Discrete Element Method-Based Simulation for Rice Straw Comminution and Device of Parameter Optimization

Featured Application

The proposed DEM framework, which incorporates wet adhesion, flexible deformation, and fracture behaviors, can be directly applied to the design and operational optimization of rice stubble-crushing and residue-returning equipment under wet paddy-field conditions. In practice, it provides (i) a simulation-based tool for identifying blockage- and entanglement-prone regions and for refining key structural components such as the throwing chamber and rotor–blade assembly; (ii) quantitative guidance for selecting operating parameters (e.g., rotor speed, forward speed, and chamber clearance) to achieve high comminution efficiency and improved throwing uniformity; and (iii) a reusable calibration–validation workflow that can be readily extended to other biomass materials (e.g., wheat straw and rapeseed stalks) by updating material and contact parameters. Furthermore, the framework has the potential to be integrated into digital prototyping and controller parameter tuning, thereby reducing trial-and-error field tests, shortening development cycles, and improving operational stability under variable moisture conditions.

Abstract

To mitigate the entanglement, agglomeration, and unstable conveying of high-moisture rice residues during stubble crushing for field incorporation, a discrete element method (DEM)-based modeling and optimization framework was developed to enhance the performance of a stubble-crushing device under wet paddy-field conditions. The device structure and kinematics were first analyzed, and the physical and mechanical properties of the residues were obtained through field measurements. A hollow wet–flexible straw model was then proposed to account for both mechanical breakage and moisture-induced adhesive interactions. Key contact and material parameters were calibrated using DEM simulations coupled with laboratory shear and three-point bending tests, showing good agreement with experimental trends. The validated model was subsequently extended to the device scale to characterize the cyclic capture–acceleration–throwing behavior of residues inside the crushing chamber. The individual and interactive effects of rotor speed, forward speed, and throwing-chamber clearance on comminution efficiency and conveying stability were investigated. A multi-objective response surface optimization identified an optimal parameter combination of 2000 rpm rotor speed, 0.87 m s⁻¹ forward speed, and 10.5 cm clearance. Under these conditions, the comminution rate reached 96.94%, and the coefficient of variation in throwing uniformity was 8.71%. Field validation further confirmed the reliability of the simulation results, with relative errors below 6%. Overall, the proposed framework provides an effective tool for the design optimization and parameter selection of wet-residue comminution equipment.

1. Introduction

Rice is one of the major staple crops in China, with a large and widely distributed planting area [1]. With the promotion of straw resource utilization and conservation tillage in rice–wheat rotation regions, stubble chopping and incorporation have become essential operations in post-harvest field management of rice [2]. As an important agronomic practice, straw incorporation not only enhances the stability of topsoil aggregates and increases soil microbial communities, thereby improving soil fertility [3], but also improves the moisture conditions of surface soil, promoting crop growth and development and ultimately increasing crop yield and water use efficiency [4]. However, a large amount of high-moisture rice straw stubble remains in the field after harvesting. Its high flexibility and pronounced adhesive characteristics pose significant challenges for stubble elimination, chopping, and incorporation operations [5]. Compared with dry (low-moisture) straw, high-moisture rice stubble is more prone to bending, entanglement, and agglomeration during comminution, resulting in unstable material flow. This instability further causes non-uniform chopping, localized clogging, and increased energy consumption [6,7], which severely constrains improvements in the operational efficiency and working quality of stubble elimination devices.

To accurately predict the motion behavior of rice straw within a stubble elimination device, it is essential to develop a discrete element model (DEM) that can realistically represent its mechanical response and interaction characteristics [8]. In existing studies, the straw motion process inside stubble elimination devices has often been described using rigid straw models [9,10]. Although such models can substantially simplify the modeling procedure and reduce computational cost, they neglect the shear, bending, and large-deformation behaviors of straw under loading, making it difficult to accurately reproduce the actual stress state and motion patterns of straw under comminution conditions [11]. To address this limitation, various flexible modeling approaches have been proposed, which have improved the representation of straw flexibility to some extent [12]. For example, Tang et al. [13] employed multi-sphere chains or bonded-particle models to simulate the flexible deformation of straw and achieved good simulation performance under bending and compression conditions. Cheng et al. [14] developed modeling and analysis of the mechanical response of straw during cutting and compression based on parallel-bond or elasto-plastic contact models, thereby improving the prediction accuracy of the comminution process to a certain degree. Nevertheless, some models still exhibit limitations in describing the fracture behavior of wet straw and the post-fracture dynamic evolution of fragments. Therefore, developing an efficient modeling strategy that balances the representation of straw flexibility and fracture characteristics while maintaining a controllable computational cost remains a critical challenge in current research.

The appropriate selection of inter-particle contact models also has a direct impact on simulation accuracy. In existing studies, the Hertz–Mindlin (no-slip) model has been widely adopted to describe contact and collision behaviors of straw particles [15,16], and models such as the elasto-plastic adhesive (EEPA) model have also been introduced to account for elasto-plastic deformation effects [17]. However, these models generally have difficulty in capturing the pronounced adhesion and delayed/hysteretic detachment characteristics between wet straw materials. In contrast, the Johnson–Kendall–Roberts (JKR) contact model can effectively represent particle contact behaviors under strong adhesive conditions. It has been validated in simulations of root–soil systems and other interactions in wet cohesive soil environments [18,19], providing a feasible approach for describing adhesive interactions among wet straw particles. Overall, for the comminution conditions of wet and adhesive rice stubble in paddy fields, there is an urgent need to develop a straw model that can simultaneously characterize flexible deformation, fracture–fragmentation, and wet adhesive interactions, while maintaining high computational efficiency. Such a model is crucial for supporting mechanistic analyses of straw motion within stubble elimination devices and for optimizing key structural and operational parameters.

Therefore, this study focuses on wet rice straw and a stubble elimination device and conducts structural and operational parameter optimization based on the DEM. The main contents are as follows: (1) key parameters affecting crushing performance were first identified, and field pre-tests were conducted to characterize the stubble under real operating conditions, providing a basis for comparison with the numerical simulations; (2) a rice straw DEM was developed that incorporates flexibility and adhesive characteristics, and the relevant contact parameters were calibrated; (3) a DEM simulation model of the stubble elimination device was established to analyze the effects of key parameters on stubble flow behavior and comminution performance; and (4) the critical parameters of the device were optimized using response surface methodology and the results were validated through field experiments. The findings of this study provide a theoretical basis and technical support for the structural improvement and intelligent design of high-efficiency stubble elimination devices for wet, flexible rice straw.

