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学者姓名：张挺
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Abstract ：
To address complex flood wave propagation problems characterized by discontinuity and anisotropic superposition, Split Coefficient-based Physical Informed Neural Network (SC-PINN) is proposed. The Split Coefficient (SC) strategy is employed to decompose the spatial features of flood waves along different propagation directions. Spatial derivatives, matching each spatial feature component, are obtained through the Taylor series, ensuring that each component contains only the information of waves propagating in a single positive or negative direction. This approach captures flow characteristics in each direction, thereby reducing the spectral bias encountered by PINN when learning complex flow regimes during flood wave propagation. To verify the effectiveness and accuracy, the proposed SC-PINN is applied to three classical dam-break scenarios. Additionally, an investigation is conducted into why the SC strategy assists PINN in improving the accuracy of flood forecasting. The results indicate that as the changing rate in water depth increases, the flow characteristics of asymmetric propagation and superposition become more pronounced, which leads to PINN failing to capture the complex flow regime effectively. In contrast, the proposed SC-PINN splits the total changing rate in water depth along different propagation directions, enabling the network model to independently learn the changing rate component in water depth in each direction. Consequently, the new method accurately captures not only the strong discontinuity regions in shallow water flow but also the phenomena of double shock system, vortex, and wake formed by the interaction between flood waves and obstacles. Furthermore, the proposed approach successfully describes asymmetric flow around the dam breach and local high-water levels induced by irregular breaches. It provides a potent solution for addressing complex flood wave propagation problems characterized by discontinuity and anisotropic superposition.
Keyword ：
Asymmetric propagation Asymmetric propagation Physical Informed Neural Network Physical Informed Neural Network Shallow water flows Shallow water flows Split Coefficient Split Coefficient Strong discontinuous flows Strong discontinuous flows
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| GB/T 7714 | Zhan, Changxun , Zhang, Ting , Zhang, Siqian et al. Solving complex flood wave propagation using split Coefficient-based Physical Informed Neural Network [J]. | JOURNAL OF HYDROLOGY , 2025 , 654 . |
| MLA | Zhan, Changxun et al. "Solving complex flood wave propagation using split Coefficient-based Physical Informed Neural Network" . | JOURNAL OF HYDROLOGY 654 (2025) . |
| APA | Zhan, Changxun , Zhang, Ting , Zhang, Siqian , Yang, Dingying . Solving complex flood wave propagation using split Coefficient-based Physical Informed Neural Network . | JOURNAL OF HYDROLOGY , 2025 , 654 . |
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In scenarios involving coupled excitations from multiple forces, structures exhibit complex vibrational patterns with superimposed high and low-frequency. This is particularly evident in thin-walled structures such as submarine pipelines, where the coupling of internal and external flows leads to more intricate superimposed vibrations compared to scenarios with only internal flow excitation. However, neural networks encounter challenges in capturing these superimposed vibrations due to inherent spectral bias. To address this, the multiple Fourier features physics-informed neural network (MFF-PINN) is proposed. Through multiple Fourier mappings for refined multi-scale and multi-frequency decomposition, facilitating PINN in accurately capturing multifrequency superposed vibrations. Additionally, the correspondence between hyperparameters and eigenvector frequencies is established, while the effects of different hyperparameters and number of mappings on the network is analyzed. The MFF-PINN with multiple mapping decomposition outperforms single mapping in synchronizing the learning of high and low-frequency, improving convergence speed and enhancing the ability to handle multi-frequency superposition. It provides an effective solution for modeling and simulating multifrequency superposed problems in science and engineering.
