The drying shrinkage of geopolymers poses significant limitations on their potential as constructive materials.In this study,the drying shrinkage of metakaolin-based geopolymer(MKG)with different initial water/solid r...The drying shrinkage of geopolymers poses significant limitations on their potential as constructive materials.In this study,the drying shrinkage of metakaolin-based geopolymer(MKG)with different initial water/solid ratios and pore structures was investigated experimentally.According to mini-bar shrinkage experiments,the drying shrinkage-water loss relation of MKG showed two-stage behavior.The initial water/solid ratio influences the critical water loss and span of the pausing period of the shrinkage curves but not the general trend.Combined with the microstructure characterization and physical estimation,the underlying dependency of the shrinkage on the pore structure of the binder was elucidated.Capillary pressure,surface energy change,and gel densification dominate the drying shrinkage of MKG at different water loss stages.The findings indicate that besides porosity control,finer tuning of the pore size distribution is needed to control the drying shrinkage of MKG.展开更多
Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil...Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.展开更多
基金Project supported by the National Key R&D Program of China(No.2018YFB0605700)and the National Natural Science Foundation of China(Nos.51879230 and 51778570)。
文摘The drying shrinkage of geopolymers poses significant limitations on their potential as constructive materials.In this study,the drying shrinkage of metakaolin-based geopolymer(MKG)with different initial water/solid ratios and pore structures was investigated experimentally.According to mini-bar shrinkage experiments,the drying shrinkage-water loss relation of MKG showed two-stage behavior.The initial water/solid ratio influences the critical water loss and span of the pausing period of the shrinkage curves but not the general trend.Combined with the microstructure characterization and physical estimation,the underlying dependency of the shrinkage on the pore structure of the binder was elucidated.Capillary pressure,surface energy change,and gel densification dominate the drying shrinkage of MKG at different water loss stages.The findings indicate that besides porosity control,finer tuning of the pore size distribution is needed to control the drying shrinkage of MKG.
基金supported by CITRIS and the Banatao Institute,Air Force Office of Scientific Research(Grant No.FA9550-22-1-0420)National Science Foundation(Grant No.ACI-1548562).
文摘Conventional wing aerodynamic optimization processes can be time-consuming and imprecise due to the complexity of versatile flight missions.Plenty of existing literature has considered two-dimensional infinite airfoil optimization,while three-dimensional finite wing optimizations are subject to limited study because of high computational costs.Here we create an adaptive optimization methodology built upon digitized wing shape deformation and deep learning algorithms,which enable the rapid formulation of finite wing designs for specific aerodynamic performance demands under different cruise conditions.This methodology unfolds in three stages:radial basis function interpolated wing generation,collection of inputs from computational fluid dynamics simulations,and deep neural network that constructs the surrogate model for the optimal wing configuration.It has been demonstrated that the proposed methodology can significantly reduce the computational cost of numerical simulations.It also has the potential to optimize various aerial vehicles undergoing different mission environments,loading conditions,and safety requirements.
