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Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns

Applied Mathematics and Mechanics(English Edition) 2023年第7期44卷 1085-1100页

作者： M.KIM G.LIN Samsung Electronics Yongin-siGyeonggi-do 17113Republic of Korea Department of Mathematics Purdue UniversityWest LafayetteIN 47907-2067U.S.A. School of Mechanical Engineering Purdue UniversityWest LafayetteIN 47907-2067U.S.A.

This paper develops a deep learning tool based on neural processes(NPs)called the Peri-Net-Pro,to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified *** particular,image classification and regression studies are conducted by means of convolutional neural networks(CNNs)and ***,the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method(FEM)in generating crack pattern ***,case studies are conducted with the prototype microelastic brittle(PMB),linear peridynamic solid(LPS),and viscoelastic solid(VES)models obtained by using the peridynamic *** case studies are performed to classify the images by using CNNs and determine the suitability of the PMB,LBS,and VES ***,a regression analysis is performed on the crack pattern images with NPs to predict the crack *** regression analysis results confirm that the variance decreases when the number of epochs increases by using the *** training results gradually improve,and the variance ranges decrease to less than *** main finding of this study is that the NPs enable accurate predictions,even with missing or insufficient training *** results demonstrate that if the context points are set to the 10th,100th,300th,and 784th,the training information is deliberately omitted for the context points of the 10th,100th,and 300th,and the predictions are different when the context points are significantly ***,the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the ***,if the NPs are employed for training,the missing information of the training data can be supplemented to predict the results.

关键词： neural process(NP) peridynamics crack pattern molecular dynamic(MD)simulation machine learning Gaussian process regression convolutional neural network(CNN)

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pattern transition and sluggish cracking of colloidal droplet deposition with polymer additives

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Science China(Physics,Mechanics & Astronomy) 2013年第9期56卷 1712-1718页

作者： ZHANG YongJian LIU ZhengTang ZANG DuYang QIAN YiMeng LIN KeJun State Key Laboratory of Solidification Processing School of Materials Science and EngineeringNorthwestern Polytechnical University Key Laboratory of Space Applied Physics and Chemistry(Ministry of Education) School of ScienceNorthwestern Polytechnical University

Drying of colloidal droplets often develops versatile *** study the drying deposition of both colloidal droplets containing silica nanoparticles and the silica colloidal droplets with polyethylene oxide(PEO) *** focus on the effect of polymer additives on the deposition formation and the cracking dynamics by using in-situ microscope *** PEO additives,a transition from ring-like deposition to uniform deposition is observed,and the cracking dynamics is greatly *** PEO additives enhance the adsorption of particles at the air-water interface,thus forming the network structure at the interface which blocks the outward capillary *** contributes to the uniform ***,the multi-distribution of the aggregates size enhances the non-homogeneity of the drying film and consequently results in multi-nucleation of *** reduces the stress accumulation that drives the crack propagation and may be responsible for the sluggish cracking dynamics.

关键词： sessile droplet drying crack pattern coffee effect

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Formation of wavy-ring crack in drying droplet of protein solutions

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Science China(Technological Sciences) 2018年第7期61卷 949-958页

作者： GAO MengNi HUANG XianFu ZHAO YaPu State Key Laboratory of Nonlinear Mechanics Institute of Mechanics Chinese Academy of Sciences School of Engineering Science University of Chinese Academy of Sciences

The formations of desiccation cracks and their pattems in drying droplets of protein solutions are studied experimentally. The solvent evaporation causes the dehydration self-organization phenomenon in colloidal droplets, followed by the formations of desiccation cracks. Two categories of highly ordered crack patterns, which we name ＂daisy＂ and ＂wavy-ring＂, are identified in the drying droplets. We explore the shifting of crack patterns from the ＂daisy＂ to the ＂wavy-ring＂ by varying the concentration of protein droplets. The results show that the concentration correlates with the pattern of deposition film directly, and modulates the periodicity of the crack pattern. We investigate the formations and periodicities of these two kinds of crack patterns, and obtain the scaling law of periodicity of the ＂wavy-ring＂ crack pattern. The relationship between the deposition pattern and the highly ordered crack patterns is also examined. This study will help in understanding the formation mechanisms of crack patterns in drying droplets of protein solutions and assist the future design of crack patterns in practical applications.

关键词： coffee-ring effect spiral crack self-organization crack pattern desiccation

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Blast response of full-size concrete walls with chemically reactive enamel （CRE）-coated steel reinforcement

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2016年第9期17卷 689-701页

作者： Dong-ming YAN Hua-wei YIN Cheng-lin WU Yan-long LI Jason BAIRD Gen-da CHEN School of Civil and Architectural Engineering Zhejiang University Hangzhou 310058 China College of Civil Engineering Hunan University Changsha 410082 China Department of Civil Architectural and Environmental Engineering Missouri University of Science and Technology Rolla 654009 USA State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area Xi'an University of Technology Xi'an 710048 China Rock Mechanics and Explosives Missouri University of Science and Technology Rolla 654009 USA Center for Infrastructure Engineering Studies Missouri University of Science and Technology Rolla 654009 USA

In this study, two full-size concrete wails were tested and analyzed to demonstrate the effectiveness of a chemically reactive enamel （CRE） coating in improving their mechanical behavior under blast loading： one with CRE-coated rebar and the other with uncoated rebar. Each wall was subjected in sequence to four explosive loads with equivalent 2, 4, 6-trinitrotoluene （TNT） charge weights of 1.82, 4.54, 13.6, and 20.4 kg. A finite element model of each wall under a close-in blast load was developed and validated with pressure and strain measurements, and used to predict rebar stresses and concrete surface sWain distributions of the wall. The test results and visual inspections consistently indicated that, compared with the barrier wall with uncoated reinforcement, the wall with CRE-coated rebar has fewer concrete cracks on the front and back faces, more effective stress transfers from concrete to steel rebar, and stronger connections with its concrete base. The concrete surface strain distributions predicted by the model under various loading conditions are in good agreement with the crack patterns observed during the tests.

关键词： Chemically reactive enamel (CRE) coating Blast loading crack pattern Bond strength Finite element model

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