Biosystems Engineering最新文献_第7页

Investigation on heat transfer characteristic of deformable high aspect-ratio biomass particle system in rotary drums with baffles 可变形高长径比生物质颗粒系统挡板转鼓传热特性研究

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104299

Conghui Gu , Yufan Zhang , Yuan Liu , Huiqian Yin , Xingyu Liang , Jingyu Zhu , Kaiyuan Deng , Zhulin Yuan

With the development of carbon reduction technologies, the energy conversion efficiency of the wet biomass particle system has attracted increasing attention. Heat transfer within these systems is a critical factor influencing combustion performance. In this study, experimental and simulation methods were used to investigate the behaviour of deformable high aspect-ratio biomass particles within different structural rotary drums. The particle heat transfer model of deformable high aspect-ratio biomass particles was established and validated. By comparing the simulation results with experimental data, good consistency was demonstrated. It systematically investigates the effects of drum rotation speed, the number of baffles, and drum temperature on the mixing and heat transfer processes of the biomass particle system. It was found that increasing the drum rotation speed facilitates heat transfer in the particles. The best heat transfer efficiency was observed at a speed of 10 r min⁻¹. As the mixing index improved, the heat exchange between particles became more efficient, and higher mixing index led to improved heat transfer efficiency. The highest mixing index reached 0.55, resulting in a notable increase in temperature. Moreover, increasing the number of baffles improved the uniformity of the particle temperature distribution, and optimising the drum temperature further enhanced heat transfer. The optimal results were achieved with 6 baffles and the drum wall temperature of 403.15 K. These findings provide valuable insights for improving energy efficiency in biomass systems.

随着碳减排技术的发展，湿生物质颗粒系统的能量转换效率越来越受到人们的关注。这些系统内的传热是影响燃烧性能的关键因素。在这项研究中，采用实验和模拟的方法来研究可变形的高纵横比生物质颗粒在不同结构的旋转鼓中的行为。建立并验证了可变形高展弦比生物质颗粒的传热模型。通过仿真结果与实验数据的比较，证明了较好的一致性。系统地研究了转鼓转速、挡板数和转鼓温度对生物质颗粒系统混合和传热过程的影响。结果表明，提高转鼓转速有利于颗粒内的传热。在速度为10 r min−1时，传热效率最高。随着混合指数的提高，颗粒间的换热效率提高，混合指数越高，换热效率越高。混合指数最高达到0.55，导致温度显著升高。此外，增加挡板的数量改善了颗粒温度分布的均匀性，优化转鼓温度进一步增强了传热。当挡板6个，鼓壁温度为403.15 K时，效果最佳。这些发现为提高生物质系统的能源效率提供了有价值的见解。

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引用次数: 0

Variations of multiple environmental factors and multi-objective optimisation control in a pig house 猪舍多环境因素变化与多目标优化控制

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104300

Xiaofei Sun , Shengchao Wang , Qiuju Xie , Congcong Sun , Haiming Yu , Wenfeng Wang

With the increasing scale and intensification of pig farming, the environmental quality of pig houses has become crucial for pigs' health and reproductive ability. Numerous environmental factors in the pig house have formed a complex nonlinear microclimate environment that is dynamic, time-varying, and coupled, making it difficult to achieve precise control of the environment. In this study, sixty days of indoor environmental data were collected by an Internet of Things platform to analyse the diurnal changes in indoor temperature, humidity, and concentrations of CO₂ and NH₃, as well as the seasonal changes in winter and summer. A multi-objective control strategy for optimising the pig house environment using Double Deep Q-Network (DDQN) was established. The results showed that 1) there were significantly higher correlations between multiple environmental factors in summer than in winter; 2) the determination coefficients R² of the multiple linear regression models constructed with indoor temperature and CO₂ concentration as dependent variables reached 0.915 and 0.778, respectively; 3) the DDQN control strategy kept the indoor temperature variation within ±1.7 °C, compared to ±2.1 °C with the traditional temperature threshold control strategy (TTCS); 4) the total running time of the three fans in a day under the DDQN control strategy was 28.01 h, with the total power consumption of 11.4 kWh, and an energy-saving rate of 7.39 % and the indoor various environmental factors are closer to the setting values. This research offers feasible reference for the intelligent and energy-saving environmental regulation in large-scale pig production for precise environmental control.

