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This study was undertaken to develop a new index, the Airport Runway Roughness Index (ARRI), that considers the effect of runway space when evaluating airport runway roughness. The difference between roadway and runway roughness evaluations is analysed in detail and the spatial effect is considered for a proposed runway roughness evaluation model. The evaluation of runway roughness should fully account for the dynamic responses of different typical aircraft during taxiing at the most unfavourable speed for the full cross-section of the runway. ARRI is defined according to the normalised weighted average of the vibration responses of typical aircraft models at the most unfavourable excitation waveband, the most unfavourable taxiing speed, and the most unfavourable lateral offset position within the longitudinal distance of the runway. Using runway three-dimensional full-section unevenness data collection equipment, a runway roughness evaluation procedure that is based on ARRI is proposed. The ARRI system provides qualitative improvement in terms of its representation ability, the rationality of the evaluation criteria, and the correlation of the indicators. [ABSTRACT FROM AUTHOR]

This study was undertaken to develop a new index, the Main Landing Gear Cumulative Stroke (MLGCS) index, to evaluate airport runway roughness. Using ADAMS/Aircraft software, we first developed and validated a virtual prototype model of the Boeing B737-800 aircraft and then employed the model to predict the aircraft's dynamic responses with regard to roughness. Using this prototype model and comparing it to the International Roughness Index (IRI) that is designed to evaluate roadway roughness, we established a landing gear cumulative stroke (LGCS) model to represent runway roughness. The results show that taxiing speed is an important factor using the LGCS model. According to the most unfavorable scenario, we determined the LGCS of the nose landing gear to be 100 km/h and that of the main landing gear to be 60 km/h. These results underscore that, based on correlation analysis, using the cumulative stroke of the main landing gear is more reasonable than using that of the nose landing gear to evaluate runway roughness. Furthermore, based on measured data for 37 runways, a comparison of commonly used roughness indices indicates that the MLGCS index proposed in this paper is superior to both the Boeing Bump Index and the IRI. [ABSTRACT FROM AUTHOR]

With the rapid development of the vehicle chassis control and autonomous driving technology, it is more and more urgent to realize the active steering technology of autonomous driving stability control. Under emergency conditions, the adhesion constraints, the model uncertainty, and the strong nonlinearity of vehicle bring great challenges to active steering control. In this paper, a model predictive control method for an active steering system based on a nonlinear vehicle model is proposed to solve the problems of adhesion constraint, model uncertainty, and external disturbance in the active steering system. Based on the real-time measurement of vehicle state, a new optimization method is proposed in this paper, which has good performance in dealing with the uncertainty and nonlinearity of the model. The control method transforms the constraint problem into quadratic programming and nonlinear programming. In order to ensure the control accuracy when the vehicle enters the nonlinear area, the control model is built with the combination of the nonlinear tire model and the 2DOF model. The control model is built based on Simulink, and the effectiveness of the controller is the verified joint simulation of Simulink and CarSim. The hardware-in-the-loop (HIL) test bench based on LabVIEW RT is built and tested in order to verify the feasibility and real effect of the controller. Simulation and HIL test results demonstrate that, compared with PID controller, the model predictive controller can accomplish the driving task well and improve the vehicle handling stability. [ABSTRACT FROM AUTHOR]

