Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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2012 - 20206

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Now showing 1 - 6 of 6
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    Reliability modeling considerations for emerging cyber-physical power systems
    (IEEE, 2018-06-24) Aravinthan, V; Balachandran, T; Ben-Idris, M; Fei, W; Heidari-Kapourchali, M; Hettiarachchige-Don, A. C. S; Jiang, J. N; Lei, H; Lei, C. C; Mitra, J; Ni, M; Papic, M; Parvania, M; Sephary, M; Singh, C; Srivastava, A; Stefanov, A; Sun, H; Tindemans, S
    Power system operation considering an increasingly complex cyber infrastructure may be one of the key factors of the next generation power systems. The effective operation of a power system in a massively deployed cyber network environment will be affected by cyber network reliability. Therefore, it is vital not only to understand the operation of a cyber network and its reliability, but also it is critical to integrate the interdependency of cyber and power systems into power system planning and operations. This requires a three-layer approach to reliability modeling and evaluation. The cyber and power layers are interconnected by the information layer. The objective of this paper is to define the three-layer model and report a generalized framework for combined reliability modeling.
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    Design evolution of engineering systems using bond graphs and genetic programming
    (Pergamon, 2016-02-01) Samarakoon, B. L; Gamage, L. B; de Silva, C. W
    This paper presents a scheme of evolutionary design optimization, which integrates modeling with bond graphs and optimization using genetic programming for multi-domain engineering systems, particularly mechatronic systems. The performance of the developed system is studied using both experimentation and simulation. During the evolutionary optimization, in addition to the desired response error, system complexity is also taken into account. For the experimental study, the method is implemented in an industrial fish processing machine at the Industrial Automation Laboratory of the University of British Columbia, and the obtained results for suggested design modifications are studied and tested. The drawbacks of the fitness calculation methodologies that are presented in literature are identified and improved fitness functions are developed for evolutionary design in the present work. While previous work has investigated the integration of bond graphs and genetic programming for designing an engineering system, the present work specifically addresses the application of the developed method for the design improvement of an industrial machine. The proposed method is applicable particularly to existing engineering systems, first because the initial model can be tested by comparing its simulated results with the corresponding results from the actual physical system, and second because the design improvements as suggested by the evolutionary design framework, which is developed in the present work, may be implemented and tested against the behavior of the corresponding model.
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    Modeling of optical carrier recovery using four wave mixing technique for binary phase shift keying systems
    (IEEE, 2015-04-07) Malasinghe, L. P; Weerasuriya, R
    Modern communication networks uses optical fibre extensively. The transport networks are upgrading its capacity continuously to provide the bandwidth requirement of the customer requirements. To provide such an increase in bandwidth, the transmission networks are moving from Amplitude Shift Keying modulation methods to Phase Shift Keying methods. In phase shift keying systems, data reception and regeneration required phase synchronization. This requires original optical carrier phase information. In this paper, we report a model for optical carrier recovery for optical synchronization of a Binary Phase Shift Keying input by exploiting Four Wave Mixing in Highly Non-Linear Fibers. The noise influence from the signal laser for the recovered carrier was analyzed.
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    Modeling of compressed air requirement for different weft yarns on air jet weaving
    (IEEE, 2017-05-29) Gunarathna, D. P. I. U; Kumari, R. K. D. N; Subawickrama, S. G. A. K; Fernando, E. A. S. K; Jayawardena, T. S. S
    Although air-jet looms run faster than mechanical weft insertion looms such as rapier or shuttle looms, but their production cost can be higher when their air usage is not optimized. The amount of air required for weft insertion varies greatly for each yarn type and also depends on several key factors. One of these factors is the yarn's air friendliness, which is the measure of how easy it is to project yarns across a web with an air column. In practice, the same air pressure is applied to all yarn types since determining the optimum air pressure for each yarn type is laborious and time intensive. Consequently, a considerable amount of air is wasted and the weaving efficiency of the loom is compromised, especially when weaving with air friendly yarns. A scientific approach that predicts the best pressure for main nozzle and sub nozzles for each yarn type can mitigate the waste of compressed air usage. Thus, a mathematical model that can be applied to optimize the compressed air usage is presented.
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    Development of an artificial neural network model to simulate the growth of microalga Chlorella vulgaris incorporating the effect of micronutrients
    (Elsevier, 2020-03-20) Liyanaarachchi, V. C; Nishshanka, G. K. S. H; Nimarshana, P. H.V; Ariyadasa, T. U; Attalage, R. A
    Artificial neural network (ANN) models can be trained to simulate the dynamic behavior of biological systems. In the present study, an ANN model was developed upon multilayer perceptron neural network architecture with 23-20-1 configuration to predict the cell concentration of microalga Chlorella vulgaris at a given time. Irradiance level, photoperiod, temperature, air flow rate, CO2 percentage of the air stream, initial cell concentration, cultivation time and the nutrient concentrations of the media were considered as the input variables of the model. Resilient backpropagation learning algorithm was used to train the model by means of 484 experimental data belonging to four studies. Bias and accuracy factors of the developed model fall into the range of 0.95–1.11 indicating the model has an excellent prediction ability. Parity plot showed a good agreement between the predicted and experimental values with R2 = 0.98. Relative importance of the inputs was evaluated using Garson’s algorithm. The results of the study indicated that CO2 supply had the highest impact on the growth of C. vulgaris within the selected range of input parameters. Among macronutrients and micronutrients, highest influence was demonstrated by nitrogen and copper respectively.
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    PublicationOpen Access
    Modeling of microstructural evolution in an MCrAlY overlay coating on different superalloy substrates
    (Springer US, 2012-02-01) Karunaratne, M. S. A; Martino, Irene Di; Ogden, Sarah L; Oates, David L; Thomson, R C
    A multicomponent, one-dimensional diffusion model that was developed for simulating microstructure evolution in coated gas turbine blade systems has been used to compare the phase structures of three MCrAlY coated superalloy systems. The model is based on finite differences and incorporates oxidation and equilibrium thermodynamic computations. The superalloy substrates considered were the nickel-based superalloy CMSX-4, a high-Cr singlecrystal superalloy, and a cobalt-based MAR-M509, and these were all coated with an MCrAlY bond coat of similar composition. The results predicted by the model have been compared with similar experimental systems. The model can predict many features observed experimentally and therefore can be expected to be a useful tool in lifetime prediction and microstructural assessment of turbine blade systems based on superalloys. The work also highlighted the fact that for a given coating, the phase evolution within system is dependent on the substrate material.