Keyword ：
Multiple fourier feature Multiple fourier feature Multiple frequency superposition Multiple frequency superposition Physics-informed neural network Physics-informed neural network Submarine pipeline Submarine pipeline Vortex-induced vibration Vortex-induced vibration
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| GB/T 7714 | Zhang, Ting , Yan, Rui , Zhang, Siqian et al. Multi-frequency superposed vortex-induced vibration modeling based on multiple Fourier features physics-informed neural network [J]. | THIN-WALLED STRUCTURES , 2025 , 212 . |
| MLA | Zhang, Ting et al. "Multi-frequency superposed vortex-induced vibration modeling based on multiple Fourier features physics-informed neural network" . | THIN-WALLED STRUCTURES 212 (2025) . |
| APA | Zhang, Ting , Yan, Rui , Zhang, Siqian , Yang, Dingying , Zhan, Changxun . Multi-frequency superposed vortex-induced vibration modeling based on multiple Fourier features physics-informed neural network . | THIN-WALLED STRUCTURES , 2025 , 212 . |
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对于大规模运动模拟问题而言,近邻点的搜索效率将对整体的运算效率产生显著影响.本文基于关联性分析建立kd-tree的最大深度dmax与粒子总数N的自适应关系式,提出了kd-tree自动终止准则,即ATC-kd-tree,同时还考虑了叶子节点大小阈值no对近邻搜索效率的影响.试验表明,ATC-kd-tree具有更高的近邻搜索效率,相较于不使用自动终止准则的kd-tree搜索效率最高提升46％,且适用性更强,可求解不同N值的近邻搜索问题,解决了粒子总数N发生改变时需要再次率定最大深度dmax的问题.同时,本文还提出了网格搜索法组合坐标下降法的两步参数优化算法GSCD法.通过2维阿米巴虫形状的参数优化试验发现,GSCD法可更为快速地率定ATC-kd-tree的可变参数,其优化效率比网格搜索法最高提升了205％,相较于改进网格搜索法最高提升了90％.研究结果表明,ATC-kd-tree和GSCD法不仅提高了近邻搜索的效率,也为复杂运动中近邻粒子搜索问题提供了一种更为高效的解决方案,能够显著降低计算资源的消耗,进一步提升模拟的精度和效率.
Keyword ：
kd-tree kd-tree 坐标下降法 坐标下降法 粒子近邻搜索 粒子近邻搜索 网格搜索法 网格搜索法 自适应 自适应
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| GB/T 7714 | 张挺 , 王宗锴 , 林震寰 et al. 基于自动终止准则改进的kd-tree粒子近邻搜索研究 [J]. | 工程科学与技术 , 2024 , 56 (6) : 217-229 . |
| MLA | 张挺 et al. "基于自动终止准则改进的kd-tree粒子近邻搜索研究" . | 工程科学与技术 56 . 6 (2024) : 217-229 . |
| APA | 张挺 , 王宗锴 , 林震寰 , 郑相涵 . 基于自动终止准则改进的kd-tree粒子近邻搜索研究 . | 工程科学与技术 , 2024 , 56 (6) , 217-229 . |
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Granular fracture holds significant implications in material mechanics. However, the previous studies in ordinary state-based peridynamic (OSB-PD) framework often neglect the internal crystalline structure of particles or only consider limited crystal orientation. To address this gap, a novel OSB-PD model for granular fracture within polycrystalline materials is proposed, in which the periodic functions are incorporated in the PD strain energy density, taking into account the inherent random orientation in cubic crystals. By comparing energy density from PD and the classical continuum mechanics, four PD material parameters are defined. Moreover, the corresponding surface correction method in the global coordinate system is also proposed. Several numerical examples including fracture analysis of polycrystalline materials are conducted to validate the effectiveness of the proposed method. The proposed ordinary state-based peridynamic model offers a fresh perspective for investigating granular fracture behaviors within polycrystalline materials.
