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Traumatic rupture of the aorta (TRA) is highly lethal in motor vehicle collisions (MVCs). Human body biomechanical models (HUBMs) show promise as useful tools for better comprehension of the mechanical response and injury mechanism of TRA. However, the prediction accuracy largely depends on its material modeling. This study investigated the performance of the isotropic elastic material constitutive model (Mat_001) and the orthotropic elastic material constitutive model (Mat_002) for predicting aortic dynamic response and injury in blunt side impacts, via simulations against vertical and oblique side impact cadaver tests with previously developed HUBM. The aortic strain response, peak intraluminal pressure, thoracic deflections and injury distributions were compared. It was found that both Mat_001 and Mat_002 could be used to model aortic tissue, but Mat_002 presented more sensitivity to blunt side impacts in terms of strain responses. The thoracic rib cage and internal organs sustained more severe injuries under the oblique left side impact (AIS 5+) than those under the vertical left side impact (AIS 4+), whereas the aortic peri-isthmus exhibited a higher TRA risk in the vertical side impact than that in the oblique side impact, with regard to the peak longitudinal strain. The TRA could result from the aorta's sudden stretch, and the peak longitudinal strain could be regarded as a main contributing factor to it. This study enhances our understanding of the aortic material modeling and injury mechanisms. [ABSTRACT FROM AUTHOR]

This paper presents a sliding mode control based on particle swarm optimization neural network and adaptive reaching law, and the proposed control method solves the problem of chattering and tracking performance degradation of a multi-joint manipulator caused by uncertainties such as external disturbances and modeling error. First, to address the problem that the precise dynamic system of the manipulator is difficult to establish, the radial basis function neural network (RBFNN) is used to approximate the uncertainty of the manipulator model, and the parameters of the neural network are optimized through the adaptive natural selection particle swarm optimization algorithm (ASelPSO) to improve the approximation ability and reduce the approximation error. Second, to eliminate chattering, adaptive reaching law is selected to improve the dynamic quality of approaching motion. Finally, a comparative simulation experiment is carried out with a 3-DOF manipulator as the research object. The results show that the control method has obvious improvements in eliminating chattering, improving tracking accuracy, and increasing convergence speed, which verifies the feasibility and superiority of the control scheme. [ABSTRACT FROM AUTHOR]

Hepatic injury induced by blunt abdominal impact is a major cause of death in vehicle crashes. However, few works have been done effectively in cadaver experiments to clarify the liver's specific dynamic behavior and mechanical characteristics. This paper described the dynamic behavior and mechanical characteristics of the liver under blunt impacts to the upper abdomen with the finite element model (FEM) of the Chinese human body — the 50 percentile-sized male (CHUBM-M50). The simulation matrix, three directions (frontal, oblique, lateral), and four speeds included in each group were designed with a 23.4 kg, rigid cylindrical impactor aligning the T11 level. The liver deformation contours displayed compression against the spine and rotation in the horizontal plane, which were the two main features in liver motion. Pressure distribution in the liver capsule and parenchyma was discussed to elucidate the biomechanical characteristics related to impact direction. Generally, the stress distribution in the capsule was 10 times higher than that in the parenchyma. A discussion of the injury mechanism of the liver capsule and parenchyma observed in the simulations was given upon the pressure distribution. It demonstrated that the capsule could protect liver parenchyma at low-speed impacts and should not be neglected for understanding liver injury mechanisms. [ABSTRACT FROM AUTHOR]

Thermal conditions are strongly changeable in a vehicle cabin, where passengers could suffer consecutive self‐thermoregulation to such dynamic changing thermal stresses, though its HVAC system works well. To observe human overall and local thermal sensations in dynamic thermal conditions, a series of experiments under various conditions were carried out in a cabin‐like climate chamber. The results showed that the head, chest, back, and hands during hot exposure are warmer leading to the overall thermal sensation being hot. The thermal sensation of the head was warmer than the overall thermal sensation. During cold exposure, arms, hands, legs, and feet were the main areas causing coldness. In a dynamic thermal environment, the previous skin temperature state and thermal sensation form a thermal sensation overshoot, causing a shift in the body's neutral temperature point. This study proposes a thermal sensation model for the prediction of human thermal sensation local and overall based on skin temperature changes in a dynamic environment. Considering the airflow characteristics in the cabin, the human body is set into seven local parts in the local thermal sensation model. To compensate for sensation overshoot from this, defining recovery points rp for local parts differentiate temperature setpoints according to the experienced thermal state so that the effect resulting from the dynamic condition is integrated into the model algorithm. The model provides a scientific basis for guiding design optimization and intelligent regulation in the dynamic environment of the vehicle cabin, so as to achieve efficient energy utilization. [ABSTRACT FROM AUTHOR]

A neural network control strategy based on the immune algorithm is proposed to address the problem of chattering and trajectory tracking performance degradation of multi-joint manipulators due to unknown backlash-like hysteresis and system uncertainty. The controller is designed based on backstepping sliding mode technology and is suitable for Euler-Lagrange nonlinear systems. Firstly, to deal with the uncertainty problem of the manipulator model, we consider using radial basis function neural networks (RBFNN) to approximate, and on this basis, we add immune algorithms to optimize the RBFNN parameters to improve the approximation ability. Secondly, to compensate for the unknown backlash-like hysteresis error and RBFNN approximation error, an adaptive reaching law is designed to improve the control accuracy and reduce chattering to a certain extent. Finally, taking a 3-DOF manipulator as the research object, based on comparative simulation results, the feasibility and superiority of this control method can be proven. [ABSTRACT FROM AUTHOR]

The optimal initial pre-conditioning parameter is essential to properly adjust the temperature within the cabin in an effective and accurate way, especially while passengers' thermal comfort and energy-saving properties are both considered. Under the various environmental thermal loads, the pre-conditioning solutions resulting from those pre-fixed cooling parameters are unfeasible for achieving accurately passengers' comfort temperature. In addition, it is also difficult in such a narrow car space to identify a lot of local attributes due to the different material properties and sizes of a variety of structural parts that have various thermal responses to environmental conditions. This paper presents a data-driven decision model to numerically identify the degrees of the cabin thermal characteristic to determine satisfactory pre-conditioning parameter schemes. Initially, based on the thermal data within a vehicle recorded through the whole year at a selected hot climate region of the Middle East, the study levels multiple climate scenes corresponding to change in the cabin air temperature. Then three classification algorithms (Support Vector Machines, Decision Tree, and K-nearest neighbor model) are used to comparatively identify climate levels according to the input conditions. Based on the identified climate level, an appropriate parameters scheme for this level is applied. A comprehensive evaluation index (CEI) is proposed to characterize the passengers' satisfaction in numerical computation, on considering multi-satisfaction objectives including Predicted Mean Vote (PMV), local temperature, air quality, and energy efficiency; and it formulates the pre-conditioning parameter scheme for each climate scene with CEI. Several scene cases are carried out to verify the effectiveness of the proposed models. The result shows that the pre-conditioning schemes of the model can effectively satisfy passengers in multi-satisfaction objectives. [ABSTRACT FROM AUTHOR]

