Purpose: Extreme support vector regression (ESVR) has been widely used in the design, analysis and optimization of engineering systems of its fast training speed and good computational ability. However, the ESVR model is only able to utilize one-fidelity information of engineering system. To solve this issue, this paper extends extreme support vector regression (ESVR) to a multi-fidelity surrogate (MFS) model which can make use of a few expensive but higher-fidelity (HF) samples and a lot of inaccurate but cheap low-fidelity (LF) samples, named ESVR-MFS.Design/methodology/approach: In the ESVR-MFS model, a kernel matrix is designed to evaluate the relationship between the HF and LF samples. The root mean square error of HF samples is used as the training error metric, and the optimal hyper-parameters of the kernel matrix are obtained through a heuristic algorithm.Findings: A number of numerical problems and three engineering problems are used to compare the ESVR-MFS model with the single-fidelity ESVR model and two benchmark MFS models. The results show that the ESVR-MFS model exhibits competitive performance in both numerical cases and practical cases tested in this work.Practical implications: The proposed approach exhibits great capability for practical multi-fidelity engineering design problems.Originality/value: A MFS model is proposed based on ESVR, which can make full use of the advantages of both HF data and LF data to achieve optimal results at same or lower cost.

The combine harvester equipped with attitude-adjustment functionality significantly enhances its adaptability to complex terrain but often struggles to maintain the reliability of its mechanisms. Therefore, investigating the dynamic load characteristics of the attitude-adjustment mechanism becomes imperative. This article employed the DEM–FMBD (Discrete Element Method–Flexible Multibody Dynamics) bidirectional coupling simulation method to establish a multibody dynamic model of a tracked combine harvester. The study delved into the interaction mechanism and dynamic stress response characteristics between the tracked chassis and the complex terrain under various height adjustments, lateral adjustment angles, longitudinal adjustment angles, and different field-ridge crossing methods. Finally, the accuracy of the coupled simulation model was validated through a constructed stress detection system. The research findings revealed that the displacement and tilt angle deviation of the hydraulic cylinders utilized to execute the chassis adjustment actions in the constructed coupled simulation model was less than 5%, and the deviation between the simulation results and the actual maximum dynamic stress under multiple working conditions ranged from 7% to 15%. This verification confirmed the effectiveness of the DEM–FMBD coupled simulation method. Under different adjustment conditions, the maximum stress position was consistently distributed in the same area of the left-front and left-rear rotating arms. The primary and secondary effects of the various parts of the adjustment mechanism on the overall reliability of the chassis were as follows: left front > right front > left rear > right rear. By implementing the middle height with the adjustment strategy, the dynamic stress extreme value of the adjustment mechanism can be effectively reduced by 21.98%, thereby enhancing the structural stability of the chassis.

Background: Following the popularization of high-yielding rice in China, fast and efficient mechanized harvesting proved challenging. In addition, the physical characteristics of rice grains and stems are substantially affected during harvest by the field environment and harvest time. However, the combine harvester driver is focused on maximizing the outputs and does not consider the adverse effects of these factors during the rice harvest. Methods: We investigated the effects of the harvest time, spatial position, and temperature on the static friction coefficient of rice grains and stems of high-yielding rice using a field experiment. Results: The result difference in the static friction coefficient between the parallel and perpendicular placements of the rice stem on the steel plate was 9%, indicating that the contact configuration had a significant impact. The region, harvest time, and temperature significantly affected the static friction characteristics of the rice grains and stems. The most significant differences were observed in the X-direction. Conclusions: The optimum harvest time was 10:11 a.m.–3:30 p.m. and the optimum temperature was above 16.5 °C. A quantitative analysis of the effects of the harvest time and temperature on the static friction characteristics of rice provides reliable data for machine design optimization and standardization of harvests operations.