A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttriastabilized zirconia anodes in solid oxide fuel cells(SOFCs)to their effective macroscopic properties.A vari...A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttriastabilized zirconia anodes in solid oxide fuel cells(SOFCs)to their effective macroscopic properties.A variety of microstructures are generated by the discrete element method and the meso‑scale kinetic Monte Carlo method.Then,the finite element method and the homogenization theory are used to calculate the effective elastic modulus(E),Poisson’s ratio(υ),shear modulus(G)and coefficient of thermal expansion(CTE)of representative volume elements.In addition,the triple-phase boundary length density(LTPB)is also calculated.The convolutional neural network(CNN)based deep learning model is trained to find the potential relationship between the microstructures and the five effective macroscopic properties.The comparison between the ground truth and the predicted values of the new samples proves that the CNN model has an excellent predictive performance.This indicates that the CNN model could be used as an effective alternative to numerical simulations and homogenization because of its accurate and rapid prediction performance.Hence the deep learning-based homogenization framework could potentially accelerate the continuum modeling of SOFCs for microstructure optimization.展开更多
The effect of fluoride ions on the formation and dissolution behaviour of anodic oxide films on Ti has been investigated in acidic fluoride media (pH=1) using impedance and galvanostatic techniques. A5 the fluoride io...The effect of fluoride ions on the formation and dissolution behaviour of anodic oxide films on Ti has been investigated in acidic fluoride media (pH=1) using impedance and galvanostatic techniques. A5 the fluoride ion concentration and temperature increase the rate of oxide film formation decreases while the dissolution process increases. oxide film formed at high tem-perature and formation voltage was found to contain more defect sites in the film than that formed at a lower one. Activation energies are calculated during the oxide film formation and dissolution and found to be 20.76 and 28.72 kJ/mol, respectively. Formation rate and reciprocal capacitance data are reported as a function of polarizing current density. Values are recorded for the electrolytic parameters A and B. Potentiostatic curves are derived from the galvanostatic results.展开更多
During the operation of sandy railways, the challenge posed by wind-blown sand is a persistent issue. An in-depth study on the influence of wind-blown sand content on the macroscopic and microscopic mechanical propert...During the operation of sandy railways, the challenge posed by wind-blown sand is a persistent issue. An in-depth study on the influence of wind-blown sand content on the macroscopic and microscopic mechanical properties of the ballast bed is of great significance for understanding the potential problems of sandy railways and proposing reasonable and adequate maintenance and repair strategies. Building upon existing research, this study proposes a new assessment indicator for sand content. Utilizing the discrete element method(DEM) and fully considering the complex interactions between ballast and sand particles, three-dimensional(3D) multi-scale analysis models of sandy ballast beds with different wind-blown sand contents are established and validated through field experiments. The effects of varying wind-blown sand content on the microscopic contact distribution and macroscopic mechanical behavior(such as resistance and support stiffness) of ballast beds are carefully analyzed. The results show that with the increase in sand content, the average contact force and coordination number between ballast particles gradually decrease, and the disparity in contact forces between different layers of the ballast bed diminishes. The longitudinal and lateral resistance of the ballast bed initially decreases and then increases, with a critical point at 10% sand content. At 15% sand content, the lateral resistance is mainly shared by the ballast shoulder. The longitudinal resistance sharing ratio is always the largest on the sleeper side, followed by that at the sleeper bottom, and the smallest on the ballast shoulder. When the sand content exceeds 10%, the contribution of sand particles to stiffness significantly increases, leading to an accelerated growth rate of the overall support stiffness of the ballast bed, which is highly detrimental to the long-term service performance of the ballast bed. In conclusion, it is recommended that maintenance and repair operations should be promptly conducted when the sand content of the ballast bed reaches or exceeds 10%.展开更多
To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal test...To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.展开更多
This work presents a review of the findings into the ability of a digitally based particle packing algorithm, called DigiPac, to predict bed structure in a variety of packed columns, for a range of generic pellet shap...This work presents a review of the findings into the ability of a digitally based particle packing algorithm, called DigiPac, to predict bed structure in a variety of packed columns, for a range of generic pellet shapes frequently used in the chemical and process engineering industries. Resulting macroscopic properties are compared with experimental data derived from both invasive and non-destructive measurement techniques. Additionally, fluid velocity distributions, through samples of the resulting bed structures, are analysed using lattice Boltzmann method （LBM） simulations and are compared against experimental data from the literature.展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.11932005,12172104)the National Key R&D Program of China(No.2018YFB1502602)Shenzhen Science and Technology Innovation Commission(JCYJ20200109113439837).