In reliability analysis, the probability density function (PDF) of the directional importance sampling method is based on a multi-dimensional vector (i.e., multivariate), thus it is inefficient to obtain the important directional vectors (IDVs) by sampling each dimensional component randomly. In this paper, an efficient solution approach of vector-angle geometric mapping is proposed. Firstly, the angles between IDVs and the design point position vector are set as the important direction angles (IDAs) in the standard Gaussian space. By exploring the geometric relationship between IDVs and IDAs, the PDF of multi-dimensional IDV can be converted into the PDF of one-dimensional IDA, following which, the cumulative distribution function of IDA is derived by integration. Further, the cumulative distribution is sampled uniformly using the Latin hypercube technique, and then the uniform IDAs are generated by inversion. Finally, the IDVs are shown by geometric mapping of the IDAs. The research results show that the PDF of IDA is jointly determined by the two parameters, dimensionality and reliability index. Therefore, the distribution characteristics of IDA can be explored and diagrammatically represented, and the obtained IDVs can be used repeatedly for other reliability analysis with the same mentioned parameters to improve the computational efficiency. The applicability, accuracy, and robustness of the proposed approach are proved on illustrative examples, battery pack and truss structure engineering applications. [ABSTRACT FROM AUTHOR]

With the different physiological properties and thermal conditions, different body parts of passengers have inconsistent thermal sensations and thermal requirements in a highly non-uniform and transient vehicle cabin thermal environment. Determining the thermal comfort requirements for different body parts of a passenger is essential for effectively supplying warm air to the right human part especially for electric vehicles with energy-saving attributes. In this paper, a comprehensive numerical model that integrates human thermal regulation mechanism and dynamic environmental characteristics is established to calculate the thermal comfort for passengers via thermal responses to a dynamic environment. The numerical computation sets up such a model structure, firstly considering human thermal regulation functions into the thermal response to the in-cabin dynamic thermal distributions, then combining Berkeley thermal comfort model to identify the thermal comfort level at different body parts, that would implement total numerical simulations to get thermal comfort evaluation, independent of human subjective feedbacks. The model is validated by experiments with an acceptable error and implemented for a cabin heating case study. The models can effectively predict the thermal comfort and thermal requirements of various body parts in a dynamic environment with human thermoregulation, as an important tool for designing a non-uniform environment. [ABSTRACT FROM AUTHOR]

• YOLOv8-GP based on key point detection is proposed. • C2f in the backbone network is replaced by C2f-Faster-EMA. • Multi-scale feature fusion is achieved by using BiFPN in Neck network. • While reducing the calculation, the AP of grape is improved by 3.3 %. • The positioning error of the picking point is kept within 30 pixels. Precise positioning of fruit and picking point is crucial for harvesting table grapes using automated picking robots in an unstructured agricultural environment. Most studies employ multi-step methods for locating picking points based on fruit detection, leading to slow detection speed, cumbersome models, and algorithmic fragmentation. This study proposes an improved YOLOv8-GP (YOLOv8-Grape and picking point) model based on YOLOv8n-Pose to solve the problem of simultaneous detection of grape clusters and picking points. YOLOv8-GP is an end-to-end network that integrates object detection and key point detection. Specifically, the Bottleneck in C2f is replaced with FasterNet Block that incorporates EMA (Efficient Multi-Scale Attention), resulting in C2f-Faster-EMA. BiFPN is applied to substitute the original PAN as Neck network. The FasterNet Block, designed based on partial convolution (PConv), reduces redundant computation and memory access, thereby extracting spatial features more efficiently. The EMA attention mechanism achieves performance gains with lower computational overhead. Furthermore, BiFPN is employed to enhance the effect of feature fusion. Experimental results demonstrate that YOLOv8-GP achieves AP of 89.7 % for grape cluster detection and a Euclidean distance error of less than 30 pixels for picking point detection. Additionally, the number of Params is reduced by 47.73 %, and the model complexity GFlops is 6.1G. In summary, YOLOv8-GP offers excellent detection performance, while the reduced number of parameters and model complexity contribute to lower deployment costs and easier implementation on mobile robots. [ABSTRACT FROM AUTHOR]

Steering angle and driving force saturations are inevitable in autonomous vehicle motions, which will degrade the motion control performance and even deteriorate the driving safety. In addition, the strong coupling of lateral and longitudinal motions of autonomous vehicles also contributes to the saturations damage and increases the control difficulty. To reduce these negative impacts on motion control, a novel motion controller for automated vehicles is presented in this paper. Firstly, an extended 3 degree of freedom vehicle dynamic model is built with the unknown saturations, strong-coupled motion characteristics and external disturbance considered simultaneously. Then, a coordinated controller is designed to complete motion control task based on sliding-mode control (SMC) and non-singular fast terminal sliding-mode control (NFTSM). Moreover, a three-layer radial basis function neural network (RBFNN) is adopted as a compensator to approximate modelling uncertainties. Based on Lyapunov stability theory, the uniform ultimate boundednesses of motion control errors are guaranteed. Simulation results show that the proposed controller owns smaller steady-state errors and faster convergence speed. [ABSTRACT FROM AUTHOR]

Airfoil optimization algorithm is studied and a hybrid PSO and GA method is proposed in this paper. After function test, it shows that algorithm is well in convergence performance, fast speed, and optimization capability. Then, the airfoil parametric expression theory is analyzed. A new airfoil is obtained after combining CFD and PSO-GA optimization. The aerodynamic of new airfoil is compared with the airfoil optimized by GA-PSO and basic airfoil NACA0018. The results indicate that new airfoil is better than the other two airfoils in lift coefficient, lift-drag ratio, and surface pressure. At last, wing-sail of new airfoil and NACA0018 wing-sail are designed and manufactured. Both of them are applied in land-yacht robot linear motion and steering motion experiment. For the linear motion, in the situation of wind speed being 15 m/s and angle of attack being 5, running speed of robot with optimized new wing-sail is 1.853 m/s. In steering motion, trajectory with new wing-sail is closer to the real situation and it gets more thrust. The experiments data verify that the simulation results are correct and PSO-GA algorithm is effective. [ABSTRACT FROM AUTHOR]

