Runzhi Liang

This study investigates noise detection and damping-based noise mitigation strategies for cavity structures, with a specific focus on addressing noise issues in the conveyor trough of combine harvesters. Despite its practical significance, research on the noise generation mechanisms, transmission paths, and control measures for conveyor troughs remains limited, particularly under varying operational conditions. To bridge this gap, this work integrates experimental measurements with numerical simulations to systematically analyze and optimize the noise reduction performance of the conveyor trough. Noise measurements were conducted using the sound intensity method, revealing sound pressure levels in the range of 93–95 dB. Frequency spectrum analysis identified key noise sources and dominant frequency components. Finite element analysis (FEA) and vibration modal testing were performed to uncover critical noise-inducing factors, including chain meshing impacts and structural resonances. Based on these findings, a damping optimization strategy was proposed by incorporating constrained damping layers to attenuate vibration and reduce noise in targeted frequency bands. The effectiveness of this approach was validated through multiple coherence analysis, which confirmed significant suppression of structural vibration noise in the 0–500 Hz range, while experimental results showed that the optimized conveyor trough structure achieved a maximum reduction of 0.4071 dB in continuous equivalent A-weighted sound pressure under load conditions. This research provides a comprehensive methodology for noise control and structural optimization of conveyor trough systems, offering valuable theoretical and practical insights for enhancing the operational comfort and environmental performance of combine harvesters.

The noise generated by combine harvesters during operation has drawn growing attention, particularly that of the conveying trough shell, whose noise generation mechanism remains unclear. This study investigated the vibration radiation noise characteristics of conveying troughs by analyzing a chain system with 83 links using numerical simulation and experimental validation. A dynamic model of the conveyor chain system was developed, and the time domain reaction force at the bearing support was used as excitation for the trough shell’s finite element model. Modal and harmonic response analyses were performed to obtain the vibration response, which served as an acoustic boundary input for the LMS Virtual Lab. The indirect boundary element method was used to compute the radiated noise, achieving coupled modeling of chain system vibration and trough shell noise. Simulation results revealed that the maximum radiated noise occurred at approximately 112 Hz, closely matching experimental data. Comparative analysis of transmitted noise at 500 Hz and 700 Hz showed acoustic power levels of 98.4 dB and 109.52 dB, respectively. Results indicate that transmitted noise dominates over structural radiation in energy contribution, highlighting it as the primary noise path. This work offers a validated prediction model and supports noise control design for combine harvester conveying troughs.

Sustainable agriculture and soil conservation methods are integral to ensuring food safety and mitigating environmental impacts worldwide. However, crop residue/straw serves many vital functions from tillage to harvest, so that quantifying the appropriate amount of Crop Straw Cover (CSC) on the soil surface is crucial for monitoring tillage intensity and crop yield performance. Thus, a novel research study is conducted to develop an innovative approach for accurately estimating and mapping the Wheat Straw Cover (WSC) percentage through two different multispectral satellites (Sentinel-2B MSI and Landsat-8 OLI-TIRS), using remote sensing-based techniques in Changshu County, China. The field measurements were collected from 80 distinct sites and eight images were acquired through both satellites for the analysis process by applying Crop Residue Indices (CRIs). The results indicate that the coefficients of determination (R²) of the Normalized Difference Tillage Index (NDTI) computed by Sentinel-2 and Landsat-8 were 0.80 and 0.70, respectively, and the root-mean-square deviation (RMSD) values were in the range from 6.88 to 12.04% for CRIs for both satellite data. Additionally, the comparative analysis of the developed model revealed that NDTI was R² = 0.85 and R² = 0.77, followed by STI, R² = 0.82 and R² = 0.80 and NDRI, R² = 0.69 and R² = 0.56 for Sentinel-2B and Landsat-8 data, respectively. Hence, the correlation strength of NDTI, STI and NDRI with WSC percentages was markedly superior by using Sentinel-2B spectral data compared to Landsat-8 ones. Moreover, the NDTI of Sentinel-2B data was the most accurate in mapping the WSC percentage in four categories, with an overall accuracy of 86.53% (κ = 0.78), surpassing the other CRI indices. Therefore, these findings suggest that the multispectral imagery of Sentinel-2B bolstered with enhanced temporal and spatial data was superior for precisely estimating and mapping the WSC percentage compared to Landsat-8 data over a large-scale agricultural region.