2. Materials and Methods

2.1. Structure and Working Principle of the Rice Straw Stubble Elimination Device

The high-efficiency rice straw stubble elimination and incorporation machine mainly consists of a tractor, a stubble elimination unit, a hitch system, and a power transmission system. The transmission system transfers the power output from the tractor power take-off shaft to the chopping-shaft (knife-shaft) assembly, thereby providing power for straw chopping and throwing operations. The stubble elimination unit is responsible for chopping and throwing rice straw, by fragmenting the straw and discharging it rearward. The hitch system is used to connect the machine to a four-wheel tractor, as shown in Figure 1. Among these components, the straw stubble elimination unit is the key module of the high-efficiency machine, and its structural configuration is illustrated in Figure 2a. The high-efficiency rice straw stubble elimination device primarily comprises an arc-shaped housing, a chopping-shaft assembly, and a feeding inlet. The core component of the device is multiple sets of rotary throwing–chopping flail blades mounted on the chopping shaft. These blades are uniformly distributed along the circumferential direction of the rotor to form “rotary throwing” functional units. An arc-shaped housing encloses the rotor, forming a rotary throwing chamber between the rotor periphery and the inner wall of the housing. As shown in section A–A (side view), the rotor is concentrically arranged within the housing, and a certain working clearance is maintained between the blade tips and the arc-shaped housing. This clearance ensures smooth material passage while providing sufficient space for effective shearing and impact actions. A combined flail-blade configuration—integrating the chopping capability of straight blades with the improved L-shaped blades—was adopted to enhance blade wear resistance and improve straw comminution quality.

The working principle of the stubble elimination device is schematically illustrated in Figure 2b. During operation, the power from the tractor power take-off shaft is transmitted through the gearbox and transmission mechanism, where it undergoes secondary speed increase, and then drives the chopping shaft to rotate at high speed. Consequently, the combined flail blades mounted on the shaft cut and chop the wet, flexible rice straw on the soil surface. The cut straw is then mechanically conveyed into the curved housing through the inlet clearance $m$. Inside the housing, the straw is further fragmented into short segments or fibrous forms under the combined actions of repeated chopping, tearing, and kneading generated by the interaction between the flail blades and the stationary blades. Finally, driven by the mechanical throwing effect of the high-speed rotating shaft, the processed straw is uniformly discharged from the outlet of the curved housing and spread onto the soil surface [20]. Considering the mechanical characteristics of wet, flexible rice straw and the stubble elimination process [21], the chopping-shaft rotational speed directly determines the blade tip speed and the number of effective interactions per unit time. It is therefore a key dynamic parameter influencing straw loading intensity, fragmentation efficiency, and energy consumption. The forward speed of the machine determines the amount of straw entering the rotary throwing chamber per unit time, thereby directly affecting the material load level, operational continuity, and clogging risk, and serves as an important operational parameter reflecting the matching relationship between the working conditions and the device capacity [22]. The clearance height $H$ of the rotary throwing chamber regulates the available motion space and confinement level of straw inside the chamber, and thus controls the contact frequency and interaction intensity between the straw and both the flail blades and stationary blades. This makes $H$ a critical structural parameter affecting comminution refinement and material passability [23]. When $H$ is small, the straw is closer to the high-speed blade region, resulting in higher mechanical action intensity and shear frequency per unit volume, which is beneficial for improving chopping efficiency and refinement. In contrast, a larger $H$ increases chamber volume and passability, which can effectively reduce clogging risk and improve throughput; however, the intensity of each comminution action is relatively weakened. Therefore, the chopping-shaft speed, forward speed, and rotary throwing chamber clearance were selected as the key influencing factors for parameter analysis and optimization in this study, which is of great significance for achieving efficient stubble elimination, reducing energy consumption, and enhancing operational stability.

2.2. Field Pre-Test

To characterize the fragmentation behavior of wet rice stubble under actual operating conditions and to provide a reliable benchmark for validating the numerical simulations, a field pre-test was carried out in a rice experimental field located in Jingkou District, Zhenjiang City, Jiangsu Province. The stubble used in this study was collected on the third day after harvesting with a combine harvester, and samples were taken during this period (Figure 3). The soil in the test area was classified as clay loam. The surface-layer soil moisture content was 42.68%, determined using the oven-drying method, and the field surface evenness was controlled within ±3.5 cm. The rice cultivar was Wukejing 7375, with a post-harvest stubble height of approximately 200 mm, and a straw coverage mass of about 2.2 kg·m⁻² per unit area. To quantify the physical properties of the wet straw, its moisture content was systematically measured. The results showed that the mean moisture content of the straw was 62.45% ± 3.73%. The relatively high moisture content markedly increased straw flexibility and surface adhesion, making bending, entanglement, and agglomeration more likely during crushing compared with dry straw. Therefore, it is necessary to analyze the fragmentation behavior based on real field conditions.

2.3. Discrete Element Modeling of Rice Straw and the Stubble-Crushing Device

2.3.1. Physical Properties of Rice Stubble

In this study, rice straw samples were collected from an experimental field located in Jingkou District, Zhenjiang City, Jiangsu Province, China. The rice cultivar used was Wukejing 7375. After harvesting with a combine harvester, the stubble height remaining in the field was approximately 200 mm, and the straw coverage density was 2.2 kg/m². Straw samples were collected three days after harvesting. The collected straw samples were oven-dried at 100–110 °C until a constant mass was achieved. Four replicate measurements were conducted, and the average moisture content of the straw was determined to be 62.45% ± 3.73%, as illustrated in Figure 4a. In agricultural machinery and stubble elimination studies, straw with a moisture content of ≥50% is generally regarded as high-moisture straw; accordingly, it is defined as wet straw in this study. Since the cross-section of rice straw is approximately circular and the straw diameter varies slightly among internodes, a vernier caliper was used to measure the axial dimensions and wall thickness of the straw. A total of 30 straw stems were measured, as shown in Figure 4b. The mean diameter was calculated using the following equation [24]:

$$D={\displaystyle \frac{1}{30}}{\displaystyle \sum _{i=1}^{i=30}{D}_i}$$

(1)

where $D_i$ is the diameter measured in the $i$-th measurement, and $i$ denotes the measurement index (i.e., the number of measurements).

The measured mean outer diameter, mean inner diameter, and mean wall thickness of rice straw were 6.6 mm, 4.52 mm, and 1.05 mm, respectively.

2.3.2. DEM of Wet, Flexible Rice Straw

Straw, as a typical biomass material, exhibits macroscopic mechanical behavior that is closely related to its microstructural characteristics [25]. Among these properties, flexibility and fracture behavior directly affect the accuracy and computational efficiency of DEM simulations [26]. To simultaneously characterize the bending deformation and fracture behavior of wet straw with a relatively low computational cost, a hollow wet flexible straw DEM was developed in this study. As shown in Figure 5, the geometric radius of each individual spherical particle in the simulation was set to 0.55 mm, while the contact radius was defined as 0.65 mm. Considering the hollow tubular anatomical structure of rice straw, hollow cylindrical ring-shaped particles were employed instead of conventional solid elements (outer diameter: 6.6 mm; inner diameter: 5.5 mm; wall thickness: 1.1 mm), enabling the model geometry and mechanical response to more closely resemble those of real straw. Adjacent elements were connected by bonded contacts, which effectively captured the bending, tensile, and fracture behavior of straw under loading conditions. The total length of a single straw model was set to 200.2 mm, consistent with the typical stubble straw length encountered in agricultural production, thereby enhancing the applicability of the model under practical operating conditions.