随着生猪养殖规模和集约化程度的不断提高，猪舍环境质量对猪的健康和繁殖能力至关重要。猪舍内众多的环境因素形成了一个复杂的非线性、动态、时变、耦合的小气候环境，难以实现对环境的精确控制。本研究通过物联网平台采集60 d室内环境数据，分析室内温度、湿度、CO2和NH3浓度的日变化以及冬、夏两季的季节变化。建立了基于双深度q网络（DDQN）的猪舍环境多目标优化控制策略。结果表明：1)夏季多环境因子间的相关性显著高于冬季；2)以室内温度和CO2浓度为因变量构建的多元线性回归模型的决定系数R2分别达到0.915和0.778；3) DDQN控制策略将室内温度变化控制在±1.7℃以内，而传统温度阈值控制策略（TTCS）将室内温度变化控制在±2.1℃以内；4) DDQN控制策略下三台风机一天总运行时间为28.01 h，总功耗为11.4 kWh，节能率为7.39%，室内各环境因子更接近设定值。本研究为规模化养猪生产中智能化、节能化的环境调控提供了可行的参考。

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引用次数: 0

CKSP: Cross-species knowledge sharing and preserving for universal animal activity recognition CKSP：跨物种知识共享和保护，用于普遍的动物活动识别

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104303

Axiu Mao , Meilu Zhu , Zhaojin Guo , Zheng He , Tomas Norton , Kai Liu

Deep learning techniques are dominating automated animal activity recognition (AAR) tasks with wearable sensors due to their high performance on large-scale labelled data. However, current deep learning-based AAR models are trained solely on datasets of individual animal species, constraining their applicability in practice and performing poorly when training data are limited. In this study, a one-for-many framework is proposed, dubbed Cross-species Knowledge Sharing and Preserving (CKSP), based on sensor data from diverse animal species. Given the coexistence of generic and species-specific behavioural patterns among different species, a Shared-Preserved Convolution (SPConv) module is designed. This module assigns an individual low-rank convolutional layer to each species for extracting species-specific features and employs a shared full-rank convolutional layer to learn generic features. This enables the CKSP framework to learn inter-species complementarity and alleviates data limitations via increasing data diversity. Considering the training conflict arising from discrepancies in data distributions among species, a Species-specific Batch Normalisation (SBN) module is devised that involves multiple BN layers to separately fit the distributions of different species. To validate CKSP's effectiveness, experiments are performed on three public datasets from horses, sheep, and cattle, respectively. The results show that this approach remarkably boosts the classification performance compared to the baseline method (one-for-one framework) solely trained on individual-species data, with increments of 6.04 %, 2.06 %, and 3.66 % in accuracy, and 10.33 %, 3.67 %, and 7.90 % in F1-score for the horse, sheep, and cattle datasets, respectively. This proves the promising capabilities of the method in leveraging multi-species data to augment classification performance.

深度学习技术由于其在大规模标记数据上的高性能，在可穿戴传感器的自动动物活动识别（AAR）任务中占据主导地位。然而，目前基于深度学习的AAR模型仅在单个动物物种的数据集上进行训练，这限制了其在实践中的适用性，并且在训练数据有限的情况下表现不佳。在本研究中，基于不同动物物种的传感器数据，提出了一个“一对多”的框架，称为跨物种知识共享和保护（CKSP）。考虑到不同物种间的共性行为模式和物种特异性行为模式并存，设计了共享保存卷积（SPConv）模型。该模块为每个物种分配一个单独的低阶卷积层来提取物种特有的特征，并使用一个共享的全阶卷积层来学习通用特征。这使得CKSP框架能够学习物种间的互补性，并通过增加数据多样性来缓解数据限制。考虑到物种间数据分布差异引起的训练冲突，设计了一个物种特异性批归一化（SBN）模块，该模块涉及多个批归一化层，分别拟合不同物种的分布。为了验证CKSP的有效性，分别在马、羊和牛的三个公共数据集上进行了实验。结果表明，与单物种数据训练的基准方法（一对一框架）相比，该方法的分类准确率分别提高了6.04%、2.06%和3.66%，马、羊和牛的f1得分分别提高了10.33%、3.67%和7.90%。这证明了该方法在利用多物种数据来增强分类性能方面的潜力。

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引用次数: 0

FEM-SPG coupling modelling and reinforced soil effect of maize root-soil composite 玉米根-土复合材料FEM-SPG耦合模型及加筋土效应

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104293

Yiwen Yuan , Mingxuan Du , Yanlin Zhou , Yueqian Yang , Xin Zhang , Shuhong Zhao

Root-soil composites, formed by root stubble remaining in the field and the surrounding soil, are major factors affecting the quality of maize seeding operations under conservation tillage system. Addressing this problem requires the development of efficient stubble management equipment. A dynamic simulation model of the cutting effect of a blade is established by coupling finite element method (FEM) and smoothed particle Galerkin (SPG) models. After drawing 3D solid models, soil was discretised into SPG particles, and stubble was divided into finite meshes and given different material properties respectively based on their measured data. Based on the mechanical properties of fibre-reinforced composite material, soil and stubble model were bonded as a composite by contact setting. The coupling between FEM and SPG and the non-uniqueness problem of this contact model were dealt with by the co-nodes. Direct shear simulations at two positions of the maize root-soil composite were carried out using SPG-FEM model and verified experimentally by in-situ tests. Test results showed that shear stress and force predicted by SPG-FEM model were in good agreement with the measured results. The results showed that the reinforcement ability of roots to soil was related to their contact area with soil and distribution uniformity. The effective circumference was shown to be able to predict the depth of maximum shear strength of root-soil composite. These current research results are useful for improving the reliability of numerical simulations of crop root-soil composite and optimising agricultural operation components.

保留在田间的根茬与周围土壤形成的根土复合物是影响保护性耕作制度下玉米播种作业质量的主要因素。解决这一问题需要开发高效的残茬管理设备。采用有限元法（FEM）和光滑粒子伽辽金（SPG）模型相结合的方法，建立了叶片切削效果的动态仿真模型。在绘制三维实体模型后，将土壤离散为SPG颗粒，并根据其实测数据将残茬划分为有限网格，分别赋予其不同的材料属性。基于纤维增强复合材料的力学特性，采用接触设置的方法将土体与残茬模型粘结为复合材料。利用协节点处理了有限元法与SPG法的耦合问题以及该接触模型的非唯一性问题。采用SPG-FEM模型对玉米根土复合材料的两个位置进行了直接剪切模拟，并通过现场试验进行了验证。试验结果表明，SPG-FEM模型预测的剪切应力和剪切力与实测结果吻合较好。结果表明，根系对土壤的加固能力与其与土壤的接触面积和分布均匀性有关。有效周长可以预测根土复合材料的最大抗剪强度深度。这些研究成果对于提高作物根系-土壤复合数值模拟的可靠性和优化农业经营要素具有重要意义。

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引用次数: 0

Modal frequency analysis for Camellia oleifera with sympodial branching growth mode using allometric scaling law 用异速标度法分析油茶对称分枝生长模式的模态频率

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104296

Lewei Tang , Huiyu Zhang , Shiyi Zhang , Rui Pan , Mingliang Wu

In this paper, the branching growth mode of Camellia oleifera is considered to be a sympodial branching pattern. A simplified fractal model of Camellia oleifera is established with several similar basic units. The morphological parameters of the basic bifurcated units in the tree model are correlated according to the biological allometric scaling law. For the basic bifurcated unit, a cantilever trunk modelled as the Euler-Bernoulli beam with two lateral branches is obtained regardless of the mass of foliage. The modal frequency of the basic bifurcated unit is calculated using Hamilton's principle. Finite element simulation using ANSYS software is performed to obtain the simulation modal frequencies in comparison with the theoretical frequencies. An empirical formula relating to the modal frequencies is derived from the perspective of energy transfer. The effect of branch angle on the modal frequency is further investigated. Besides, the empirical formula is extended for the calculation of the modal frequency of three basic bifurcated units. The modal frequency in branch mode is also obtained and compared with the simulation results. In the field measurement, two coefficients involving the lateral branching ratio and the slenderness coefficient were identified from the morphological measurements. The modal frequency for the basic bifurcated unit and the tree were measured using the dynamic signal analyser. The relative errors of the modal frequency from the empirical formulas and the field experiments are less than 6 %, which demonstrates the feasibility of the modal frequency analysis of Camellia oleifera using the proposed empirical formulas.

