Traditional airflow field measurement methods for cotton picker fans (e.g., Pitot tube method) can only obtain discrete point data and fail to collect full-flow field information; although Computational Fluid Dynamics (CFD) simulation enables full-flow field analysis, it is restricted by high computational cost and long cycle, making real-time monitoring difficult. To address these issues, this study proposed a fast and accurate dynamic monitoring method for the airflow field at the outlet of cotton picker fans based on digital twin (DT) technology. Firstly, a high-fidelity CFD model of the centrifugal fan was established, and unsteady simulations were carried out across 1000-4000 r/min to obtain full-flow field data including velocity and pressure distributions. Secondly, four surrogate models—polynomial response surface (PRS), Radial Basis Function (RBF), Kriging (KRG) and support vector regression (SVR)—were compared to select the optimal one for fast airflow field prediction, and the fuzzy C-means (FCM) clustering algorithm was introduced to screen representative core sampling nodes from the original dataset to reduce computational overhead. Thirdly, the optimized KRG model was encapsulated into a. NET standard class library by Matlab, and a windows forms (WinForm) human-machine interface (HMI) platform was developed via C# hybrid programming. Finally, a pneumatic conveying test bench was built, equipped with a PNP Hall effect sensor for rotational speed measurement, and combined with a planar guide rail system to collect high-precision wind speed data at key nodes, completing system performance verification. CFD simulation results showed that fan rotational speed correlated with the average wind speed in the central flow domain: the average wind speed increased approximately linearly at low rotational speeds, while growth slowed with nonlinear characteristics at high rotational speeds. With rising rotational speed, the wind speed difference at the outlet increased and inhomogeneity intensified, reaching 68.86% at 3 500 r/min. Among the four surrogate models, the KRG model performed best, with its root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) all lower than the others. Compared with uniform sampling, FCM clustering reduced training samples by 8.84%, improved response speed by 8.86% and decreased prediction error by 3.9353%. The monitoring system built with 821 clustered representative nodes had an average response time <1 s under all tested rotational speeds. Experimental verification indicated that the average prediction error between system output and measured values was within 9.26%, with the lowest error of 4.90% at 4 000 r/min. Dense grid tests at 3500 r/min showed that the relative error of most measurement points was still below 8% even near the volute tongue, where airflow separation and eddies frequently occurred. The proposed monitoring method effectively integrates CFD simulation, surrogate modeling and FCM clustering technology, enabling fast and accurate full-flow field monitoring of cotton picker fans. Its real-time response and prediction accuracy meet agricultural machinery operational requirements, providing technical support for monitoring and optimizing cotton picker pneumatic conveying systems, offering a transferable reference for airflow field monitoring of other agricultural equipment, and promoting digital twin technology in agricultural engineering.

Tracked combine harvesters have widely used in the hilly and mountainous terrains. It is highly demanded for the highly adaptive adjustment mechanism of the chassis, due to the complex operational environments. However, conventional systems can often suffer from irrational structural parameters, leading to large fluctuations in the track tension, excessive hydraulic loads, high risk of track derailment, and compromised stability during slope-crossing operations. Such challenges have severely limited the machine reliability, service life, and operational efficiency in real field conditions. The objective of this study was to design and then optimize an omnidirectional adjustable chassis. The coordinated lateral and longitudinal attitude adjustments were performed to maintain the stable track tension in order to reduce the requirements of the actuator thrust. The generalizable modeling and optimization framework was also constructed using terrain-adaptive platforms. A parametric model was then established for both the lateral and longitudinal adjustment mechanisms of the chassis. There were geometric relationships between critical link circumferences, hydraulic cylinder strokes, chassis attitude, the resulting track deformation, and tension states. Multiple working conditions were simulated to represent the real terrain scenarios, including the variations in the slope angle, chassis height, and combined attitude states. The structural parameters were also optimized under multi-constraint conditions using the AutoDesign module within the RecurDyn multibody dynamics platform. All components after optimization remained within the allowable stress and stroke limits, with the minimum hydraulic effort. As a result, the front and rear hydraulic cylinder strokes increased by 10.54% and 22.82%, respectively, leading to the reductions of 19.54% and 20.04% in the maximum thrust. The efficiency of the energy transfer was effectively improved to lower the mechanical impact on the support structure. In addition to the physical parameter optimization, a surrogate model was developed to rapidly predict the track circumference under different adjustment conditions. A multilayer perceptron neural network was employed to train the surrogate using adaptive moment estimation. The nonlinear mapping was approximately realized among tension wheel position, hydraulic stroke, and track circumference. The model was also trained on the simulation-generated data in the full adjustment range, and then validated on unseen test scenarios. The better performance was achieved in a root mean square error of 0.514 4 and a coefficient of determination of 0.997 2, indicating the excellent predictive accuracy and generalization suitable for engineering applications. The surrogate model was then embedded into the grey wolf optimizer. The global optimization was then performed on the tension wheel configuration. The optimal tension wheel was then positioned at 296.11 mm from the rearmost support roller. The track circumference remained consistently close to the actual value of 4590 mm under all attitude combinations and chassis height levels. Extensive validation experiments were performed on a full-scale physical prototype of the tracked combine harvester equipped with the optimal chassis. The prototype was tested under a series of lateral and longitudinal adjustment scenarios. Compared with the pre-optimization configuration, the optimal chassis reduced the maximum cylinder thrust by 2.5%-31.8% and the average thrust by 2.94%-19.73%, indicating the high effectiveness of the optimization. Furthermore, the track sag was consistently maintained within the effective tension range of 120-150 mm among the tested configurations. The smoothness, mechanical stability, and robustness of the tension control were substantially improved after adjustment under dynamic attitudes. The mechanical shock loads were reduced during adjustment in order to minimize the risk of slippage and derailment. Ground contact consistency and traction performance were enhanced during operation on uneven terrain. In summary, an integrated approach was combined with parametric modeling, neural network-based surrogate modeling, and optimization. The posture adjustment and track tension control were realized in the tracked agricultural machinery. The optimal chassis system significantly improved the mechanical efficiency to maintain the stable track tension. The practical applicability was obtained to reduce the actuator load in the combine harvesters. Moreover, the modeling and optimization framework demonstrated that the strong generalization was suitable for the construction equipment, autonomous ground vehicles, and mobile robots on complex terrains.