To account for the effects of straw flexibility and wet adhesiveness on device performance, the BondingV2 model was adopted to simulate the fracture behavior of a single flexible rice straw stem [27]. In this model, a parallel bond is formed at the contact point between two adjacent particles, producing a bonded interaction. The bond effect can be regarded as a set of springs distributed around the spherical particles, enabling the transmission of both forces and moments. During model construction, the bonded connections established between particles can effectively represent the cohesion among straw fibers [28]. When the bond is subjected to bending, compression, or tension, relative motion occurs between the two connected units, and the forces and moments acting between them can be expressed as follows:

$$\left\{\begin{array}{l}\delta F_b^n=-v_n{k}_{n}A{\delta }_t\\ \delta F_b^t=-v_t{k}_{t}A{\delta }_t\\ \delta M_b^n=-{\omega }_n{k}_{t}J{\delta }_t\\ \delta M_b^t=-{\omega }_t{k}_n{\displaystyle \frac{J}{2}}{\delta }_t\end{array}\right.$$

(2)

$$\left\{\begin{array}{l}A=\pi R_B^2\\ J={\displaystyle \frac{1}{2}}\pi R_B^4\end{array}\right.$$

(3)

where $\delta F_b^n$ is the normal force (N); $\delta F_b^t$ is the tangential force (N); $\delta M_b^n$ is the normal moment (N·m); $\delta M_b^t$ is the tangential moment (N·m); ${\omega }_n$ is the normal angular velocity (rad/s); ${\omega }_t$ is the tangential angular velocity (rad/s); $v_n$ is the normal velocity (m/s); $v_t$ is the tangential velocity (m/s); $k_n$ is the normal contact stiffness (N/m³); $k_t$ is the tangential contact stiffness (N/m³); $A$ is the contact area between the units (m²); $J$ is the moment of inertia of the spherical unit (m⁴); $R_B$ is the bond radius (mm); and ${\delta }_t$ is the simulation time step (s).

When the stress in the bond exceeds the maximum normal stress ${\sigma }_{\mathrm{m}\mathrm{a}\mathrm{x}}$ and/or the maximum tangential stress ${\tau }_{\mathrm{m}\mathrm{a}\mathrm{x}}$, the bond between the units breaks. The bond failure criterion is given as follows:

$$\left\{\begin{array}{l}{\sigma }_{\max }<{\displaystyle \frac{-F_n}{A}}+{\displaystyle \frac{2M_b^t}{J}}{R}_B\\ {\tau }_{\max }<{\displaystyle \frac{-F_t}{A}}+{\displaystyle \frac{2M_b^n}{J}}{R}_B\end{array}\right.$$

(4)

The Hertz–Mindlin model with JKR contact comprehensively accounts for cohesive forces, van der Waals forces, and adhesion and hysteretic effects between particles [29]. By further modifying the contact force calculation in the discrete element method, it facilitates the simulation of adhesive behavior during particle separation [30]. Therefore, the JKR contact model was adopted in this study to characterize the adhesive behavior of wet straw, as illustrated in Figure 4b. This model incorporates contact parameters such as the surface energy between particles, and the corresponding formulations are given as follows:

$$\gamma ={\displaystyle \frac{{\lambda }^2}{R_1}}-\sqrt{{\displaystyle \frac{4\pi \alpha \lambda }{E^{\ast }}}}$$

(5)

$$F_{JKR}=-4\sqrt{\pi \alpha E^{\ast }{\lambda }^3}+{\displaystyle \frac{4E^{\ast }}{3R_1}}{\lambda }^3$$

(6)

When the particles are not in physical contact and the separation distance reaches the maximum detachment gap, the adhesive force reaches its peak value, expressed as:

$$F_{\max }=-1.5\pi \gamma R_1$$

(7)

where $F_{\mathrm{m}\mathrm{a}\mathrm{x}}$ is the peak pull-off force during the separation of two adhesive particles.

2.3.3. DEM of the Stubble Elimination Device

To facilitate data processing while maintaining computational efficiency, the geometric structure of the stubble elimination device was reasonably simplified. During model establishment, detailed features such as the bearing housings, bearings, bolts, and nuts on the cutter roller were omitted to avoid excessive redundant elements that would otherwise increase computational complexity, as shown in Figure 6. A three-dimensional simplified model of the stubble elimination device was developed in SolidWorks 2021. The model mainly consists of a rotating drum surrounding the main chopping shaft and an outer housing that encloses the drum. Several cutting tools are uniformly arranged along a helical line on the drum to achieve straw cutting and comminution. The housing serves to constrain the material trajectory, define the comminution region, and ensure operational safety.

2.4. Calibration of Characteristic Parameters of Rice Stubble

To improve the predictive accuracy of the DEM and to more realistically represent the physical behavior of rice straw, model parameters must be calibrated [31]. The calibration tests are shown in Figure 7. Because straw crushing intrinsically involves cutting, a cutting test was used to calibrate the bonding parameters of the straw model [32]. A 150 mm-long straw segment was placed horizontally on the cutting fixture platform with the blade surface oriented perpendicular to the straw. Shearing was performed using a texture analyzer (EZ-LX, Shimadzu Instruments Suzhou Co., Ltd., Suzhou, China) at a loading rate of 10 mm/min. During each test, the computer automatically recorded the load and displacement data and generated the load–displacement curve. The test was repeated 30 times to ensure stable and reliable results. In addition, to obtain the actual bending performance of straw and to emulate straw–machine interactions during stubble crushing, a three-point bending test was conducted to determine bending stiffness [33]. Straw specimens of the same length (150 mm) were placed on supports with a span of 60 mm and tested using an ETM-103A universal testing machine (Shenzhen Wance Testing Equipment Co., Ltd., Shenzhen, China). A cylindrical indenter applied loading at 10 mm/min while the displacement–load relationship was recorded in real time. Considering the inherent variability of biomass materials, 30 specimens were tested to obtain representative bending-response data.

2.5. Simulation Design

2.5.1. Simulation Parameter Settings

The contact models were configured in the EDEM software 2022. For both rice straw and the stubble elimination device, intrinsic properties and contact parameters needed to be defined. Based on previous studies, additional test results, and relevant literature [34,35], the simulation parameters for rice straw and the stubble elimination device are listed in

Table 1. Discrete element simulation parameter settings.