本文认为油茶的分枝生长模式为对形分支模式。用几个相似的基本单位建立了油茶的简化分形模型。根据生物异速缩放规律，将树模型中基本分叉单元的形态参数进行关联。对于基本分叉单元，无论叶片质量如何，都可以得到具有两个侧枝的欧拉-伯努利梁模型的悬臂主干。利用哈密顿原理计算了基本分岔单元的模态频率。利用ANSYS软件进行有限元仿真，得到仿真模态频率，并与理论频率进行比较。从能量传递的角度推导了与模态频率有关的经验公式。进一步研究了分支角对模态频率的影响。并将经验公式推广到三个基本分叉单元的模态频率计算。得到了分支模态频率，并与仿真结果进行了比较。在野外测量中，从形态测量中确定了两个系数，包括侧枝比和长细系数。利用动态信号分析仪测量了基本分岔单元和树的模态频率。经验公式与田间试验的模态频率相对误差小于6%，证明了利用该经验公式进行油茶模态频率分析的可行性。

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引用次数: 0

Digital simulation of condensation and water loss in ventilated lychee packaging 通风荔枝包装中凝结和水分损失的数字模拟

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104297

Jicheng Lin , Xiaodan Zhang , Yihong Jiang , Dongfeng Liu , Wei Cai , Guopeng Lin , Zhiwu Ding , Enli Lü , Jiaming Guo

Lychee quality deteriorates rapidly post-harvest. Appropriately ventilated packaging can help maintain quality during the supply chain. However, interruptions in the cold chain can lead to temperature differences between the inside and outside of packages, increasing condensation and liquid water formation, which negatively affects lychee quality. This study used numerical simulations to analyse forced ventilation in different packaging structures, focusing on how the number of top openings affects internal temperature, humidity, condensation, and water loss. Additionally, the stacking of packaged lychee was modelled to predict water loss and condensation under actual supply conditions. The results showed that increasing the number of package openings reduces humidity differences and condensation, with eight openings resulting in 9.18 % less water loss rate compared to six openings. Six openings reduced condensation by 23.67 % compared to four openings. Furthermore, during storage and transportation, the amount of water loss and condensation varied by location, with lychees near the air outlet losing less water but experiencing more condensation. The findings of this study provide insights into reducing in-package condensation and water loss in the lychee supply chain, offering a reference for optimising storage and transportation strategies.

荔枝的品质在收获后迅速恶化。适当通风的包装可以帮助在供应链中保持质量。然而，冷链的中断会导致包装内外的温差，增加凝结和液态水的形成，这对荔枝的质量产生负面影响。本研究使用数值模拟分析了不同包装结构的强制通风，重点研究了顶部开口的数量如何影响内部温度、湿度、冷凝和水分损失。此外，还对包装荔枝的堆积进行了模拟，以预测实际供应条件下的水分损失和凝结。结果表明，增加包装开口的数量可以减少湿度差和结露，与6个开口相比，8个开口的失水率降低了9.18%。与四个开口相比，六个开口减少了23.67%的凝结。此外，在储存和运输过程中，水分损失和凝结的量因地点而异，靠近出风口的荔枝失水较少，但凝结较多。本研究结果为减少荔枝供应链中的包装冷凝和水分损失提供了见解，为优化储存和运输策略提供了参考。

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引用次数: 0

Inversion of on-tree peach firmness via high-fidelity fruit finite element models and sim-to-real deep transfer learning 基于高保真水果有限元模型和模拟到真实深度迁移学习的树上桃子坚固度反演

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-09-25 DOI: 10.1016/j.biosystemseng.2025.104291

