Temperature distribution prediction using CFD simulation for silo stored barley

Abstract

The long-term preservation of bulk stored grains is critical for the food security of South Africa. Temperature is a key driver in grain degradation, specifically for barley, which has the unique quality metric of germination energy, which is highly sensitive to storage temperature. Grain management companies rely on various measurement techniques and conditioning factors to facilitate safe storage conditions, with temperature monitoring forming the backbone of these systems. Temperature monitoring of bulk grain stores is, however, complicated by costing, maintenance and implementation difficulties. Furthermore, due to a lack of quantitative data on the propagation of heat specifically from hotspots, temperature monitoring remains under-implemented, and the data underutilised, jeopardising grain stores. Previous research on the transfer of heat in grain silos has seldom considered hotspot induced heating, and none has modelled barley specifically. Furthermore, most of the previous research have been limited to one- and two-dimensional simplifications, rather than three-dimensional analyses owing to earlier computational constraints. In this study, a three-dimensional CFD porous media grain bulk model with a hotspot has been developed to quantify the heat transfer driven by insect induced hotspots in a packed bed of barley. The model is validated by comparison to results from an experimental model and benchmarked against results from literature. The movement of hotspot temperature profiles over two weeks of progression have been characterised in terms of temperature profile velocity and effective detection distance. Based on these results recommendations for improved temperature measurement interpretation and equipment installation methods are presented to address the current shortcomings for a major grain management company in South Africa.