Parameter	Value
Straw density (kg/m3)	240
Poisson’s ratio of straw	0.4
Shear modulus of straw (pa)	1 × 106
Density of chopping blade (kg/m3)	7865
Poisson’s ratio of chopping blade	0.3
Shear modulus of chopping blade (pa)	7.9 × 1010
Coefficient of restitution between straw and blade	0.3
Static friction coefficient between straw and blade	0.42
Rolling friction coefficient between straw and blade	0.01
Coefficient of restitution between straw particles	0.28
Static friction coefficient between straw particles	0.54
Rolling friction coefficient between straw particles	0.05
Normal stiffness per unit area (N/m3)	7.6 × 108
Tangential stiffness per unit area (N/m3)	6.8 × 108
Normal bond strength (pa)	7.7 × 106
Tangential bond strength (pa)	7.2 × 106
Surface energy between straw particles (J/m2)	0.55
Bonded disk scale	1.1
Contact radius (mm)	0.65
Normal range (N/m3)	5.3 × 107
Shear range (N/m3)	4.7 × 107

. The contact parameters of rice straw were calibrated through the cutting tests. Subsequently, RSM was employed to determine the optimal combination of Bonding parameters, including the normal/tangential stiffness per unit area and the normal/tangential bond strengths. Finally, the surface energy in the JKR model was determined by comparing the simulated and experimental angles of repose of rice straw.

It should be emphasized that, during parameter calibration, multiple trial simulations were conducted for both the bond strength and the JKR surface energy. The results showed that when these parameters varied within ±10% of the calibrated values, the trends of the overall bending stiffness, peak shear load, and angle of repose remained consistent. For simulation pre-processing in EDEM, the time step was set to 2 × 10⁻⁶ s, the number of iterations was set to 20,000, and the total simulation time was 2 s, to ensure computational efficiency and numerical stability. By defining an orientation matrix, straw particles were randomly generated in the vertical direction within the particle factory plane, with a generation rate of 360 stems·s⁻¹. The generation rate of 360 stems·s⁻¹ was determined by converting the straw coverage density in the field, with a straw distribution density of 300 stems·m⁻².

2.5.2. Performance Indices

The working performance of the straw stubble elimination device was evaluated through experiments. According to the quality standard for straw incorporation operations (NY/T 500—2002) [36], the evaluation indices were determined as the straw chopping rate $R_1$ and the rotary throwing uniformity $R_2$:

$$R_1={\displaystyle \frac{m_1}{m_2}}$$

(8)

In the equation, $m_1$ is the mass of chopped straw within the monitored sampling area (g), and $m_2$ is the total mass of straw within the monitored sampling area (g).

$$R_2={\displaystyle \frac{1}{\overline{M}}}\sqrt{{\displaystyle \frac{1}{4}}{\displaystyle {\sum }_{i=1}^5{\left(M_{zi}-\overline{M}\right)}^2}}\times 100\%$$

(9)

In the equation, $\overline{M}$ is the average straw mass in the monitored sampling areas (g); $i$ is the sampling point index; and $M_{zi}$ is the total straw mass at sampling point $i$ (g).

For each operating pass of the stubble elimination device, five sampling areas were selected at equal intervals along the length direction of the test section. Each sampling area covered 1 m². All chopped straw within each area was collected, and a vibrating sieve was used to remove impurities such as soil and gravel mixed with the straw. The cleaned straw was then weighed. The coefficient of variation (CV) of straw mass in the sampling areas was calculated to characterize the throwing uniformity of straw distribution.

2.5.3. Experimental Design

Because field operations are subject to complex and variable conditions and because field testing is difficult to implement, time-consuming, and resource-intensive, full-factorial multi-parameter field experiments are often impractical in engineering applications. Therefore, this study adopted a combined strategy of single-factor field tests and multi-factor simulation experiments to improve experimental efficiency while ensuring the reliability of the results. This approach also enhanced overall research efficiency and reduced the uncertainty associated with large-scale multi-factor field testing. Based on preliminary work, three key operating parameters were identified as having major effects on performance: rotor speed (A), forward speed (B), and throwing-chamber clearance (C). The crushing rate (R₁) and the coefficient of variation in throwing uniformity (R₂) were selected as the primary evaluation indicators. In addition, operational phenomena that may affect continuity—such as blockage, entanglement, and abnormal vibration—were recorded to support determination of reasonable parameter ranges, narrowing factor levels for multi-factor testing, and refinement of the simulation model. The test identifiers are listed in

Table 2. Test coding for the single-factor experiments.

Test No	Chopping-Shaft Rotational Speed (rpm)	Machine Forward Speed (m/s)	Rotary Throwing Chamber Clearance (cm)
1	1600, 1800, 2000, 2200, 2400	1	10
2	2000	0.6, 0.8, 1, 1.2, 1.4	10
3	2000	1	6, 8, 10, 12, 14

.

Because different factors of the stubble elimination device exert varying influences on the straw chopping rate and rotary throwing uniformity, and potential interaction effects may exist among these factors, the ranges of the experimental factors were further narrowed based on the results of the single-factor tests. With the straw chopping rate $R_1$ and rotary throwing uniformity $R_2$ as the response indices, an RSM experiment with three factors and three levels was conducted using a central composite design (CCD) approach. The three factors were defined as follows: $A$, chopping-shaft rotational speed; $B$, machine forward speed; and $C$, rotary throwing chamber clearance. These factors were treated as interaction variables, and the corresponding factor codes and design levels are listed in

Table 3. Coding and levels of factors for the multi-factor experiments.

Level	A (rpm)	B (m/s)	C (cm)
−1	1800	0.8	8
0	2000	1	10
1	2200	1.2	12

. A total of 17 experimental runs were performed, and the experimental scheme is presented in

Table 4. Multifactorial experimental design.

Test No	A (rpm)	B (m/s)	C (cm)
1	1	−1	0
2	−1	0	1
3	0	0	0
4	0	0	0
5	0	1	1
6	−1	0	−1
7	−1	1	0
8	1	1	0
9	0	0	0
10	0	−1	1
11	0	0	0
12	1	0	−1
13	1	0	1
14	−1	−1	0
15	0	1	−1
16	0	−1	−1
17	0	0	0

. The experimental data were analyzed using Design-Expert 13 software. Based on the Box–Behnken experimental design approach, 17 simulation experiments were determined, and the simulated results of the chopping and spreading device are summarized in Table 4. Quadratic multiple regression analysis was then performed on the experimental data to identify the optimal combination of parameters.

2.6. Field Experiment

Based on the simulation results and the actual operating performance of the prototype, field experiments on straw chopping and incorporation were conducted in a harvested rice field at the experimental base in Jingkou District, Zhenjiang City, Jiangsu Province, China. The experiments were carried out in accordance with the operating specifications of the Chinese national standard GB/T 24675.6—2009 (Conservation tillage machinery—Straw chopping and incorporation machine) [37] and the agricultural industry standard NY/T 500—2002 (Operational quality of straw incorporation machines). The experimental procedure is shown in Figure 8. For field validation, a tractor-mounted rice stubble elimination device was used. During the experiment, the prototype had a working width of 2.4 m, a forward speed of 0.87 m/s, and a chopping-shaft rotational speed of 2000 rpm. Under normal operating conditions, the device traveled along the rice field and performed one-pass chopping of the upright rice stubble remaining after harvesting. Throughout the operation, the device ran smoothly, with no noticeable clogging or abnormal vibration. After the test, the chopped straw length distribution and the mass of the thrown straw were obtained through manual measurement and an electronic balance with an accuracy of 0.01 g.