Jiaqi Xiong , Yilei Hu , Xianbin Gu , Ce Yang , Di Cui

Firmness is an important attribute of peaches related to texture and ripeness. The inversion of on-tree peach firmness contributes to monitoring fruit ripeness and determining the optimal harvest time for enhancing fruit quality. However, accurate inversion of fruit firmness is still challenging because of the limited number, range and distribution of training datasets. Therefore, an acoustic vibration method based on high-fidelity fruit finite element (FE) models and sim-to-real deep transfer learning was proposed for the inversion of the on-tree peach firmness with limited samples. Ten FE models of peaches comprising skin, flesh, pits and kernels were constructed to generate simulated vibration responses of on-tree peaches during fruit ripening. A 1D-Inception-SE network was established to extract features from the vibration responses to characterise peach firmness. To reduce the difference in feature distribution between the simulated dataset and the experimental dataset, a deep transfer learning method based on a domain adversarial neural network was introduced. The results indicated that the high-fidelity FE models of peaches were reliable for simulating the vibration behaviours of on-tree peaches during fruit ripening. The proposed deep transfer learning method raised the R² of the test set from 0.79 to 0.83. Compared to the 1D-Inception-SE model with 320 experimental samples, the deep transfer learning model with both simulated data and 160 experimental samples achieves superior performance while requiring only half the number of experimental samples. The results demonstrated that the proposed method could efficiently and effectively improve the firmness inversion accuracy for on-tree peaches by measuring limited samples.

硬度是桃子的重要属性，与质地和成熟度有关。桃树上硬度的反演有助于监测果实成熟度，确定最佳采收时间，提高果实品质。然而，由于训练数据集的数量、范围和分布有限，水果硬度的准确反演仍然具有挑战性。为此，提出了一种基于高保真水果有限元模型和模拟到真实深度迁移学习的声振动方法，用于有限样本条件下桃的树上硬度反演。建立了包括果皮、果肉、果核和果仁在内的10个桃有限元模型，模拟了桃果实成熟过程的振动响应。建立1D-Inception-SE网络，从振动响应中提取特征来表征桃子的硬度。为了减小模拟数据集与实验数据集特征分布的差异，提出了一种基于领域对抗神经网络的深度迁移学习方法。结果表明，所建立的高保真桃有限元模型能够较好地模拟果实成熟过程中的振动行为。提出的深度迁移学习方法将测试集的R2从0.79提高到0.83。与具有320个实验样本的1D-Inception-SE模型相比，同时具有模拟数据和160个实验样本的深度迁移学习模型在只需要一半实验样本的情况下取得了更好的性能。结果表明，该方法可以有效地提高对有限样本桃的硬度反演精度。

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引用次数: 0

Improved steering model and integrated robust control for a curved path-tracking controller for headland turns 改进转向模型和集成鲁棒控制的弯径跟踪控制器

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-09-23 DOI: 10.1016/j.biosystemseng.2025.104294

Yueqi Ma, Guohui Fu, Chao Ban, Tong Su, Ruijuan Chi

The accuracy of the rice transplanter's curved path-tracking during headland turns significantly impacts the row spacing precision of rice transplanting, especially in the initial stage of each row operation. This study aims to improve the curved path-tracking accuracy of rice transplanters. To achieve this, an improved transplanter steering model and tracking error model are developed based on the transplanter's steering characteristics, using the transplanting arm array centre as the reference point. Building upon the proposed tracking error model, an integrated robust curved path-tracking controller is proposed, combining low-frequency disturbance observer-based feedforward control, Linear Quadratic Regulator control, H-infinity control, and quadratic stability. This controller is robust to perturbation parameters and disturbances caused by uneven paddy field bottom, sideslip, path curvature, and model linearisation, and it also has a rapid convergence rate. Model comparison results indicated that the turning radii predicted by the proposed transplanter steering model closely match the actual turning radii of the rice transplanter, outperforming the conventional Ackermann steering model. Additionally, the controller using the transplanting arm array centre as the reference point exhibited higher tracking accuracy for transplanting arm array centre compared to using the rear axle centre as the reference point. Ablation experiments demonstrated the effectiveness of each component in the proposed control method, among all components, the low-frequency disturbance observer-based feedforward control had the most significant impact on accuracy improvement. Overall, the proposed curved path-tracking controller exhibited high accuracy for curved path-tracking control and effectively meets the operational requirements of the rice transplanter.

