Advancing the CANWET watershed model and decision support system by utilizing high performance parallel computing functionality
Watershed modeling is widely used to better understand processes and help inform planning and watershed management decisions. Examples include identifying impacts associated with land use change; investigating outcomes of infrastructure development, predicting effects of climate change. The proposed project will see the evolution of a desktop based watershed modeling and decision support system to a web based tool that will allow greater access by decision makers and stakeholders. By this means we will advance the idea of evaluating cumulative effects in the watershed decision making process rather than the current practice of assessing proposed changes in isolation.
The proposed software evolution will take advantage of high performance computing by porting existing code to a higher performing language and restructuring to operate using parallel or multi-core processing. The result is expected to be a dramatic reduction in simulation run times. Reduced run times will facilitate the use of automatic calibration routines used to conduct model setup, reducing costs. It will also enable rapid response if the simulation were to be re-run by a request through the web-based user interface. The designed web-based tool will be used by decision and policy makers in the watersheds that contribute to Lake Erie to understand the sources of pollution especially phosphorus which is a major contributor to Lake Erie eutrophication problems and develop policies in supporting a wide variety of watershed planning and ultimately help achieve the Federal and Ontario government commitments to reduce 40% phosphorus entering Lake Erie by 2025.
Assessment and adaptation strategies for a changing climate: future wind loading on buildings in Toronto
Maintaining resiliency of Canada’s built environment against extreme wind hazard is necessary to sustain the prosperity of our communities. Buildings are becoming more complex, lighter and taller making them more prone to wind effects. This is further aggravated by the long-term effects of climate change, and the associated uncertainty of future wind load characteristics. Historical climate data is no longer enough for long-term planning and adaptation in urban environments. The formulation of adaptation strategies to mitigate the effects of climate change in cities will require a collaborative effort that draws on expertise, tools, and approaches from a variety of disciplines.
This project will investigate the response of selected tall, highly flexible structures together with their surroundings in downtown Toronto under the new wind conditions due to climate change. Structures that are currently safe and serviceable under wind loading may experience issues (large accelerations, member failures) when the wind loading characteristics change with the changing climate. The multi-disciplinary project team will capitalize on the availability of large archives of climate model output, new tools of downscaling, and extensive computational resources. This technical expertise and infrastructure will enable the translation of knowledge of global climate change into actionable knowledge useful to practitioners in the area of urban building design. This project will deliver sustainability and resiliency-focused design, as well as retrofit recommendations for practitioners and decision makers with a direct benefit to the residents of Toronto.
Assessment of Prototype Scour Data
Scour is the removal of riverbed sediment which can be induced due to the presence of channel contraction, hydraulic structures, natural fluctuations in discharge, or changes in sediment supply in a fluvial environment. Scour and erosion have been identified as the primary cause of the majority of bridge failures in North America. Several investigators have concluded that about 50% of all bridge collapses occur due to scourͲrelated complications. The prevalence of scourͲinduced bridge collapses is indicative of the criticality of scour estimation in the interest of public safety as well as mitigation of infrastructure costs, as this type of collapse often requires significant investment for design and construction of replacement bridges, fault analysis and potential rehabilitation. The available bridge design codes are mostly extracted from estimates of scour at the laboratory scale (experiments in reducedͲscale physical models), acquired under highly controlled conditions. Some sources of model inaccuracy include scale effects in physical hydraulic modelling; a lack of understanding of the flow physics of the phenomenon; and the limitations of current computational methods used to model sediment transport. A way to improve the prediction ability of current scour methodologies could be using observed scour values in real rivers to verify/correct such methods, as this proposed research intends to do. The aim of the present project is to study prototype scour data at various field site as well as to use computational tools to assess the efficacy of scour estimation methods for investigating the possible improvements to existing approaches.
Computational high-throughput screening of catalyst materials for renewable fuel and feedstock generation
According to a World Energy Council Report, population growth and rising standards of living across the world will at least double global energy demand by 2050. Simultaneously, carbon dioxide emissions must be reduced significantly to prevent a catastrophic rise in global temperatures. Clean and abundant renewable energy sources are available; unfortunately, the intermittency of solar and wind power is a prevailing problem which is limiting the potential for widespread use. Our project seeks to address both of these issues through development of novel catalysts to electrochemically convert CO2 captured from power plants into fuels and other higher value chemical feedstocks using renewable electricity. This innovative strategy will (1) provide a long term storage solution by converting renewable electricity into a stable chemical fuel, (2) provide a means to intelligently recycle CO2 rather than storing it in deep underground aquifers, and (3) provide a cleaner and cheaper pathway for production of industrial chemical feedstocks and fuels. This could be a truly disruptive technology which would allow Canadian led manufacturing of high value chemicals and fuels in a low-cost and low-carbon fashion. Additionally, there are large benefits to Canada’s energy sector by facilitating the dispatchability of renewable power.