3. Results

3.1. Measurement of Field Pre-Test Results

Field pre-tests indicated that wet rice straw exhibited increased flexibility and pronounced surface adhesion, making it more susceptible to bending, entanglement, and agglomeration inside the crushing chamber. These behaviors led to fluctuations in feeding, localized retention, and intermittent impact loads, thereby significantly affecting crushing quality and operational continuity (Figure 9). Compared with dry straw, under high-speed operation of the crushing device, wet adhesive straw subjected to impact and shear tended to show a “tension–bending–rebound” deformation response. When the impact energy was insufficient or the discharge space was constrained, straw readily became entangled and accumulated at the inlet, along the chamber wall, and in the transition zone of the throwing section, which in turn triggered blockage. Blockage occurred mainly under conditions of relatively low rotor speed or inappropriate forward speed. At low rotor speeds, crushing and conveying capacity were inadequate, resulting in a higher proportion of long straw segments that were prone to forming entanglements. When forward speed was excessive, the instantaneous feeding rate increased, raising the probability of agglomeration and entanglement; the effective flow cross-section within the chamber decreased and material retention intensified, manifested by distinct short-term torque peaks and ultimately necessitating shutdown for cleaning. In addition, the required clearing time varied with the degree of entanglement, the blockage location, and the cleaning method. These observations not only elucidate the flow and fragmentation behavior of wet straw under real operating conditions but also provide field-based evidence for subsequent narrowing of parameter ranges and multi-factor simulation-based optimization.

3.2. Validation of Model Parameters

To investigate the effects of straw wet adhesion on the performance of stubble crushing and residue returning and to verify the accuracy of the developed wet–flexible rice stubble mechanical model, bending and shear tests were conducted (Figure 10a,b), and the simulation results were compared with the experimental measurements. As shown in Figure 10c, under bending conditions, both the simulated and measured displacement–load curves exhibited three stages—initial bending, nonlinear deformation, and fracture—with consistent overall trends. In the small-displacement range, the two curves overlapped closely, indicating that the model captured the initial flexibility well. With increasing displacement, the peak load, its corresponding displacement, and the post-fracture load drop agreed well with the experimental results. As shown in Figure 10d, under shear conditions, the curves showed typical stage characteristics, including a linear response, strengthening, and completion of shearing, and the simulated curve matched the measured curve well in terms of stage-wise evolution and overall shape. The local discrepancies were mainly attributed to specimen-to-specimen variability, moisture-content fluctuations, and clamping conditions, and the errors remained within an acceptable range. Overall, the model reproduced the mechanical responses of wet rice stubble under both bending and shear conditions with good fidelity, demonstrating high reliability.

3.3. Analysis of Simulation Results

3.3.1. Flow and Comminution Characteristics of Straw

To characterize the flow and comminution behavior of straw inside the chopping chamber, the temporal evolution of straw velocity was examined, as shown in Figure 11. Overall, straw motion follows a clear progression from initial capture to fully developed agitation: particles entering the chamber are first concentrated near the inlet with relatively low velocities, then rapidly accelerate as interactions with the rotating components intensify, leading to a pronounced expansion of high-velocity regions. As the process reaches a quasi-steady state, energetic particle motion persists and straw is repeatedly subjected to impact, bending, and shear, indicating that comminution is governed by sustained capture–acceleration–collision cycles rather than single-pass breakage. This dynamic mechanism promotes continuous fragmentation and dispersion of straw within the chamber, consistent with the finer fragment distribution and crushing performance reported in subsequent sections.

3.3.2. Analysis of Straw Motion Trajectories

To investigate the crushing and flow characteristics of straw within the crushing chamber, the velocity distribution and motion state of straw at different time instants were analyzed. As illustrated in Figure 12, the color contour represents the magnitude of straw velocity. It can be observed from Figure 12 that the motion velocity of straw in the crushing chamber exhibits a pronounced stage-wise evolution over time. During the initial stage (approximately 0.15 s), the straw is mainly distributed near the inlet with relatively low velocities, indicating that it is being captured and beginning to come into contact with the crushing components. Subsequently, during the intermediate stage (approximately 0.25–0.5 s), the straw is rapidly accelerated under the combined action of the rotor and flail blades, and the high-velocity region gradually expands. This suggests that the interaction between the straw and the crushing device is significantly enhanced, and the crushing process enters an efficient operating phase. In the later stage (approximately 0.75–1.5 s), a wide range of high-speed motion persists within the chamber. Under intense agitation as well as repeated impact and shear actions, the straw is progressively refined and dispersed, ultimately forming a finer particle distribution. Overall, straw crushing is not accomplished in a single event but rather through a dynamic process of “capture–acceleration–intense agitation/repeated action.” This evolution highlights the critical role of continuous energy input and repeated mechanical interactions within the crushing chamber in achieving sufficient fragmentation.

As shown in Figure 12b, straw motion near the chopping shaft exhibits not only circumferential rotation but also evident axial transport and periodic tumbling, forming wave-like/spiral trajectories. This indicates that straw continuously migrates along the axial direction under the combined effects of blade arrangement, axial clearances, and confinement by the housing/screen, rather than remaining at a fixed axial position. Such spatio-temporal mixing increases the frequency of “approach–collision/shearing–detachment–recontact” events across different regions, which helps reduce localized accumulation and promotes a more uniform material distribution. Meanwhile, the repeated tumbling and bending–rebound during axial migration enhances stress concentration and facilitates coupled bending–shear failure, leading to a mixed fragmentation mechanism dominated by impact, bending, shearing, and friction rather than pure shearing alone.

3.3.3. Analysis of Straw Comminution Energy and Interaction Forces

As shown in Figure 13a–c, the evolution of straw energy and straw–blade interaction forces exhibits clear stage-dependent behavior and a strong dependence on rotational speed. After entering the chamber, straw is rapidly captured and accelerated, leading to a sharp increase in average energy. This is followed by a high-energy fluctuation stage (approximately 0.20–1.05 s), during which pronounced energy oscillations and impulsive force peaks occur, indicating repeated ejection–fallback–recapture cycles accompanied by intense impact, shearing, and frictional interactions. During this stage, the mean interaction force reaches its maximum (around 0.45–0.60 s). As comminution proceeds, both the magnitude and frequency of force peaks decrease, reflecting progressive refinement of straw and a transition from impact-dominated fragmentation to conveying and discharge of finer fragments.

