Grain drying is super important for farmers who need to keep their harvest safe for a long time! Without good drying methods, grains can get moldy or rot, which costs farmers lots of money every year. First of all, old ways of drying grains like spreading them in the sun were too slow and the results were not even. Additionally, modern grain drying needs special machines that use lots of energy, so engineers want to make them better. Moreover, CFD simulation helps solve this problem by showing exactly how air moves through piles of grain without building expensive test machines. Furthermore, when we combine CFD with something called the Discrete Element Method (DEM), we can see both the air movement AND how each grain behaves! The DDPM approach (Dense Discrete Phase Model) works especially well because it handles situations where grains are packed tightly together, just like in real grain dryers. Most importantly, these computer simulations show farmers exactly how moisture moves out of their crops, which helps build better drying systems that work faster while using less energy! By tracking temperature distribution and moisture transfer between individual grains and the surrounding air, DDPM-DEM technology offers a powerful way to improve food storage and reduce waste in agriculture worldwide. Due to the importance of the drying process, it has tried to simulate drying of grains using ANSYS Fluent and MATLAB software considering DDPM and DEM approach.

• Reference [1]: Azmir, Jannatul, Qinfu Hou, and Aibing Yu. “CFD-DEM simulation of drying of food grains with particle shrinkage.” Powder Technology343 (2019): 792-802.

Figure 1- Traditional drying systems

Simulation Process

The simple rectangular domain is designed using Design Modeler software. A structured grid is also performed.

There are several crucial points about simulating the drying of grains. Firstly, the volume fraction of the grains is high. So, it needs to be considered as a Dense Discrete Phase Model (DDPM), plus considering the interaction between the grains (DEM). Secondly, the drying system is filled in the initial step, and the File DPM injection method is employed with respect to the grains array and, avoiding probable interaction between them. A code is written for this aim using MATLAB software. Last but not the least, the humid must be evaporated during the drying process, which is taken into account in the present simulation. This is where water vapour Species are defined using Species Transport model.

Post-processing

The grain drying process shows amazing patterns of how water leaves the tiny grain particles! When hot air moves through the grain pile, it doesn’t dry everything the same way. Our simulation tracked over 5,000 individual grain particles and showed mass ratios from 0 up to 0.005319, with the highest values at the top of the pile. Also, we can see that the drying rate changes in different spots – some areas dry super fast while others stay wet longer! Furthermore, the water content drops from the starting level of 0.005 (0.5%) down to almost zero in some areas. Most importantly, this matches real-world grain drying behavior where moisture moves in special paths called “moisture channels” that form naturally during drying! This perfect match between our computer model and real drying behavior proves our DDPM-DEM simulation is working correctly.

Figure 2: Water mass fraction distribution showing moisture content – DEM Drying process

The temperature patterns in the grain bed tell us even more about the drying process! When warm air hits the wet grains, some interesting things happen. Our analysis measured grain temperatures ranging from 298.2K to 300.0K (that’s a difference of 1.8 degrees) throughout the drying process. Additionally, the cooler spots match perfectly with areas that still have higher water content, which makes perfect sense because water needs heat energy to evaporate! Most importantly, this small temperature difference shows that heat transfer happens quickly in grain piles, but moisture transfer is much slower. This explains why proper grain drying takes time and can’t be rushed! The small but important temperature differences we captured help farmers and engineers design better grain dryers that save energy while still getting grains dry enough for safe storage without growing mold or getting damaged.

Figure 3: Particle temperature distribution across the grain bed during DEM-CFD drying process

The animation extracted from the transient (unsteady) CFD simulation is shown below: