Detailed modelling and simulation of single-phase transformers for research and educational purposes

<span>COVID-19 pandemic, despite its devastating impact, accelerated the shift to e-learning in higher education. Particularly in the electrical machines courses, that often include laboratory experiments. However, no detailed models of transformers, developed in Simulink/MATLAB®, were reported in the literature. Hence, in this paper, a virtual laboratory consists of models of single-phase transformers was built for the first time. The proposed models are easy to use and modify, and allow all machines’ parameters to be altered for students to replicate easily to support and enhance the learning process of electrical machines courses. Consequently, the developed models are effective tools for educational and research purposes. Dynamic models of single-phase, two-winding, transformers and step-up and step-down auto-transformers were developed using Simulink/MATLAB®. Two different approaches for modelling were proposed, the block diagram representation and Simscape based models. The two modelling methods were validated against the built-in transformer model. The developed models have been successfully integrated into electrical engineering courses at Middle Technical University, Baghdad, Iraq. Therefore, all developed models are freely available online at a dedicated repository.</span>

Covid spirals: a phase diagram representation of COVID-19 effective reproduction number Rt

In this paper, we propose a phase diagram representation of COVID-19 effective reproduction number R_t. Specifically, we express R_t as a function of the estimated infected individuals. This function plots a particular clockwise spiral that allows to easily compare the evolution of the number of new infected individuals at different dates and, possibly, provide some hints on the future progression of the infection.

Detailed modelling and simulation of different DC motor types for research and educational purposes

The devastating impact of Covid-19 pandemic accelerated the shift to e-learning in the higher education. Particularly in the electrical machines courses, that often include laboratory experiments. However, no detailed models of DC motors, developed in MATLAB/Simulink, were reported in literature. Hence, in this paper, a virtual laboratory consist of models of DC motors was built for the first time. The proposed models are easy to use and modify, and allow all machines’ parameters to be altered for students to replicate easily to support and enhance the learning process of electrical machines courses. Consequently, the developed models are effective tools for educational and research purposes. Dynamic models of DC motors were developed using MATLAB/Simulink, namely separately excited, permanent magnet, shunt-connected and series-connected DC motors. Two different approaches for modelling were proposed, the block diagram representation and Simscape based models. The two modelling methods were validated against the built-in DC machine model. The proposed models are easy to use and modify, and allow all machines’ parameters to be altered for monitoring and comparison purposes. Consequently, the developed models are effective tools for research and educational purposes.

Multimedia Learning Modules (MLMs) Based on Local Wisdom in Physics Learning To Improve Student Diagram Representations in Realizing the Nature of Science

This research was conducted to determine the feasibility of the instrument diagram representation test and the effectiveness of Multimedia Learning Modules (MLMs) integrated local wisdom in physics learning activities. The study design used a pretest-posttest control group design. The research instrument consisted of tests and non tests. The test instrument was in the form of five items arranged according to the diagram representation indicators, namely drawing diagrams and their components and performing mathematical calculations according to the diagram explanation. Non-test instrument is a questionnaire study of test instruments. The validation of the test instrument was carried out using Aiken's V. Data analysis techniques used the General Linear Model (GLM) with a significance level of 0.05 to test the effectiveness of integrated local wisdom MLMs in improving student diagram representation. The results showed that the overall item items were declared valid with Aiken's V score in the range of 0.88 to 0.92 and the integrated local wisdom MLMs were effectively used in physics learning activities to improve student diagram representation based on Mean Difference (MD) values of -54,449.

Solving Delete Free Planning with Relaxed Decision Diagram Based Heuristics

We investigate the use of relaxed decision diagrams (DDs) for computing admissible heuristics for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of two novel DD encodings for a DFP task: a multivalued decision diagram that includes the sequencing aspect of the problem and a binary decision diagram representation of its sequential relaxation. We present construction algorithms for each DD that leverage these different perspectives of the DFP task and provide theoretical and empirical analyses of the associated heuristics. We further show that relaxed DDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while DD-based heuristics trail the state of the art, even small relaxed DDs are competitive with the linear programming heuristic for the DFP task, thus, revealing novel ways of designing admissible heuristics.

A new heuristics for the event ordering in binary decision diagram applied in fault tree analysis

Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Since their introduction, binary decision diagrams proved to be a valuable tool for complete quantification of hard fault tree models. As is known, the size of the binary decision diagram representation is mainly determined by the quality of the selected fault tree event ordering scheme. Finding the optimal event ordering for binary decision diagram representation is a computationally intractable problem, for which reason heuristic approaches are applied to find reasonable good ordering schemes. The existing method for finding optimal ordering schemes related to special types of fan-in 2 read-once formulas is employed in our research to develop a new heuristic for fault tree. Various fault tree simplification methods are used for the sake of reducing fault tree model discrepancy from fan-in 2 read-once formulas. The reduced fault tree is traversed in a depth-first manner, as for every gate, the best ordering scheme is chosen from selected sets of input permutations. The quality of the final event ordering scheme is compared to orderings produced with depth-first left most heuristic on a set of fault tree models addressed in the literature as well as on a set of our hard models. Our method proves to be a useful heuristic for finding good static event ordering, and it compares favourably to the known heuristic based on a depth-first left most assignment approach.