• The method monitors rigid pavement strength without interference to airfield operations. • Over two years, the slab modulus stays constant; the subgrade reaction modulus decreases. • Merging instrumentation and simulation samples creates a diverse pavement database. • Neural network models effectively correlate instrumentation data with rigid airfield pavement properties. The pavement strength evaluation plays a vital role in the airfield operation and management. Traditionally, the evaluation has relied on the Heavy Weight Deflectometer (HWD) test. This method encounters challenges, including interruptions in airfield operations, limited coverage of inspection locations, extensive time required for data collection and analysis. This paper introduces a methodology for evaluating the structural strength of airfield rigid pavement using instrumentation data, aiming to enhance both the efficiency and accuracy of such evaluations. Strain responses, collected using the instrumentation system at Chengdu Tianfu International Airport under HWD loading, are extensively analyzed. A three-dimensional numerical simulation model encompassing nine slabs was formulated, leveraging finite element analysis to replicate pavement working conditions that were not covered by the instrumentation samples. The merging of the instrumentation and simulation samples resulted in a comprehensive database for diverse pavement working conditions. This facilitated the establishment of a data-driven framework for the fusion of mechanical models. From this database, a neural network mapping model was developed to correlate the instrumentation data with the pavement slab modulus and subgrade reaction modulus. These correlations were subsequently employed for the calculation of the pavement classification number (PCN). The primary advantages of the proposed method include real-time monitoring and evaluation of airfield rigid pavement strength without interference to airfield operations. The developed method has been deployed at seven hub airports in China, encompassing Shanghai Pudong International Airport (PVG), Beijing Capital International Airport (PEK), and Chengdu Tianfu International Airport (TFU), etc. [ABSTRACT FROM AUTHOR]

Dynamic soil stress in the ground induced by aircraft loads can lead to excessive and differential settlements. The effect of aircraft speeds on the dynamic stress is complicated due to runway roughness, lift force and wave propagation. In this paper, a virtual prototype model of B777-300 was initially developed to investigate the aircraft loads impacted by runway roughness and lift force. Subsequently, a finite element model of the runway was established for the dynamic stress in the ground subjected to moving aircraft loads. Results show that as aircraft speed increases, the aircraft-load-induced dynamic stress in the ground first increases and then decreases. The critical speed of dynamic stress accompanied with a peak stress varies with soil depth. In the shallow soil layers, dynamic stress in the ground is mainly affected by runway roughness and lift force. The critical speed of dynamic stress is close to the sensitive speed corresponding to peak aircraft loads. With the increase of soil depth and aircraft speed, the effect of dynamic amplification due to wave propagation becomes more pronounced. In the deep soil layers, the critical speed of dynamic stress approaches the Rayleigh wave velocity of soils, especially when soft soil layers exist. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of airfield pavement damage is crucial to airport flight safety and airfield pavement maintenance. An efficient and lightweight detection algorithm that can be embedded into the mobile detection device has been in urgent demand. However, traditional Convolutional Neural Networks (CNNs) usually generate redundant feature maps during feature extraction or use extra operations during feature fusion to gain better performance, which greatly challenges the efficiency of the algorithm. We approached this issue by proposing an accurate and efficient detection algorithm, the YOLOv5-APD. The algorithm improves the model performance in two ways: Speeding up training and inferencing by using cheaper operations during feature extraction; Reducing the model complexity by removing redundant nodes during feature fusion. We verified the detection performance of YOLOv5-APD on a self-made dataset and compared it with the other state-of-the-art (SOTA) models. Then ablation experiments were carried out to investigate the effects of the proposed model design and the impact of image augmentation. Results showed that the proposed YOLOv5-APD model outperformed the SOTA algorithms in model performance and efficiency, which attained the optimal performance mean average precision (mAP) of 0.924. The proposed model also achieved the fastest inference speed of 142 frame-per-second (FPS), with a model footprint of 8.3 G FLOPs and 8 MB Parameters. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of airfield pavement damage is crucial to airport flight safety and airfield pavement maintenance. An efficient and lightweight detection algorithm that can be embedded into the mobile detection device has been in urgent demand. However, traditional Convolutional Neural Networks (CNNs) usually generate redundant feature maps during feature extraction or use extra operations during feature fusion to gain better performance, which greatly challenges the efficiency of the algorithm. We approached this issue by proposing an accurate and efficient detection algorithm, the YOLOv5-APD. The algorithm improves the model performance in two ways: Speeding up training and inferencing by using cheaper operations during feature extraction; Reducing the model complexity by removing redundant nodes during feature fusion. We verified the detection performance of YOLOv5-APD on a self-made dataset and compared it with the other state-of-the-art (SOTA) models. Then ablation experiments were carried out to investigate the effects of the proposed model design and the impact of image augmentation. Results showed that the proposed YOLOv5-APD model outperformed the SOTA algorithms in model performance and efficiency, which attained the optimal performance mean average precision (mAP) of 0.924. The proposed model also achieved the fastest inference speed of 142 frame-per-second (FPS), with a model footprint of 8.3 G FLOPs and 8 MB Parameters. [ABSTRACT FROM AUTHOR]

