Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Power loss and junction temperature analysis of power semiconductor devices

Abstract: A newly developed electrothermal calculation method is implemented to estimate the power loss and working temperature of insulated gate bipolar transistor (IGBT) devices. Based on the measurement of the IGBT's characteristics, the exact estimation of power loss considering the junction temperature is introduced. Then, the thermal network is used to calculate the working temperature. The comparison between experimental and calculation results shows that this method is effective as a designing step with only the time-domain voltage and current data obtained from simulation results.

A new grain orientation spread approach to analyze the dynamic recrystallization behavior of a cast-homogenized Mg-Zn-Zr alloy using electron backscattered diffraction

Abstract: A new grain orientation spread approach (GOS ≤ 5°) was proposed to study DRX of a Mg-Zn-Zr alloy during hot deformation. DRXed grains possessed random texture while the deformed grains contributed to basal texture. The alloy exhibited rapid DRX at low deformation strains followed by near-saturated behavior as strain increased.

A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry

Abstract: With the emerging trend of green supply chain management, supplier selection and order allocation based on green criteria have become very important in this competitive world. During the selection process of the eligible suppliers, qualitative as well as quantitative factors should be considered. In this paper, a novel mathematical model is developed to select a set of suppliers, and assign the order quantity. Due to the importance of environmental concerns, both qualitative and quantitative environmental criteria are taken into account in this research. The proposed model comprises two phases namely a two-stage QFD, and a stochastic multi-objective mathematical model. The stochastic (scenario) approach helps to manage the uncertainty in the order allocation process. Furthermore, trapezoidal fuzzy numbers are utilized to handle the vagueness in human thoughts. The application of the proposed model is shown in beverages industry.

Assessing the impact of climate change on building heating and cooling energy demand in Canada

Abstract: In recent years, the building sector has received increasing attention with attempts to limit its energy consumptions and GHG emissions. In fact, buildings account for more than 30% of the overall energy demand worldwide, with projections for increases in this quota due to climate changes, urbanization, and higher living comfort standards. This study investigates the effects of climate changes on the heating and cooling energy demand of buildings in the most populated urban region in Canada, i.e. the city of Toronto in Ontario. Statistical and dynamical downscaling methods are utilized to generate several future weather files, starting from different baseline climates including the old Canadian Weather Year for Energy Calculation CWEC (representing the 1959–1989 period) and the new CWEC 2016 (representing the 1998–2014 period). In dynamical downscaling, a regional climate model is used to obtain a finer resolution than traditional general circulation models. The generated future weather data sets are then used for simulating the energy demand of 16 building prototypes. The simulation results show an average decrease of 18%–33% for the heating energy use intensity, and an average increase of 15%–126% for the cooling energy use intensity by 2070, depending on the baseline climatic file of use and building typology. The forecasted GHG emissions of each building prototype are then discussed. The results demonstrate the need to perform building modelling with sensitivity analysis of future climate scenarios in order to design more resilient buildings.