Advancing sustainable aerodynamic solutions with improved modeling
Within current aerospace design, it is necessary to over-engineer features to ensure stability and safety under emergency conditions. It would be ideal to develop capabilities to reduce the size of large elements of commercial aircraft with reliable technologies that ensure safe operation under hazardous conditions. A key advantage of the synthetic jet is that no bulky air source and supply system is required to provide actuation to the flow. The planned changes to the aircraft structure that increase fuel economy and reduce weight will ensure the success of the economically important Canadian aerospace industry.
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.
Agile computing for rapid DNA sequencing in mobile platforms
DNA can now be measured in terms of electronic signals with pocket-sized semiconductor devices instead of 100-pound machines. This has started to transform DNA analysis into a mobile activity with the possibility to track and analyze the health of organisms at unprecedented levels of detail, time, and population. But the remote cloud-based computer services currently needed to process the electronic signals generated by these miniature DNA-meters cost over $100 to complete an initial analysis on one human genome and consume over 1000 Watts of power. Also, the cost of wirelessly transmitting measured data to these cloud-based analyzers can exceed $1000 per human genome. Further, reliance on external high-performance compute services poses a greater risk for compromising the security of the DNA data collected. This project proposes the construction of a specialized high-performance miniature computer – the agile base caller (ABC) – built from re-configurable silicon chips that can connect directly to the DNA-meter and analyze the DNA it measures in real-time.
The ABC, by virtue of its size, will preserve the mobility of emerging DNA measurement machines, and will enable them to analyze data for less than $1 while consuming less than 10 Watts. These cost/power performance improvements will significantly drop the barriers to the application of genomic analysis to non-laboratory settings. For example, they will allow continuous monitoring of Canada’s food supply for the presence of harmful biological agents with the possibility of cutting analysis delays from weeks to hours.
Agile real time radio signal processing
Canadian VLBI capability has been missing for a decade. Jointly with Thoth Technology Inc we propose to restore domestic and international VLBI infrastructure that will be commercialized by Thoth Technology Inc. This project will implement and optimize multi-telescope correlation and analysis software on the SOSCIP BGQ, Agile and LMS platforms. The resulting pipeline package will allow commercial turnkey VLBI delivery by Thoth Technology Inc to domestic and international customers into a market of about $10 million/year
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.
Atomic-scale modeling of halide perovskites for optoelectronics and photovoltaics
The proposed applied research is of strategic importance to Ontario. The government of Ontario has repeatedly affirmed its commitment to creating a culture of environmental sustainability in the province, most recently via the Long-Term Energy Plan (2012). The Long-Term Energy Plan sets a 20-year course for Ontario’s clean energy future, and its priorities include the continued development of a diverse supply mix, including more renewable energy sources, fostering a culture of energy-efficiency, and encouraging the development of a clean energy economy. It specifically “encourages the development of renewable sources of energy such as wind, solar, hydro and bio-energy.” Efficient and economical photovoltaic systems such as those that will be facilitated via this project will play a very important role in realizing this objective. The proposed research will further support Ontario’s economy by generating new opportunities in the advanced materials and solar technology sectors, and by training a cadre of highly qualified personnel who will be poised to assume positions of global leadership in these industries.
Continuous vital sign monitoring using intelligent bed sheet
Studio 1 labs developed wireless intelligent bed sheet patient monitoring system that continuously captures client vital signs. In collaboration with Dr. Laura Nicholson and York University’s Faculty of Health, Studio 1 labs will work with SOSCIP infrastructure to match vital signs with gold standards approved medical decides for the highest level of accuracy through clinical validation and scientific evidence. With millions of data points collected from each device to output clinical grade quality information continuously, AI solutions allow modeling to predict health emergencies and diseases. This contributes to efficient health monitoring solutions that are simple and effective for use by older adults and healthcare providers.
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.
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.
