This study systematically examines the energy dissipation mechanisms and ballistic characteristics of foam sandwich panels(FSP)under high-velocity impact using the explicit non-linear finite element method.Based on th...This study systematically examines the energy dissipation mechanisms and ballistic characteristics of foam sandwich panels(FSP)under high-velocity impact using the explicit non-linear finite element method.Based on the geometric topology of the FSP system,three FSP configurations with the same areal density are derived,namely multi-layer,gradient core and asymmetric face sheet,and three key structural parameters are identified:core thickness(t_(c)),face sheet thickness(t_(f))and overlap face/core number(n_(o)).The ballistic performance of the FSP system is comprehensively evaluated in terms of the ballistic limit velocity(BLV),deformation modes,energy dissipation mechanism,and specific penetration energy(SPE).The results show that the FSP system exhibits a significant configuration dependence,whose ballistic performance ranking is:asymmetric face sheet>gradient core>multi-layer.The mass distribution of the top and bottom face sheets plays a critical role in the ballistic resistance of the FSP system.Both BLV and SPE increase with tf,while the raising tcor noleads to an increase in BLV but a decrease in SPE.Further,a face-core synchronous enhancement mechanism is discovered by the energy dissipation analysis,based on which the ballistic optimization procedure is also conducted and a design chart is established.This study shed light on the anti-penetration mechanism of the FSP system and might provide a theoretical basis for its engineering application.展开更多
The objective of this work is to develop new biosourced insulating composites from rice husks and wood chips that can be used in the building sector. It appears from the properties of the precursors that rice chips an...The objective of this work is to develop new biosourced insulating composites from rice husks and wood chips that can be used in the building sector. It appears from the properties of the precursors that rice chips and husks are materials which can have good thermal conductivity and therefore the combination of these precursors could make it possible to obtain panels with good insulating properties. With regard to environmental and climatic constraints, the composite panels formulated at various rates were tested and the physico-mechanical and thermal properties showed that it was essential to add a crosslinker in order to increase certain solicitation. an incorporation rate of 12% to 30% made it possible to obtain panels with low thermal conductivity, a low surface water absorption capacity and which gives the composite good thermal insulation and will find many applications in the construction and real estate sector. Finally, new solutions to improve the fire reaction of the insulation panels are tested which allows to identify suitable solutions for the developed composites. In view of the flame tests, the panels obtained are good and can effectively combat fire safety in public buildings.展开更多
How can we regulate an invasive alien species of high commercial value?Black locust(Robinia pseudoacacia L.)has a unique capacity for seed dispersal and high germination.Field surveys indicate that black locust increa...How can we regulate an invasive alien species of high commercial value?Black locust(Robinia pseudoacacia L.)has a unique capacity for seed dispersal and high germination.Field surveys indicate that black locust increases its growing area with sprouting roots and the elongation of horizontal roots at a soil depth of 10 cm.Therefore,a method to regulate the development of horizontal roots could be eff ective in slowing the invasiveness of black locust.In this study,root barrier panels were tested to inhibit the growth of horizontal roots.Since it is labor intensive to observe the growth of roots in the fi eld,it was investigated in a nursery setting.The decrease in secondary fl ush,an increase in yellowed leafl ets,and the height in the seedlings were measured.Installing root barrier panels to a depth of 30 cm eff ectively inhibit the growth of horizontal roots of young black locust.展开更多
Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large ove...Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.展开更多
Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent developm...Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.展开更多
Reconstituted wood panels have several advantages in terms of ease of manufacturing,but their shorter life span results in a huge amount of reconstituted wood panels being discarded in sorting centers yearly.Currently...Reconstituted wood panels have several advantages in terms of ease of manufacturing,but their shorter life span results in a huge amount of reconstituted wood panels being discarded in sorting centers yearly.Currently,the most common approach for dealing with this waste is incineration.In this study,reconstituted wood panels were converted into activated biochar through a two-step thermochemical process:(i)biochar production using pilot scale fast pyrolysis at 250 kg/h and 450℃;and(ii)a physical activation at three temperatures(750℃,850℃ and 950℃)using an in-house activation furnace(1 kg/h).Results showed that the first stage removed about 66% of the nitrogen from the wood panels in the form of NO,NH3,and trimethylamine,which were detected in small amounts compared to emitted CO_(2).Compared to other types of thermochemical conversion methods(e.g.,slow pyrolysis),isocyanic acid and hydrogen cyanide were not detected in this study.The second stage produced activated biochar with a specific surface area of up to 865 m^(2)/g at 950℃.The volatile gases generated during activation were predominantly composed of toluene and benzene.This two-step process resulted in nitrogen-rich carbon in the form of pyrrolic and pyridinic nitrogen.Activated biochars were then evaluated for their SO_(2) retention performance and showed an excellent adsorption capacity of up to 2140 mg/g compared to 65 mg/g for a commercial activated carbon(889 m^(2)/g).End-of-life reconstituted wood panels and SO_(2) gas are problematic issues in Canada where the economy largely revolves around forestry and mining industries.展开更多
Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and ...Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.展开更多
The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovol...The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.展开更多
Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincia...Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.展开更多
This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV tech...This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.展开更多
The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scatt...The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.展开更多
In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is con...In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is constrained by certain limitations. Notably, the efficiency of solar PV modules on the ground peaks at a maximum of 25%, and there are concerns regarding their long-term reliability, with an expected lifespan of approximately 25 years without failures. This study focuses on analyzing the thermal efficiency of PV Modules. We have investigated the temperature profile of PV Modules under varying environmental conditions, such as air velocity and ambient temperature, utilizing Computational Fluid Dynamics (CFD). This analysis is crucial as the efficiency of PV Modules is significantly impacted by changes in the temperature differential relative to the environment. Furthermore, the study highlights the effect of airflow over solar panels on their temperature. It is found that a decrease in the temperature of the PV Module increases Open Circuit Voltage, underlining the importance of thermal management in optimizing solar panel performance.展开更多
Truss core sandwich panels reinforced by carbon fibers were assembled with bonded laminate facesheets and carbon fiber reinforced truss cores. The top and bottom facesheets were interconnected with truss cores. Both e...Truss core sandwich panels reinforced by carbon fibers were assembled with bonded laminate facesheets and carbon fiber reinforced truss cores. The top and bottom facesheets were interconnected with truss cores. Both ends of the truss cores were embedded into four layers of top and bottom facesheets. The mechanical properties of truss core sandwich panels were then investigated under out-of-plane and in-plane compression loadings to reveal the failure mechanisms of sandwich panels. Experimental results indicated that the mechanical behavior of sandwich structure under in-plane loading is dominated by the buckling and debonding of facesheets.展开更多
Dent resistance of automobile body panels is an important property for automobile design and manufacture, but the study on this aspect is not still profound. This study is to summarize the testing methods and physical...Dent resistance of automobile body panels is an important property for automobile design and manufacture, but the study on this aspect is not still profound. This study is to summarize the testing methods and physical significations of static and dynamic dent resistance of automobile body panels combined with the author's study, and to analyze the dent behaviors in the round. Several influence factors on dent resistance are expatiated including the mechanical properties of materials, stress states after forming, bake hardening ability, modulus, methods of testing, and structure of specimens and so on. The automotive lightweight and application of high strength steel sheets and aluminum alloys sheets are analyzed, and the significance of testing of dent resistance, especially for dynamic dent resistance of auto-panels, and the finite element simulation analysis are emphasized. To explain the physical phenomenon of dent behaviors, the latest and concerned study results are also discussed. According to this study, a dent resistance test and evaluation standard of Society of Automotive Engineers of China for automotive body panel is presented and is being carried out, and an industry conference is hold to discuss the working-out of the standard, a primary schedule of this standard is confirmed now. The study can guide the further testing and study of dent resistant of auto-panels.展开更多
The thermal protection performance of superalloy honeycomb structure in high-temperature environments are important for thermal protection design of high-speed aircrafts. By using a self-developed transient aerodynami...The thermal protection performance of superalloy honeycomb structure in high-temperature environments are important for thermal protection design of high-speed aircrafts. By using a self-developed transient aerodynamic thermal simulation system, the thermal protection performance of superalloy honeycomb panel was tested in this paper at different transient heating rates ranging from 5℃/s to 30℃/s, with the maximum instantaneous temperature reaching 950℃. Furthermore, the thermal protection performance of superalloy honeycomb struc- ture under simulated thermal environments was computed for different high heat- ing rates by using 3D finite element method, and a comparison between calcu- lational and experimental results was carded out. The results of this research provide an important reference for the design of thermal protection systems com- prising superalloy honeycomb panel.展开更多
