Predictions of Stored Grain Condition using Computational ... · CFD modeling is a reliable method...
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E. Kaloudis 1 , S. Bantas 1 , V. Sotiroudas 1 and C.G. Athanassiou 2 Predictions of Stored Grain Condition using Computational Fluid Dynamics Modelling TM NC-213 The U.S. Quality Grains Research Consortium 1 Centaur Analytics, Inc., [email protected] 2 Laboratory of Entomology and Agricultural Zoology, University of Thessaly, Greece February 27, 2019
Transcript of Predictions of Stored Grain Condition using Computational ... · CFD modeling is a reliable method...
E. Kaloudis1, S. Bantas1, V. Sotiroudas1 and C.G. Athanassiou2
Predictions of Stored Grain Condition using Computational
Fluid Dynamics Modelling
TM
NC-213 The U.S. Quality Grains Research Consortium
1Centaur Analytics, Inc., [email protected] of Entomology and Agricultural Zoology, University of Thessaly, Greece
February 27, 2019
© Centaur Analytics, Inc. NC-213
Introduction
Grain condition
Biotic:• Grain• Insects & mites• Microflora
Abiotic:• Dust & foreign material• Intergranular air• Water vapor• Storage structure• Aeration system
Computational Fluid Dynamics (CFD)
Using numerical analysis to predict the movement of fluids, heat and mass transfer
Industry applications:Aerospace, Automotive, Heat exchangers, Weather forecast, Biomedical
Advantages:• Detailed analysis of physical phenomena• Analysis of the entire storage space• Geo-location specific• Historical weather data and/or forecasts can
be used to simulate and/or predict the grain condition
© Centaur Analytics, Inc. NC-213
CFD simulation procedure
Modelling
Storage facility (geometry)
Domain discretization
Grain properties - boundary conditions
Solver
Results
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Transport equations
Modelling
transient convection diffusion sources
𝜕Φ
𝜕𝑡+ 𝑢𝛻Φ = 𝛻 𝐷Φ 𝛻Φ + 𝑆Φ
CO2 O2
air flow temperature moisture content
• grain respiration• porous media• latent heat
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Modelling
● Temperature
● Relative humidity
● Barometric pressure
● Wind velocity
● Solar radiation
Interaction with the ambient environment
● Heat transfer (reflectivity, conductivity)
● Gas permeability (fumigant losses)
Storage construction materials
Aeration system
Grain Quality prediction
Boeotia, Greece
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Grain quality test case
• Boeotia, Greece• storage period: Aug to Sept. 2018• steel silo• H=9.5m/31ft, D=10m/33ft• durum wheat, 500 tons• fill ratio = 80.6%• multiple areas of different
moisture content values• T, r.h. sensorsgrain
headspace
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Input data
Weather data – Aug. 2018 to Sept. 2018
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Simulation results
Grain temperature
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Simulation results
Moisture content (w.b.)
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Simulation results
video link: https://youtu.be/ukQouSSyOXg
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Simulation results
Oxygen (O2) concentration
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Simulation results
Carbon Dioxide (CO2) concentration
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Simulation results
Dry Matter Loss
Aeration
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Aeration test case
• South Africa• storage period: Oct. to Nov. 2018• concrete silo• H=36.9m/121ft, D=15.2m/50ft• white maize• 4200 tons• 45kW aeration fan
headspace
grain
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Input data
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Simulation results
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Simulation results
video link: https://youtu.be/CmaXcPDv9oE
Conclusions
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● CFD modeling is a reliable method to predict grain condition
● Prediction capabilities months ahead
● Customizable to different storage types (silo bins, bags, bunkers,
warehouses)
● Customizable to grain types and geographic locations
● Correlation with sensor data could improve model predictions
● Aeration procedure could be optimized by taking into account ambient
climate conditions
● Minimize energy costs for fan operation
Conclusions
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Thank you!
CENTAUR.AG