Fast and accurate biophotonic simulations for personalized photodynamic cancer therapy treatment planning
There are many medical uses of light, both for diagnostic purposes (medical imaging) and for treatment. We will build a very fast and accurate simulator of where light inserted with fiber optic probes travels within a person’s body; by using this simulator we can enable a promising new cancer therapy, among other medical applications. The key use of our light simulator in this project will be for photodynamic therapy (PDT), a promising new cancer therapy. It uses non-toxic light activated drugs (called a photosensitizer) which is harmless until it is activated by light of a certain wavelength; where the activated photosensitizer destroys cells. Hence if we can localize the light to a cancerous tumour, we can destroy it with minimal damage to surrounding healthy tissue and with fewer side effects and less cost than would be achieved with ionizing radiation therapy or surgery. PDT is used today to destroy “superficial” cancers, such as those on the skin where it is easy to localize the light to the tumour.
PDT can also be used to destroy tumours within the body by inserting fiber optic probes through needles into the body. The key challenge is that the light reflects, refracts and is absorbed in complex ways when it leaves the fiber, making it hard for a physician to determine where to place the fibers and whether the tumour will be completely destroyed. We aim to fill this gap by developing a fast and accurate simulator that can determine the light density throughout a person’s tissue that will result from a given fiber optic probe placement and light input intensity. To do so we will compute the path taken by hundreds of millions of simulated photons — a task so computationally intense that we will use an unconventional IBM computing platform that uses special programmable hardware to offload key calculations from the conventional processor. We believe that by doing so we can speed up the computation and reduce the power required by a factor of 60. This large speed up will allow us not just to simulate one probe placement but many possible light probe placements — we seek to return the best probe placement and the expected treatment results to the physician in less than an hour. Overall this project will pave the way for a much-needed new, less invasive, more effective, and lower-cost cancer treatment. It will also use a new style of computation where special hardware does much of the computation, instead of a general purpose CPU, showing how such an approach can be used to make future computers faster and less power-hungry.
Full Monte: fast hardware for Monte Carlo biophotonic simluations
There are many medical applications of light, such as bioluminescent imaging (BLI) and photodynamic therapy for cancer treatment (PDT). These applications require rapid and accurate simulation of how light will scatter and be absorbed in complex human tissue in order to image tissue (e.g. BLI), or to determine where a light-sensitive drug will be activated and destroy cells (e.g. PDT). This project will use agile computing (FPGA hardware) to simulate light propagation very accurately with the Monte Carlo method but much more quickly and power-efficiently than in a conventional computer. The first aspect of the project is to complete our prototype agile implementation of this simulator and scale up its performance and the problem sizes it can handle. The second aspect is to complete a workflow using this simulator along with meshing, visualization and light source placement optimization to make a clinically useful PDT treatment planning system.
Generalized heterogeneous radio signal processing
A new generation of radio telescopes is opening new windows on the Universe, allowing astronomers to observe the cosmos in unprecedented ways. Powered by the ongoing revolution in computing, these new telescopes operate at the cutting edge of digital technologies. New algorithms are being developed at a spectacular pace, and we are forging a new partnership with the Markham branch of Advanced Micro Devices (AMD) to add to these, developing new tools and opening new possibilities in radio astronomy, software defined radio, and similar telecommunications technologies. High-cadence mitigation of Radio Frequency Interference (RFI), advanced Digital Beamforming techniques, and Dynamic Spectral reshaping will all be developed and ported to an open software framework, allowing it to be used on a wide variety of computational and signal processing hardware.
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.
High-fidelity aerodynamic analysis of unmanned multirotor vehicles
The objective of the proposed research is to gain a better understanding of the complex aerodynamics of small multirotor vehicles, such as quadcopters. This will enable Aeryon Labs, a Canadian company and world leader in small unmanned systems, to improve the development cycles of their products and respond faster to customer needs. Multirotor vehicles are popular platforms for many remote sensing applications because of the relative ease to control them at hover. During fast flight, however, control becomes challenging, because of highly nonlinear aerodynamics. These nonlinearities are due to the small-scale aerodynamics typical for these vehicles, and the interaction of the flow fields of several rotors that operate in close proximity. The proposed research builds on existing research on multirotor-vehicle aerodynamics. In order to expand our understanding of the complex aerodynamics of multirotor vehicles, we propose to model a quadcopter using Computational Fluid Dynamics (CFD). The CFD results will be compared with the lower-fidelity predictions and experimental results. The investigation of the predictive method will benefit from experiments performed in the large low-speed wind tunnel at Ryerson University, flight tests and an existing collaboration with Aeryon Labs. As part of the proposed research project one postdoctoral fellow, one doctoral student and two MASc students will receive training in the area of applied aerodynamics. The results will benefit Aeryon Labs with superior design tools that will improve their product line. Small aerial systems represent a rapidly expanding market segment worldwide, in which Canadian companies, such as Aeryon, play an important role.
