Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

Fandi Zeng, Hongwei Diao, Yinzeng Liu, Dong Ji, Meiling Dou, Ji Cui, Zhihuan Zhao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Fandi ZengCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.ORCID 0000-0002-5630-2580

Hongwei DiaoCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.ORCID 0009-0007-7461-9489

Yinzeng LiuCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.

Dong JiCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.ORCID 0000-0002-7593-3513

Meiling DouCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.

Ji CuiCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Zhihuan ZhaoCollege of Mechanical and Electronic Engineering, Shandong Agriculture and Engineering University, Jinan 250100, China.

Funding

Program for Innovative Research Team in SDAEU sgykycxtd2020-03

6 · The paper itself

Abstract

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 10

Indexed as

AlgorithmsZea maysCalibrationComputer SimulationNeural Networks, ComputerDEMGA–BPmaize strawneural networkpeak compression force

Identifiers

PMID39204915

DOI10.3390/s24165217

PMCPMC11359212

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.