In arable farming, precision matters. Seed placement influences how evenly crops emerge, how effectively land is used, and how efficiently seeds and fertiliser are consumed. For agricultural machinery manufacturers, improving that level of precision is not just a design objective. It directly affects field performance and harvest potential.
Amazone, a global agricultural machinery manufacturer headquartered in Osnabrück, Germany, set out to improve seed placement in pneumatic seeding machines. To do that, the team turned to DEM-CFD simulation to better understand seed behaviour inside the pneumatic conveying system and optimise the design of the machine.
Client Snapshot
Founded in 1883, Amazone is a global leader in agricultural machinery. The company offers systems for arable farming across seed drills, soil tillage, fertilisation, crop protection, and more. Its focus on continuous innovation has helped it develop advanced and cost-effective machinery aimed at helping customers maximise their harvests.
The Challenge
Agricultural equipment manufacturers face growing pressure to deliver machinery that supports high-yield harvests with greater efficiency and precision. In arable farming, seed drills play a major role in crop establishment, and accurate seed placement is essential. Each seed needs the right spacing to support plant growth, while also limiting unnecessary use of expensive seeds and fertiliser.
That challenge becomes more difficult inside pneumatic seeding machines, where seeds move through a conveying system shaped by dynamic particle interactions and airflow. Understanding that transport process is not straightforward. The behaviour is complex, conventional testing offers limited insight, and large-scale experimental investigation can be difficult and expensive. Amazone needed a way to study this process more effectively and work toward a more ideal distribution of seeds while reducing material usage.
The Solution
To address the challenge, the project focused on modelling a critical subsystem of the pneumatic seeding machine. This included the particle tank, dosing unit, pneumatic conveying line, riser tube, and distributor head. The team reduced the full system to its most relevant parts and developed a bidirectionally coupled DEM-CFD simulation to capture the interaction between seed particles and the surrounding airflow.
The study analysed particle-fluid interactions in a pneumatic system handling wheat grains. Simcenter EDEM software was used to model the wheat as non-spherical ellipsoidal particles so the seed geometry could be represented more accurately. The model included standard friction, drag, and lift forces, while calibration tests such as static angle of repose and inclined plane testing were used to ensure realistic particle behaviour. For the CFD side of the model, AcuSolve simulations used the Spalart-Allmaras turbulence model to represent airflow, along with non-spherical drag and lift models to account for particle aspect ratio.
The team then defined an air intake velocity of 20 m/s and a particle feed rate of 0.18 kg/s. Momentum exchange between the solid seed phase and the fluid air phase was modelled so that each influenced the other throughout the simulation. Simcenter EDEM software determined particle behaviour, while AcuSolve updated the fluid field and communicated drag and lift forces back into the particle model.
Why This Approach Mattered
This simulation approach gave Amazone’s engineers a clearer view of behaviour inside the seed drills and helped them optimise the design in ways that physical testing alone could not easily achieve. The mass flow analysis along the riser indicated optimal centring of seeds before they entered the distribution head, helping support consistent sowing patterns. The coupled DEM-CFD model also enabled the team to represent the seed transport process more accurately and visualise the particle-fluid multiphase flow inside the distributor head of the pneumatic seeding machine.
For Amazone, that meant more than simply running a simulation. It meant building a predictive model that could support design decisions with deeper insight into how seeds moved through the system and how distribution could be improved.
The Results
The deployment of DEM-CFD simulation delivered clear results. Amazone reduced development time by roughly 40% by limiting reliance on expensive physical prototypes and shortening the development cycle. The simulation also improved understanding of the conveying line’s internal dynamics, giving engineers access to insights that were nearly impossible to observe through conventional physical testing alone.
The project also helped optimise seed distribution. Simulation results showed a strong correlation with experimental test data and were evaluated using the measured Coefficient of Variance for particle mass across outlets. These experiments helped the team examine mass distribution behaviour along the riser and support more consistent sowing patterns.
Alongside that, precise seed placement reduced material usage, helping conserve expensive seeds while maximising yields. This supported more sustainable and efficient farming practices. Improved efficiency in seed usage and higher yields also meant farmers could improve profitability and reduce input costs.
Jan Bruns, calculation engineer at Amazone, summarised the outcome clearly: the combined DEM-CFD simulation approach delivered precise seed placement, optimised harvest output, reduced seed waste, accelerated development, and supported more efficient and more sustainable farming solutions.
The Bottom Line
Amazone’s work shows how simulation can strengthen precision agriculture by helping engineers model the complex interaction between particles and airflow inside pneumatic seeding systems. By combining Simcenter EDEM software with AcuSolve, the team improved seed placement, reduced development time, supported more sustainable resource use, and created a stronger foundation for future innovation in agricultural machinery.
For manufacturers looking to improve machine performance in arable farming applications, this kind of simulation-led development can open the door to better process understanding, faster optimisation, and more efficient product design.
Source basis for factual accuracy: customer background, challenge, subsystem scope, modelling approach, calibration tests, airflow setup, quoted outcomes, and the reported 40% development-time reduction all come from your uploaded case study.
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Content adapted from the original case study published by Altair.
