A 3D transient CFD model to predict heat and moisture transfer in on-farm stored grain silo through parallel computing using compiler directives: Impact of discretization methods on solution efficacy

Abstract

Computational fluid dynamics model parameters highly depend on the storage structure, grain conditions, and airflow properties. Solution methods obtained for the lab-scale bins cannot be extrapolated for a relatively large on-farm silo. To validate the above hypothesis, a model was developed and validated with stored barley aeration in a 1000t silo. A mathematical function was used to initialize the discrete initial conditions in the silo followed by using DEFINE_macros to execute parallel computing. Time-step analysis was conducted followed by optimization of the solution methods in terms of the accuracy and time frame of the model’s output. Results showed that with a time-step of 4 s, temperature and moisture error were 1.1–1.7 °C and 0.3% wb while mean relative deviation were 3.1–4.4% and 2.2%, respectively. COUPLED algorithm resulted in a similar accuracy as of SIMPLE scheme except within spatial and transient formulations. However, the former algorithm was observed to take almost 190% more time than the latter scheme, limiting the simulation efficiency in an on-farm silo. Green Gauss Node-based gradient technique was found to be the appropriate for discretizing large silos. Model showed 45 kJ kg−1 energy emission that decreased the cooling potential of air in silo. As field evaluation of aeration strategies are time-consuming, this model can be used to obtain results that could shape the stored grain management practice.