This paper focuses on the mobile robot path planning problem of optimizing the performance metrics of bidirectional A* algorithm in randomized two-dimensional map environments. An algorithm called direction constraints adaptive extended bidirectional A* (DCAE-BA*), which is an improvement of the traditional target dynamic bidirectional A* algorithm (TTD-BA*), is proposed to improve the performance metrics of the algorithm. Regarding the improvement, we propose the adaptive extension method and the direction-constrained optimal node extension method (DCONE). Simulation experiments were conducted for DCAE-BA*, TTD-BA* and traditional A* algorithm (A*) in a large number of random two-dimensional map environments. The simulation experimental scenarios consider four types of start and end point relative directions and three obstacle proportions to objectively and comprehensively evaluate the performance of the proposed algorithms. The results show that different scenarios have a significant impact on the algorithm performance metrics. Finally, the overall performance of the proposed algorithm is evaluated with a large number of experiments in "random" scenarios, and the results show that DCAE-BA* obtains significantly better search time for all three obstacle proportions, and better path length and number of expanded nodes for 10% and 25% obstacle proportions. The effectiveness of the proposed DCAE-BA* algorithm is demonstrated, which provides an essential reference for the path planning of mobile robots in a random 2D map environment. • DCAE-BA* is improved by adaptive extension and direction-constrained optimal node extension (DCONE). • The Start-End Dir and obstacle proportions have a significant impact on the algorithm metrics. • Overall, the DCAE-BA* has the highest computational efficiency for all three types of obstacle proportion. • DCAE-BA* optimizes the path length in the "diagonal" scenario most significantly. • As the proportion of obstacles increases, the metric optimization effect decreases. [ABSTRACT FROM AUTHOR]

The dynamics of assembled structures are significantly dependent on joints. Joints parameters, owing to be difficultly measured, are always ignored by pure rigid in numerical modeling and it will result in unreliable or even error descriptions. 1~3 Hz deviations may cause resonance especially in engineering optimization issues; hence it is necessary to identify joints parameters for structural dynamics investigation. In present work, multiobjective optimization algorithms are used to identify joints parameters by approaching actual test results in each series of structure to decrease unreliable or error for numerical modeling. Taking automotive dynamics with seam-welding and spot-welding as examples, the relationship of joints parameters perturbation and structural dynamics is derived to give the selecting reason of parameters’ identification. The actual dynamics of an SUV’s frame and a thin-walled part in BIW (body in white) are utilized to validate the methodology. Results demonstrate that the validated model has enough accuracy to reflect the dynamics of the actual structure. The methodology provides reliable guarantee for dynamic analysis and the design of structure. [ABSTRACT FROM AUTHOR]

• Modeling algorithms for human thermal sensation integrating physiological responses. • The change rate reveals TS and avoids the inconsistency of raw physiological levels. • CRPR and CRMAP are significantly different in warm chamber than in the cool. • Case analysis verifies feasibility of proposed thermal sensation model with 0.8 R 2. Background and Objectives: Thermal conditions are changeable in cabin space, where occupants could suffer consecutive self-thermoregulation to such changing thermal stresses. Thermal environment management is expected to be purposefully auto-adjustable for the environment by recognizing individual real-time thermal sensations. Current thermal sensation evaluation models are developed for virtual simulations rather than for realistic scenarios, challenging to evaluate human thermal sensation in the field surveys. Methods: The study constructs a human thermal sensation model via human physiological responses to evaluate the human thermal sensation in the actual vehicle environment. The thermal sensation model forms with exponential functions to clarify the relationship between thermal sensation and pulse rate and blood pressure, which successfully expresses the approximately linear trend around neutral sensation and compensates for the end-points bias. The study set up experimental cases to determine the parameter states in the thermal sensation model. Firstly, subjective thermal sensation scoring was performed by combing with an established seven-point-scale questionnaire survey system for human thermal sensation. Wearable sensors are then applied to measure the human physiological response, including blood pressure B P , pulse rate P R and blood oxygen saturation S p O 2. Results: The subjects revealed significantly higher pulse rates (positively correlated) and lower blood pressure (negatively correlated) in the warm chamber than in the cool chamber. The defined parameter change rate effectively reveals the trend of human thermal sensation and avoids the inconsistency of raw physiological response levels. The change rate in P R and M A P between the thermal sensation in cold -3 and hot +3 is about a 10% difference. Conclusions: Based on the thermal sensation model algorithm, model parameters were fitted by the subjects' thermal sensation voting and the change rate of their physiological responses. With the coefficient of determination (R 2) of the regression over 0.8, the proposed thermal sensation model can be employed for human thermal sensation evaluation. The physiological thermoregulatory responses effectively indicate the thermal state of the human body and can be used in thermal environments in conjunction with human smart wearable devices. [ABSTRACT FROM AUTHOR]

• A detection model of sesame and weeds based on YOLOv4, YOLO-sesame, is proposed. • Using local importance pooling to add attention mechanism to SPP structure. • Use SE block to improve the logic module of local importance pooling. • The ASFF structure is integrated to solve the problem of missing detection. • Effectively improve the detection accuracy while maintaining a fast detection speed. Weeds have a significant impact on sesame throughout its early stages of development, thus they must be rigorously controlled. However, the shape of sesame seedlings and weeds are similar, and the size specifications are not defined, making reliable weed detection difficult. To achieve the goal of weed recognition, the majority of solutions now use a deep learning model to learn the weed image. Weed targets with big variances in size and specification are easy to overlook with the current deep learning algorithm. As a result, standard deep learning models have room for improvement when it comes to sesame and weed recognition rates. The YOLO-sesame model is proposed to improve the efficiency and accuracy of sesame weed identification. Based on the YOLOv4 model, an attention mechanism is introduced. Local importance pooling is added to the SPP layer, on which the SE module is used as a logical module. To address the issue of large differences in target size and specifications, an adaptive spatial feature fusion structure is included at the feature fusion level. The experimental results suggest that the YOLO-sesame model proposed in this study outperforms mainstream models such as Fast R-CNN, SSD, YOLOv3, YOLOv4, and YOLOv4-tiny in terms of detection performance. Sesame crops and weeds received F1 scores of 0.91 and 0.92, respectively, while the mAP was 96.16%. The detecting frame rate was 36.8 per second. In conclusion, the YOLO-sesame model successfully meets the needs for accurate sesame weed detection. [ABSTRACT FROM AUTHOR]