Keyword ：
Cubic crystals Cubic crystals Grain orientation Grain orientation Granular fracture Granular fracture Peridynamics Peridynamics Polycrystalline materials Polycrystalline materials
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| GB/T 7714 | Zhang, Ting , Gu, Tiantian , Jiang, Jin et al. An ordinary state-based peridynamic model for granular fracture in polycrystalline materials with arbitrary orientations in cubic crystals [J]. | ENGINEERING FRACTURE MECHANICS , 2024 , 301 . |
| MLA | Zhang, Ting et al. "An ordinary state-based peridynamic model for granular fracture in polycrystalline materials with arbitrary orientations in cubic crystals" . | ENGINEERING FRACTURE MECHANICS 301 (2024) . |
| APA | Zhang, Ting , Gu, Tiantian , Jiang, Jin , Zhang, Jianzhi , Zhou, Xiaoping . An ordinary state-based peridynamic model for granular fracture in polycrystalline materials with arbitrary orientations in cubic crystals . | ENGINEERING FRACTURE MECHANICS , 2024 , 301 . |
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为准确获取山区小流域的降水空间分布及其资源量, 采用Kriging插值法对低分辨率卫星数据进行空间降尺度处理, 通过长短期记忆网络(Long Short-Term Memory, LSTM)将局部卫星与观测数据进行降水融合, 引入前期降水信息加强卫星与观测降水之间的时间相关性, 并利用该模型进行流域降水空间分布估计。结果表明: 从空间分布来看, 融合模型对暴雨中心位置的捕捉更加精确; 从降水量来看, 所提模型在短时强降水下的探测率和临界成功指数分别为0.60和0.50, 能够改善原始低分辨率卫星降水数据, 使其更接近实际情况; 从雨量站数量来看, 融合降水的精度随着站点数量的增加而提高, 当站点数量达到某个临界值时, 融合降水的精度趋于稳定。Kriging-LSTM模型为准确获取山区小流域的降水资源提供了新思路。
Keyword ：
Kriging插值法 Kriging插值法 山区小流域 山区小流域 长短期记忆网络(LSTM) 长短期记忆网络(LSTM) 降水空间估计 降水空间估计 降水融合 降水融合
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| GB/T 7714 | 詹昌洵 , 张挺 , 蒋嘉伟 . 基于多源数据的山区小流域降水融合模型 [J]. | 水科学进展 , 2024 , 35 (1) : 74-84 . |
| MLA | 詹昌洵 et al. "基于多源数据的山区小流域降水融合模型" . | 水科学进展 35 . 1 (2024) : 74-84 . |
| APA | 詹昌洵 , 张挺 , 蒋嘉伟 . 基于多源数据的山区小流域降水融合模型 . | 水科学进展 , 2024 , 35 (1) , 74-84 . |
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Neural network models have been widely used in runoff forecasting, but are often criticized for their lack of physical interpretability. In this study, we present a simple but useful approach to developing hydrological models by designing neural networks based on the principles of runoff generation and concentration, which we refer to as a Hydrological Process-based Neural Network (HPNN) model. The Convolutional neural network (CNN) and softmax function are used because of their similar formula to the conventional runoff generation and unit hydrograph approach used in hydrology. We apply the HPNN model and four other benchmark models to forecast runoff in two catchments (Yutan and Chenda) in China. Results show that the HPNN model has higher computational efficiency, its parameters are interpretable and closely linked to the processes of runoff generation and concentration, and the HPNN model outperforms conventional GRU-based models.
Keyword ：
HPNN model HPNN model Neural network Neural network Physical interpretability Physical interpretability Runoff forecasting Runoff forecasting
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| GB/T 7714 | Gao, Shuai , Zhang, Shuo , Huang, Yuefei et al. A hydrological process-based neural network model for hourly runoff forecasting [J]. | ENVIRONMENTAL MODELLING & SOFTWARE , 2024 , 176 . |
| MLA | Gao, Shuai et al. "A hydrological process-based neural network model for hourly runoff forecasting" . | ENVIRONMENTAL MODELLING & SOFTWARE 176 (2024) . |
| APA | Gao, Shuai , Zhang, Shuo , Huang, Yuefei , Han, Jingcheng , Zhang, Ting , Wang, Guangqian . A hydrological process-based neural network model for hourly runoff forecasting . | ENVIRONMENTAL MODELLING & SOFTWARE , 2024 , 176 . |
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In addressing the intricate dynamic responses of pipeline conveying fluid characterized by spatiotemporal multiscales and multi-modal contributions, Fourier feature-embedded physics-information neural network (FF-PINN) is proposed. By introducing Fourier feature mapping to decompose the temporal and spatial scale information, FF-PINN precisely captures the relatively low-frequencies on the macroscopic time scale as well as the relatively high-frequencies on the microscopic scale of the pipeline's vibration. This approach significantly overcomes the spectral bias encountered by PINN when learning high-frequency information. To verify the effectiveness and accuracy of this method, the proposed FF-PINN is applied to solve the pipeline conveying fluid model with fixed support at both ends. The relative L2 error between the obtained results and the reference solution is 1.8 x 10-2, concurrently with a significant reduction in computational time. Additionally, an analysis of hyperparameter sigma selection is conducted to evaluate its impact on the performance of FF-PINN, while establishing the correspondence between hyperparameter and eigenvector frequency. The results demonstrate that choosing appropriate hyperparameters empowers FF-PINN to better learn the vibration of specific frequencies, enabling the accurate modeling of pipeline vibrations' dynamic response. It provides a potent solution for solving spatiotemporal multi-scale complexity problems involving the superposition of high-and low-frequencies.