插秧机在海陆转弯时的曲线路径跟踪精度对水稻插秧的行距精度有显著影响，特别是在每行操作的初始阶段。本研究旨在提高水稻插秧机的曲线路径跟踪精度。为了实现这一目标，以移植臂阵列中心为参考点，基于移植臂的转向特性，建立了改进的移植臂转向模型和跟踪误差模型。基于所提出的跟踪误差模型，提出了一种基于低频扰动观测器的前馈控制、线性二次型调节器控制、h∞控制和二次型稳定性相结合的综合鲁棒曲线路径跟踪控制器。该控制器对扰动参数和水田底部不平整、侧滑、路径曲率、模型线性化等引起的扰动具有鲁棒性，且收敛速度快。模型对比结果表明，所提出的移栽机转向模型预测的转向半径与水稻移栽机的实际转向半径接近，优于传统的Ackermann转向模型。此外，与使用后桥中心作为参考点相比，以移植臂阵列中心为参考点的控制器对移植臂阵列中心的跟踪精度更高。烧蚀实验验证了所提控制方法各组成部分的有效性，其中基于低频扰动观测器的前馈控制对精度提高的影响最为显著。总体而言，所提出的曲线路径跟踪控制器具有较高的曲线路径跟踪控制精度，能够有效地满足插秧机的操作要求。

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引用次数: 0

Research on intelligent maize targeted fertilisation method based on BPNN PID adaptive position feedback regulation 基于bp神经网络PID自适应位置反馈调节的智能玉米定向施肥方法研究

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-09-22 DOI: 10.1016/j.biosystemseng.2025.104292

Wenqi Zhou, Huaiyu Liu, Yao Wang, Cunliang Liu, Han Tang, Qi Wang, Jinwu Wang

Given problems, such as low accuracy of fertiliser application control, large positioning errors, and poor fault monitoring effects in targeted fertilisation operations, this study proposes an intelligent maize-targeted fertilisation method based on a Backpropagation Neural Network (BPNN) Proportional-Integral-Derivative (PID) adaptive position feedback regulation. With the STM32 microcontroller as the master-slave controller, an intelligent maize-targeted fertilisation system was developed through the design of multi-sensor fusion, control parameter calculation and optimisation, construction of a fertilisation drive device, and fault monitoring system. BPNN PID adaptive optimisation was used to control the angular displacement of the fertiliser applicator, and automatic control technology drove the targeted fertilisation mechanism. By integrating dual photoelectric sensors to detect the target maize, an encoder collects the angular displacement of the fertiliser applicator, a ranging sensor monitors the fertiliser amount in the fertiliser box, a pressure sensor monitors the status of the fertiliser pipe, a positioning sensor monitors the operation speed, and multi-machine communication processes the fertilisation operation data. Targeted control and fault monitoring of fertilisation operations under multi-sensor fusion were realised. The adjustment time of the optimisation algorithm is 0.9 s, and the response is fast. Experiments show that the accuracy of fertiliser application control is greater than 95 %, the average positioning error of fertilisation is less than 28.1 mm, the fault alarm success rate reaches 97 %, and the average response time of fault alarm is less than 0.45 s. The intelligent maize-targeted fertilisation method in this study can achieve precise fertilisation control in maize-targeted fertilisation operations.

针对定向施肥操作中存在的施肥控制精度低、定位误差大、故障监测效果差等问题，提出了一种基于反向传播神经网络（BPNN）比例-积分-导数（PID）自适应位置反馈调节的玉米定向施肥智能方法。以STM32单片机为主从控制器，通过多传感器融合设计、控制参数计算与优化、施肥驱动装置搭建、故障监测系统等，研制了玉米定向智能施肥系统。采用bp神经网络PID自适应优化控制施肥器的角位移，自动控制技术驱动目标施肥机构。通过集成双光电传感器对目标玉米进行检测，编码器采集施肥器的角位移，测距传感器监测施肥箱内的施肥量，压力传感器监测施肥管状态，定位传感器监测施肥速度，多机通信处理施肥操作数据。实现了多传感器融合下施肥作业的针对性控制和故障监测。优化算法的调整时间为0.9 s，响应速度快。实验表明，该系统施肥控制精度大于95%，施肥平均定位误差小于28.1 mm，故障报警成功率达97%，故障报警平均响应时间小于0.45 s。本研究提出的智能玉米定向施肥方法可以在玉米定向施肥操作中实现精准的施肥控制。