Collision avoidance is one of the most important requirements for autonomous vehicles, particularly in complex and congested traffic scenarios where trajectories have little safety redundancy. However, simultaneously reaching the required accuracy and universal feasibility for different collision-avoidance behaviours is difficult due to the multi-state coupled motion of vehicles. To achieve the maximum traversability and ensure the safety of autonomous vehicles in any complex scenarios, we propose a quasi-critical collision-avoidance strategy based on a newly developed algorithm: the exclusive area-of-relative-velocity vector. This strategy first involves the construction of an exclusive area-of-velocity vector for each object vehicle to extract its position relative to the subject vehicle. In this procedure, to establish a subject-motion-decoupled scenario, projective transformation is applied to regularise the moving elliptical contour of the subject vehicle as a settled circle while retaining all positional relationships between the subject and object vehicles using the invariants. Subsequently, a group of escaping conditions for this exclusive area are established to express this quasi-critical collision-avoidance strategy explicitly and mathematically. The ultimate ability to escape from such an area is determined through theoretical derivations and experiments according to vehicle dynamics. In terms of real scenario data, a set of escaping equations is established to calculate the escaping conditions subject to the current state and the ultimate motion ability. Via scenario verifications, this strategy is shown to represent the safety boundary accurately and ensure quasi-critical collision-avoidance conditions under complex scenarios. • The multi-state coupled motion of self-driving vehicles can be effectively decoupled while retaining their positional relationships using the motion-decoupled extraction method proposed in this study. • The exclusive area of relative velocity vector algorithm proposed in this study produces trajectories that are free of potential collisions and available for tracking. • We propose a collision avoidance strategy with equivalence, apriority, behaviour universality, and spatio-temporal uniformity for autonomous vehicles in complex traffic condition. [ABSTRACT FROM AUTHOR]

• An image restoration method based on convolutional autoencoder is proposed. • Use the area growth method to divide and code the obstruction according to the contour. • Predict the overall outline of citrus based on the unobstructed citrus outline. • Fill the area to be repaired with the selection method of similar pixels. • The repair rate and image recognition rate have been effectively improved. At present, machine vision and deep learning theories have been widely used in fruit recognition and picking. However, in the process of identification and picking, there are often situations where the target is blocked, existing methods cannot accurately identify, or the identification accuracy rate is low. For improving the recognition rate of fruits under occlusion, this paper proposes an image restoration method based on a convolutional auto-encoder. This method first encodes the obstruction and then determines the general shape of the occluded fruit and compares the general shape with the coding part is combined to determine the area to be repaired. Finally, the area to be repaired is filled with pixels to realize the image repair and recognition of the occlusion map. The average repair rate of the method proposed in this paper is 95.96%, the restoration rate is 3.52% higher than the traditional convolution method, the L2 loss value is 0.63% lower than the traditional convolution method, the average detection accuracy of the restored fruits is 94.77%. [ABSTRACT FROM AUTHOR]

• A multi-scale feature extraction module (MFE) is designed by using multi-layer dilated convolution to obtain the multi-scale feature information of pest image with a large receptive field without increasing the amount of model parameters. • A deep feature extraction module (DFE) is proposed. Firstly, the deep feature information of pest image is obtained by multiple convolution operations, and then the obtained deep feature image is restored to the size of the original image by deconvolution. At the same time, the deep and shallow feature information are fused by multiple skip-connection structure (Zhang et al., 2019) to reduce the loss of feature information. • We propose a network model based on multi-scale feature fusion to accurately identify and classify crop pests. Experiments on 12 types of pest datasets show that our method has better performance in pest identification and classification than other advanced methods, its classification accuracy (ACC) reaches 98.2%, and the model training time is only 197 min. Crop diseases and insect pests are a serious natural disaster, which needs to be predicted and monitored in time to ensure the output of crops. Due to the wide variety of pests and the similar morphology of crops in the early stages of growth, it is difficult for agricultural workers to accurately identify various types of pests. Crop insects have brought huge challenges to the prevention and control of plant diseases and insect pests. In response to this problem, we propose a way of classification of crop pests based on multi-scale feature fusion(MFFNet) to accurately recognizes and classifies crop pests. First, the multi-scale feature extraction module (MFE) is designed by using dilated convolution to obtain the multi-scale feature map of the pest image. At the same time, extracted the deep feature information of the image by the feature extraction module (DFE). Finally, the features extracted separately by the multi-scale feature extraction module (MFE) and the feature extraction module (DFE) were fused thus achieving accurately classified and identified the crops insects by the way of end-to-end. Experiments show that our proposed method has obtained excellent classification performance on the dataset of 12 types of pests, its classification accuracy rate (ACC) reached 98.2%. [ABSTRACT FROM AUTHOR]

To study the influence of anisotropic transport properties of porous layers (PLs) on the dynamic performance (DP) of fuel cells (FC), a numerical model was developed. The model uses self-compiled codes to couple the actual structure of PLs with its transport capability, while introducing a non-homogeneous model that reflects the effect of the actual agglomerate structure of the catalyst layer on the electrochemical reaction, based on the consideration of the internal heat transport and external heat exchange of FC, the three-phase transformation of water, the change of momentum, energy, species, and water transport with time, and finally realizes the transient changes of complete transport and reaction process inside the FC. Using the current density as the input load, the law of the anisotropic transport properties of PLs on the FC's DP was explored. The results show that among all properties, the proton conductivity has the most significant influence on DP indexes such as steady-state transition time, over/undershoot amplitude, the electrical conductivity and diffusivity have similar and significant effects, the thermal conductivity and permeability have minor and most negligible effects, respectively. When designing the material, structure and composition of the PLs, the focus should be on protons, electron conductivity and diffusivity. • The effect of porous layers anisotropy on the transient behavior of FC was analyzed. • Coupled structural parameters and transport properties of the porous layers. • The effect of the CL's actual agglomerate structure on FC performance is considered. • Pointed out the porous layers transport capability that should be enhanced in focus. [ABSTRACT FROM AUTHOR]

Based on the requirements of regulation ECE R66, a finite element (FE) model for bus body section is built with its simulation accuracy verified by test, and the rollover process and structural deformation of bus are analyzed. On this basis a FE model for body section with occupants is then constructed to investigate the motion path, injury status and their evaluation of occupants at different positions with and without safety belt worn. [ABSTRACT FROM AUTHOR]

