Adam Rysanek

Associate Professor

Relevant Thesis-Based Degree Programs

 
 

Great Supervisor Week Mentions

Each year graduate students are encouraged to give kudos to their supervisors through social media and our website as part of #GreatSupervisorWeek. Below are students who mentioned this supervisor since the initiative was started in 2017.

 

Dr. Rysanek is an exceptional supervisor and a wonderful human being. He is not only my supervisor but also my mentor, friend and role model!
He is brilliant, highly knowledgeable, very enthusiastic about his research field and always has creative and cool research ideas. He is supportive and caring and he is always there for his students and willing to help.

He is kind, respectful and patient with his students. He has such a positive attitude and his critiques are always constructive and helpful. He is approachable, friendly and fun to be with. He cares about the wellbeing of his students and always makes his students feel confident.

He absolutely has an amazing inspiring personality! I am grateful and lucky to have Dr. Rysanek as my supervisor and love working with him! He is definitely a #GreatSupervisor!

Sarah Crosby (2019)

 

Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Predicting window opening states in buildings using machine learning with real and synthetic datasets (2024)

As HVAC systems become more prevalent in densified residential buildings, natural ventilation and its benefits have been pushed out the window. These benefits, including energy reductions and air quality improvements, can be harnessed most effectively if building designers can estimate the natural ventilation rate. Since all ventilation models rely heavily on the window opening angle, some form of a sensor is necessary. Prior works have attempted this with traditional algorithms with limited success. Tangentially, a lot of effort has been invested in using machine learning to locate windows on facades. This work combines both of these realms, using photographic techniques and machine learning to not just locate windows but also predict their opening state. This thesis can be broken down into three main parts: the data collection, the preliminary machine learning approach, and the hybrid dataset machine learning approach. The results show the machine learning model can predict window opening state with over 90% accuracy; most windows perform similarly, with the exception of windows with a more head-on angle. Furthermore, using 3D modelling and photorealistic renders to supplement the real images of the dataset proves to be a promising avenue for continued work in this domain. The combination of real and synthetic images, which creates a hybrid dataset, improves model performance in some situations. Aside from these results, these datasets will be made public for others to approach the problem; this includes both the real and synthetic images. The publication of this dataset can facilitate further research into this topic; possible routes that can be taken are presented while also considering the implications and limitations of the research.

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Learning from long-term care: COVID-19 and architecture for ageing (2022)

Throughout the pandemic, long-term care (LTC) facilities have repeatedly been identified as the location of some of the most infectious and deadly outbreaks of COVID-19. As specialized facilities where mostly elderly residents are housed and cared for in congregate settings, LTC communities are more vulnerable to infectious diseases as many residents are frail and live with chronic illnesses and comorbidities. This thesis examines the relationship between COVID-19 outbreaks and infections within LTC facilities in British Columbia (BC) seeking out possible building design factors that might have influenced risk of exposure or severity of COVID-19 outbreaks. The study first assesses the impact of COVID-19 on global LTC facilities, identifying suspected determinants of COVID-19 infections and mortality rates. The disparities of LTC systems are then discussed through the concept of a ‘syndemic’ which suggests that pre-existing crises in within LTC such as funding, staffing, and crowding exacerbated the impact of COVID-19. The historical design and aesthetics of LTC architecture are then scrutinized, tracing lineages from eldercare and healthcare architecture through the rise of modernism and postmodernism. Through historical analysis and photographic comparison, the study considers how the progenitors of LTC have influenced the design and reputation of contemporary facilities. Turning to the built conditions of LTC facilities in BC, satellite imagery and Streetview remote site visits are used to classify BC’s LTC facilities into typologies according to building footprints and massing to observe trends within the spatial arrangement of floorplans. Lastly, the investigation undertakes a regional cohort analysis of BC’s 355 LTC facilities by linking administrative survey data from BC Office of the Seniors Advocate and COVID-19 outbreak data from the BC Centre for Disease Control’s weekly pandemic reports. Timelines and data graphs are used to illustrate the course of the pandemic as it occurred in BC’s LTC facilities during the observation period of March 5, 2020-Feburary 9, 2022. BC’s LTC facilities are sorted according to chronology, regional health authorities, resident population size, repeat outbreaks, highest infection rate, and legislative compliance to assess the influence of building design on occurrence of outbreaks and resident attack rates.

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Integrating membrane-assisted radiant cooling panels in building energy simulation (2021)

The world is in critical need of technologies that will make a significant andimmediate impact in our fight against climate change. As global temperaturesrise, building cooling demands could rise by 72% by the year 2100,meaning that the development of energy efficient space cooling technologiesis becoming increasingly important. Radiant cooling panels have shown a lotof potential as an energy efficient method of supplying space cooling. However,they need to operate alongside dehumidification in many environmentsso that air moisture does not condense on their chilled surfaces.This thesis focuses on the development of the membrane assisted radiantcooling panel, a technology used to provide energy-efficient space cooling inhot and humid climates without the need for mechanical dehumidification.A heat balance model is developed that estimates the operational membranetemperature and cooling capacity of a membrane assisted panel. The modelis then calibrated using data collected from a field experiment in Singapore.Additionally, a framework is developed that allows the heat transfer modelto operate within a TRNSYS environment. This allows for the energy simulationof buildings that utilize membrane assisted panels for sensible spacecooling. The framework is then used to predict the potential energy savingsthat could be obtained by implementing this technology in both Singapore and Vancouver.The membrane temperatures predicted by the calibrated heat transfermodel differ from those observed through experimentation by 0.21°C. Themodel is sufficiently accurate for condensation mitigation, however, concernsregarding the coefficients used to model natural convection, along with thedata used for calibration, need to be addressed before the model can beapplied to different panel geometries. While some aspects of the TRNSYSframework need to be further developed, it was found through simulationthat membrane assisted radiant cooling can provide significant energy savingsin both tropical and temperate climates.The framework developed in this study will bring membrane assistedradiant cooling closer to widespread implementation, as modelers will beable to optimize the design of a radiant system before its construction in abuilding.

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News Releases

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