The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. General Corteva R&D Examples
3.1.1. Potential for Breathing in a Volatile Pesticide
3.1.2. Reactor Design
3.1.3. Home Fumigation
3.1.4. Quantifying Efficacy and Bystander Exposure from Expanded Uses of Sulfuryl Fluoride
3.1.5. Drift Loading to Stream and Backwater Regions: Refinement of Acute Exposure
3.1.6. Predicting Pesticide Volatility through Coupled above/below Ground Multiphysics Modeling
3.1.7. Why Holes Form in Oil-in-Water-Sprayed Liquid Sheets, Leading to Sheet Breakup
3.2. Corteva Manufacturing Examples
- Achieving a better understanding of the physical phenomena in mixing-based processes.
- Prototyping mixing designs at manufacturing scale cost-effectively compared to experiments.
- Evaluating impacts of design/parameter changes through computer simulations.
- Understanding transient effects and mixing heterogeneities during scale-up.
- Troubleshooting and hypothesis testing;
- Feasibility studies;
- Engineering design, redesign, and optimization;
- Flow-physics-based visualization.
- Flows in batch and continuous processes.
- Mixing and mass transfer in stirred tank reactors;
- ○
- Mixing/blend time calculations;
- ○
- Power requirement calculations;
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- Vortexing and splashing computations;
- ○
- Non-Newtonian fluid mixing;
- ○
- Multiple miscible fluid blending;
- ○
- Gas–liquid mixing and mass transfer.
- Flow in static/in-line mixers.
- Flows in jet mixers/tanks/non-standard geometries.
- Free surface flows in centrifugation.
- Tank recirculation flows.
- Gas entrainment in recirculation flows;
- ○
- Mixing and hydrodynamics in crystallization;
- ○
- Mixing and mass transfer in fermentation and chlorination;
- ○
- Chemical reactions coupled with mixing and mass transfer.
3.2.1. Mixing and Hydrodynamics in Crystallization
- Mixing hydrodynamics and statistics;
- Power number;
- Mixing time.
3.2.2. Reactor Yield Mixing-Based Investigation
- Fluid flow and hydrodynamics (i.e., vortex formation);
- Mixing performance (dead velocity zones, flow recirculation regions).
- Evaluate the impact of mode of addition of reactants;
- ○
- Top surface vs subsurface loading provided the opportunity to increase yield, batch size, improve cycle time and increase production rates.
- Investigate new reactor designs for upcoming/new commercial campaigns when there is a question about yield performance for mixing sensitive reactions, impeller location, retrofit tank shape, or optimal batch size.
- Understand key mixing scenarios affecting the yield performance of the cyclization step of the chemical manufacturing process.
3.2.3. Non-Newtonian Miscible Fluid Mixing in Formulations
- Proposing optimal impeller designs (narrow blade hydrofoils) and optimal impeller placement with original batch size.
- Investigating single phase and miscible non-Newtonian fluid blending.
- Quantifying the effect of miscible fluid blending on velocity profiles, blend times and power consumption.
- Modeling single phase and miscible non-Newtonian fluid blending.
- Quantifying the effect of non-Newtonian flow on velocity profiles.
- Quantifying the effect of miscible fluid blending on the following:
- ○
- Velocity profiles;
- ○
- Blend times;
- ○
- Power consumption.
- Determining the optimal impeller placement for blend time reduction.
3.2.4. Agitation Design Modifications in Formulations
- Validation of modifications to agitator design with the addition of a second hydrofoil impeller.
- Comparison of current and modified designs.
3.2.5. Bi-Phasic Immiscible Mixing CFD Model
- Increasing the thermowell ID from 0.125″ to 0.375″ at 100 RPM does not help break up the toluene–HCl layer.
- Introducing baffles with thermowell and pH probes does not help the liquid layer to break up when operating RPM < Njd (just dispersed speed).
- Increasing agitation to 500 RPM breaks up the two liquid layers, inducing maximum interfacial surface area.
3.2.6. Solid–Liquid Mixing in Formulations
- To determine if the operating RPM was sufficient to entrain the powder particles in the bulk liquid.
- To quantify the effect of powder incorporation on velocity, blend times, power consumption, and cloud heights.
3.2.7. Static Mixer Flow Modeling for Nozzle Design Comparison
- To understand the impact of key dimensions in the static mixer nozzle design.
- To quantify the impact of nozzle dimensions on the energy dissipation rate and droplet break-up through the simulation of turbulent energy dissipation rates.
- To compare twelve different nozzle variants for reducing fouling.
3.2.8. Jet Mixing Modeling
- To model flow hydrodynamics in continuous flow systems like jet mixers with flat and dished bottom heads.
- To quantify the effect of inlet nozzle flow and tank bottom head geometry on the following:
- ○
- Mixing hydrodynamics;
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- Mixing/blend times;
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- Spatial/transient tracer concentration.
3.2.9. Continuous Flow Process CFD Modeling
- To quantify the (%) volume of annular flow region within given thresholds.
- To guide the optimal nozzle placement in a continuous flow reactor for minimizing dead zones associated with low velocity thresholds.
- To quantify the effect of viscosity on velocity profiles, and in turn its effect on the wall heat transfer coefficient.
3.2.10. Fermentation, Integrating Biochemical Kinetics with CFD
- Heterogeneous distribution of gluconic acid that can result in an overall yield loss.
- Slight differences in DO concentration between CFD and perfectly mixed ODE solution.
- Differences in the results caused by the underlying spatial variations in the gas–liquid mass-transfer coefficient across fluids.
4. Discussion
- For extrapolating field study observations to different and diverse scenarios or when testing on mammals such as humans cannot be performed.
- For achieving a better understanding of the physical phenomena in mixing-based processes.
- For prototyping mixing designs at manufacturing scale cost-effectively, as compared to experiments.
- Understanding transient effects and mixing heterogeneities during scale-up.
5. Future Work
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
C | concentration [g·m−3] |
D | diffusion coefficient [m2·s−1] |
u = V | velocity vector [m·s−1] |
p | pressure [kg·m−1·s−2] |
F = fG | external body forces acting on the fluid (such as gravity) [kg·m·s−2] |
μ | dynamic viscosity [kg·m−1·s−1] |
ζ | kinematic viscosity [m2·s−1] |
ρ | fluid density [kg·m−3] |
I | identity matrix |
T | temperature [°K] |
τ | shear stress tensor [kg·m−1·s−2] |
D/Dt | material derivative (the rate of change following a moving fluid parcel) [s−1] |
k | material thermal conductivity [W·m−1·°K−1] |
et | specific internal energy (internal energy per unit mass) [J·kg−1] |
Sg | generation source term for energy (radiation effects, thermal heating from electrical current, etc.) [W·m−1] |
Cv | heat energy absorbed/released per unit mass (constant volume) [J·kg−1·°K−1] |
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Hanspal, N.; Cryer, S.A. The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems. Fluids 2024, 9, 186. https://doi.org/10.3390/fluids9080186
Hanspal N, Cryer SA. The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems. Fluids. 2024; 9(8):186. https://doi.org/10.3390/fluids9080186
Chicago/Turabian StyleHanspal, Navraj, and Steven A. Cryer. 2024. "The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems" Fluids 9, no. 8: 186. https://doi.org/10.3390/fluids9080186