• An algorithm for detecting ripe fruit under natural illumination and occlusion conditions is proposed. • A graying factor K is proposed to remove the complex background of colorful ripe fruits. • Improving the Hough Transform by selecting equidistant points and increasing judgement threshold. • A real contour Hough Transform was proposed to detect ripe fruits or occluded circles. For an accurate detection of ripe fruits under uneven illumination and ubiquitous occlusion conditions, this paper proposes a method to detect and locate ripe fruits based on machine vision. There are four key steps in this method including image graying and background removal, binary image optimization, true contour fragments extraction, and fruit fitting. For testing the proposed method, field experiments were conducted with tomato and citrus, and the ripe fruits in complex environments were successfully detected and located. From the detection experiments, it showed that the recognition rate for ripe fruits in the near zone of the proposed method was higher than 97.44%, the average time consumption was 0.2966 s, and the positioning error was less than 4.41%. In addition, it can be concluded from the comparative experiment that the proposed method is superior to conventional Hough transform, random Hough transform, and other methods based on deep learning in terms of detection rate, time performance and positioning accuracy. Therefore, it can be applied to picking robots for real-time detecting and locating ripe fruits. [ABSTRACT FROM AUTHOR]

A numerical simulation of combined natural convection and radiation in a square enclosure heated by a centric circular cylinder and filled with absorbing-emitting medium is presented. The ideal gas law and the discrete ordinates method are used to model the density changes due to temperature differences and the radiation heat transfer correspondingly. The influence of Rayleigh number, optical thickness and temperature difference on flow and temperature fields along with the natural convection, radiation and total Nusselt number at the source surfaces is studied. The results reveal that the radiation heat transfer as well as the optical thickness of the fluid has a distinct effect on the fluid flow phenomena, especially at high Rayleigh number. The heat transfer and so the Nusselt number decreases with increase in optical thickness, while increases greatly with increase in temperature difference. The variation in radiation heat transfer with optical thickness and temperature difference is much more obvious as comparison with convection heat transfer. [ABSTRACT FROM AUTHOR]

Internal short circuits and thermal runaway in lithium-ion batteries (LIBs) are mainly caused by deformation-induced failures in their internal components. Understanding the mechanisms of mechanical failure in the internal materials is of much importance for the design of LIB pack safety. In this work, the constitutive behaviors and deformation-induced failures of these component materials were tested and simulated. The stress-strain constitutive models of the anode/cathode and the separator under uniaxial tensile and compressive loads were proposed, and maximum tensile strain failure criteria were used to simulate the failure behaviors on these materials under the biaxial indentations. In order to understand the deformation failure mechanisms of ultrathin and multilayer materials within the prismatic cell, a mesoscale layer element model (LEM) with a separator-cathode-separator-anode structure was constructed. The deformation failure of LEM under spherical punches of different sizes was analyzed in detail, and the results were experimentally verified. Furthermore, the n-layer LEM stacked structure numerical model was constructed to calculate the progressive failure mechanisms of cathodes and anodes under punches. The results of test and simulation show the fracture failure of the cathodes under local indentation will trigger the failure of adjacent layers successively, and the internal short circuits are ultimately caused by separator failure owing to fractures and slips in the electrodes. The results improve the understanding of the failure behavior of the component materials in prismatic lithium-ion batteries, and provide some safety suggestions for the battery structure design in the future. [ABSTRACT FROM AUTHOR]

• An algorithm for identifying and locating sugarcane stem nodes is proposed. • Use the horizontal projection function of binary image to intercept the region of interest. • Use vertical projection function and local pixels to locate sugarcane stem nodes. • The algorithm has good performance in recognition rate, accuracy and time-consuming. Aiming at the difficulty of sugarcane nodes identification and location during automatic cutting of sugarcane seeds, based on machine vision system, this paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Firstly, according to the color and texture characteristics of yellow sugarcane, the captured RGB image of sugarcane was converted into HSV color image. Then, the S-component image in the HSV image was extracted, and the S-component image was binarized by the Otsu algorithm to obtain the binary image, and then the binary image was processed by morphological closed operation, which can eliminate the noise and fill holes of binary image. Subsequently, the sugarcane area was segmented as the region of interest through the horizontal projection of the binary image, which lowered the amount of calculation while reducing interference. Finally, the vertical projection function of the binary image of the region of interest was established, and the function was continuously derived to obtain the minimum points, and then the position of sugarcane nodes was preliminarily determined by the obtained minimum points. Then, the final position of the sugarcane nodes were determined according to the number of nodes to be identified and the sum of pixels of each 5 columns on both sides of the minimum points. The pixel column positions corresponding to the minima of the sum are the accurate nodes positions determined by the proposed algorithm. The experimental results show that the algorithm proposed in this paper has a single node identification rate of 100%, with an average time consumption of 0.15 s, and a position deviation of less than 0.34 mm; a double nodes identification rate of 98.5%, with an average time consumption of 0.21 s, and a position deviation of less than 0.42 mm. Compared with other nodes identification algorithms mentioned in this paper, it has higher identification rate and accuracy. [ABSTRACT FROM AUTHOR]

In view of the disadvantages of the existing pose estimation algorithm, which has low real-time performance and the positioning accuracy will be greatly reduced in dynamic scene, a compound deep learning and parallel computing algorithm (DP-PE) is proposed. The detection algorithm based on deep learning is used to detect dynamic objects in the environment, and the dynamic feature points are removed before the matching of feature points to reduce the impact of dynamic objects on the positioning accuracy; A method for distinguishing "pseudo-dynamic objects" is proposed to solve the problem that the stationary vehicles and pedestrians in the environment are regarded as dynamic objects. The parallel computing framework for feature point extraction and matching is established on CPU-GPU heterogeneous platform to speed up DP-PE; In the localization part of DP-PE, we propose a 3D interior point detection strategy to achieve parallel search of map points, and the saturated linear kernel function is used to act on reprojection error to realize the parallelization of pose optimization. We verify the algorithm on KITTI dataset, the experimental results show that average speedup ratio of feature point extraction and matching is 6.5 times, and the overall computational efficiency of DP-PE is about 7 times higher than that before acceleration, which can realize high precision and efficient pose estimation in dynamic scene. [ABSTRACT FROM AUTHOR]

• A prediction point Hough transform with a maximum error of 0.5°is proposed. • Based on the G-B and 2G-B-R grayscale factors, a new grayscale factor is proposed. • The least square method is used to optimize the traditional Hough transform. Accurate extraction of navigation path is very important for automated navigation of agricultural robots. Based on the machine vision system, this paper proposes a new algorithm for the fitting of navigation path of greenhouse cucumber-picking robots. Aiming at the problems that the traditional Hough transform has a large amount of calculation and the least square method has a low precision, the prediction point Hough transform algorithm is proposed to extract the navigation path. The prediction-point Hough transform contains 4 parts: intercept area of interest, image segmentation, navigation point extraction, navigation path fitting. In this paper, only the final 160-pixel rows of image captured by the camera are taken as the region of interest. In the image segmentation stage, this paper proposes a new graying factor. For navigation path extraction, a regression equation is used to determine the prediction point, and finally the proposed prediction point Hough transform is used to fit the navigation path. The experimental results show that the proposed grayscale factor can well segment cucumber plants and soil, and the segmentation effect is better than 2G-B-R and G-B grayscale factors. The proposed prediction point Hough transform fits the navigation paths with an average error less than 0.5°, which is 10.25° lower than the average error of the least-square method. Also, the computation time of the proposed Hough transform is 17.92 ms. Compared with the traditional Hough transform, it saves 35.20 ms. [ABSTRACT FROM AUTHOR]