文摘A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttriastabilized zirconia anodes in solid oxide fuel cells(SOFCs)to their effective macroscopic properties.A variety of microstructures are generated by the discrete element method and the meso‑scale kinetic Monte Carlo method.Then,the finite element method and the homogenization theory are used to calculate the effective elastic modulus(E),Poisson’s ratio(υ),shear modulus(G)and coefficient of thermal expansion(CTE)of representative volume elements.In addition,the triple-phase boundary length density(LTPB)is also calculated.The convolutional neural network(CNN)based deep learning model is trained to find the potential relationship between the microstructures and the five effective macroscopic properties.The comparison between the ground truth and the predicted values of the new samples proves that the CNN model has an excellent predictive performance.This indicates that the CNN model could be used as an effective alternative to numerical simulations and homogenization because of its accurate and rapid prediction performance.Hence the deep learning-based homogenization framework could potentially accelerate the continuum modeling of SOFCs for microstructure optimization.
文摘The effect of fluoride ions on the formation and dissolution behaviour of anodic oxide films on Ti has been investigated in acidic fluoride media (pH=1) using impedance and galvanostatic techniques. A5 the fluoride ion concentration and temperature increase the rate of oxide film formation decreases while the dissolution process increases. oxide film formed at high tem-perature and formation voltage was found to contain more defect sites in the film than that formed at a lower one. Activation energies are calculated during the oxide film formation and dissolution and found to be 20.76 and 28.72 kJ/mol, respectively. Formation rate and reciprocal capacitance data are reported as a function of polarizing current density. Values are recorded for the electrolytic parameters A and B. Potentiostatic curves are derived from the galvanostatic results.
基金supported by the National Natural Science Foundation of China (Grant No. 52372425)the Fundamental Research Funds for the Central Universities (Science and Technology Leading Talent Team Poject) Grant No. 2022JBXT010。
文摘During the operation of sandy railways, the challenge posed by wind-blown sand is a persistent issue. An in-depth study on the influence of wind-blown sand content on the macroscopic and microscopic mechanical properties of the ballast bed is of great significance for understanding the potential problems of sandy railways and proposing reasonable and adequate maintenance and repair strategies. Building upon existing research, this study proposes a new assessment indicator for sand content. Utilizing the discrete element method(DEM) and fully considering the complex interactions between ballast and sand particles, three-dimensional(3D) multi-scale analysis models of sandy ballast beds with different wind-blown sand contents are established and validated through field experiments. The effects of varying wind-blown sand content on the microscopic contact distribution and macroscopic mechanical behavior(such as resistance and support stiffness) of ballast beds are carefully analyzed. The results show that with the increase in sand content, the average contact force and coordination number between ballast particles gradually decrease, and the disparity in contact forces between different layers of the ballast bed diminishes. The longitudinal and lateral resistance of the ballast bed initially decreases and then increases, with a critical point at 10% sand content. At 15% sand content, the lateral resistance is mainly shared by the ballast shoulder. The longitudinal resistance sharing ratio is always the largest on the sleeper side, followed by that at the sleeper bottom, and the smallest on the ballast shoulder. When the sand content exceeds 10%, the contribution of sand particles to stiffness significantly increases, leading to an accelerated growth rate of the overall support stiffness of the ballast bed, which is highly detrimental to the long-term service performance of the ballast bed. In conclusion, it is recommended that maintenance and repair operations should be promptly conducted when the sand content of the ballast bed reaches or exceeds 10%.
基金the National Natural Science Foundation of China (Nos. 50674083 and 51074162) for its financial support
文摘To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.
文摘This work presents a review of the findings into the ability of a digitally based particle packing algorithm, called DigiPac, to predict bed structure in a variety of packed columns, for a range of generic pellet shapes frequently used in the chemical and process engineering industries. Resulting macroscopic properties are compared with experimental data derived from both invasive and non-destructive measurement techniques. Additionally, fluid velocity distributions, through samples of the resulting bed structures, are analysed using lattice Boltzmann method （LBM） simulations and are compared against experimental data from the literature.
