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Ran Chen
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3 records found
1
Journal article
(2026)
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Jiaxiu Zhang, Wei Zhao, Ran Chen, Liangliang Nan, Wenhao Chen, Mingqiang Wei
Porous sandwich structures, particularly aluminum foam sandwiches (AFS), are widely used in lightweight and impact-resistant applications, yet their mechanical performance remains difficult to predict due to irregular and multiscale pore morphologies. Traditional constitutive models and current deep learning methods fall short in capturing the complex structure–property relationships of those materials. Accordingly, this work proposes a three-dimensional (3D) pore cloud representation learning method tailored for energy absorption prediction. A novel digital descriptor, termed the pore cloud, is constructed from 3D scans of real AFS cores to preserve detailed pore-level geometric and topological information. A comprehensive structure–property dataset is subsequently generated by integrating these pore cloud features with energy absorption data obtained through finite element analysis (FEA). Furthermore, this work develops PoreNet, a point cloud-based deep learning architecture that learns the direct mapping from mesoscale pore morphology to macroscopic mechanical response. The experimental results demonstrate that PoreNet achieves a high prediction accuracy of 95.12%, robust generalization across variable porosities, and fast convergence within 30 min on a consumer-grade Graphics Processing Unit (GPU). It outperforms both traditional analytical models and baseline neural networks. In addition, this study demonstrates the effectiveness of pore-level geometric learning in structure–property modeling and offers a scalable, data-driven framework for the design and optimization of advanced porous sandwich composites. The dataset and the proposed algorithm are publicly available at https://crescentrosexx.github.io/pore-net/.
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Porous sandwich structures, particularly aluminum foam sandwiches (AFS), are widely used in lightweight and impact-resistant applications, yet their mechanical performance remains difficult to predict due to irregular and multiscale pore morphologies. Traditional constitutive models and current deep learning methods fall short in capturing the complex structure–property relationships of those materials. Accordingly, this work proposes a three-dimensional (3D) pore cloud representation learning method tailored for energy absorption prediction. A novel digital descriptor, termed the pore cloud, is constructed from 3D scans of real AFS cores to preserve detailed pore-level geometric and topological information. A comprehensive structure–property dataset is subsequently generated by integrating these pore cloud features with energy absorption data obtained through finite element analysis (FEA). Furthermore, this work develops PoreNet, a point cloud-based deep learning architecture that learns the direct mapping from mesoscale pore morphology to macroscopic mechanical response. The experimental results demonstrate that PoreNet achieves a high prediction accuracy of 95.12%, robust generalization across variable porosities, and fast convergence within 30 min on a consumer-grade Graphics Processing Unit (GPU). It outperforms both traditional analytical models and baseline neural networks. In addition, this study demonstrates the effectiveness of pore-level geometric learning in structure–property modeling and offers a scalable, data-driven framework for the design and optimization of advanced porous sandwich composites. The dataset and the proposed algorithm are publicly available at https://crescentrosexx.github.io/pore-net/.
Journal article
(2021)
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Pengqian Luan, Yunting Liu, Yongxing Li, Ran Chen, Chen Huang, Jing Gao, Frank Hollmann, Yanjun Jiang
An aqueous chemoenzymatic cascade reaction combining Pd-catalyzed C-C formation and enzymatic C=C asymmetric hydrogenation (AH) was developed for enantioselective synthesis of tertiary α-aryl cycloketones in good yields and excellent enantioselectivities. The stereopreference of the enzyme in AH of α-aryl cyclohexenones was studied. An enantiocomplementary enzyme was obtained by site-directed mutation.
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An aqueous chemoenzymatic cascade reaction combining Pd-catalyzed C-C formation and enzymatic C=C asymmetric hydrogenation (AH) was developed for enantioselective synthesis of tertiary α-aryl cycloketones in good yields and excellent enantioselectivities. The stereopreference of the enzyme in AH of α-aryl cyclohexenones was studied. An enantiocomplementary enzyme was obtained by site-directed mutation.
Most of previous studies optimize maintenance time window scheduling problem under a given train schedule, leading to a relatively poor quality of maintenance time window schedule, increasing the influence on traffic assignment. In order to reduce the negative effects on maintenance schedule and improve the utilization of railway resources, we consider integrating maintenance time window scheduling and train timetabling. In this way, more reasonable maintenance time window schedule can be obtained. We propose a mixed integer programming model and in particular we focus on the characteristics of the problem, including the speed limits affected by maintenance tasks on a double-track railway line. The benefits of the proposed integrated optimization model are demonstrated by numerical experiments.
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Most of previous studies optimize maintenance time window scheduling problem under a given train schedule, leading to a relatively poor quality of maintenance time window schedule, increasing the influence on traffic assignment. In order to reduce the negative effects on maintenance schedule and improve the utilization of railway resources, we consider integrating maintenance time window scheduling and train timetabling. In this way, more reasonable maintenance time window schedule can be obtained. We propose a mixed integer programming model and in particular we focus on the characteristics of the problem, including the speed limits affected by maintenance tasks on a double-track railway line. The benefits of the proposed integrated optimization model are demonstrated by numerical experiments.