumes and hold thousands of items.Traditional discrete-event cell-based approach to simulatesuch networks becomes computationally challenging.An alternative approach,in which the traffic isrepresented by segments of fluid flow of different density instead of individual packages,is presentedin this paper to address this challenge.The proposed fluid-based simulation approach is developedusing a Hybrid Petri Nets framework.The underlying model is a combination of an extension of aBatches Petri Nets(BPN)and a Stochastic Petri Nets(SPN).The extensions are in the inclusion ofrandom elements and relaxation of certain structural constraints.Some adaptations are also made to fitthe target system modeling.The approach is presented with an example.展开更多
Inertial navigation represents a unique method of navigation,in which there is no dependency on external sources of information.As opposed to other position fixing navigation techniques,inertial navigation performs th...Inertial navigation represents a unique method of navigation,in which there is no dependency on external sources of information.As opposed to other position fixing navigation techniques,inertial navigation performs the navigation in a relative sense with respect to the initial navigation state of the moving platform.Hence,inertial navigation systems are not prone to jamming,or spoofing.Inertial navigation systems have developed vastly,from their occurrence in the 1940s up to date.The accuracy of the inertial sensors has improved over time,making inertial sensors sufficient in terms of size,weight,cost,and accuracy for navigation and guidance applications.Within the past few years,inertial sensors have developed from being purely mechanical into incorporating various technologies and taking advantage of numerous physical phenomena,from which the dynamic forces exerted on a moving body could be computed accurately.Besides,the evolution of inertial navigation scheme involved the evolution from stable-platform inertial navigation system,which were mechanically complicated,to computationally demanding strap-down inertial navigation systems.Optical sensory technologies have provided highly accurate inertial sensors,at smaller sizes.Besides,the vibratory inertial navigation technologies enabled the production of Micro-electro-machined inertial sensors that are extremely low-cost,and offer extremely low size,weight and power consumption,making them suitable for a wide range of day-to-day navigation applications.Recently,advanced inertial sensor technologies have been introduced to the industry such as nuclear magnetic resonance technology,coldatom technology,and the reintroduction of fluid-based inertial sensors.On another note,inertial sensor errors constitute a huge research aspect in which it is intended for inertial sensors to reach level in which they could operate for substantially long operation times in the absence of updates from aiding sensors,which would be a huge leap.Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors.In this review,the inertial sensor technologies are covered extensively,along the future trends in the inertial sensors’technologies.Besides,this review covers a brief overview on the inertial error modeling techniques used to enhance the performance of low-cost sensors.展开更多
Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological an...Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications.However,high predicted accuracy by the traditional sequential method requires a large amount of experimental data,which is not practical in some situations.To conquer these problems,artificial intelligence has promoted the rapid development of material science in recent years,bringing hope to solve these challenges.Here we propose a Kriging machine learning model with an active candidate region,which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region,to predict the mechanical and rheolo-gical performance of liquid gating system with an active minimal size of ex-perimental data.Besides this,this new machine learning model can instruct our experiments with optimal size.The methods are then verified by liquid gating membrane with magnetorheological fluids,which would be of wide interest for the design of potential liquid gating applications in drug release,microfluidic logic,dynamic fluid control,and beyond.展开更多
基金supported by China Postdoctoral Science Foundation(Grant No.2014M561037)President Fund of University of Chinese Academy of Sciences(Grant No.Y3510213N00)+2 种基金National Natural Science Foundation of China(Grant No.51276046)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20112302110020)National Natural Science Foundation of China(Grant No.51325603)
文摘In this paper,the field synergy principle is firstly performed on the viscoelastic fluid-based nanofluid and other relevant fluid in channel at turbulent flow state to scrutinize their heat transfer performance based on our direct numerical simulation database.The cosine values of intersection angle between velocity vector and temperature gradient vector are calculated for different simulated cases with varying nanoparticle volume fraction,nanoparticle diameter,Reynolds number and Weissenberg number.It is found that the filed synergy effect is enhanced when the nanoparticle volume fraction is increased,nanoparticle diameter is decreased and Weissenberg number is decreased,i.e.the heat transfer is also enhanced.However,the filed synergy effect is weakened with the increase of Reynolds number which may be the possible reason for the power function relationship in empirical correlation of heat transfer between heat transfer performance and Reynolds number with the constant power exponent lower than 1.Finally,it is also observed that the field synergy principle can be used to analyze the heat transfer process of viscoelastic fluid-based nanofluid at the turbulent flow state even if some negative cosine values of intersection angle exist in the flow field.