Comparing different rotational speeds, both energy levels and interaction forces increase markedly with speed, with 2200 rpm consistently producing higher values than 2000 rpm and 1800 rpm. This demonstrates that higher rotational speed enhances straw energy uptake and intensifies blade loading, thereby accelerating comminution. However, it also implies increased mechanical load and energy consumption, highlighting the trade-off between fragmentation efficiency and operational cost.

3.3.4. Torque Analysis During Device Operation

Figure 14 shows the time-varying torque responses of the stubble elimination device at different chopping-shaft speeds. For all rotational speeds (1800, 2000, and 2200 rpm), the torque exhibits a consistent three-stage pattern: an initial increase as straw is captured, a fluctuation stage with multiple peaks (approximately 0.2–1.0 s) corresponding to intermittent impact and shearing events, and a gradual decline as straw becomes progressively fragmented. With increasing rotational speed, both the peak torque and the fluctuation amplitude increase markedly, with 2200 rpm producing the highest torque levels, followed by 2000 rpm and 1800 rpm. This indicates that higher speed intensifies straw–blade interactions and increases the torque load due to stronger reaction forces during impact and shearing. The pronounced torque fluctuations reflect the discontinuous nature of straw–blade contact during comminution. While higher rotational speed can enhance fragmentation intensity, it also subjects the blades to greater mechanical loading, implying increased wear risk and highlighting the need to balance comminution effectiveness with operational durability in practical applications.

In the stubble elimination device, the power consumption is primarily determined by the torque generated on the rotating components due to particle interactions. The relationship among power $P$, torque $M$, and angular velocity $\omega$ can be expressed as:

$$P=M\times \omega$$

(10)

That is, the power equals the product of torque and angular velocity. When the device operates at a constant rotational speed, the power increases linearly with increasing torque. Under fluctuating particle loads, the instantaneous power varies accordingly with the torque. For the three rotational speed conditions, the maximum instantaneous power consumption of the stubble elimination device was 614.04 W, 1089.35 W, and 1611.86 W, respectively.

3.3.5. Length Distribution of Chopped Straw

As shown in Figure 15, both the spatial distribution of straw in the crushing chamber and the final length distribution exhibit similar overall patterns across rotor speeds. A clear recirculating band forms near the rotor periphery, indicating that straw is fragmented progressively through repeated “capture–acceleration–co-motion/adhesion–ejection–fallback–recapture” cycles. In the chamber, shorter fragments are mainly concentrated in the upper curved passageway and the upper-right region, implying that intensive impact and shearing occur there and that finely chopped material is transported upward by inertia and/or airflow. In contrast, the inlet and lower regions still contain occasional aggregation and dragging of longer pieces, which contributes to the residual “long-tail” in the length distribution.

Figure 15c further quantifies the speed effect: increasing rotor speed from 1800 to 2200 r/min shifts the length distribution toward shorter segments (higher fraction in the short-length bins and reduced medium-to-long fractions), demonstrating that higher speed strengthens impact/shear intensity and improves fragmentation completeness. Nevertheless, a small proportion of long pieces persists at all speeds, suggesting that rotor speed alone cannot fully eliminate occasional discharge of insufficiently fragmented straw.

3.4. Optimization Design of the Stubble Elimination Device

To further optimize the structural and operational parameters of the stubble elimination device and identify the optimal parameter combination, a multi-factor experimental design was implemented, with the results summarized in

Table 5. Results of the multi-factor experiments.

Test No	A (rpm)	B (m/s)	C (cm)	R1	R2
1	1	−1	0	89.86	10.98
2	−1	0	1	90.55	10.92
3	0	0	0	96.29	10.33
4	0	0	0	97.22	10.55
5	0	1	1	94.98	8.71
6	−1	0	−1	86.24	9.95
7	−1	1	0	89.8	10.23
8	1	1	0	98.16	8.97
9	0	0	0	95.35	10.22
10	0	−1	1	94.21	8.76
11	0	0	0	95.98	9.61
12	1	0	−1	92.55	10.43
13	1	0	1	98.93	9.91
14	−1	−1	0	86.63	9.73
15	0	1	−1	97.74	10.03
16	0	−1	−1	84.91	9.99
17	0	0	0	95.86	10.67

. Based on the quadratic orthogonal rotational combination design, a second-order regression analysis was carried out. Regression models $Y_1$ and $Y_2$ were developed for the straw chopping rate $R_1$ and the rotary throwing uniformity $R_2$, respectively. The corresponding regression equations are presented below, followed by an analysis of their statistical significance.

$$\begin{array}{l}{Y}_1=96.14+3.285A+3.134B+2.154C+1.283AB+0.518AC-3.015BC\\ -2.96A^2-2.068B^2-1.113C^2\end{array}$$

(11)

$$\begin{array}{l}{Y}_2=20.476-0.693A-1.19B-0.388C+0.123AB+0.128AC-0.273BC\\ +0.216A^2+0.0358B^2+0.1107C^2\end{array}$$

(12)

The significance of the regression equations and their coefficients was evaluated, and the analysis of variance (ANOVA) results for the effects of the factors on the straw chopping rate and rotary throwing uniformity are presented in

Table 6. Analysis of variance (ANOVA) results for the multi-factor optimization models.

Evaluation Indicators	Source of Variance	Sum of Squares	Degree of Freedom	Mean	F	p	Significance
R1	Model	311.98	9	34.66	41.56	<0.0001	**
	A	86.33	1	86.33	103.51	<0.0001	**
	B	78.56	1	78.56	94.20	<0.0001	**
	C	37.11	1	37.11	44.49	0.0003	**
	AB	6.58	1	6.58	7.89	0.0262
	AC	1.07	1	1.07	1.28	0.2944
	BC	36.36	1	36.36	43.60	0.0003
	A2	36.89	1	36.89	44.23	0.0003	**
	B2	18.00	1	18.00	21.58	0.0024	**
	C2	5.21	1	5.21	6.25	0.0410	*
	Residual	5.84	7	0.8340
	Lack of Fit	3.92	3	1.31	2.73	0.1785
	PureError	1.92	4	0.4793
	CorTotal	317.82	16
R2	Model	17.06	9	1.90	63.80	<0.0001	**
	A	3.84	1	3.84	129.13	<0.0001	**
	B	11.33	1	11.33	381.31	<0.0001	**
	C	1.20	1	1.20	40.43	0.0004	**
	AB	0.0600	1	0.0600	2.02	0.1982
	AC	0.0650	1	0.0650	2.19	0.1826
	BC	0.2970	1	0.2970	10.00	0.0159
	A2	0.1960	1	0.1960	6.60	0.0371	*
	B2	0.0054	1	0.0054	0.1811	0.6832
	C2	0.0516	1	0.0516	1.74	0.2289
	Residual	0.2080	7	0.0297
	LackofFit	0.0600	3	0.0200	0.5413	0.6793
	PureError	0.1479	4	0.0370
	CorTotal	17.27	16

Note: ** indicates an extremely significant effect (p < 0.01), and * indicates a significant effect (0.01 < p < 0.05).