Airport runway roughness can be characterised by power spectral density (PSD) and two spectrum parameters, namely, the unevenness index and waviness. In order to find suitable spectrum parameters for airport runway roughness, seven runway profiles in China were measured to expand the current database of runway profiles. The statistical distributions of the spectrum parameters for runway roughness then were calculated, and the effects of the spectrum parameters on aircraft vibrations were analysed. The results show that the mode of the unevenness index is 0.13, which is a little lower than that of an A-level road, and the mode of waviness is 2.2, which is higher than the waviness recommended in ISO road classification. Moreover, as the unevenness index and the waviness increase, the aircraft vibrations become more severe. The aircraft vibration responses can be represented as a function of waviness, unevenness index, and speed, with a high correlation. At a constant speed, the relationship between the threshold values of the unevenness index and waviness can be shown as a straight line in a log plot. Finally, taking 200 km/h as the representative speed, threshold curves of the spectrum parameters, which are based on ride comfort levels and statistical analysis are proposed for the the simulation of aircraft vibration. [ABSTRACT FROM AUTHOR]

Under long-term tram loads, cracking occurs on the composite pavement surrounding an embedded tram track. However, in the current pavement design of the shared right-of-way section of a tram track, the trackside pavement fatigue characteristics are not considered. The investigation of the real dynamic strain response of the trackside composite pavement under tram loads is crucial for pavement system design. In this paper, a three-dimensional finite element model of tram-track- composite pavement was established. The rationality of this model was verified by a field pavement dynamic test. The influence of the track irregularity and tram speed on the dynamic characteristics of the pavement surrounding the track was explored. The trackside pavement strain distribution along the longitudinal and vertical directions of the track had been studied. The high-frequency strain components caused by the random shortwave track irregularity excitation were found in the strain frequency domain curves in different directions, and the track irregularity amplifies the influence of the tram speed on the surrounding pavement dynamic response. The loading frequency is an important input for determining the dynamic model and fatigue characteristics of asphalt materials. At the same time, the lab test loading frequency and fatigue life corresponding to the mixture were calculated by the mechanistic coupled model and empirical damage evolution equation. It provides an analytical base for the design of pavement surrounding embedded tram tracks. • Three-dimensional FEM of tram-track-asphalt pavement was established. • The trackside pavement stress distribution had been studied. • Reason for the cracking of the trackside pavement were explained. • Indoor loading frequency of trackside pavement was higher than that of municipal pavement. [ABSTRACT FROM AUTHOR]

• Extreme value distribution better represents aircraft wheel wander. • The damage caused by each load repetition is considered individually. • The load transfer effect is taken into account using finite element analysis. • The proposed method typically produces smaller CDF values. This paper proposes an approach to determine cumulative damage factors (CDFs) based on Miner's law to improve airport runway rigid pavement design. The approach uses measured aircraft wheel wander data and constructed influence surfaces. In this study, we developed an aircraft wheel wander measuring system and measured actual aircraft wheel loading locations at the Shanghai Hongqiao International Airport (SHA). We constructed influence surfaces using finite element analysis (FEA) and multivariate interpolation. We conducted FEA of a nine-slab model to simulate load transfer effects. Based on the measured wheel wander distribution and the constructed influence surface, a cumulative damage surface construction approach for CDF determination is proposed. The preliminary benefits of the proposed cumulative damage surface method are that (1) the damage caused by each load repetition is considered individually for the CDF calculation and (2) the load transfer effect is taken into account using FEA simulations. Also, the field data we collected at SHA show that, compared to normal (uniform) distribution, extreme value distribution better represents aircraft wheel wander. A comparison between the CDFs calculated using normal distribution and extreme value distribution indicates that the maximum CDF is increased by 210% using extreme value distribution. According to the several cases analyzed in this study, the proposed cumulative damage surface method typically produces smaller CDF values than those calculated using the traditional Civil Aviation Administration of China method. Traditional airport runway rigid pavement design methods could be improved and optimized in terms of accuracy and performance using the proposed cumulative damage surface method for CDF determination. [ABSTRACT FROM AUTHOR]