The local buckling of stiffened panels is one of possible failure modes and concerned by engineers in the preliminary design of lightweight structures. In practice,a simplified model,i.e.,a rectangular plate with elas...The local buckling of stiffened panels is one of possible failure modes and concerned by engineers in the preliminary design of lightweight structures. In practice,a simplified model,i.e.,a rectangular plate with elastically restrained along its unloaded edges,is established and the Ritz method is usually employed for solutions. To use the Ritz method,however,the loaded edges of the plate are usually assumed to be simply supported. An empirical correction factor has to be used to account for clamped loaded edges. Here,a simple and efficient method,called the quadrature element method(QEM),is presented for obtaining accurate buckling behavior of rectangular plates with any combinations of boundary conditions, including the elastically restrained conditions. Different from the conventional high order finite element method(FEM),non-uniformly distributed nodes are used,and thus the method can achieve an exponential rate of convergence. Formulations are worked out in detail. A computer program is developed. Improvement of solution accuracy can be easily achieved by changing the number of element nodes in the computer program. Several numerical examples are given. Results are compared with either existing solutions or finite element data for verifications. It is shown that high solution accuracy is achieved. In addition,the proposed method and developed computer program can allow quick analysis of local buckling of stiffened panels and thus is suitable for optimization routines in the preliminary design stage.展开更多
In recent years,field trials of non-pillar longwall mining using complete backfill have been implemented successively in the Chinese coal mining industry.The objective of this paper is to get a scientific understandin...In recent years,field trials of non-pillar longwall mining using complete backfill have been implemented successively in the Chinese coal mining industry.The objective of this paper is to get a scientific understanding of surface subsidence control effect using such techniques.It begins with a brief overview on complete backfill methods primarily used in China,followed by an analysis of collected subsidence factors under mining with complete backfill.It is concluded that non-pillar longwall panel layout cannot protect surface structures against damages at a relatively large mining height,even though complete backfill is conducted.In such cases,separated longwall panel layout should be applied,i.e.,panel width should be subcritical and stable coal pillars should be left between the adjacent panels.The proposed method takes the principles of subcritical extraction and partial extraction;in conjunction with gob backfilling,surface subsidence can be effectively mitigated,thus protecting surface buildings against mining-induced damage.A general design principle and method of separated panel layout have also been proposed.展开更多
基金the National Natural Science Foundation of China(Grant Nos.11972096,12372127 and 12202085)the Fundamental Research Funds for the Central Universities(Grant No.2022CDJQY004)+4 种基金Chongqing Natural Science Foundation(Grant No.cstc2021ycjh-bgzxm0117)China Postdoctoral Science Foundation(Grant No.2022M720562)Chongqing Postdoctoral Science Foundation(Grant No.2021XM3022)supported by the opening project of State Key Laboratory of Explosion Science and Technology(Beijing Institute of Technology)The opening project number is KFJJ23-18 M。
文摘This study systematically examines the energy dissipation mechanisms and ballistic characteristics of foam sandwich panels(FSP)under high-velocity impact using the explicit non-linear finite element method.Based on the geometric topology of the FSP system,three FSP configurations with the same areal density are derived,namely multi-layer,gradient core and asymmetric face sheet,and three key structural parameters are identified:core thickness(t_(c)),face sheet thickness(t_(f))and overlap face/core number(n_(o)).The ballistic performance of the FSP system is comprehensively evaluated in terms of the ballistic limit velocity(BLV),deformation modes,energy dissipation mechanism,and specific penetration energy(SPE).The results show that the FSP system exhibits a significant configuration dependence,whose ballistic performance ranking is:asymmetric face sheet>gradient core>multi-layer.The mass distribution of the top and bottom face sheets plays a critical role in the ballistic resistance of the FSP system.Both BLV and SPE increase with tf,while the raising tcor noleads to an increase in BLV but a decrease in SPE.Further,a face-core synchronous enhancement mechanism is discovered by the energy dissipation analysis,based on which the ballistic optimization procedure is also conducted and a design chart is established.This study shed light on the anti-penetration mechanism of the FSP system and might provide a theoretical basis for its engineering application.
文摘The objective of this work is to develop new biosourced insulating composites from rice husks and wood chips that can be used in the building sector. It appears from the properties of the precursors that rice chips and husks are materials which can have good thermal conductivity and therefore the combination of these precursors could make it possible to obtain panels with good insulating properties. With regard to environmental and climatic constraints, the composite panels formulated at various rates were tested and the physico-mechanical and thermal properties showed that it was essential to add a crosslinker in order to increase certain solicitation. an incorporation rate of 12% to 30% made it possible to obtain panels with low thermal conductivity, a low surface water absorption capacity and which gives the composite good thermal insulation and will find many applications in the construction and real estate sector. Finally, new solutions to improve the fire reaction of the insulation panels are tested which allows to identify suitable solutions for the developed composites. In view of the flame tests, the panels obtained are good and can effectively combat fire safety in public buildings.
