Research Publications

Now showing 1 - 3 of 3

Embargo
Computational Modelling of Drying Process in a Novel Solar Dryer Design with Experimental Validation
(SLIIT, Faculty of Engineering, 2024-10) Gunathilaka, R.A.C.K.; Kumar, R; Chatterjee, S; Bandara, R.M.P.S.
Crops and food products are dried by a variety of conventional methods, including open-air drying, smoking, and oven-drying for preservation purposes. Due to inherent drawbacks in the conventional drying methods, such as higher energy consumption, possible contamination and uncontrollable drying conditions, solar drying is preferred over the said drying methods. A solar dryer utilizes solar energy to dry crops, food products etc. by harnessing the heat energy from the sun to reduce the moisture content of the substances. The study focuses on modelling the drying process in an indirect type novel solar dryer through computational modelling with subsequent experimental validation of the temperature and air velocity profiles. The solar dryer is comprised of a divergent section, a convergent section, an absorber plate, a drying chamber, an outlet and trays. The Computational Fluid Dynamics (CFD) approach has been adopted in modelling the drying process and ANSYS Fluent has been used as the CFD tool. The computational mesh is comprised of 621,106 tetrahedral mesh elements. Pressurevelocity- coupling numerical scheme was used for discretizing the Navier-Stokes and other transport equations. A realizable k-ε model was applied in modelling turbulence. CFD simulations were conducted for three different mass flow rates of air: 0.0872 kg/s, 0.0636 kg/s, and 0.0447 kg/s at a solar insolation of 996 W/m². CFD simulations provided a comprehensive insight into the temperature and velocity profiles within the solar dryer. Furthermore, modelling results are well aligned with the experimental measurements taken on the solar dryer, confirming the reliability and accuracy of the computational model. The findings of this study will contribute as a platform for optimizing the performance of solar dryer designs.
Open Access
Translation and Validation of the ‘Indian Scale for Assessment of Autism’ on a Sinhala-Speaking Population of 3- to 12-year-olds in Colombo and Gampaha Districts
(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Pathiraja, G.A.P.S.S.O; Ponnamperuma, L
There is a significant need for appropriate culturally sensitive, standardized screening tools in many countries including Sri Lanka for the accurate identification of ASD which leads to specific interventions and good prognosis. The study’s aim was to investigate psychometric properties by translating and validating the Indian Scale for the Identification of Autism on a Sinhala-speaking population of 3 to 12-year-olds in Colombo and Gampaha districts to increase the efficiency and quick screening of autism in routine clinics. The methodology included the systematic forward and backward translation, Delphi process and data collection from clinical and non-clinical samples from Sinhala speaking parents of 3- to 12-year-olds in Colombo and Gampaha districts. The study had a good internal consistency reliability measured through Cronbach’s alpha of .927. There was high sensitivity and specificity measures whereby a cutoff score of 68 was ensured through the Receiver Operator Characteristic Curve. Overall, the Indian Scale for Assessment of Autism is suitable to be used in routine clinics in Colombo and Gampaha districts.
Open Access
Knowledge Discovery with Data Mining for Predicting Students’ Success Factors in Tertiary Education System in Sri Lanka
(University of Moratuwa, Sri Lanka, 2017-10-31) Kasthuriarachchi, K. T. S; Liyanage, S. R
Knowledge discovery in educational data would be so basic to determine better expectations on the undergraduates. Distinguishing proof of the components influence to the execution of undergraduates in light of various attributes will be supportive for instructors, educators and managers viewpoints. This paper endeavors to utilize different data mining ways to deal with find forecast manages in undergraduates’ data to distinguish the components influence to the scholarly accomplishment in their tertiary education. The approach of this exploration observed the aftereffects of three mining algorithms with about 3800 undergraduates’ records and the calculation which demonstrated the most elevated exactness has chosen as the best model and the connections acquired through that were gotten to foresee various elements against the objective of whether they will get the degree or not following three years of the university life. Naïve Bayes, Decision Tree and Support Vector Machine were used in predicting the most affecting factors to the performance of students. According to the prediction accuracy levels, the results of Decision Tree were selected since it outperforms the rest for the selected data set. Finally, the results were evaluated using a correlation analysis to select the most prominent factor. According to the test, the age, past failure modules, performance of past semesters were selected as the most influencing factors to the success or failure of the students in tertiary education system in Sri Lanka.

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