Objective. The current study sought to compare the effects of the addition of Qingshen granules to conventional Western medicine on immune function in patients with comorbid chronic renal failure and damp-heat syndrome and to explore the possible mechanisms responsible for any differences observed. Methods. Through a multicenter, randomized, controlled study, a total of 282 eligible patients were divided into experimental (n = 136) and control groups (n = 146). All of the patients were treated with conventional Western medical therapy. The experimental group also received Qingshen granules three times daily for 12 weeks. Clinical efficacy was observed in the two groups. Peripheral blood levels of CD4+ T cells, CD8+ T cells, Th17 cells, nuclear factor-κB p65 (NF-κB p65) activity, serum interleukin-17 (IL-17), serum interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), tumor necrosis factor receptor-associated factor 6 (TRAF6), fibronectin (FN), and type IV collagen (Col-IV) were detected in both groups. Results. The total clinical curative effective rate was significantly higher p < 0.05 in the experimental group (79.41%) than in the control group (67.12%). Before treatment, there were no significant differences in CD4+/CD8+ T cell ratio, Th17 cell level, NF-κB p65 activity, serum IL-17, IL-6, TNF-α, TRAF6, FN, and Col-IV between the experimental and control groups p > 0.05 ; however, all of the measures were significantly higher than those observed in a healthy comparison group (p < 0.05 or p < 0.01). After treatment, the above indexes in the experimental group were significantly lower than those before treatment (p < 0.05 or p < 0.01). Similarly, NF-κB p65 activity, serum IL-17, TNF-α, TRAF6, FN, and Col-IV in the control group were significantly lower than the levels observed prior to treatment (p < 0.05 or p < 0.01); however, while all of the other indexes were lower than those observed before treatment, the differences were not statistically significant p > 0.05 . Conclusion. Qingshen granules adjust immune dysfunction, improve immunity mediated inflammatory response, and attenuate renal fibrosis in patients with comorbid chronic renal failure and damp-heat syndrome. [ABSTRACT FROM AUTHOR]

The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better. [ABSTRACT FROM AUTHOR]

For electric power steering system (EPS), road interference, noise of the sensor, and the uncertainty of the steering system may make EPS control effect and the driver's road sense worse. EPS system which takes advantage of good current tracking ability, good anti-interference ability, and good operation stability is becoming more and more important in automotive research. The traditional H∞ control algorithm can solve the system uncertainty theoretically, but it cannot solve the contradiction between robustness and performance without considering the performance of the system. Therefore, this paper proposes a EPS current tracking method based on the hybrid sensitivity H∞ control algorithm, which takes the current tracking performance as one of the control objectives, so that the system can maximize the robustness and performance. Firstly, the dynamic model of EPS is established. Then, the two-degree-of-freedom vehicle model and tire model are introduced. The state space equation of the system is constructed on the basis of the system state space with random disturbance signals, the hybrid sensitivity H∞ controller is designed in the sensitivity index design, and the proposed algorithm can use weighting function to minimize the performance of the current tracking error as well as the robustness of the yaw rate error in response to robustness. Simulation analysis and experimental verification of EPS system are also carried out. The results show that the control method of the hybrid sensitivity H∞ can better achieve EPS target current tracking, effectively suppress the effect of external interference and noise, improve the system performance and robustness, ensure the driver get good road sense, and improve the system of steering stability. [ABSTRACT FROM AUTHOR]

Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability. [ABSTRACT FROM AUTHOR]