Pub Date : 2021-03-11DOI: 10.23967/WCCM-ECCOMAS.2020.347
Hai Xiao, Menglong Liu, Shifeng Guo, F. Cui
Fiber reinforced polymers (FRPs) are increasingly used in thick primary load-bearing structures, while manufacturing and in-service defects occur with a higher chance as the composite thickness increases, which entails the nondestructive detection and evaluation of potential structure defects. This study focuses on the imaging qualities of defects at different depth in thick FRPs via total focusing method (TFM), aiming at determining the optimum imaging strategy for thick FRPs (25 mm for discussion). Dynamic homogenization based on Floquet theory and numerical finite element analysis are performed to interrogate the wave propagation characteristics. The Frequency-dependent time correction method for TFM imaging (F-TFM) is proposed for accurate defect imaging in periodically layered crossply FRP. Finally, the results show that the proposed F-TFM method is able to detect and locate the defects of 2 mm size at all possible depth.
{"title":"Total Focusing Method for Imaging Defect in CFRP Composite with Anisotropy and Inhomogeneity","authors":"Hai Xiao, Menglong Liu, Shifeng Guo, F. Cui","doi":"10.23967/WCCM-ECCOMAS.2020.347","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.347","url":null,"abstract":"Fiber reinforced polymers (FRPs) are increasingly used in thick primary load-bearing structures, while manufacturing and in-service defects occur with a higher chance as the composite thickness increases, which entails the nondestructive detection and evaluation of potential structure defects. This study focuses on the imaging qualities of defects at different depth in thick FRPs via total focusing method (TFM), aiming at determining the optimum imaging strategy for thick FRPs (25 mm for discussion). Dynamic homogenization based on Floquet theory and numerical finite element analysis are performed to interrogate the wave propagation characteristics. The Frequency-dependent time correction method for TFM imaging (F-TFM) is proposed for accurate defect imaging in periodically layered crossply FRP. Finally, the results show that the proposed F-TFM method is able to detect and locate the defects of 2 mm size at all possible depth.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132397349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-11DOI: 10.23967/WCCM-ECCOMAS.2020.081
Qunsheng Huang, Amine Abdelmoula, Gerasimos Chourdakis, J. Rauleder, B. Uekermann
. Modeling a rotor blade flow field involves computing the blade motion, elastic deformation, and the three-dimensional forces and moments for specific trim conditions. Such a complex multi-physics problem, which includes a strong fluid-structure interaction, should be modeled by coupling separate solvers which are specialized on solving single-physics problems. In this work, we present a modular and extensible TAU-CAMRAD II coupling environment using the preCICE coupling library [1]. In this coupling, the aerodynamic forces and moments were computed with the CFD solver TAU. The blade control angle for the CFD simulation were determined by the CSD solver CAMRAD II. We vali-dated the implementation using a modified model of the HART-II rotor at an advancing ratio of µ =0.3. Besides the potential that this work unlocks for future simulations of an active rotor, it also serves as an example of using preCICE for geometric multi-scale (1D-3D) coupling of closed-source solvers for periodic phenomena.
{"title":"CFD/CSD Coupling for an Isolated Rotor Using preCICE","authors":"Qunsheng Huang, Amine Abdelmoula, Gerasimos Chourdakis, J. Rauleder, B. Uekermann","doi":"10.23967/WCCM-ECCOMAS.2020.081","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.081","url":null,"abstract":". Modeling a rotor blade flow field involves computing the blade motion, elastic deformation, and the three-dimensional forces and moments for specific trim conditions. Such a complex multi-physics problem, which includes a strong fluid-structure interaction, should be modeled by coupling separate solvers which are specialized on solving single-physics problems. In this work, we present a modular and extensible TAU-CAMRAD II coupling environment using the preCICE coupling library [1]. In this coupling, the aerodynamic forces and moments were computed with the CFD solver TAU. The blade control angle for the CFD simulation were determined by the CSD solver CAMRAD II. We vali-dated the implementation using a modified model of the HART-II rotor at an advancing ratio of µ =0.3. Besides the potential that this work unlocks for future simulations of an active rotor, it also serves as an example of using preCICE for geometric multi-scale (1D-3D) coupling of closed-source solvers for periodic phenomena.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115286905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-08DOI: 10.23967/WCCM-ECCOMAS.2020.280
Sebastian Kaltenbach, P. Koutsourelakis
The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a probabilistic perspective that simultaneously identifies predictive, lower-dimensional coarse-grained (CG) variables as well as their dynamics. We make use of the expressive ability of deep neural networks in order to represent the right-hand side of the CG evolution law. Furthermore, we demonstrate how domain knowledge that is very often available in the form of physical constraints (e.g. conservation laws) can be incorporated with the novel concept of virtual observables. Such constraints, apart from leading to physically realistic predictions, can significantly reduce the requisite amount of training data which enables reducing the amount of required, computationally expensive multiscale simulations (Small Data regime). The proposed state-space model is trained using probabilistic inference tools and, in contrast to several other techniques, does not require the prescription of a fine-to-coarse (restriction) projection nor time-derivatives of the state variables. The formulation adopted is capable of quantifying the predictive uncertainty as well as of reconstructing the evolution of the full, fine-scale system which allows to select the quantities of interest a posteriori. We demonstrate the efficacy of the proposed framework in a high-dimensional system of moving particles.
