Progressive Failure Analysis of Composite Laminates Subjected to Transverse Loading with Augmented Finite Element Method

In this paper, two-dimensional (2D) orthotropic augmented finite element method (A-FEM) is applied to account for progressive failure of composite laminates under transverse loading, which considers all major cracking modes (delamination, fiber kinking/rupture matrix cracking). High-fidelity simulations of different stacking composite laminates under transverse loading are implemented. Both predicted load-deflection curves and damage evolution are in good agreement with that of experimental results, which demonstrates the numerical capability of A-FEM. In addition, the influence of stacking sequence on the failure mechanism is also studied by predicted damage evolution of laminates with different stacking sequence [90(2)degrees/0(2)degrees](4S), [90(4)degrees/0(4)degrees](2S) and [90(8)degrees/0(8)degrees](S). Results show that the tensile matrix crack in the bottom laminar is always the first damage mode for the composite laminate, and the subsequent crack propagation is related to ply orientation of adjacent plies which have a blocking effect on crack propagation

Numerical Simulation on Air-Liquid Transient Flow and Regression Model on Air-Liquid Ratio of Air Induction Nozzle

Air induction nozzle (AIN) has a special Venturi structure that has been widely used in the field of reducing the probability of drift of pesticide droplets and realizing precise application. The present research mainly adopts the method of comparative test and analyzes the difference between AIN and standard fan nozzle. However, the research on internal flow characteristics and air–liquid ratio (ALR) of AIN is very limited. In order to detect the air-liquid transient flow distribution and the influence of the geometric parameter structure of Venturi on the air–liquid ratio in the air induction nozzle, numerical simulation and air-liquid ratio prediction model of AIN combined with TD (Turbo Drop series) type Venturi tubes and ST110 (standard nozzle series) type fan nozzles are used. Based on the VOF (volume of fluid) model and Realizable k-ε turbulence control method, the TD-ST combined AIN is simulated numerically using open input and exit boundary conditions. The results show that the transient flow characteristic of the combined AIN is determined by the geometric structure of the Venturi tube, and the internal velocity and pressure change significantly at the Venturi angle. Under the same ST110 fan nozzle, the size of the larger TD Venturi tube will decrease the air phase content in the air–liquid flow. TD03-ST06 combined AIN has a maximum volume flow of 0.0092 (L/min) under 0.6 MPa. The air–liquid ratio regression model is established by designing the intake volume measurement system. According to this model, the influence law of tube size and spray parameters on the air–liquid ratio can be clarified. After variance analysis, it is proved that this model is suitable for air–liquid ratio prediction of TD-ST combined AIN. This study clarifies the air–liquid coupling law inside AIN and provides some reference for the quantitative analysis of the relationship between the geometric parameters, spray parameters, and the air–liquid ratio

Progressive fracture analysis of the open-hole composite laminates: experiment and simulation

A conforming augmented finite element method (C-AFEM) is extended to predict the failure process in inter-ply of fiber-reinforced composite laminates. Combining with cohesive zone model (CZM) element, the delamination between inter-plies can also be captured. Matrix fracture, fiber fracture and delamination in inter-plies will occur when the composite laminate fracture, which is a complicated process, and the current method appears capable of modelling failure mechanism of composite laminates, which is demonstrated mesh-independent, high-fidelity and robust. Different stacking open-hole composite laminates subject to tension load are tested by electrical measurement and Digital Image Correlation (DIC) system. The proposed C-AFEM is applied to model progressive failure of open-hole composite laminates and results are found to agree with experiments very well

A Red Fluorescent Protein-Based Probe for Detection of Intracellular Reactive Sulfane Sulfur

Reactive sulfane sulfur, including persulfide and polysulfide, is a type of regular cellular component, playing an antioxidant role. Its function may be organelle-dependent; however, the shortage of probes for detecting organellar reactive sulfane sulfur has hindered further investigation. Herein, we reported a red fluorescent protein (mCherry)-based probe for specifically detecting intracellular reactive sulfane sulfur. By mutating two amino acid residues of mCherry (A150 and S151) to cysteine residues, we constructed a mCherry mutant, which reacted with reactive sulfane sulfur to form an intramolecular –Sn– bond (n ≥ 3). The bond largely decreased the intensity of 610 nm emission (excitation at 587 nm) and slightly increased the intensity of 466 nm emission (excitation at 406 nm). The 466/610 nm emission ratio was used to indicate the relative abundance of reactive sulfane sulfur. We then expressed this mutant in the cytoplasm and mitochondria of Saccharomyces cerevisiae. The 466/610 nm emission ratio revealed that mitochondria had a higher level of reactive sulfane sulfur than cytoplasm. Thus, the mCherry mutant can be used as a specific probe for detecting reactive sulfane sulfur in vivo