. As indicated in Table 6, the p-values of both regression models are less than 0.01, demonstrating that the models are highly significant. The corresponding coefficients of determination ($R^2$) are 0.9816 and 0.9780, respectively. In addition, the p-values of the lack-of-fit terms are all greater than 0.05, indicating that the regression equations adequately describe the relationships between the influencing factors and the response variables. Based on the F-values of the individual terms, within the selected factor level ranges, the chopping-shaft rotational speed (A) has the greatest effect on the straw chopping rate, followed by the machine forward speed (B), the rotary throwing chamber clearance (C), the quadratic effect of chopping-shaft speed (A²), the interaction between chopping-shaft speed and rotary throwing chamber clearance (BC), the quadratic effect of forward speed (B²), the interaction between chopping-shaft speed and forward speed (AB), the quadratic effect of rotary throwing chamber clearance (C²), and the interaction between chopping-shaft speed and rotary throwing chamber clearance (AC). Similarly, for the rotary throwing uniformity of chopped straw, the machine forward speed (B) exerts the most significant influence, followed by the chopping-shaft rotational speed (A), the rotary throwing chamber clearance (C), the interaction between chopping-shaft speed and rotary throwing chamber clearance (BC), the quadratic effect of chopping-shaft speed (A²), the interaction between chopping-shaft speed and rotary throwing chamber clearance (AC), the interaction between chopping-shaft speed and forward speed (AB), the quadratic effect of rotary throwing chamber clearance (C²), and the quadratic effect of forward speed (B²).

For both the straw chopping rate and the rotary throwing uniformity of chopped straw, the relationships between the predicted values and the experimentally measured values of the regression models are shown in Figure 16. The predicted values and measured values are generally distributed along a diagonal trend, with most data points closely clustered around the fitted line. Over the entire prediction range, the data points are relatively evenly distributed, and good agreement is observed between the predicted and measured values. This indicates that the regression models exhibit high predictive accuracy.

3.5. Response Surface Analysis

Based on the regression analysis, the response surfaces illustrating the interactive effects of chopping-shaft rotational speed, machine forward speed, and rotary throwing chamber clearance on the straw chopping rate and rotary throwing uniformity are shown in Figure 17. The results indicate that pronounced interaction effects exist among the factors, and within certain parameter ranges, optimal performance regions can be identified.

Regarding straw chopping rate, the response surfaces for the interactions among chopping-shaft speed, forward speed, and rotary throwing chamber clearance all show an increase followed by a decrease. At low shaft speeds, insufficient blade impact and cutting yield a low chopping rate; increasing speed enhances comminution and raises the chopping rate. However, excessive speed shortens straw residence time in the chamber, leading to premature discharge and reduced chopping. Forward speed and chamber clearance govern the straw feed and discharge rates, respectively; when properly matched with shaft speed, they provide appropriate residence time and loading conditions and thus maximize chopping rate, whereas mismatching causes clogging or early discharge and lowers performance.

Regarding rotary throwing uniformity, the response surfaces under the interaction of different factors are relatively smooth, and the variation range of the coefficient of variation is small. This indicates that the device can maintain good rotary throwing uniformity over a relatively wide parameter range. When the chopping-shaft speed, forward speed, and rotary throwing chamber clearance are well matched, the straw feeding, comminution, and throwing processes remain stable, resulting in a uniform distribution. Conversely, parameter mismatch may cause localized straw accumulation or concentrated discharge, thereby deteriorating throwing uniformity. Therefore, the rational matching of chopping-shaft rotational speed, machine forward speed, and rotary throwing chamber clearance is crucial for simultaneously improving the straw chopping rate and ensuring rotary throwing uniformity.

Using the optimization module of Design-Expert 13 software, the optimal combinations of operational and structural parameters for the stubble elimination device were obtained. The experimental factors were optimized with the objectives of maximizing the straw chopping rate and minimizing the coefficient of variation in rotary throwing uniformity, in that order. According to the bench test conditions and operational requirements, constraint conditions for the objective functions were defined, as expressed in Equation (13), and the optimization problem was subsequently solved.

$$\left\{\begin{array}{l}1800\le A\le 2200\\ 0.8\le B\le 1.2\\ 8\le C\le 12\\ \max y_1=f_1\left(A,B,C\right)\\ \min y_2=f_2\left(A,B,C\right)\end{array}\right.$$

(13)

As a result, multiple optimal combinations of operational and structural parameters were generated. Considering the practical operating requirements of the stubble elimination device, the optimal parameter combination was determined as follows: a chopping-shaft rotational speed of 2000 rpm, a machine forward speed of 0.87 m/s, and a rotary throwing chamber clearance of 10.5 cm. Under this parameter combination, the straw chopping rate reached 96.94%, and the coefficient of variation in rotary throwing uniformity was 8.71%.

3.6. Field Validation

To verify the reliability of the optimal parameter combination for the rice straw stubble elimination device, chopping experiments were conducted using a combination of field tests and simulation analysis, as shown in Figure 18. A total of five experimental runs were performed, and the straw chopping rate as well as the coefficient of variation in rotary throwing uniformity were recorded for each test. The calculated results are summarized in

Table 7. Results of simulation and field validation experiments.

Test	Chopping Rate (%)		Relative Error (%)	Coefficient of Variation (%)		Relative Error (%)
	Simulation	Measured		Simulation	Measured
1	96.94	91.25	5.9	8.71	9.11	4.4
2		92.57	4.5		9.02	3.4
3		91.73	5.4		8.99	3.1
4		92.81	4.3		8.95	2.7
5		90.38	6.7		9.36	6.9
Mean value		91.75	5.4		9.09	4.1

. A comparison between the five field experiments and the corresponding simulation results indicates that the measured straw chopping rates were consistently high, with all experimental groups achieving chopping rates above 90% and an average value of 91.75%. This demonstrates that, under the optimal parameter combination, the device can achieve sufficiently effective straw comminution and meets the basic requirements for field operations. From the results of individual tests, the straw chopping rates predicted by the simulations exhibit trends consistent with the field measurements. The relative errors between the simulated and measured chopping rates range from 4.3% to 6.7%, with an average relative error of 5.4%. This level of error falls within the acceptable range commonly reported in comparative studies between agricultural machinery simulations and field measurements, indicating that the established DEM simulation model can effectively reproduce the straw fragmentation behavior observed during actual comminution processes.

With respect to the rotary throwing uniformity of chopped straw, the measured coefficients of variation from the five experimental runs remained at relatively low levels, ranging from approximately 8.95% to 9.36%, with an average value of 9.09%. This indicates that the chopped straw was distributed uniformly in the field, which is beneficial for subsequent straw incorporation operations and for improving overall field operation quality. Overall, the good agreement between the simulation predictions and field measurements demonstrates that the optimal parameter combination can stably achieve both a high straw chopping rate and favorable throwing uniformity under actual field operating conditions. These results provide reliable support for the structural optimization and parameter selection of rice straw stubble elimination devices.