Design of OLED materials for manufacturing and improved product quality
Organic light emitting diodes (OLEDs) present a unique opportunity to produce thinner and more efficient lighting and displays. This will change the way we interact with light. The main barrier to mass adoption of OLEDs is the manufacturing process, due to the need for high throughput while maintaining nanoscale precision. High throughput operation requires materials that can undergo elevated temperature without decomposing. Our objective is to use computational chemistry to model innovative materials that can withstand these elevated temperatures while still providing high performing OLEDs. We will simulate targeted compounds using SOSCIP’s computer cluster examining properties relevant to OLED manufacturing processes. Promising materials will be synthesized and their properties experimentally measured then compared to the simulation results. The most promising materials will then be integrated into OLEDs and characterized by OTI Lumionics in their pilot scale manufacturing line located in Toronto, ON.
Detailed computational fluid dynamics modeling of UV-AOPs photoreactors for micropollutants oxidation in water and wastewater
Micropollutants such as bisphenol-A and N-nitrosodimethylamine pose a significant threat to aquatic life, animals, and humans beings due to their persistent and potentially carcinogenic nature. While most conventional water treatment methods cannot remove these contaminants, ultraviolet-driven (UV) advanced oxidation processes (AOPs) are effective in degrading micropollutants. As UV-AOPs require electrical energy to enable the treatment, energy costs present a barrier to the widespread adoption of this technology. In this project, we focus on the optimization of UV-AOPs-based reactors to enhance their degradation performance while reducing their energy consumption. In this respect, we will develop a detailed numerical model that integrates hydraulics, optics and chemistry to investigate UV-AOP photoreactors in a comprehensive manner.
The resulting information will then be utilized to design the next-generation of UV-AOP photoreactors commercialized by Trojan Technologies. The design space will be explored by high-performance computer simulations of full-scale photoreactors rather than simplified or scaled-down models. This will be accomplished by leveraging opensource software, artificial-intelligence optimization techniques and the second-to-none parallel-computing capabilities offered by Blue Gene/Q. Once the optimization of UVAOPs-based reactors is complete, the advanced modeling results generated using Blue Gene/Q will be utilized in the development of a simplified model for sizing purposes. This will be accomplished through combined use of metamodeling techniques and cloud computing. In brief, the concept is to simplify the detailed model developed earlier so that it can be simulated using hand-held mobile devices, which will allow the company’s sales personnel to market the optimized reactors. Consequently, it will allow the company to increase its competitiveness on global scale as well as to increase the rate of adoption of advanced water treatment technologies by water utilities and end-users.
Electrochemical Fischer-Tropsch synthesis of renewable liquid fuels from CO2
Renewable electricity costs have been rapidly declining, enabling clean consumption of energy in many sectors. However, there is still demand for energy-dense liquid fuels, such as in heavy freight and air transportation. In this project, we will harness machine learning to develop technologies that enable the synthesis of liquid fuels from carbon dioxide and/or synthesis gas using renewable electricity. Industrially, liquid fuels can be synthesized from a mixture of carbon monoxide and hydrogen called synthesis gas (syngas). However, this process requires high temperatures and pressures, and is itself responsible for significant greenhouse gas emissions. We propose the use of electrocatalysis to produce these liquid fuels. To accomplish this, we will use computational modeling and machine learning methods to design electrocatalysts that efficiently convert CO2 or syngas into dense chemical fuels. These computational efforts will be validated through a parallel experimental approach that includes the fabrication of new catalyst formulations and the construction of prototype electrochemical flow cells. This project will enable the synthesis of clean, energy-dense liquid fuels that can replace the use of fossil-derived fuels in industry and transportation sectors. This project is a logical extension of the existing CO2 related projects previously underway with SOSCIP. This project will allow the research to achieve the next milestones in the overall goal to achieve beneficial conversion of CO2.
High performance computing for assessing and mitigating the effect of extreme wind on building and cities
As the second largest country in the world, Canada’s diverse geography and climate increases our cities exposure to different types of natural hazards, such as snow storms, hurricanes, tornadoes and floods. The insurance industry estimates that insured catastrophic losses in North America average $80B per year. In Toronto (2005), for example. a single tornado event resulted in $500M loss. This is further compounded by changes in climate, population growth and aging infrastructure.
To maintain the prosperity of our communities, it is imperative that a comprehensive framework be developed to assess and mitigate the impacts of extreme climate on cities. The current project aims to develop a multi-scale climate responsive design framework that accounts for the complex interaction between buildings and wind (including hurricane and tornado). This computational framework, at neighborhood scale, models urban micro-climate necessary to assess the impact of changing city topology on the pedestrian level wind, air quality and to generate boundary conditions for small-scale simulations. At building scale, it develops a full numerical aeroealstic model (e.g. building model that flex) immersed in turbulent city flows, for the first time. This frame work when integrated with artificial intelligence based optimization procedures, allow optimizing tall building aerodynamics (shape) and dynamics (structural systems) appropriate for current era of booming tall building construction.
