The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have eme...The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.展开更多
Archean greenstone belts and Proterozoic granulite mobile belts are products of fundamentally different tectonic processes that culminated in different levels of crustal incision.The present study focuses on graphite-...Archean greenstone belts and Proterozoic granulite mobile belts are products of fundamentally different tectonic processes that culminated in different levels of crustal incision.The present study focuses on graphite-bearing fluid inclusions from two such terrains in India,the Angul domain of Eastern Ghats Mobile Belt and Hutti-Maski schist belt of the eastern Dharwar greenstone-granite belt.In beth cases,a high population of such inclusions within the fluid inclusion assemblage rules out the possibility of graphite being a captive phase,and instead confirms that it was deposited by the fluid within the inclusion cavity.Graphite is usually observed to be occurring with either pure water or a pure carbonic( CO_2 only)liquid,or with a CH_4 dominated carbonic liquid without vapor at room temperature.Graphite precipitation in inclusions is brought about by reaction of the CO2 and CH4 trapped as a homogeneous fluid to give rise to H_2O and C(graphite).Molar volume calculations for the CO_2-CH_4 mixture assuming an appropriate PVTX relationship indicates that there is a substantial increase in volume with decreasing pressure at a given temperature.The reaction producing graphite and H_2O from CH_4 and CO_2 involves substantial volume reduction,and hence would be favored when the rock undergoes rapid exhumation.Graphite-beating inclusions in quartz in a late-stage leucosome from migmatites in the Angul domain of the EGMB are accompanied by other fluid inclusion evidence for isothermal decompression.In the Hutti-Maski schist belt of the eastern Dharwar Craton,graphite-bearing inclusions occur in structurally controlled quartz veins(often auriferous)within metamorphosed mafic volcanics(schists and amphibolites).The Raman spectra indicate that graphites in fluid inclusions from the Hutti-Maski schist belt have both ordered(O)and the disordered(D)peaks,whereas those from the Angul area of EGMB lack the disordered(D)peaks, with both having perfectly symmetrical‘S’peak.This implies that in both belts,exhumation from the burial depth maxima was a rapid process.However,the Hutti-Maski schist belt experienced a lower amount of uplift than the Angul domain,where the driving mechanism led to a deeper level of incision.This difference in the extent and rate of exhumation is speculated to be related to a fundamental difference in the nature of tectonism.A more detailed comparative study of the fluid inclusion characteristics would possibly throw more light on the changing tectonic style from the Archean to the Proterozoic,a topic that is extensively debated.展开更多
基金financial support received from DST-SERBSRG/2020/000997,Indiathe initiation grant received from IIT Kanpur。
文摘The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.
文摘Archean greenstone belts and Proterozoic granulite mobile belts are products of fundamentally different tectonic processes that culminated in different levels of crustal incision.The present study focuses on graphite-bearing fluid inclusions from two such terrains in India,the Angul domain of Eastern Ghats Mobile Belt and Hutti-Maski schist belt of the eastern Dharwar greenstone-granite belt.In beth cases,a high population of such inclusions within the fluid inclusion assemblage rules out the possibility of graphite being a captive phase,and instead confirms that it was deposited by the fluid within the inclusion cavity.Graphite is usually observed to be occurring with either pure water or a pure carbonic( CO_2 only)liquid,or with a CH_4 dominated carbonic liquid without vapor at room temperature.Graphite precipitation in inclusions is brought about by reaction of the CO2 and CH4 trapped as a homogeneous fluid to give rise to H_2O and C(graphite).Molar volume calculations for the CO_2-CH_4 mixture assuming an appropriate PVTX relationship indicates that there is a substantial increase in volume with decreasing pressure at a given temperature.The reaction producing graphite and H_2O from CH_4 and CO_2 involves substantial volume reduction,and hence would be favored when the rock undergoes rapid exhumation.Graphite-beating inclusions in quartz in a late-stage leucosome from migmatites in the Angul domain of the EGMB are accompanied by other fluid inclusion evidence for isothermal decompression.In the Hutti-Maski schist belt of the eastern Dharwar Craton,graphite-bearing inclusions occur in structurally controlled quartz veins(often auriferous)within metamorphosed mafic volcanics(schists and amphibolites).The Raman spectra indicate that graphites in fluid inclusions from the Hutti-Maski schist belt have both ordered(O)and the disordered(D)peaks,whereas those from the Angul area of EGMB lack the disordered(D)peaks, with both having perfectly symmetrical‘S’peak.This implies that in both belts,exhumation from the burial depth maxima was a rapid process.However,the Hutti-Maski schist belt experienced a lower amount of uplift than the Angul domain,where the driving mechanism led to a deeper level of incision.This difference in the extent and rate of exhumation is speculated to be related to a fundamental difference in the nature of tectonism.A more detailed comparative study of the fluid inclusion characteristics would possibly throw more light on the changing tectonic style from the Archean to the Proterozoic,a topic that is extensively debated.