Although certain experimental combinations in Table 5 exhibit slightly higher values for individual indicators (such as R₁ or R₂), the optimal parameter set identified in this study was not determined based on the maximization of a single metric. Instead, it was derived from a multi-objective optimization framework that balances and coordinates key factors including crushing efficiency, spreading uniformity, and operational stability under field conditions. Further field validation results demonstrate that this optimal parameter combination exhibits greater stability across varying operating conditions, indicating superior robustness and engineering applicability.

4. Discussion

Based on the pronounced flexibility, hollow structural characteristics, and strong wet adhesion behavior of rice straw under high-moisture conditions, this study developed a discrete element modeling framework that integrates hollow tubular geometry, flexible bonded-particle mechanics, and adhesive contact interactions, and applied it to the mechanistic analysis and parameter optimization of straw comminution in a stubble elimination device. Compared with conventional DEM approaches for biomass comminution, the proposed framework extends the modeling fidelity while maintaining computational feasibility at the machine scale.

• Methodological advancement relative to existing DEM-based straw models

Previous DEM studies on straw or biomass comminution have largely relied on simplified assumptions, including rigid or weakly flexible particles, solid-filled geometries, and purely frictional contacts, which are generally adequate for dry or low-moisture conditions. In contrast, wet rice straw in paddy fields exhibits pronounced bending deformation, progressive fracture, and moisture-induced inter-particle adhesion, and neglecting these characteristics can lead to inaccurate predictions of force transmission, fragmentation behavior, and material flow. To address this limitation, the present study discretized rice straw using a hollow tubular multi-sphere representation, which captures the primary load-bearing features of the thin-walled stem while reducing particle number and bonded interactions relative to solid-filled models. By coupling the Bonding V2 model with the JKR adhesive contact formulation, the proposed DEM framework simultaneously accounts for elastic deformation, bond-failure-driven fragmentation, and wet adhesive interactions, enabling a more realistic description of straw behavior during bending, cutting, impact, and post-fracture aggregation. Validation through bending and cutting tests shows good agreement between simulated and experimental load–displacement responses, peak loads, and fracture evolution, confirming the physical representativeness of the calibrated stiffness, bonding strength, and surface energy parameters under the target moisture condition. Compared with earlier DEM studies that focus primarily on local material behavior or simplified fracture criteria, this work advances the state of the art by supporting whole-device-scale simulation and subsequent operational parameter optimization under wet and adhesive working conditions.

2.

Influence of modeling simplifications and computational considerations.

In the DEM, the flail blades were simplified as rigid bodies fixed to the rotating shaft, although in practice they are mounted via pins and possess limited rotational freedom. Under the high-speed operating conditions considered in this study, centrifugal forces cause the blades to remain predominantly in a radially extended and quasi-stable posture. Simulation–experiment comparisons indicate that this simplification has a limited influence on macroscopic performance indicators such as torque, chopping rate, and throwing uniformity within the investigated parameter range, while substantially reducing model complexity and computational cost. Moreover, the hollow tubular discretization inherently reduces the number of particles and bonded contacts compared with solid-filled straw models, thereby decreasing the scale of contact detection and bond force calculations per time step. Although the present study focuses on mechanistic fidelity and engineering applicability rather than explicit benchmarking of computational efficiency, the adopted modeling strategy offers clear potential advantages in terms of scalability for device-level simulations and parametric studies.

3.

Engineering implications and parameter optimization

Building upon the validated DEM framework, a multi-factor optimization of chopping-shaft rotational speed, machine forward speed, and rotary throwing chamber clearance was conducted using response surface methodology. Significant interaction effects among these parameters were identified. The chopping-shaft speed exerts the strongest influence on straw chopping rate, whereas machine forward speed plays a more critical role in determining throwing uniformity. The optimal parameter combination (2000 rpm, 0.87 m/s, and 10.5 cm) achieves a balanced performance in terms of effective comminution, material passability, and uniform straw distribution. Field experiments confirm the reliability of the simulation-based predictions, with relative errors remaining within acceptable limits. These results demonstrate that DEMs incorporating wet adhesion and flexible fracture behavior can serve as effective tools for guiding the structural design and operational optimization of straw comminution equipment under realistic paddy field conditions.

4.

Future work

Despite the encouraging results, this study still has several limitations. The DEM parameters were calibrated for a single rice variety at a specific moisture level, and the influences of stem morphological variability and moisture-dependent mechanical responses were not explicitly considered. Moreover, several practical factors relevant to field robustness—such as soil–straw interactions, ground constraints, air resistance, long-term energy consumption, blade wear, and non-steady straw mass flow—were not incorporated into the current simulations. In addition, although the JKR model was employed to represent wet adhesion, alternative adhesive formulations (e.g., EEPA) may lead to different detachment and dissipation behaviors, thereby affecting agglomeration intensity, residence time, torque fluctuation, and throwing uniformity. Future work will extend the proposed framework to multiple biomass types, rice varieties, and moisture conditions through systematic recalibration, while integrating coupled straw–soil–machine interaction and time-dependent wear/feeding boundary conditions. Such developments will enable more robust multi-objective optimization and control-oriented design, providing stronger theoretical and engineering support for high-efficiency and energy-saving residue comminution equipment under wet and adhesive paddy-field conditions.

5. Conclusions

• Wet-field bottlenecks were identified and benchmarked. High-moisture rice stubble became more flexible and adhesive, intensifying bending, entanglement, and agglomeration during crushing, which destabilized feeding and increased blockage risk, guiding subsequent DEM modeling and key parameter selection.
• A hollow flexible rice-straw DEM was developed. Considering the hollow tubular structure of rice straw, the model significantly reduced the number of bonding elements, thereby improving computational efficiency while preserving the capability to represent flexible deformation and fracture/fragmentation. The simulated bending and shear responses showed good agreement with experimental results, confirming the rationality and reliability of the model for reproducing the mechanical behavior of wet straw.
• A DEM was developed to reveal straw–machine interactions. Straw followed a cyclic “capture–acceleration–throwing–falling–recapture” trajectory, with fragmentation concentrated near the rotor periphery. Higher rotor speed increased kinetic energy and contact forces, improving crushing intensity but also raising torque and energy consumption.
• Response surface methodology was used to determine the optimal operating parameter combination. The optimal conditions were a rotor speed of 2000 rpm, a forward speed of 0.87 m/s, and a throwing-chamber clearance of 10.5 cm. Under these conditions, the straw crushing rate reached 96.94%, and the coefficient of variation in throwing uniformity was 8.71%. Field validation tests showed trends consistent with the simulation results, demonstrating the effectiveness and applicability of the proposed modeling and optimization approach.