文摘High volume conveyor systems in distribution centers have very large footprint and can handlelarge volumes and hold thousands of items.Traditional discrete-event cell-based approach to simulatesuch networks becomes computationally challenging.An alternative approach,in which the traffic isrepresented by segments of fluid flow of different density instead of individual packages,is presentedin this paper to address this challenge.The proposed fluid-based simulation approach is developedusing a Hybrid Petri Nets framework.The underlying model is a combination of an extension of aBatches Petri Nets(BPN)and a Stochastic Petri Nets(SPN).The extensions are in the inclusion ofrandom elements and relaxation of certain structural constraints.Some adaptations are also made to fitthe target system modeling.The approach is presented with an example.
基金Dr.Naser El-Sheimy research funds from NSERC and Canada Research Chairs programs(Grant No.RT691875).
文摘Inertial navigation represents a unique method of navigation,in which there is no dependency on external sources of information.As opposed to other position fixing navigation techniques,inertial navigation performs the navigation in a relative sense with respect to the initial navigation state of the moving platform.Hence,inertial navigation systems are not prone to jamming,or spoofing.Inertial navigation systems have developed vastly,from their occurrence in the 1940s up to date.The accuracy of the inertial sensors has improved over time,making inertial sensors sufficient in terms of size,weight,cost,and accuracy for navigation and guidance applications.Within the past few years,inertial sensors have developed from being purely mechanical into incorporating various technologies and taking advantage of numerous physical phenomena,from which the dynamic forces exerted on a moving body could be computed accurately.Besides,the evolution of inertial navigation scheme involved the evolution from stable-platform inertial navigation system,which were mechanically complicated,to computationally demanding strap-down inertial navigation systems.Optical sensory technologies have provided highly accurate inertial sensors,at smaller sizes.Besides,the vibratory inertial navigation technologies enabled the production of Micro-electro-machined inertial sensors that are extremely low-cost,and offer extremely low size,weight and power consumption,making them suitable for a wide range of day-to-day navigation applications.Recently,advanced inertial sensor technologies have been introduced to the industry such as nuclear magnetic resonance technology,coldatom technology,and the reintroduction of fluid-based inertial sensors.On another note,inertial sensor errors constitute a huge research aspect in which it is intended for inertial sensors to reach level in which they could operate for substantially long operation times in the absence of updates from aiding sensors,which would be a huge leap.Inertial sensors error modeling techniques have been developing rapidly trying to ensure higher levels of navigation accuracy using lower-cost inertial sensors.In this review,the inertial sensor technologies are covered extensively,along the future trends in the inertial sensors’technologies.Besides,this review covers a brief overview on the inertial error modeling techniques used to enhance the performance of low-cost sensors.
基金This study was supported by the National Natural Science Foundation of China(52025132,21975209,and 21621091)the National Key R&D Program of China(2018YFA0209500).
文摘Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications.However,high predicted accuracy by the traditional sequential method requires a large amount of experimental data,which is not practical in some situations.To conquer these problems,artificial intelligence has promoted the rapid development of material science in recent years,bringing hope to solve these challenges.Here we propose a Kriging machine learning model with an active candidate region,which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region,to predict the mechanical and rheolo-gical performance of liquid gating system with an active minimal size of ex-perimental data.Besides this,this new machine learning model can instruct our experiments with optimal size.The methods are then verified by liquid gating membrane with magnetorheological fluids,which would be of wide interest for the design of potential liquid gating applications in drug release,microfluidic logic,dynamic fluid control,and beyond.