A Model for Identifying Soybean Growth Periods Based on Multi-Source Sensors and Improved Convolutional Neural Network

The identification of soybean growth periods is the key to timely take field management measures, which plays an important role in improving yield. In order to realize the discrimination of soybean growth periods under complex environments in the field quickly and accurately, a model for identifying soybean growth periods based on multi-source sensors and improved convolutional neural network was proposed. The AlexNet structure was improved by adjusting the number of fully connected layer 1 and fully connected layer 2 neurons to 1024 and 256. The model was optimized through the hyperparameters combination experiment and the classification experiment of different types of image datasets. The discrimination of soybean emergence (VE), cotyledon (VC), and first node (V1) stages was achieved. The experimental results showed that after improving the fully connected layers, the average classification accuracy of the model was 99.58%, the average loss was 0.0132, and the running time was 0.41 s/step under the optimal combination of hyperparameters. At around 20 iterations, the performances began to converge and were all superior to the baseline model. Field validation trials were conducted applying the model, and the classification accuracy was 90.81% in VE, 91.82% in VC, and 92.56% in V1, with an average classification accuracy of 91.73%, and single image recognition time was about 21.9 ms. It can meet the demand for the identification of soybean growth periods based on smart phone and unmanned aerial vehicle (UAV) remote sensing, and provide technical support for the identification of soybean growth periods with different resolutions from different sensors

A Conforming A-FEM for Modeling Arbitrary Crack Propagation and Branching in Solids

In this paper, an improved conforming AFEM (C-AFEM) for efficient modeling of arbitrary crack propagation and branching is proposed and validated. An explicit formulation for branching cracks has been derived within the C-AFEM framework. The conjugate gradient method is integrated into the C-AFEM formulation to solve the local problem that consists of all elements traversed by single or multiple cracks. Multiple numerical evidences show that this new approach can substantially improve the modeling efficiency. The solution accuracy and numerical robustness are also significantly improved

Optimal Design and Dynamic Characteristic Analysis of Double-Link Trapezoidal Suspension for 3WPYZ High Gap Self-Propelled Sprayer

A fast spraying speed, wide working area, and easy operation are the operational advantages of high-clearance boom sprayers. To address the issue of spray boom mechanical vibration affecting the spraying effect, a double-link trapezoidal boom suspension is designed for the 3WPYZ sprayer. This suspension can achieve passive vibration reduction, active balance, and ground profiling. The kinematic model of the boom suspension is established based on D’Alembert’s principle and the principle of multi-body dynamics, and the design factors affecting the stability of the boom are determined. Through orthogonal experimental design and virtual kinematics simulation, the influence of the boom length and orifice diameter of each part on the swing angle and the natural frequency of the boom suspension is investigated. Design-Expert 8.0.6 software is used to analyze and optimize the test results. The optimization results show that, when the connecting boom length LAB is 265 mm, the inner boom suspension boom length LAD is 840 mm, the outer boom suspension boom length LBC is 1250 mm, and the throttle hole diameter d is 4 mm; the maximum swing angle of the boom suspension is reduced by 53.02%. In addition, the natural frequency of the boom is reduced from 1.3143 rad/s to 1.1826 rad/s, and the dynamic characteristic optimization effect is remarkable. The modal analysis results show that the first sixth-order vibration test frequency of the boom sprayer suspension designed in this paper meets the requirements and avoids the influence of external factors. Field tests show that, when the sprayer is excited by the environment at 3.5° to 4°, the boom suspension can reduce the vibration transmitted by the body to a reasonable range. The static analysis shows that the structural design of this study reduces the stress at the connection of the end boom suspension, the maximum displacement, and the maximum stress of the inner boom suspension. The test results of the dynamic characteristics of the implement are basically consistent with the virtual model simulation test results, thus achieving the optimization objectives