• A median point Hough transform with a maximum error of 0.5°was proposed. • Based on the 2G-B-R factor, a new grayscale factor is proposed to realize the segmentation of crops and soil. • Get the coordinate of the navigation points through the relative coordinate center of the pixel. The extraction of navigation paths is essential for the autonomous navigation of an agricultural robot. As most of the current navigation path extraction algorithms are time-consuming, difficult and susceptible to interference, the median point Hough transformation algorithm was proposed in this paper based on an optical system to fit the navigation paths of a greenhouse tomato-cucumber spraying robot quickly and accurately. The algorithm consists of four parts: interception of the target area, image segmentation, navigation points extraction and navigation path fitting. Image segmentation was based on the improved grayscale factor and Otsu method to achieve the segmentation of soil and plants. The navigation points then were extracted based on the relative coordinate center of the white pixel points of the binary image. Finally, the navigation path was fitted by the median point Hough transformation method. The experimental results show that the maximum error of the navigation path fitted by the improved Hough transformation was 0.5° and the time taken was 7.13 ms, which is 45. 24 ms faster than the traditional Hough transform and on average 5.1° more accurate than the least squares method. [ABSTRACT FROM AUTHOR]

In online battery model identification by recursive least squares (RLS), the identification biases are generated by the noises in the voltage and current measurements, further resulting in the accuracy degradation of model-based state of charge (SOC) estimation. Firstly, the detailed formula derivation presents the relationship between noise variances and identification biases in least squares. Then, through the practical identification on a general battery model, the consistent results from the formulas and simulations both adequately and quantitatively verify that the model identified by RLS is biased, when either only one of voltage and current measurements or both are corrupted by noises. To further assess the noise effects on SOC and parameter estimations, a conventional co-estimation algorithm joining RLS and extended Kalman filter (EKF) is applied into the simulations and experiments especially under noise corrupted measurements, the numerical results show that the estimation accuracy degradation generated by noises is quite considerable. Hence, bias compensation RLS and EKF co-estimation algorithms are proposed to alleviate the impact of the noises. Simulation and experiment studies show that the proposed algorithms can compensate the model identification biases caused by noises and can enhance SOC estimation accuracy under noise corrupted measurements. • Noise corruption on battery model identification bias in RLS is investigated. • Biases generated even only voltage or current measurement is corrupted by noise. • Noise effect on conventional RLS based co-estimation algorithm is investigated. • Three bias compensation RLS based co-estimation algorithms are proposed. • The superiority of the proposed methods is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

Blunt cardiac injury (BCI) in the traffic accidents usually causes serious consequences like heart failure or even death, and the impact of steering wheel to the thorax is one of the main causes of blunt heart injury. Therefore, more realistic heart models are needed to predict the biomechanical response and related injury of the heart during a thorax-to-steering wheel impact. This study developed three-dimensional finite element (FE) models of lungs, aorta and heart, and validated the models through published cadaver tests by comparing the contact force and intracardiac pressures in four heart chambers. Then, various simulations of thorax-to-steering wheel impact were carried out at different impact heights (lower, middle and higher) and different inclination angles of steering wheel (15∘, 30∘, 45∘ and 60∘) to investigate the effects of impact height and inclination angle on heart injury. The result showed that (1) the biomechanical response of the heart model was agree with the test data; (2) the contact force, myocardial stress and intracardiac pressure were decreased when the inclination angle was increased; (3) when the impact height was middle and the inclination angle of steering wheel was 15∘, the myocardial stress and intracardiac pressure were both maximum which indicated that the blunt heart injury was more likely to occur in this condition. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive fuzzy prescribed time H ∞ congestion control problem for a class of high speed transmission control protocol/active queue management (HSTCP/AQM) systems subject to asymmetric input saturation and external disturbance. Firstly, a saturation-based finite-time performance function (SFTPF) is constructed by introducing a nonnegative modified signal associated with input saturation errors into user-defined performance boundaries, which features the capability of ensuring the user-appointed tracking performance when no saturation exists and degrading it once saturation occurs, thereby effectively increasing the flexibility and reliability of the control scheme. Then, by designing a novel shifting function, the condition that the initial value of the queue tracking error remains within that of SFTPF is relaxed, which avoids the SFTPF being redeveloped for different initial queue tracking errors. Thereafter, an auxiliary system and H ∞ control theory are employed to eliminate the adverse effect caused by input saturation and external disturbance, respectively. Moreover, through the Lyapunov stability analysis, it is proven that, under our proposed adaptive fuzzy congestion control scheme, all the closed-loop signals are bounded, and the connection between input saturation and performance-related constraint is established. Finally, a set of simulation results are presented to demonstrate the feasibility and effectiveness of the designed congestion controller. • A new shifting function is developed to relax the initial condition restriction. • An error transformation function is designed to solve asymmetric constrained issues. • The performance constraint boundaries can be resiliently enlarged and recovered. • An adaptive fuzzy practical prescribed time congestion control scheme is proposed. [ABSTRACT FROM AUTHOR]

• A semicircular baffle flow field with helical structure is proposed. • Considering the anisotropic mass and heat transfer properties of the porous layers. • Considering the actual agglomerate structure of the cathode catalyst layer. • The optimized flow field increases the fuel cell's net power density by 11.42 %. To effectively improve the fuel cell (FC) mass transport capacity, a new flow field (FF) design with helical baffle at the cathode is proposed, which facilitates gas flow and mass transfer in both through- and in-plane directions. To fully understand the influence of various design parameters on the FC performance, a series of studies were carried out with a semicircular baffle as an example. The effect of the baffle structure on the complex heat and mass transport process is studied in detail to obtain the optimal baffle structure parameters. To simulate the complete transport process, a three-dimensional, multiphase, non-isothermal steady-state model was developed, embedding the anisotropic transport properties caused by the porous layer structures and the heterogeneous model of the actual agglomerate structure of the catalyst layer in the model. The results show that the helical baffles induce cross flow under the ribs while inducing forced convection, enhancing the oxygen supply in both directions. The FF structure with baffle height and pitch of 0.4 mm and 1.0 mm respectively has the maximum net power density. Taking the relative humidity = 50 % and 100 % as an example, the net power density is increased by 11.42 % and 5.72 % respectively compared with the original FF. [ABSTRACT FROM AUTHOR]

