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As off-road vehicles, in addition to field transportation, another vital function of agricultural tractors is to provide power for field machinery. Therefore, the dynamic performance of the power take-off (PTO) driveline directly affects the field reliability of tractors. Firstly, a torsional vibration coupled spatial dynamics model of the power take-off driveline is proposed according to the classical machine driveline dynamics and gear dynamics theory. In the dynamics model, the interactions among the vertical, lateral, and rotational motions of the driveline parts are fully included. The coupling vibrations from internal excitations (such as tooth surface friction, gear time-varying mesh, and engine pulse) and external excitations (such as field machinery load) are also considered. Secondly, the simulation results of the model are obtained using the numerical solving algorithm ode15s. The actual experiment is carried out on the indoor Tractor PTO Test Bench. Then, the model is verified by comparing the test results with the simulation results. Finally, the dynamic characteristics of the whole driveline are revealed under different drive modes, especially strong interactions between the driveline and field machinery in low-speed and heavy-load mode. The gear mesh forces and the root mean square (RMS) values of the acceleration amplitude for the main parts generally decrease gradually with the increase in the PTO rotation speed and the decrease in PTO torque. Furthermore, the model can be applied to reliability assessment, for instance, vibration, damage, and fatigue of off-road vehicles considering gear transmissions, particularly in a field working environment.

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Aiming at the serious problem of agricultural tractor emission pollution, especially the limitation of nitrogen dioxide (NOx) and soot emissions, we took an agricultural diesel engine as the research object, and a diesel engine combustion chamber model was established for both simulated calculations and experimental verification analysis. The in-cylinder pressure and heat release obtained from the combustion chamber model simulation calculations were within 6% error of the experimental data. The overall trend of change was basically consistent. The established model can simulate the working conditions of the experimental engine relatively well. An artificial neural network (ANN) was also established as an agent model based on the indentation rate, tab depth, and combustion chamber depth as the input, and NOx and soot as the output. The decision coefficients of the ANN model were R2 = 0.95 and 0.93, with corresponding Mean Relative Error (MRE) values of 10.13 and 8.18%, respectively, which are within the generally required interval, indicating that the obtained ANN model has good adaptability and accuracy. On the basis of the general particle swarm optimization (PSO) algorithm, an improved PSO algorithm was proposed, in which the inertia factor is continuously adjusted with the help of the skip line function in the optimization process so that the inertia factor adapts to different rates and adjusts the magnitude of the corresponding values in different periods. The improved PSO algorithm was used to optimize the optimal input parameter matching of the agent model to form a new combustion chamber structure, which was imported into CONVERGE CFD software for emission simulation and comparison with the emissions of the original combustion chamber. It was found that the NOx reduction was about 1.21 g·(kW·h)−1, and the soot reduction was about 0.06 g·(kW·h)−1 with the new combustion chamber structure. The ANN + PSO optimization method proved to be effective in reducing the NOx and soot emissions of diesel engine pollutants, and it may also provide a reference and ideas for the design and development of relevant agricultural engine combustion chamber systems.

Tractors are becoming increasingly intelligent due to developments in electronics and information technology. Amid the large quantities of information in the driving cab, drivers may lack the attention resources demanded when operating, thus leading to neglect of important information. Therefore, the interface design stage must consider the human factors of the design. Designers must study and optimize the factors influencing attention allocation related to the interface design. In this study, the criticality of information is first proposed for quantifying the importance of various information in the display interface. Subsequently, the factors affecting attention allocation are investigated through experimental methods. Finally, a matching index is proposed to evaluate the interface design with respect to attention allocation. This enables the optimization of the interface design by matching attention allocation and criticality of the information. The matching index can thus serve as a design specification for tractor head-up display interfaces. K E Y W O R D S attention allocation, criticality of information, effort, eye-tracking, matching index 1 | INTRODUCTION Tractors are an important piece of agricultural machinery, with a wide variety of operating functions, such as sowing, and plowing, which improve agricultural productivity. The operating environment of tractors is complex, and the operating conditions poor. During busy seasons, the driver may work more than 10 hr every day. Consequently, tractor drivers experience a high labor intensity. Therefore, it is particularly important to consider ergonomic features during the tractor cab design stage, so that the designed cab can not only meet the operational requirements of the driver, but also enable the driver to operate efficiently and comfortably. The tractor display interface is the interactive interface between the driver and the tractor. It provides the driver with the required information, such as the internal parameters of the tractor, the position of the tractor, the status of the farm tools, and environmental conditions, among others. The tractor display interface is an extremely important part of the tractor cab. The design of the display interface determines the speed and accuracy at which the driver can perceive information. Due to recent improvements in agricultural machinery automation levels, large-wheeled tractors are becoming increasingly intelligent and electromechanically integrated. Driver tasks are shifting from solely operational to both operational and supervisory. At the same time, due to the development of automotive electronics, information, and other technologies, the amount of information that needs to be monitored simultaneously is gradually increasing. Since human attention resources are limited, drivers must reasonably allocate attention resources to deal with a variety of information. If gauges are amassed randomly, without any design specification during the display interface design stage, drivers can easily overlook key inf...