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引用次数: 0

Improving sediment discharge efficiency in drip emitters via Tesla-inspired microchannels: PyFluent simulation and SHAP-based structural insights 通过特斯拉启发的微通道提高滴灌器的沉积物排放效率：PyFluent模拟和基于shap的结构见解

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-09-22 DOI: 10.1016/j.biosystemseng.2025.104295

Peng Hou , Tuo Yin , Shengqi Jian , Yan Li , Xinhao Gao , Xueli Zhang , Changjian Ma

Sediment deposition is a critical factor contributing to emitter clogging and flow instability in drip irrigation systems, particularly under sediment-laden water conditions. At the micro-scale (10–1000 μm), flow and particle transport within emitter channels are governed by complex interactions involving confinement effects, turbulent structures, and particle–wall interactions. However, the mechanisms controlling sediment migration and removal remain insufficiently understood, and there is a lack of robust modelling tools to support emitter design under such conditions. In this study, a novel Tesla-inspired bidirectional microchannel was proposed to improve hydraulic performance and sediment discharge efficiency. A high-resolution Euler–Lagrange two-phase flow model was developed using PyFluent, integrating key physical processes including Schiller–Naumann drag, Saffman lift, turbulent dispersion, and rebound boundary conditions to simulate sediment behaviour at particle scale. Simulation results revealed that the inclusion of reverse-flow units significantly enhanced shear zones and vortex intensity, leading to a 97.18 % increase in turbulent kinetic energy (TKE, CFD simulation). Under different forward- and reverse-flow unit configurations, PSD and QSDV both decreased by 22.73 %–53.40 %. Variations under different channel widths and depths showed different ranges due to QSDV being normalised by volume (all CFD simulation results). Contribution analysis using SHapley Additive exPlanations (SHAP) identified hydraulic diameter and the number of forward-flow units as dominant structural factors influencing sediment transport through their effects on local energy dissipation and flow field reorganisation. These findings provide a physically interpretable and practically applicable modelling framework for optimising emitter design. This study proposed approach offers new insights into the coupling between microchannel geometry and sediment dynamics, supporting the development of anti-clogging strategies in drip irrigation systems using non-conventional water sources.

泥沙淤积是滴灌系统中造成灌水器堵塞和流量不稳定的一个关键因素，特别是在含泥沙的水条件下。在微尺度（10-1000 μm）下，发射器通道内的流动和粒子输输受到约束效应、湍流结构和粒子壁相互作用等复杂相互作用的控制。然而，控制沉积物迁移和移除的机制仍然没有得到充分的了解，并且缺乏强大的建模工具来支持在这种条件下的发射器设计。本研究提出了一种新型的受特斯拉启发的双向微通道，以提高水力性能和排沙效率。利用PyFluent开发了一个高分辨率的欧拉-拉格朗日两相流模型，该模型集成了包括席勒-瑙曼阻力、Saffman升力、湍流弥散和反弹边界条件在内的关键物理过程，以模拟颗粒尺度上的沉积物行为。模拟结果表明，加入逆流单元显著增强了剪切区和涡流强度，导致湍流动能增加97.18% （TKE， CFD模拟）。在不同的正、逆流单元配置下，PSD和QSDV均降低22.73% ~ 53.40%。由于QSDV按体积归一化（所有CFD模拟结果），不同通道宽度和深度下的变化范围不同。利用SHapley加性解释（SHAP）的贡献分析发现，水力直径和前向流单元数量通过对局部能量耗散和流场重组的影响，是影响输沙的主要结构因素。这些发现为优化发射器设计提供了物理解释和实际应用的建模框架。该研究提出的方法为微通道几何形状和沉积物动力学之间的耦合提供了新的见解，支持使用非常规水源的滴灌系统的防堵塞策略的发展。

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