{"title":"Physics-Aware, Deep Probabilistic Modeling of Multiscale Dynamics in the Small Data Regime","authors":"Sebastian Kaltenbach, P. Koutsourelakis","doi":"10.23967/WCCM-ECCOMAS.2020.280","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.280","url":null,"abstract":"The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a probabilistic perspective that simultaneously identifies predictive, lower-dimensional coarse-grained (CG) variables as well as their dynamics. We make use of the expressive ability of deep neural networks in order to represent the right-hand side of the CG evolution law. Furthermore, we demonstrate how domain knowledge that is very often available in the form of physical constraints (e.g. conservation laws) can be incorporated with the novel concept of virtual observables. Such constraints, apart from leading to physically realistic predictions, can significantly reduce the requisite amount of training data which enables reducing the amount of required, computationally expensive multiscale simulations (Small Data regime). The proposed state-space model is trained using probabilistic inference tools and, in contrast to several other techniques, does not require the prescription of a fine-to-coarse (restriction) projection nor time-derivatives of the state variables. The formulation adopted is capable of quantifying the predictive uncertainty as well as of reconstructing the evolution of the full, fine-scale system which allows to select the quantities of interest a posteriori. We demonstrate the efficacy of the proposed framework in a high-dimensional system of moving particles.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-09DOI: 10.23967/WCCM-ECCOMAS.2020.146
E. Alami, F. Fekak, L. Garibaldi, A. Khalfi
During its life expectancy, a RC structure is exposed to corrosion. This phenomenon attacks the reinforcement and lead to the creation of a third material that is rust, at the expense of steel. This corrosion material takes more volume than the lost volume of steel and generates internal stresses that lead to the deterioration of the steel-concrete interface and to the cracking of the concrete cover. The distribution of the rust around the reinforcement for a natural corrosion is non-uniform and irregular. This distribution is associated to corrosion “pits” that are localized and concentrated in the regions exposed to corrosion. To better study and understand the phenomenon of pitting corrosion, a 2D numerical model is adopted. This model associates the corrosion of the reinforcement to a single pit, located at the top of the rebar. A model that take into account the damaging of the concrete in compression and tension is used and an interface between the two materials that models a tangential and normal contact is adopted.
{"title":"Numerical Modelling of Pitting Corrosion in RC Structures","authors":"E. Alami, F. Fekak, L. Garibaldi, A. Khalfi","doi":"10.23967/WCCM-ECCOMAS.2020.146","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.146","url":null,"abstract":"During its life expectancy, a RC structure is exposed to corrosion. This phenomenon attacks the reinforcement and lead to the creation of a third material that is rust, at the expense of steel. This corrosion material takes more volume than the lost volume of steel and generates internal stresses that lead to the deterioration of the steel-concrete interface and to the cracking of the concrete cover. The distribution of the rust around the reinforcement for a natural corrosion is non-uniform and irregular. This distribution is associated to corrosion “pits” that are localized and concentrated in the regions exposed to corrosion. To better study and understand the phenomenon of pitting corrosion, a 2D numerical model is adopted. This model associates the corrosion of the reinforcement to a single pit, located at the top of the rebar. A model that take into account the damaging of the concrete in compression and tension is used and an interface between the two materials that models a tangential and normal contact is adopted.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124247587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-22DOI: 10.23967/WCCM-ECCOMAS.2020.297
M. Horsch, S. Chiacchiera, B. Schembera, M. Seaton, I. Todorov
The European Materials and Modelling Ontology (EMMO) has recently been advanced in the computational molecular engineering and multiscale modelling communities as a top-level ontology, aiming to support semantic interoperability and data integration solutions, e.g., for research data infrastructures. The present work explores how top-level ontologies that are based on the same paradigm - the same set of fundamental postulates - as the EMMO can be applied to models of physical systems and their use in computational engineering practice. This paradigm, which combines mereology (in its extension as mereotopology) and semiotics (following Peirce's approach), is here referred to as mereosemiotics. Multiple conceivable ways of implementing mereosemiotics are compared, and the design space consisting of the possible types of top-level ontologies following this paradigm is characterized.