Improved numerical combustion models for understanding and predicting nvPM/Soot formation and emissions in aviation gas turbine engines
Aviation gas turbine engines that burn hydrocarbon based fuels emit nanometer-sized carbonaceous non-volatile (not readily vaporized) particulate matter (nvPM) in addition to the usual gaseous emissions, such as green-house gases (GHG, largely CO2, actually a combustion product), nitric oxide (NOx) and carbon monoxide (CO). Also known as soot, smoke, or black carbon, these very small size nvPM has been shown to impact global warming and climate change by altering the radiation balance in the atmosphere through induced cloud cover and deposition of PM on arctic ice.
For these reasons, the manufacturers of gas turbine engines are today facing more and more stringent governmental and/or environmental regulations pertaining to PM emissions and there is a pressing need for reduced emission strategies. Unfortunately, the physical processes governing how nvPM and its precursors are formed in the high pressure flames and combustion systems of gas turbines is currently a matter of intense debate and a complete fundamental understanding of soot formation and emission processes is not firmly established.
The proposed two-year research project will consider the development of new and improved mathematical theory and computational models for understanding and predicting nvPM formation and emissions in aviation gas turbine engines. Through collaboration with the industrial partner, Pratt & Whitney Canada Corp. (P&WC), this new knowledge and understanding will be subsequently transferred to an industrial setting where it will be put to use in the design of next generation gas turbine engines having reduced PM emissions.
Improvement of Precipitation Gauge Collection in Remote Locations
To properly understand the global water cycle, improve analysis of climate variability, verify climate models and assist in local decision-making of surface or air transport, it is necessary to have better field tools for measurement of snow. Previous work has developed models of the Geonor precipitation guage and has included k-epsilon based numerical models of the flow around shielded gauges. To improve these results, it is essential to pursue advanced turbulence models as well as to develop benchmark experimental results. Large-Eddy Simulation, or LES, has become the method of choice for computationally-intensive simulations resolved to the necessary scales. Traditional Reynolds-averaged methods (RANS), although useful, require significant assumptions that compromise the fidelity of the flow physics obtained.
Direct Numerical Simulation (DNS) which resolves all the scales remains a prohibitive method due to its computational requirements. LES bridges between RANS and DNS, where the energetic large scales are resolved and computed directly whereas the smaller more universal scales are modeled. By modeling the subgrid scales within the inertial subrange, it is possible to extract high-fidelity flow information that can be used to improve local conditions. However, LES is computationally intensive and micro-climate modeling is beyond the capability of most desktop computers. As well, until recently LES models were either lab-developed codes or commercial codes. Lab-developed codes are difficult to transfer to industry partners as they are not necessarily client-friendly. While there exists excellent commercial codes, using these on multi-processor machines is prohibitor expensive. To model this flow, OpenFoam, an open-source turbulence code, will be used. The use of LES, instead of RANS modeling, would allow improved physical modeling of snow precipitate and ensure better comparison to real flow.
Joint optimization of route design and schedules for fixed route transit systems
The current method of optimizing routes and schedules for fixed route transit systems is sequential. Typically, route planning (involving determining route path, stops and service pattern) occurs initially, followed by schedule optimization (examining factors such as vehicle availability, operating requirements, safety restrictions, union contracts and employee pay).
This sequential optimization process produces a sub-optimal overall solution, inefficiently allocating agency resources, or allocating them in a way that may not be providing transit users with the best route and service. As a result, a method that could handle both simultaneously would be unique in the industry and extremely valuable to all fixed route transit agencies and service providers. Handling the numerous variables and constraints in route and schedule planning requires a method capable of intelligently searching for solutions.
The proposed method to tackle these dual requirements is simulation-based optimization using constraint programming—which is an optimization technique where knowledge of the problem is used to reduce the solution search space based on constraints. Evaluation of the feasible solutions is proposed to be accomplished using a simulation of the transit service that would more accurately represent service performance and passenger experience, where the structure of constraint programming methods lends themselves well to parallelization—ideal for the multi-core setup of the SOSCIP platforms.