Heat pipe cooling for battery thermal management systems (BTMSs) in electric vehicles (EVs) is growing due to its advantages of high cooling efficiency, compact structure and flexible geometry. Considering the transient conduction, phase change and uncertain thermal conditions in a heat pipe, it is challenging to obtain the dynamic thermal characteristics accurately in such complex heat and mass transfer process. In this paper, a “segmented” thermal resistance model of a heat pipe is proposed based on thermal circuit method. The equivalent conductivities of different segments, viz. the evaporator and condenser of pipe, are used to determine their own thermal parameters and conditions integrated into the thermal model of battery for a complete three-dimensional (3D) computational fluid dynamics (CFD) simulation. The proposed “segmented” model shows more precise than the “non-segmented” model by the comparison of simulated and experimental temperature distribution and variation of an ultra-thin micro heat pipe (UMHP) battery pack, and has less calculation error to obtain dynamic thermal behavior for exact thermal design, management and control of heat pipe BTMSs. Using the “segmented” model, the cooling effect of the UMHP pack with different natural/forced convection and arrangements is predicted, and the results correspond well to the tests. [ABSTRACT FROM AUTHOR]

From 1990 to approximately 50,000–120,000 people die annually of road traffic accidents in China. Traffic accidents are the main cause of death of Chinese adults aged 15–45 years. This study aimed to determine the biomechanical response and injury tolerance of the human body in traffic accidents. The subject was a 35-year-old male with a height of 170 cm, weight of 70 kg and Chinese characteristics at the 50th percentile. Geometry was generated by computed tomography and magnetic resonance imaging. A human-body biomechanical model was then developed. The model featured in great detail the main anatomical characteristics of skeletal tissues, soft tissues and internal organs, including the head, neck, shoulder, thoracic cage, abdomen, spine, pelvis, pleurae and lungs, heart, aorta, arms, legs, and other muscle tissues and skeletons. The material properties of all tissues in the human body model were obtained from the literature. Material properties were developed in the LS-DYNA code to simulate the mechanical behaviour of the biological tissues in the human body. The model was validated against cadaver responses to frontal and side impact. The predicted model response reasonably agreed with the experimental data, and the model can further be used to evaluate thoracic injury in real-world crashes. We believe that the transportation industry can use numerical models in the future to simultaneously reduce physical testing and improve automotive safety. [ABSTRACT FROM AUTHOR]

This review concentrated on proton exchange membrane fuel cell (PEMFC) water management, which introduces the latest research contents and progresses on membrane electrode assembly (MEA) components, and summarizes the influencing factors about the properties of them. The PEMFC's core component is MEA which consists of proton exchange membrane, catalyst layer, microporous layer and carbon fiber paper. The properties of each component have significant impacts on the fuel cell (FC) performance, and there will be strong interactions between some properties. Therefore, the FC performance is determined by the properties of each component and their interactions. It is the basis for the design of the high-performance FCs to clarify the influence mechanism of various properties and their interactions on the FC performance, but there is currently no summary on this aspect. Therefore, the conclusions about the same component or property drawn from all literatures are sorted and combined, so that we can intuitively know which literatures have studied and reached the conclusions, and comprehensively realize the influence mechanism of a certain property. Based on the development and achievement of each component in some aspects, this article makes some predictions and discussions on the future research direction and development prospects of each component. • The influence relationships between the properties of MEA components are discussed. • The influences of MEA components' properties on fuel cell performance are analyzed. • Pointed out the ambiguity in some conclusions and explained the possible reasons. • The joint effect of MEA components on the water management capability are presented. [ABSTRACT FROM AUTHOR]

In order to predict and evaluate the human head fractures and brain damage caused by rotational acceleration in car crashes, both geometric and finite element (FE) models for human head having Chinese body features are established and verified based on the CT scanning on a 50 percentile Chinese male volunteer. The comparison of the results of simulation on FE model in terms of kinematic and dynamic responses with those of experiment in this paper as well as Nahum's and Trosseille's classical experiments show that their intracranial pressure distributions all assume a coup-contrecoup pressure gradient distribution mode and their peak pressures agree well with each other. These demonstrate the high biofidelity of the model. [ABSTRACT FROM AUTHOR]

With a hat-shape reinforcement beam for the cross member of a SUV as an example, the effects of stamping forming-caused changes in material properties on its crashworthiness is studied. Taking forming factors into consideration with the plastic strain and thickness unevenness as the initial conditions of crash analysis, a simulation on structural crashworthiness is conducted and compared with the results of conventional crash simulation and tests. The results show that work hardening raises the stiffness of structure and may cause change in its deformation mode; and thickness unevenness has great influence on the deformation and energy absorption of structure. Hence the forming factors should be introduced into impact simulation for improve its accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In the paper, crashworthiness of the S-shaped rail extracted from the frontal frame in a car is studied. In order to reduce the peak impact force while increasing the total absorbed energy, the hybrid materials are employed in that rail, where aluminum alloy is used for its front part and advanced high strength steel (AHSS) for its back part. By designing 16 experiments based on orthogonal experiment, the effect of five influence factors with four levels on the crash performance of the steel–aluminum hybrid S-shaped front rail is emphatically investigated. These influence factors include the different material types of aluminum alloy and AHSS, the sheet thicknesses of the two parts, and length proportion for the aluminum part. The AHSS includes dual-phase (DP) steel, transformation induced plasticity (Trip) steel and Mart steel; the aluminum alloy series include 5000 and 6000 series. The research result shows that the use of steel–aluminum hybrid materials can reduce the peak impact force and the total weight for the S-shaped front rail, while the total absorbed energy can be greatly increased, so the crashworthiness and lightweight of the S-shaped front rail are significantly improved. [Copyright &y& Elsevier]