基金supported in part by the Research Faculty of Agriculture of Hokkaido University.
文摘How can we regulate an invasive alien species of high commercial value?Black locust(Robinia pseudoacacia L.)has a unique capacity for seed dispersal and high germination.Field surveys indicate that black locust increases its growing area with sprouting roots and the elongation of horizontal roots at a soil depth of 10 cm.Therefore,a method to regulate the development of horizontal roots could be eff ective in slowing the invasiveness of black locust.In this study,root barrier panels were tested to inhibit the growth of horizontal roots.Since it is labor intensive to observe the growth of roots in the fi eld,it was investigated in a nursery setting.The decrease in secondary fl ush,an increase in yellowed leafl ets,and the height in the seedlings were measured.Installing root barrier panels to a depth of 30 cm eff ectively inhibit the growth of horizontal roots of young black locust.
基金supported by the National Natural Science Foundation of China(No.52074305)Henan Scientific and Technological Research Project(No.212102210005)Open Fund of Henan Engineering Laboratory for Photoelectric Sensing and Intelligent Measurement and Control(No.HELPSIMC-2020-00X).
文摘Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.
文摘Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.
基金funded by the Ministere de l’Economie,de la Science et de l’Innovation du Quebec,the Natural Sciences and Engineering Research Council of Canada(NSERC)the Consortium de recherche et innovations en bioprocedes industriels au Quebec(Cribiq)+1 种基金the Canada Research Chair Program,the College of Abitibi-Temiscaminguethe Industrial Waste Technology Centre(Centre Technologique des Residus Industriels)through its partner on this project,Airex Energy.
文摘Reconstituted wood panels have several advantages in terms of ease of manufacturing,but their shorter life span results in a huge amount of reconstituted wood panels being discarded in sorting centers yearly.Currently,the most common approach for dealing with this waste is incineration.In this study,reconstituted wood panels were converted into activated biochar through a two-step thermochemical process:(i)biochar production using pilot scale fast pyrolysis at 250 kg/h and 450℃;and(ii)a physical activation at three temperatures(750℃,850℃ and 950℃)using an in-house activation furnace(1 kg/h).Results showed that the first stage removed about 66% of the nitrogen from the wood panels in the form of NO,NH3,and trimethylamine,which were detected in small amounts compared to emitted CO_(2).Compared to other types of thermochemical conversion methods(e.g.,slow pyrolysis),isocyanic acid and hydrogen cyanide were not detected in this study.The second stage produced activated biochar with a specific surface area of up to 865 m^(2)/g at 950℃.The volatile gases generated during activation were predominantly composed of toluene and benzene.This two-step process resulted in nitrogen-rich carbon in the form of pyrrolic and pyridinic nitrogen.Activated biochars were then evaluated for their SO_(2) retention performance and showed an excellent adsorption capacity of up to 2140 mg/g compared to 65 mg/g for a commercial activated carbon(889 m^(2)/g).End-of-life reconstituted wood panels and SO_(2) gas are problematic issues in Canada where the economy largely revolves around forestry and mining industries.
基金supported by the National Natural Science Foundation of China(Grant No.52079046).
文摘Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.
基金the Efficiency and Performance Engineering Network International Collaboration Fund(award No.of TEPEN-ICF2021-05).
文摘The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.
文摘Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.
文摘This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.
文摘In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is constrained by certain limitations. Notably, the efficiency of solar PV modules on the ground peaks at a maximum of 25%, and there are concerns regarding their long-term reliability, with an expected lifespan of approximately 25 years without failures. This study focuses on analyzing the thermal efficiency of PV Modules. We have investigated the temperature profile of PV Modules under varying environmental conditions, such as air velocity and ambient temperature, utilizing Computational Fluid Dynamics (CFD). This analysis is crucial as the efficiency of PV Modules is significantly impacted by changes in the temperature differential relative to the environment. Furthermore, the study highlights the effect of airflow over solar panels on their temperature. It is found that a decrease in the temperature of the PV Module increases Open Circuit Voltage, underlining the importance of thermal management in optimizing solar panel performance.
