Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion
Abstract
:1. Introduction
2. Materials and Methods
2.1. Operational Principle of Mud Spreader
2.2. Mechanics Model of Mud Particle Motion
2.2.1. Force Analysis on Particles
2.2.2. Differential Equation of Particle Motion
2.2.3. Trajectory Equation of Particle Motion
2.3. Simulation Model
2.3.1. Discrete Element Model
2.3.2. Single-Factor Experiment
2.3.3. Secondary Orthogonal Rotational Combination Design Experiment
2.3.4. Calculation Method of Experimental Index
3. Results and Discussion
3.1. Single-Factor Experiment Results and Discussion
3.1.1. Influence of Rotating Radius on Non-Uniformity in Spreading
3.1.2. Influence of Bending Angle on Non-Uniformity in Spreading
3.1.3. Influence of Sub-Blade Tilt Angle on Non-Uniformity in Spreading
3.1.4. Impact of Forward Velocity on Non-Uniformity in Spreading
3.1.5. Influence of Rotation Speed on Non-Uniformity in Spreading
3.2. Results and Analysis of the Secondary Orthogonal Rotational Combination Design Experiment
3.2.1. Experimental Results
3.2.2. Regression Prediction Model
3.2.3. Analysis of the Impact Intensity of Factors
3.2.4. Analysis of the Impact Effects of Interaction Factors on Non-Uniformity in Spreading
3.3. Optimization Model
3.4. Bench Experiment
4. Conclusions
- The trajectory equations governing the movement of mud particles were established, and a discrete element simulation model was developed to simulate the interaction between the mud spreader blade and mud particles. A second-order orthogonal rotation combined design experiment was conducted to obtain a regression predictive model for quantifying the non-uniformity in spreading with respect to the blade parameters. The accuracy of both the proposed simulation model and regression predictive model was validated through bench tests.
- The response surface methodology was utilized to construct a regression model for the objective function, and analysis of variance was employed to determine the significance of first-order, square terms, and interaction terms on the objective function. The results of the variance analysis indicated that the first-order terms, including rotation radius, bending angle, and forward velocity, significantly influenced non-uniformity in spreading. Additionally, the square terms for rotation radius, sub-blade tilt angle, and rotational speed had a significant impact on non-uniformity in spreading. Among the interaction terms of the second order, the interaction effects of the sub-blade tilt angle with forward velocity and the sub-blade tilt angle with rotational speed were not significant, while the interactions of other factors demonstrated significant effects on the non-uniformity in spreading.
- A multivariate single-objective optimization approach was employed to establish an optimization model for the non-uniformity in spreading. The results indicated that the optimized value for non-uniformity in spreading was achieved at a rotation radius of 188 mm, a bending angle of 121°, a sub-blade tilt angle of 30°, a forward velocity of 400 mm/s, and a rotational speed of the blade at 191 r/min.
- The optimized blade parameters were validated through bench tests. The experimental results closely aligned with the simulated results, with a relative error of 7.67% for non-uniformity in spreading. Furthermore, the optimized uniformity of mud spreading increased by 31.19%, indicating that the optimized parameters can meet the planting requirements for rice seedling cultivation in paddy fields.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Horizontal Coded Value | Rotating Radius X1 (mm) | Bending Angle X2 (°) | Sub-Blade Tilt Angle X2 (°) | Forward Velocity X4 (mm/s) | Rotation Speed X5 (r/min) |
---|---|---|---|---|---|