As a result, Ontario will save materials and energy in one of the most resource intensive sector, while enhancing the safety of Ontarians during extreme climate. For successful implementation of the framework, a high performance computing environment and experimental validations are necessary, which will be enabled by two unique research facilities in Ontario, Blue Gene Q and WindEEE Dome, respectively.
Machine Learning for Materials Discovery and Design
The objective of this project is to combine machine learning methodologies and electronic structure theory for the purpose of designing new materials through computational modelling. Achieving this goal will be important for the fields of Advanced Manufacturing and Energy (Materials). We will perform electronic structure calculations on a large database of existing materials (transition metal surfaces) and use results of these simulations as input to a machine learning model. The developed model will then be tested against new materials outside of the test set to confirm the model’s validity and transferability. This machine learning model will be used to identify new catalytic materials for use in water splitting and CO2 reforming devices. The project will combine high performance computing and machine learning to enable accelerated material discovery.
Modeling of urban wind flow and its interaction with buildings and their components
The design challenge is that as city populations rapidly increase, urban densification through vertical design will demand highly efficient, optimized and safe built environments to suit a changing climate. Therefore building and urban designers in the Architecture and Engineering (A&E) industry can benefit greatly from having access to robust, accurate, fast and cost effective wind modelling processes to assist in building and urban design performance simulation. However, urban wind flows are highly complex due to the time and spatial characteristics of the wind, its turbulence characteristics, and its interaction with the urban environment. Computational wind simulations therefore require very advanced modelling processes and demand enormous computational resources to replicate this phenomena. A validated and trusted computational wind simulation process, developed through this partnership, will offer new ways through which design practitioners can improve design, make buildings safer, more efficient and reduce building construction costs and materials through concept optimization. This research collaboration will allow the Ontario business and research community to continue to play its global and leading role in the application and export of its resources and expertise in physical testing and computational wind engineering.
Novel approaches and architecture for survivable smart grid
The term smart grid refers to a power grid in which the electricity distribution and management is upgraded by incorporating advanced two-way communications and pervasive computing capabilities for improved control, efficiency, reliability and safety [6]. Today’s power grid systems lack the ability to detect failure in ‘the last mile’ (i.e., in the power line between the user and the last transmission point). For example, during any power outage event, if the problem is within the last mile then the utility company’s operations center is unaware of the outage until notified by customers. Also, inside the power grid core systems, although the operations centre has the ability to detect a failure, their current systems lack the ability to provide an automated power backup facility. This means that when there is a core grid systems failure, their current fault notification systems can identify the failure, technicians are dispatched to diagnose and repair the fault. From the time the systems fails to the time it gets restored, the affected area/community experience power outage. In order to have (a) an automated failure notification systems in the last mile, and (b) a smart resilient system inside the grid’s core, today’s smart grid will require the ability to failure detection in the last mile, and a failure protection ability inside the core.
Our research will develop (a) an automatic fault notification systems for the last mile so that the operations center get real time notification of any failure in the last mile and can take measures to restore the outage; and (b) a new smart resilient system which would (during a power outage) automatically re-route its power to an alternate route bypassing the failed components of the grid so that it doesn’t lead to a power outage during a grid failure.
Smart computing for tornado and downburst resilient cities
High intensity wind storms such as tornadoes and downbursts significantly impact the livelihood and the economy of Southern Ontario communities. According to the Insurance Bureau of Canada, the 2005 tornado outbreak in Southern Ontario cost more than 500 million Canadian dollars in insured loss. During tornado or downburst events, the built-environment sustains severe damages from the strong vertical vortexes of tornadoes or the horizontal winds that move outward from a central location during a downburst. These wind events have complex interactions with building components that can cause cascading failures and result in flying wind-born debris. The state-of-the-art on the assessment of tornadic wind effects is limited to rating the intensity of tornadoes based on surveying the damage following the disaster and involves inferring the aerodynamics from straight wind flows that are not representative of tornadoes.
This project aims to develop a combined mechanics based and data-driven approach for high resolution numerical simulations of realistic tornado/downburst and building-cluster interactions for cities. Incorporating physics-based tornado and downburst data into structural models will improve the design of structures and allow them to withstand the most commonly observed tornadoes and downbursts. A method for estimating the damage from high intensity wind assisted by simulations of tornado/downburst interactions in user-selected urban regions will uncover the potential hazards cities are exposed to. Furthermore, the developed computational models enabled by SOSCIP’s blue gene super computer will ensure that Western University and the partner organization, RWDI Inc. (an Ontario based consulting firm focusing on the microclimate and science of buildings) will deliver optimal and innovative high intensity wind engineering solutions around the world.