{"title":"Semantic interoperability Based on the European Materials and Modelling Ontology and its Ontological Paradigm: Mereosemiotics","authors":"M. Horsch, S. Chiacchiera, B. Schembera, M. Seaton, I. Todorov","doi":"10.23967/WCCM-ECCOMAS.2020.297","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.297","url":null,"abstract":"The European Materials and Modelling Ontology (EMMO) has recently been advanced in the computational molecular engineering and multiscale modelling communities as a top-level ontology, aiming to support semantic interoperability and data integration solutions, e.g., for research data infrastructures. The present work explores how top-level ontologies that are based on the same paradigm - the same set of fundamental postulates - as the EMMO can be applied to models of physical systems and their use in computational engineering practice. This paradigm, which combines mereology (in its extension as mereotopology) and semiotics (following Peirce's approach), is here referred to as mereosemiotics. Multiple conceivable ways of implementing mereosemiotics are compared, and the design space consisting of the possible types of top-level ontologies following this paradigm is characterized.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"43 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.23967/WCCM-ECCOMAS.2020.019
J. Ruano, A. B. Vidal, J. Rigola, F. Trias
This article presents a new spectral analysis approach for dispersion error and a methodology to numerically evaluate it. In practice, this new analysis allows the numerical study of dispersion errors on all types of mesh and for multiple dimensions. Nonetheless, when mesh uniformity and one-dimensionality assumptions are imposed as in the classical method, the results of this new technique coincide with those of the classic method. We establish the theoretical basis of the approach, derive a numerical methodology to evaluate dispersion errors and assess the method after a set of numerical tests on non-uniform stretched meshes.
{"title":"A General Method to Compute Numerical Dispersion Error","authors":"J. Ruano, A. B. Vidal, J. Rigola, F. Trias","doi":"10.23967/WCCM-ECCOMAS.2020.019","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.019","url":null,"abstract":"This article presents a new spectral analysis approach for dispersion error and a methodology to numerically evaluate it. In practice, this new analysis allows the numerical study of dispersion errors on all types of mesh and for multiple dimensions. Nonetheless, when mesh uniformity and one-dimensionality assumptions are imposed as in the classical method, the results of this new technique coincide with those of the classic method. We establish the theoretical basis of the approach, derive a numerical methodology to evaluate dispersion errors and assess the method after a set of numerical tests on non-uniform stretched meshes.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130714265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.23967/WCCM-ECCOMAS.2020.022
B. Castricum, A. Brian, M. Mirkhalaf, M. Fagerström, F. Larsson
. Short Fiber Reinforced Composites (SFRCs) are being increasingly used in a variety of ap-plications due to their interesting mechanical properties and ease of processing. For SFRCs, different micro-structural parameters (in addition to the constitutive behaviour of the matrix and reinforcement fibers), such as fiber orientation distribution, fiber aspect ratio and fiber/matrix interface strength play important roles in the macroscopic mechanical behaviour. Hence, to have an accurate and reliable modelling approach, using multi-scale models is a natural choice. In this study, a coupled multi-scale model is proposed using a recently developed micromechanical model and the Finite Element Method. The proposed model enables analysis of macroscopic specimens considering micro-structural properties.