Upper asterisk arm (r = 2) | 190 | 160 | 65 | 550 | 320 |
Upper level (r = 1) | 185 | 150 | 50 | 450 | 270 |
Zero level (r = 0) | 180 | 140 | 35 | 350 | 220 |
Lower level (r = −1) | 175 | 130 | 20 | 250 | 170 |
Lower asterisk arm (r = −2) | 170 | 120 | 5 | 150 | 120 |
Variation interval (∆j) | 5 | 10 | 15 | 100 | 50 |
Test No. | X1 | X2 | X3 | X4 | X5 | Y (%) |
---|---|---|---|---|---|---|
1 | −1 | −1 | −1 | −1 | 1 | 59.55 |
2 | 1 | −1 | −1 | −1 | −1 | 40.60 |
3 | −1 | 1 | −1 | −1 | −1 | 44.41 |
4 | 1 | 1 | −1 | −1 | 1 | 45.27 |
5 | −1 | −1 | 1 | −1 | −1 | 40.83 |
6 | 1 | −1 | 1 | −1 | 1 | 44.88 |
7 | −1 | 1 | 1 | −1 | 1 | 51.42 |
8 | 1 | 1 | 1 | −1 | −1 | 54.05 |
9 | −1 | −1 | −1 | 1 | −1 | 45.26 |
10 | 1 | −1 | −1 | 1 | 1 | 35.06 |
11 | −1 | 1 | −1 | 1 | 1 | 45.98 |
12 | 1 | 1 | −1 | 1 | −1 | 56.27 |
13 | −1 | −1 | 1 | 1 | 1 | 39.65 |
14 | 1 | −1 | 1 | 1 | −1 | 40.10 |
15 | −1 | 1 | 1 | 1 | −1 | 49.74 |
16 | 1 | 1 | 1 | 1 | 1 | 53.30 |
17 | −2 | 0 | 0 | 0 | 0 | 45.69 |
18 | 2 | 0 | 0 | 0 | 0 | 43.74 |
19 | 0 | −2 | 0 | 0 | 0 | 39.65 |
20 | 0 | 2 | 0 | 0 | 0 | 53.15 |
21 | 0 | 0 | −2 | 0 | 0 | 44.05 |
22 | 0 | 0 | 2 | 0 | 0 | 44.32 |
23 | 0 | 0 | 0 | −2 | 0 | 49.29 |
24 | 0 | 0 | 0 | 2 | 0 | 45.56 |
25 | 0 | 0 | 0 | 0 | −2 | 50.37 |
26 | 0 | 0 | 0 | 0 | 2 | 51.28 |
27 | 0 | 0 | 0 | 0 | 0 | 46.26 |
28 | 0 | 0 | 0 | 0 | 0 | 47.72 |
29 | 0 | 0 | 0 | 0 | 0 | 46.53 |
30 | 0 | 0 | 0 | 0 | 0 | 45.79 |
31 | 0 | 0 | 0 | 0 | 0 | 46.53 |
32 | 0 | 0 | 0 | 0 | 0 | 46.70 |
33 | 0 | 0 | 0 | 0 | 0 | 44.73 |
34 | 0 | 0 | 0 | 0 | 0 | 47.74 |
35 | 0 | 0 | 0 | 0 | 0 | 48.71 |
36 | 0 | 0 | 0 | 0 | 0 | 45.73 |
Source of Variance | Sum of Squares | Degrees of Freedom | Mean Square | F-Value | Significance Level p |
---|---|---|---|---|---|
Model | 855.36 | 20 | 42.77 | 53.10 | <0.0001 |
X1 | 5.22 | 1 | 5.22 | 6.48 | 0.0224 |
X2 | 276.87 | 1 | 276.87 | 343.78 | <0.0001 |
X3 | 0.1849 | 1 | 0.1849 | 0.2296 | 0.6387 |
X4 | 22.25 | 1 | 22.25 | 27.62 | <0.0001 |
X5 | 1.34 | 1 | 1.34 | 1.66 | 0.2165 |
X1X2 | 110.15 | 1 | 110.15 | 136.77 | <0.0001 |
X1X3 | 51.40 | 1 | 51.40 | 63.83 | <0.0001 |
X1X4 | 15.07 | 1 | 15.07 | 18.71 | 0.0006 |
X1X5 | 52.02 | 1 | 52.02 | 64.59 | <0.0001 |
X2X3 | 62.31 | 1 | 62.31 | 77.36 | <0.0001 |
X2X4 | 80.74 | 1 | 80.74 | 100.25 | <0.0001 |
X2X5 | 27.16 | 1 | 27.16 | 33.72 | <0.0001 |
X3X4 | 0.0822 | 1 | 0.0822 | 0.1021 | 0.7538 |
X3X5 | 1.70 | 1 | 1.70 | 2.11 | 0.1673 |
X4X5 | 93.12 | 1 | 93.12 | 115.62 | <0.0001 |
X12 | 7.89 | 1 | 7.89 | 9.79 | 0.0069 |
X22 | 0.1761 | 1 | 0.1761 | 0.2186 | 0.6468 |
X32 | 12.61 | 1 | 12.61 | 15.65 | 0.0013 |
X42 | 1.06 | 1 | 1.06 | 1.31 | 0.2696 |
X52 | 34.02 | 1 | 34.02 | 42.24 | <0.0001 |
Residue | 12.08 | 15 | 0.8054 | ||
Lack of fit | 0.0535 | 6 | 0.0089 | 0.0067 | 1.0000 |
Error | 12.03 | 9 | 1.34 | ||
Sum total | 867.44 | 35 | |||
R2 = 0.9861; Adjusted R2 = 0.9675; Predicted R2 = 0.9815; Signal-to-noise ratio = 35.70 |
Test Number | Theoretical Value (%) | Test Results (%) | Relative Error (%) |
---|---|---|---|
1 | 29.63 | 31.83 | 6.91 |
2 | 29.63 | 32.62 | 9.17 |
3 | 29.63 | 32.86 | 9.83 |
4 | 29.63 | 30.89 | 4.08 |
5 | 29.63 | 32.33 | 8.35 |
Average value | 29.63 | 32.11 | 7.67 |
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Ren, J.; Chen, C.; Bao, D.; Wu, X.; Zheng, S. Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion. Agriculture 2024, 14, 344. https://doi.org/10.3390/agriculture14030344
Ren J, Chen C, Bao D, Wu X, Zheng S. Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion. Agriculture. 2024; 14(3):344. https://doi.org/10.3390/agriculture14030344
Chicago/Turabian StyleRen, Jinbo, Chongcheng Chen, Difa Bao, Xinhui Wu, and Shuhe Zheng. 2024. "Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion" Agriculture 14, no. 3: 344. https://doi.org/10.3390/agriculture14030344