Energy dissipation mechanism and ballistic characteristic optimization in foam sandwich panels against spherical projectile impact

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.

Composite Panels from the Combination of Rice Husk and Wood Chips with a Natural Resin Based on Tannins Reinforced with Sugar Cane Molasses Intended for Building Insulation: Physico-Mechanical and Thermal Properties

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.

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

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.

Regulation of the growth of sprouting roots of black locust seedlings using root barrier panels

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.

Ghost-Retina Net:Fast Shadow Detection Method for Photovoltaic Panels Based on Improved Retina Net

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.

Naturally Nitrogen-Doped Biochar Made from End-of-Life Wood Panels for SO_(2) Gas Depollution

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.

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

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.

Smart Monitoring of Solar Photovoltaic Panels by the Approach of Machine Learning

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.

How R&D investment promotes green technology innovation in the context of digitalization?-An empirical analysis based on provincial panel data

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.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

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.

Comprehensive Examination of Solar Panel Design: A Focus on Thermal Dynamics

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.

基于LabVIEW^(TM)7 Express的Remote Panels技术在化工虚拟仪器中的应用

构建了基于LabVIEWTM7 Express的燃烧热测定的化工虚拟仪器系统,用LabVIEW的Remote Panels技术的三种不同B/S方式均实现了远程测试,文中报道了将网页浏览器中操作Remote Panels方法具体应用于实例中,实现了远程实时监控及分析处理现场测试数据,结果良好。

血清外泌体microRNAs单一及组合panel对结直肠癌的诊断价值

目的 检测血清外泌体miR-15b、miR-16、miR-21和miR-31在结直肠癌患者中的表达水平,并评估其对结直肠癌的诊断价值。方法 选取2018年3月至2022年5月本院收治的123例直肠癌患者(结直肠癌组)及117例大肠腺瘤患者作为研究对象(结直肠癌组),并纳入150例健康对照者作为对照(健康对照组)。提取血清外泌体中miR-15b、miR-16、miR-21和miR-31,qRT-PCR比较4种miRNA在各组中表达水平差异。受试者工作特征(ROC)曲线评价4种miRNA单一及组合panel对结直肠癌的诊断价值。结果 结直肠癌组血清miR-15b、miR-16、miR-21和miR-31水平高于健康对照组(P <0.05),且miR-15b、miR-21和miR-31水平高于结直肠腺瘤组(P <0.05)。Ⅲ-Ⅳ期、伴淋巴结浸润、低分化程度结直肠癌组患者血清miR-15b、miR-21和miR-31水平分别高于Ⅰ-Ⅱ期、不伴淋巴结浸润、中-高分化程度患者,miR-15b、miR-21和miR-31水平与临床TNM分期、伴淋巴结浸润呈正相关,与分化程度呈负相关。4种miRNA中诊断价值最高的指标是miR-15b,其敏感度和特异度分别为81.33%和91.80%(P <0.05);且miR-15b、miR-21和miR-31的组合panel具有更好的诊断价值,其敏感度和特异度分别为95.06%和94.44%(P <0.05)。结论 miR-15b、miR-16、miR-21和miR-31在结直肠癌患者外周血血清中异常表达,miR-15b、miR-21和miR-31水平与结直肠癌的发病及进展密切相关,且miR-15b、miR-21和miR-31组合panel具有作为结直肠癌的新型诊断模型的潜力。

用Remote Panels实现远程故障诊断

介绍了Remote Panels的应用原理,并详细描述了该技术和LabVIEW图形编程语言开发的故障诊断实例。故障现场的计算机实现远程控制面板功能,而远程用户可以通过局域网或广域网登录到故障现场的计算机,控制这台计算机进行故障特征值的提取,并能够将采集到的数据通过网络传到其它用户做进一步的分析处理。

Fabrication and Testing of Carbon Fiber Reinforced Truss Core Sandwich Panels

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.

Dent Resistance for Automobile Body Panels

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.

Study on the thermal protection performance of superalloy honeycomb panels in high-speed thermal shock environments

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.

An Efficient Method for Local Buckling Analysis of Stiffened Panels

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.

Innovative backfilling longwall panel layout for better subsidence control effect-separating adjacent subcritical panels with pillars

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.

Theoretical prediction on corrugated sandwich panels under bending loads

In this paper,an aluminum corrugated sandwich panel with triangular core under bending loads was investigated.Firstly,the equivalent material parameters of the triangular corrugated core layer,which could be considered as an orthotropic panel,were obtained by using Castigliano's theorem and equivalent homogeneous model.Secondly,contributions of the corrugated core layer and two face panels were both considered to compute the equivalent material parameters of the whole structure through the classical lamination theory,and these equivalent material parameters were compared with finite element analysis solutions.Then,based on the Mindlin orthotropic plate theory,this study obtain the closed-form solutions of the displacement for a corrugated sandwich panel under bending loads in specified boundary conditions,and parameters study and comparison by the finite element method were executed simultaneously.