Optimization of Particle Distribution for Asphalt Mixtures in Screw Conveyer of Paver Based on Discrete Element Method and Response Surface Methodology

Li, Yan; Ye, Min; Zhang, Yi; Sun, Yan; Chen, Hao

doi:10.1002/adts.202300252

Cited by 1 publication

(1 citation statement)

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“…For example, Guo et al [10] used a central integrated design to evaluate the extraction process of the normalized ethanol formulation using the ethanol concentration, solvent amount, extraction time, and the number of extractions as the investigating factors to determine the optimal ethanol extraction process of dulcimer and to predict its extraction parameters. Based on the discrete element method and response surface method (RSM), Li et al [11] used the uniformity evaluation index to determine the effect of uniformity among the parameters. Shi et al [12] established models for discrete elements of deciduous date fruits and conducted Plackett-Burman tests and steepest climb tests with response surface optimization for the optimal discrete element contact parameters for deciduous date fruits.…”

Section: Introductionmentioning

confidence: 99%

Optimal Discrete Element Parameters for Black Soil Based on Multi-Objective Total Evaluation Normalized-Response Surface Method

et al. 2023

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The lack of accurate black soil simulation model parameters in the design and optimization of soil remediation equipment has led to large errors in simulation results and simulation outcomes, which to some extent restricts the development of soil remediation equipment. Accurate discrete element parameters can improve the efficiency of soil remediation equipment. To improve the reliability of the discrete element contact parameters for black soil, a set of optimal discrete element contact parameters was found that could comprehensively represent a variety of particle sizes and minimize error. In this paper, the best discrete element contact parameters were selected by using a multi-indicator total evaluation normalization method combined with the response surface method, combined with black soil solid and simulated stacking tests. First, the physical parameters of the black soil and the accumulation angle were determined. Next, Plackett–Burman tests were carried out for each grain size in turn to obtain the contact parameters that had a significant effect on the black soil accumulation angle. The important parameters obtained for different particle sizes are all as follows: black soil–black soil static friction coefficient, black soil–black soil rolling friction coefficient, and black soil–stainless steel rolling friction coefficient. In conjunction with the Plackett–Burman test screening results, the steepest climb test was designed for six grain sizes to optimize the range of values. To find the optimal contact parameters for the different particle sizes based on the final results of Box–Behnken experiments, the discrete element parameters of black soil were optimized for the different particle sizes of black soil by using the multi-indicator total evaluation normalization method and response surface method. The results showed that the black soil–black soil static friction coefficient was 1.045, the black soil–black soil rolling friction coefficient was 0.464, and the black soil–stainless steel rolling friction coefficient was 0.215. The errors for each particle size were reduced by 0.89%, 0.7%, 0.84%, 0.57%, 0.71%, and 0.76% for the best combination of parameters before and after normalization, with an average error reduction of 0.745%. This data provides some reference value for the design and optimization of soil remediation equipment.

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Section: Introductionmentioning

confidence: 99%