{"title":"A Hierarchical Coupled Multi-Scale Model for Short Fiber Composites","authors":"B. Castricum, A. Brian, M. Mirkhalaf, M. Fagerström, F. Larsson","doi":"10.23967/WCCM-ECCOMAS.2020.022","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.022","url":null,"abstract":". Short Fiber Reinforced Composites (SFRCs) are being increasingly used in a variety of ap-plications due to their interesting mechanical properties and ease of processing. For SFRCs, different micro-structural parameters (in addition to the constitutive behaviour of the matrix and reinforcement fibers), such as fiber orientation distribution, fiber aspect ratio and fiber/matrix interface strength play important roles in the macroscopic mechanical behaviour. Hence, to have an accurate and reliable modelling approach, using multi-scale models is a natural choice. In this study, a coupled multi-scale model is proposed using a recently developed micromechanical model and the Finite Element Method. The proposed model enables analysis of macroscopic specimens considering micro-structural properties.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130506329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.23967/WCCM-ECCOMAS.2020.138
D. Herz, C. Krauss, C. Zimmerling, B. Grupp, F. Gauterin
. Precise knowledge of the load history of safety-relevant structures is a central aspect within the fatigue strength design of modern vehicles. Since the experimental measurement of load variables is complex and therefore associated with high costs, vehicles require estimation of these variables in order to design even more customer-orientedly in the future and thus consistently pursue sustainable lightweight construction. Hence the data measured by sensors in today’s standard production vehicles is based on vehicle bus system signals which can be permanently retrieved. Due to the increasing availabil-ity of large quantities of recorded vehicle data, machine learning methods are moving into the focus of application. In this work, the implementation of Recurrent Neural Networks for the estimation of load-time curves is investigated. In order to close existing gaps in the state of the art, successful concepts of machine learning for sequential data, such as speech processing, are to be transferred to this application case. Long Short-Term Memory cells [1] play a central role for this type of problem. In addition to the adaptation of the network architecture, the integration of engineering knowledge is pursued within the method development process in order to increase the quality of the model. Relevant input variables are specifically selected by feature engineering and new meaningful variables are generated by filtering. Statistical analysis is used to investigate the correlation of these input signals with the estimated quantities. The development
{"title":"Estimation of Load-Time Curves Using Recurrent Neural Networks Based On Can Bus Signals","authors":"D. Herz, C. Krauss, C. Zimmerling, B. Grupp, F. Gauterin","doi":"10.23967/WCCM-ECCOMAS.2020.138","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.138","url":null,"abstract":". Precise knowledge of the load history of safety-relevant structures is a central aspect within the fatigue strength design of modern vehicles. Since the experimental measurement of load variables is complex and therefore associated with high costs, vehicles require estimation of these variables in order to design even more customer-orientedly in the future and thus consistently pursue sustainable lightweight construction. Hence the data measured by sensors in today’s standard production vehicles is based on vehicle bus system signals which can be permanently retrieved. Due to the increasing availabil-ity of large quantities of recorded vehicle data, machine learning methods are moving into the focus of application. In this work, the implementation of Recurrent Neural Networks for the estimation of load-time curves is investigated. In order to close existing gaps in the state of the art, successful concepts of machine learning for sequential data, such as speech processing, are to be transferred to this application case. Long Short-Term Memory cells [1] play a central role for this type of problem. In addition to the adaptation of the network architecture, the integration of engineering knowledge is pursued within the method development process in order to increase the quality of the model. Relevant input variables are specifically selected by feature engineering and new meaningful variables are generated by filtering. Statistical analysis is used to investigate the correlation of these input signals with the estimated quantities. The development","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132454951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.23967/WCCM-ECCOMAS.2020.264
R. Pfefferkorn, P. Betsch
. One of the most popular mixed finite elements is the enhanced assumed strain (EAS) approach. However, despite numerous advantages there are still some open issues. Three of the most important, namely robustness in nonlinear simulations, hourglassing instabilities and sensitivity to mesh distortion, are discussed in the present contribution. Furthermore, we propose a novel Petrov-Galerkin based EAS method. It is shown that three conditions have to be fulfilled to construct elements that are exact for a specific displacement mode regardless of mesh distortion. The so constructed novel element is locking-free, exact for bending problems, insensitive to mesh distortion and has improved coarse mesh accuracy.
{"title":"Open Issues on the EAS Method and Mesh Distortion Insensitive Locking-Free Low-Order Unsymmetric EAS Elements","authors":"R. Pfefferkorn, P. Betsch","doi":"10.23967/WCCM-ECCOMAS.2020.264","DOIUrl":"https://doi.org/10.23967/WCCM-ECCOMAS.2020.264","url":null,"abstract":". One of the most popular mixed finite elements is the enhanced assumed strain (EAS) approach. However, despite numerous advantages there are still some open issues. Three of the most important, namely robustness in nonlinear simulations, hourglassing instabilities and sensitivity to mesh distortion, are discussed in the present contribution. Furthermore, we propose a novel Petrov-Galerkin based EAS method. It is shown that three conditions have to be fulfilled to construct elements that are exact for a specific displacement mode regardless of mesh distortion. The so constructed novel element is locking-free, exact for bending problems, insensitive to mesh distortion and has improved coarse mesh accuracy.","PeriodicalId":148883,"journal":{"name":"14th WCCM-ECCOMAS Congress","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121432380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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