Keyword ：
Fourier feature Fourier feature Physics-information neural network Physics-information neural network Pipeline conveying fluid Pipeline conveying fluid Spatiotemporal multi-scales Spatiotemporal multi-scales Vibration characteristics Vibration characteristics
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| GB/T 7714 | Zhang, Ting , Yan, Rui , Zhang, Siqian et al. Application of Fourier feature physics-information neural network in model of pipeline conveying fluid [J]. | THIN-WALLED STRUCTURES , 2024 , 198 . |
| MLA | Zhang, Ting et al. "Application of Fourier feature physics-information neural network in model of pipeline conveying fluid" . | THIN-WALLED STRUCTURES 198 (2024) . |
| APA | Zhang, Ting , Yan, Rui , Zhang, Siqian , Yang, Dingying , Chen, Anhao . Application of Fourier feature physics-information neural network in model of pipeline conveying fluid . | THIN-WALLED STRUCTURES , 2024 , 198 . |
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The flaw tips in brittle rocks are often the sources of crack initiation and growth due to the stress concentration, which commonly governs the rock strength. However, a unified framework identifying the compression-induced crack types, ultimate failure patterns and the cracking levels of brittle flawed rocks under different mechanical confinements is not yet available. This study conducts the laboratory compression experiments with the AE monitoring to explore the failure characteristics of flawed limestone and its confinement-dependency. Four new crack types including loop crack, secondary transverse crack, near-field transverse crack and far-field transverse crack are found experimentally, and then a modified crack type classification strategy is proposed. Four failure patterns including the sigma(1)-axisymmetric flaw-disturbed spalling for uniaxial compression, the sigma(3)-transverse-symmetric flaw-disturbed spalling for biaxial compression, the sigma(1) -axisymmetric flaw-disturbed shearing for conventional triaxial compression, and the mixed sigma(3)-transverse-symmetric flaw-disturbed shearing and sigma(2)-transverse-symmetric flaw-disturbed spalling for true triaxial compression, are documented for the first time. Moreover, an acousto-mechanics-based classification methodology of rock cracking levels is established, as well as an AF (average frequency)-RA (rising angle)-based Kernel density estimation method for interpreting the rock cracking nature and the strength mechanism. This paper gets insights into the mechanical confinement-dependency of the rock failure characteristics incorporating the pre-existing flaws and help interpret the field observations.
Keyword ：
Brittle rock failure Brittle rock failure Confinement-dependency Confinement-dependency Cracking level Cracking level Crack type classification Crack type classification Failure pattern Failure pattern
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| GB/T 7714 | Zhang, Jian-Zhi , Wang, Hai-Tao , Yu, Jin et al. Compression-induced failure characteristics of brittle flawed rocks: Mechanical confinement-dependency [J]. | THEORETICAL AND APPLIED FRACTURE MECHANICS , 2024 , 134 . |
| MLA | Zhang, Jian-Zhi et al. "Compression-induced failure characteristics of brittle flawed rocks: Mechanical confinement-dependency" . | THEORETICAL AND APPLIED FRACTURE MECHANICS 134 (2024) . |
| APA | Zhang, Jian-Zhi , Wang, Hai-Tao , Yu, Jin , Cai, Wu-Qiang , Zhang, Ting . Compression-induced failure characteristics of brittle flawed rocks: Mechanical confinement-dependency . | THEORETICAL AND APPLIED FRACTURE MECHANICS , 2024 , 134 . |
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The slit-cut method for the rockburst prevention and control is believed effective with its easiness in operation, adjustment and compatibility. However, there is limited advance knowledge of the physics of the slit-cut method, which is vital for the engineering designs. In this study, the biaxial compression tests with a synchronous AE (acoustic emission)-DIC (digital image correlation) monitoring are creatively carried out on the slit-contained circular opening specimens with different slit configurations to demonstrate the academic thoughts and mechanisms of the slit-cut method. The experiments document the two typical failure types namely the internal crack propagation and the dynamic rockburst, which occupy different AE hit rate characteristics and different entropy properties. With the occurrence of rockburst, the AE hit rate presents a bouncing ascend-descend trend, and a higher disorder and chaos is faithfully exhibited. Depending on the slit parameters, the slit-cut method can efficiently mitigate rockburst in terms of the occurrence frequency and magnitude. The underlying mechanism lies in the enhancement of the shear mechanism and the development of the internal cracks through which the stored energy can be greatly dissipated. However, due to the unrestricted shear failure in the slit-contained opening specimens, the opening-scale inward instability can be triggered by the internal crack coalescence, thus posing a threat to the safety of the opening. Implementing the slit-cut method with a consideration of the insitu stress conditions is evidently essential for the safe excavation.
