Development and experimental validation of a system for agricultural grain unloading-on-the-go

Liu, Ziping; Dhamankar, Shveta; Evans, John T.; Allen, Cody M; Jiang, Chufan; Shaver, Gregory M.; Etienne, Aaron; Vyn, Tony J.; Puryk, Corwin M.; McDonald, Brandon M.

doi:10.1016/j.compag.2022.107005

Cited by 9 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, DEM is widely used to generate practical packing structures (Chen et al, 2016; Donzé et al, 2009; Sun et al, 2022). The DEM in the field of grain storage still revolves around particle compression (González‐Montellano et al, 2012), unloading (Liu, Dhamankar, et al, 2022), and the study of the temperature field of the investigated particles (Rusinek & Kobyłka, 2014). However, the effect of pore structure on the temperature field remains neglected.…”

Section: Introductionmentioning

confidence: 99%

“…However, the effect of pore structure on the temperature field remains neglected. Few researchers have constructed pore structures and analyzed the airflow distribution within the grain pile using the DEM packing model (Liu, Dhamankar, et al, 2022; Liu et al, 2022). To simplify CFD pretreatment models, in studies of near‐spherical food products such as apples (Delele et al, 2008; Han et al, 2017) and pomegranates (Ambaw et al, 2017), scholars have mainly used spheres instead of real food shapes to obtain data on local airflow distribution and temperature variation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Numerical and experimental investigation of flow and heat transfer in a fixed bed of non‐spherical grains using the DEM‐CFD method

Liu

Chen

Zheng

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

Fixed‐bed drying of grains is a widely used method for determining temperature and humidity changes and pressure drop characteristics during aeration. In this study, an accurate particle‐resolved computational fluid dynamics (CFD) numerical model was developed to investigate the flow and heat transfer in fixed beds of soybean, wheat, and maize grains. A randomly filled non‐spherical grain fixed bed structure was generated using the discrete element method (DEM), and the contact areas between the packed particles are automated. The distribution of grain particles near the wall surface was approximately circular, and the circle became more regular with increase grain sphericity. Compared with the fixed beds of soybean and maize, there was no significant stratification in the fixed beds of wheat, and the difference between the peaks and troughs of porosity oscillations was smaller. Detailed particle‐resolved CFD simulations of the flow and heat transfer in fixed beds with different grains were performed. The results showed that the pressure drop results of the DEM–CFD simulation of different grain shapes were in good agreement with the Nemec–Levec equation and experimental results. The velocity and temperature distribution in the fixed bed have obvious non‐uniform distribution characteristics, which are more visible in the maize pile. Regions with high porosity have a higher average velocity, and the temperature near the wall is higher than that at the center when the heat transfer is unbalanced. The DEM‐CFD model includes the heat conduction process between the fluid and solid phases, which is consistent with the experimental temperature results. Practical Applications In this work, an accurate particle‐resolved computational fluid dynamics numerical model was developed to investigate the flow and heat transfer in fixed beds of soybean, wheat, and maize grains. The randomly filled non‐spherical grain fixed bed structure is generated by the discrete element method (DEM), and the contact areas between the packed particles are automated treated. The pressure drop and heat transfer processes of different grains were measured using the fixed bed drying experimental platform. The results show that the pressure drop results of DEM–CFD simulation of different grain shapes are in good agreement with Nemec–Levec equation results and experimental results. The DEM‐CFD model includes the heat conduction process between fluid and solid phases, which is in close agreement with the experimental temperature results.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical and experimental investigation of flow and heat transfer in a fixed bed of non‐spherical grains using the DEM‐CFD method

Liu

Chen

Zheng

et al. 2023

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Further, the automatic offloading system can unburden the combine operator by automatically monitoring the grain cart fill status, determining the appropriate auger location, and controlling the relative vehicle position and auger on/off. They developed an automatic offloading system for agricultural combine harvesters to automate the unloading-on-the-go process 1 . Michael et al developed a coverage path planning method that mitigates harvest operations' optimization and automation, characterized by capacity limitations and features derived from real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

Grain loading condition monitoring based on dual cameras in intelligent cluster grain truck

et al. 2023

AOPC 2022: Optical Sensing, Imaging, and Display Technology

View full text Add to dashboard Cite

Intelligent harvesting is one of the important criteria to measure the development level of agricultural modernization. Coordinated operation of harvester-grain truck clusters can improve grain harvesting efficiency and reduce postproduction losses during large-scale rice/wheat concentrated harvests. Overloading the grain on the grain truck will cause serious scattering of grain, further, insufficient loading can result in wasted capacity. How to monitor the grain loading process dynamically is a pressing matter. In this paper, two cameras and a point laser were used to measure the status of grains in the truck in real-time. The loadable capacity of the grain truck can be obtained through the reconstruction of the grain truck carriage edge and the positioning of the bottom of the truck carriage. During the harvester grain unloading process, the cone tip of the wheat pile is irradiated by the laser, and the height of the wheat pile can be obtained by measuring the location of the laser point. Then insufficient loading or overloading can be avoided by controlling the speed of the unloading port. This method has been verified in the paper box. The results show that the dual-camera monitoring system can measure the volume of the grain truck in real-time, and feedback on the total amount of grain loaded in the grain truck in time, which can effectively avoid grain loss caused by excessive loading.

show abstract

LiDAR-based benchmark approach development and validation for unloading-on-the-go systems incorporating stereo camera-based perception

Jiang,

Liu,

Evans

et al. 2023

Biosystems Engineering

View full text Add to dashboard Cite

Development and experimental validation of a system for agricultural grain unloading-on-the-go

Cited by 9 publications

References 12 publications

Numerical and experimental investigation of flow and heat transfer in a fixed bed of non‐spherical grains using the DEM‐CFD method

Numerical and experimental investigation of flow and heat transfer in a fixed bed of non‐spherical grains using the DEM‐CFD method

Grain loading condition monitoring based on dual cameras in intelligent cluster grain truck

LiDAR-based benchmark approach development and validation for unloading-on-the-go systems incorporating stereo camera-based perception

Contact Info

Product

Resources

About