Renewable Energy Alternatives

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Browsing Renewable Energy Alternatives by Author "Aukah, Jackis"

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    Publication
    Prediction of Airflow and Temperature Distribution in Hybrid Solar-Biomass Dryer using Computational Fluid Dynamics
    (JOURNAL OF SUSTAINABLE RESEARCH IN ENGINEERING, 2018-09-15) Aukah, Jackis; Muvengei, Mutuku; Ndiritu, Hiram; Onyango, Calvin
    Hybrid solar-biomass dryers present a viable option for drying maize since continuous drying can be achieved. However,non-uniform drying of the product may occur in the drying chamber due to poor airflow distribution. Several studies have beenreported on modeling of solar dryers for drying of agricultural products but most researchers use one dimensional models basedon thermal analysis to simulate the drying process but this approach can only describe the flow variable in one defined directionalong the domain and therefore, cannot effectively reveal information on air flow patterns which is considered crucial foroptimization process. Three dimensional approach that provides a more realistic simulation of the drying process is necessary foroptimization of the performance of the dryer. The aim of this study was to develop a mathematical model for predicting thetemperature and air velocity distribution in hybrid solar biomass dryer. The model consists of the full set of partial differentialequations that describe the conservation of mass, momentum and heat inside the dryer. The standard model was used to describeturbulence in addition to the governing conservation equations. Simulation was done using ANSYS CFX which is a general purposeComputational Fluid Dynamics (CFD) package. The simulated airflow pattern and temperature distribution on the horizontal andvertical planes in the drying chamber was analyzed and the result revealed spatial homogeneity of drying air condition. The modelwas validated experimentally and the results showed a reasonable agreement between the experimental and simulated results witha small variation of about 0.7 0 C. This indicate that the model prediction was reasonably accurate.