Keyword ：
Biaxial compression tests Biaxial compression tests Crack propagation Crack propagation Hard rock excavation Hard rock excavation Rockburst Rockburst Slit-cut method Slit-cut method
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| GB/T 7714 | Zhang, Jian-Zhi , Zhou, Yi-Jie , Liu, Cheng-Yu et al. Experimental investigations on the failure characteristics of the slit-contained circular opening under biaxial compression: Insights into the rockburst prevention [J]. | THEORETICAL AND APPLIED FRACTURE MECHANICS , 2024 , 134 . |
| MLA | Zhang, Jian-Zhi et al. "Experimental investigations on the failure characteristics of the slit-contained circular opening under biaxial compression: Insights into the rockburst prevention" . | THEORETICAL AND APPLIED FRACTURE MECHANICS 134 (2024) . |
| APA | Zhang, Jian-Zhi , Zhou, Yi-Jie , Liu, Cheng-Yu , Yu, Jin , Li, Xing-Shang , Zhang, Ting . Experimental investigations on the failure characteristics of the slit-contained circular opening under biaxial compression: Insights into the rockburst prevention . | THEORETICAL AND APPLIED FRACTURE MECHANICS , 2024 , 134 . |
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To accurately acquire the spatial distribution and resources of precipitation in small mountainous watersheds, this study employed the Kriging interpolation method for spatial downscaling of low- resolution satellite data. It integrated local satellite and observational data using the long short- term memory (LSTM) network, enhancing the temporal correlation between satellite and observed precipitation by incorporating antecedent precipitation information. This model was further utilized to estimate the spatial distribution of watershed precipitation. The results indicate that, spatially, the fusion model captures the location of rainstorm centers with greater precision. In terms of precipitation amount, the proposed model shows a probability of detection and a critical success index of 0. 60 and 0. 50, respectively, under short- duration intense rainfall, improving the original low-resolution satellite rainfall data to better approximate actual conditions. As for the number of precipitation stations, the accuracy of the merged precipitation data increases with the number of stations, reaching stability when a critical value of station density is achieved. The Kriging- LSTM model offers a novel approach for precisely acquiring precipitation resources in small mountainous watersheds. © 2024 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. All rights reserved.
Keyword ：
Brain Brain Interpolation Interpolation Long short-term memory Long short-term memory Rain Rain Satellites Satellites Spatial distribution Spatial distribution Storms Storms Watersheds Watersheds
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| GB/T 7714 | Zhan, Changxun , Zhang, Ting , Jiang, Jiawei . Multi-source data-based precipitation fusion model for small mountainous watersheds∗ [J]. | Advances in Water Science , 2024 , 35 (1) : 74-84 . |
| MLA | Zhan, Changxun et al. "Multi-source data-based precipitation fusion model for small mountainous watersheds∗" . | Advances in Water Science 35 . 1 (2024) : 74-84 . |
| APA | Zhan, Changxun , Zhang, Ting , Jiang, Jiawei . Multi-source data-based precipitation fusion model for small mountainous watersheds∗ . | Advances in Water Science , 2024 , 35 (1) , 74-84 . |
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