Purpose: Because of the high computational efficiency, response surface method (RSM) has been widely used in structural reliability analysis. However, for a highly nonlinear limit state function (LSF), the approximate accuracy of the failure probability mainly depends on the design point, and the result is that the response surface function composed of initial experimental points rarely fits the LSF exactly. The inaccurate design points usually cause some errors in the traditional RSM. The purpose of this paper is to present a hybrid method combining adaptive moving experimental points strategy and RSM, describing a new response surface using downhill simplex algorithm (DSA-RSM). Design/methodology/approach: In DSA-RSM, the operation mechanism principle of the basic DSA, in which local descending vectors are automatically generated, was studied. Then, the search strategy of the basic DSA was changed and the RSM approximate model was reconstructed by combining the direct search advantage of DSA with the reliability mechanism of response surface analysis. Findings: The computational power of the proposed method is demonstrated by solving four structural reliability problems, including the actual engineering problem of a car collision. Compared to specific structural reliability analysis methods, the approach of modified DSA interpolation response surface for structural reliability has a good convergent capability and computational accuracy. Originality/value: This paper proposes a new RSM technology based on proxy model to complete the reliability analysis. The originality of this paper is to present an improved RSM that adjusts the position of the experimental points judiciously by using the DSA principle to make the fitted response surface closer to the actual limit state surface. [ABSTRACT FROM AUTHOR]

Summary: A simple and rapid multiplexed direct competitive chemiluminescent (CL) imaging immunoassay had been developed for the simultaneous detection of kanamycin (KAN) and streptomycin (STR), which were widely used against bacterial infections in animals. To achieve the multiplexed detection of the two targets, a microarray format based on nitrocellulose membranes (NCMs) has been designed. Then, the peroxidase activity of the captured HRP‐labelled immunocomplex in each channel was measured by an enhanced luminol‐based chemiluminescent solution using a cooled low‐light CCD with high resolution. For qualitative analysis, the limit of detection (LOD) was 0.5 ng mL−1 for KAN and 3.13 ng mL−1 for STR by the naked eye. Through the CL data collected, KAN and STR could be detected quantitatively at the LOD of 0.03 and 0.33 ng mL−1, respectively. This method can be utilised as a screening test to evaluate the presence of antibiotics in milk samples. [ABSTRACT FROM AUTHOR]

Purpose: Meta-model method has been widely used in structural reliability optimization design. The main limitation of this method is that it is difficult to quantify the error caused by the meta-model approximation, which leads to the inaccuracy of the optimization results of the reliability evaluation. Taking the local high efficiency of the proxy model, this paper aims to propose a local effective constrained response surface method (LEC-RSM) based on a meta-model. Design/methodology/approach: The operating mechanisms of LEC-RSM is to calculate the index of the local relative importance based on numerical theory and capture the most effective area in the entire design space, as well as selecting important analysis domains for sample changes. To improve the efficiency of the algorithm, the constrained efficient set algorithm (ESA) is introduced, in which the sample point validity is identified based on the reliability information obtained in the previous cycle and then the boundary sampling points that violate the constraint conditions are ignored or eliminated. Findings: The computational power of the proposed method is demonstrated by solving two mathematical problems and the actual engineering optimization problem of a car collision. LEC-RSM makes it easier to achieve the optimal performance, less feature evaluation and fewer algorithm iterations. Originality/value: This paper proposes a new RSM technology based on proxy model to complete the reliability design. The originality of this paper is to increase the sampling points by identifying the local importance of the analysis domain and introduce the constrained ESA to improve the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

The gas diffusion layer (GDL) is currently manufactured from randomly stacked carbon fibers, with each layer forming a different pore structure. This investigation attempts to know the effect of the random poral microstructures on the water transport capacity. The stochastic reconstruction approach to numerically establishes random fiber distributions in GDL and a three-dimensional two-phase volume of fluid model are developed and validated. By combining the two models the liquid water transporting inside the randomly different GDLs is numerically processed. Thus, those observed relations, such as the water coverage ratio (WCR) of the fiber layer (FL) plane and gas channel wall, saturation and pressure drop, are comparable in simulations. The results show that even if the porosity of each FL is the same, the uncertainty of liquid water movement due to the random structure can cause large state deviations with different GDLs, within the studied range. They can lead to significant differences in oxygen content within the same GDL volume and ultimately results in significantly different fuel cell performance. The mechanism of the effect of the randomness of GDL microstructure on water transport capacity is beneficial for further mining valid characteristic information of FLs and structure improvement. • The GDL pore structure was established using the stochastic reconstruction method. • A model that captures the behavior of liquid water through the pores was developed. • The effect of random stacking of fibers on liquid water transfer was investigated. • The effect of fiber layers' random arrangement on water transfer was investigated. [ABSTRACT FROM AUTHOR]

In order to realize the fuel cell (FC) operation with higher power density, researchers have made various attempts to it. The innovative design of bipolar plate (BPP) can contribute about 20% to the volumetric power density increase. Mass transfer and drainage capacity are two keys of plate design. In order to improve its mass transfer capacity, many improvements have been made, such as baffle flow field (FF) and biomimetic FF. These FFs have their own advantages and disadvantages. For facilitate comparison, this article summarizes the influence of various FF designs, structures, flow patterns and other parameters on FC performance, and gives the BPP's development direction in the future. The effects of different FF structures, geometric parameters and arrangement forms on the uniformity of i distribution are summarized. The effects of contact area, channel design parameters such as channel width/rib ratio, fillet radius and channel size on the BPP's interfacial contact resistance are reviewed. Guidance is provided for designing FFs that can achieve more uniform i distribution and smaller contact resistance. The main manufacturing processes of BPPs are reviewed, and then the difficulties that need to be overcome in order to manufacture higher performance BPPs are pointed out. • The influences of various BPP flow field designs on FC performance are summarized. • The effects of BPP on current distribution and contact resistance are summarized. • The fabrication process of BPPs and existing difficulties are summarized. • The development directions of BPP fabrication and flow field design are predicted. [ABSTRACT FROM AUTHOR]

• An ultrasonic duplex model was developed for flaw detection in long butt-weld. • Floating potential can be used to parse several geometrical parameters of flaw. • Global energy vanishing time of flaw position exhibited an upward spiral. • Flaw length effects the peak number of floating potential upper envelope curve. • The energy decay rate increases with increasing flaw orientation angle. Non-destructive testing (NDT), as an indispensable technique for assessing welding quality, is expected to continuously improve. In this study, to obtain welding flaw geometrical parameters more simply and conveniently, their correlations with floating potential were explored using groups of simulation experiments. All the simulation experiments were performed with a well-designed duplex finite element model, which can simulate ultrasonic guided wave testing (UGWT) of flaws embedded in the metro traction beam butt weld. Experimental results proved that different geometrical parameters of welding flaws exhibited different traits on floating potential. Position, length and orientation parameters of flaw related individually to the energy vanishing point, the number of peaks, the travelling time between peaks, the energy decay rate and so on. Thus, recording the floating potential of the entire UGWT process shows great promise in elucidating flaw geometrical parameter information and makes the proposed approach possible for practical use. [ABSTRACT FROM AUTHOR]