基金supported by the National Science Foundation of China under grant Nos.90816024 and 10872059the Major State Basic Research Development Program of China (973 Program) under grant No.2006CB601206+1 种基金the Program of Excellent Team inHarbin Institute of Technologythe Program for New Century Excellent Talents in Universityunder grant No.NCET-08-0152
文摘Truss core sandwich panels reinforced by carbon fibers were assembled with bonded laminate facesheets and carbon fiber reinforced truss cores. The top and bottom facesheets were interconnected with truss cores. Both ends of the truss cores were embedded into four layers of top and bottom facesheets. The mechanical properties of truss core sandwich panels were then investigated under out-of-plane and in-plane compression loadings to reveal the failure mechanisms of sandwich panels. Experimental results indicated that the mechanical behavior of sandwich structure under in-plane loading is dominated by the buckling and debonding of facesheets.
基金supported by National Hi-tech Research and Development Program of China(863 Program, Grant No. 2007AA03z551)Chongqing Municipal Technology Project of China (Grant No. 2007AA4008-4-4)
文摘Dent resistance of automobile body panels is an important property for automobile design and manufacture, but the study on this aspect is not still profound. This study is to summarize the testing methods and physical significations of static and dynamic dent resistance of automobile body panels combined with the author's study, and to analyze the dent behaviors in the round. Several influence factors on dent resistance are expatiated including the mechanical properties of materials, stress states after forming, bake hardening ability, modulus, methods of testing, and structure of specimens and so on. The automotive lightweight and application of high strength steel sheets and aluminum alloys sheets are analyzed, and the significance of testing of dent resistance, especially for dynamic dent resistance of auto-panels, and the finite element simulation analysis are emphasized. To explain the physical phenomenon of dent behaviors, the latest and concerned study results are also discussed. According to this study, a dent resistance test and evaluation standard of Society of Automotive Engineers of China for automotive body panel is presented and is being carried out, and an industry conference is hold to discuss the working-out of the standard, a primary schedule of this standard is confirmed now. The study can guide the further testing and study of dent resistant of auto-panels.
基金supported by the National Natural Science Foundation of China(11172026 and 91216301)the Specialized Research Fund for the Doctoral Program of Higher Education(20131102110014)
文摘The thermal protection performance of superalloy honeycomb structure in high-temperature environments are important for thermal protection design of high-speed aircrafts. By using a self-developed transient aerodynamic thermal simulation system, the thermal protection performance of superalloy honeycomb panel was tested in this paper at different transient heating rates ranging from 5℃/s to 30℃/s, with the maximum instantaneous temperature reaching 950℃. Furthermore, the thermal protection performance of superalloy honeycomb struc- ture under simulated thermal environments was computed for different high heat- ing rates by using 3D finite element method, and a comparison between calcu- lational and experimental results was carded out. The results of this research provide an important reference for the design of thermal protection systems com- prising superalloy honeycomb panel.
基金partially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The local buckling of stiffened panels is one of possible failure modes and concerned by engineers in the preliminary design of lightweight structures. In practice,a simplified model,i.e.,a rectangular plate with elastically restrained along its unloaded edges,is established and the Ritz method is usually employed for solutions. To use the Ritz method,however,the loaded edges of the plate are usually assumed to be simply supported. An empirical correction factor has to be used to account for clamped loaded edges. Here,a simple and efficient method,called the quadrature element method(QEM),is presented for obtaining accurate buckling behavior of rectangular plates with any combinations of boundary conditions, including the elastically restrained conditions. Different from the conventional high order finite element method(FEM),non-uniformly distributed nodes are used,and thus the method can achieve an exponential rate of convergence. Formulations are worked out in detail. A computer program is developed. Improvement of solution accuracy can be easily achieved by changing the number of element nodes in the computer program. Several numerical examples are given. Results are compared with either existing solutions or finite element data for verifications. It is shown that high solution accuracy is achieved. In addition,the proposed method and developed computer program can allow quick analysis of local buckling of stiffened panels and thus is suitable for optimization routines in the preliminary design stage.
文摘In recent years,field trials of non-pillar longwall mining using complete backfill have been implemented successively in the Chinese coal mining industry.The objective of this paper is to get a scientific understanding of surface subsidence control effect using such techniques.It begins with a brief overview on complete backfill methods primarily used in China,followed by an analysis of collected subsidence factors under mining with complete backfill.It is concluded that non-pillar longwall panel layout cannot protect surface structures against damages at a relatively large mining height,even though complete backfill is conducted.In such cases,separated longwall panel layout should be applied,i.e.,panel width should be subcritical and stable coal pillars should be left between the adjacent panels.The proposed method takes the principles of subcritical extraction and partial extraction;in conjunction with gob backfilling,surface subsidence can be effectively mitigated,thus protecting surface buildings against mining-induced damage.A general design principle and method of separated panel layout have also been proposed.