Advanced Analytics and Fault Detection of Industrial Steam Traps via Machine Learning
Steam is flexible and very cost-effective in terms of gases that can be used to transfer heat. All steam systems require that the steam arrives at the right quantity, temperature, and pressure for it to work efficiently. The presence of foreign materials such as air, condensate, and other gases can lead to a reduction in this efficiency. To that end, steam traps are used to remove condensate, debris, and other gases from the steam system to maintain operating efficiency and prevent system damage. Due to constant operation, steam traps have a high failure rate of about 20%, according to the US State Department of Energy. Pulse Industrial’s steam trap sensor provides monitoring capabilities and early detection of these steam trap failures. This leads to vast improvements in the operating performance of partner facilities and reduced operating costs. This research project will optimize the analytics and diagnostics performance of currently deployed steam trap monitors. This optimization will be done by developing the AI model using collected data from partner plants. This will allow for a holistic improvement of the overall system, which is expected to increase the accuracy performance of the steam trap sensors
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.
Automated Assessment of Forest Fire Hazards
The proposed project is to develop a Deep Reinforcement Learning (DRL) model capable of using Light Detection and Ranging (LiDAR) data to automatically analyze whether there is a risk of forest fire due to vegetation management around electric equipment. The model automatically classifies points in a LiDAR scan to determine whether there is direct contact between vegetation and electric equipment and outputs control actions for an Unmanned Aerial Vehicle (UAV) that can autonomously inspect infrastructure as needed. Our research will focus on the solution of two different but complementary machine learning problems that the DRL model will have to solve to allow for a UAV to autonomously acquire data with sufficient quality for fire risk assessment. Firstly, there is the problem of perception, i.e., to analyze raw LiDAR data and deduce classifications of individual objects and their shape and orientation. Secondly, there is the problem of control, i.e., to use the perceived state of the environment as determined by the first stage to adequately navigate through the environment until a complete scan has been obtained of the region that we are conducting a fire risk assessment for. Solving these two problems in a harmonious way that allows for an efficient, accurate and a complete scan of the environment is an open research problem in the domain of DRL that we aim to address.
Big data analysis and optimization of rural and community broadband wireless networks
Rural broadband initiative is happening in a big wave across the world. Canada, being a diverse country has a specific Internet reachability problem due to population being sparse. It is economically not viable to bring fiber to each and every house in Canada. It is not economically viable to connect every household through satellites either. Broadband Internet over wireless networks is a good option where Internet is brought over fiber to a point of presence and moved to houses over wireless.
EION is actively working in Ontario and Newfoundland to make rural broadband a possibility. Wireless networking in rural areas in Canada is a challenge in itself due to weather, terrain and accessibility. Real-time constraints such as weather, water and foliage do alter the maximum capacity of the wireless pipe. In addition the usage pattern of the houses, especially real-time video that require fast response time, require adequate planning.
This is becoming very critical as almost 80% of the traffic seems to be video related due to popularity of applications such as Netflix, Youtube and Shomi. Intelligence in wireless rural broadband networks are a necessity to bring good quality voice, video and data reliably. Optimization in system and network level using heuristics and artificial intelligence techniques based on big data analysis of video packets is paramount to enable smooth performing broadband rural networks.
In this project, we will be analyzing the big data of video packets in rural broadband networks in Ontario and Newfoundland and design optimized network design and architecture to bring reliable video services over constrained rural broadband wireless networks.
Characterization and Optimization of Heat Transfer from GRIP Metal Enhanced Surfaces
NUCAP specializes in brake pads and while improving their manufacturing process, they developed an innovative way to enhance the surface of metals in the form of small metal hooks (GRIP Metal). The raised features offer both increased surface and increased turbulence and flow mixing which can also greatly enhance convective heat transfer. The main objective of this project is to develop models which can accurately predict convective heat transfer and associated pressure drop for GRIP Metal enhanced surfaces. These will ultimately be used to optimize GRIP Metal surfaces for a wide range of heat exchange applications.
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.
Computational support for big data analytics, information extraction and visualization
The Centre for Innovation in Visualization and Data Driven Design (CIVDDD), an Ontario ORF-RE project performs research for which SOSCIP resources are needed and they were awarded NSERC CRD funding with IBM Platform [Applications of IBM Platform Computing solutions for solving Data Analytics and 3D Scalable Video Cloud Transcoder Problems] beginning in July 2015. This project involves Big Data, Visualization and Transcoding and will train many HQP. We require access to equipment capable of running a multi-core cluster using IBM Symphony and Big Insights software with IBM Platform on data analytics, visualization and transcoding. Our objectives include:
IBM Platform:
- Test the applicability of Platform Symphony to Data Analytics problems to produce demonstrations of Symphony on application domains (we started by exploring streaming traffic analysis datasets) and identify improvements to Symphony to gain IBM advantage in the marketplace.
- Design and implement methods to greatly speed-up the search for high utility frequent itemsets in big data using Symphony in a parallel distributed environment.
- Design algorithms to determine which are suitable in such an environment.
- Identify commercialization venues in application domains.
- Exploration of a Scalable Video Cloud Transcoder for Wireless Multicasts
FinTech Deep Learning in Financial Modeling
The last four decades saw the development of the financial derivatives valuation technology. Only a few practical models, however, can be solved in closed form, and as most utilize numerical methods such as finite-difference partial differential equation solvers, discrete-time trees, or Monte-Carlo simulators, these traditional methods are quite slow. Running book valuation and risk processes on hundreds of CPU cores requires an overnight process to comply with regulatory and accounting standards. This creates a huge cost(~$10 million/year) and a commensurate environmental and carbon impact. Similar issues are now facing the Insurance industry due to the accounting standard IFRS 17. The key goal of the partnership is to develop deep learning, and more generally machine and reinforcement learning, models to accelerate these processes, and to provide efficient simulation engines for asset prices, volatility surfaces, derivative valuation, and hedging.
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.
High fidelity simulations and low-order aero-acoustic modeling of engine test cells
The testing and certification of gas turbines demand the well-controlled environment provided by an engine test cell. The resonant acoustic coupling between the flow generated noise from the gas turbine exhaust and the engine test cell impacts the quality and reliability of the engine testing and certification. Predictive modeling of flow generated noise using high-fidelity numerical simulations is central to an a priori acoustic assessment and for the development of noise-mitigating designs. As part of this effort, the Multi-Physics Interaction Lab and University of Waterloo will numerically study, with the help of high fidelity, large-eddy simulations of the SOSCIP high-performance computers, the acoustic noise generation in partially confined jets undergoing a re-acceleration through the test cell ejector system. As a direct outcome, the researchers will develop a low order Aero-acoustic model that will be used by our industrial partner to predict resonant acoustic models to within +/- 20% the frequency and amplitude of the coupling phenomena. This OCE and NSERC-funding project will permit the training and mentoring of four HQP for careers in science and technology within Canada.
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.
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.
Mobilizing ‘Big energy data’ for building conservation to socialize sustainability
This project will establish the first customer-centric universal tool using mobile/cloud technology to deliver & share energy information electronically with energy customers to stimulate social change, encourage conservation & help manage/reduce GHG emissions.
A global desire to manage climate change is here. There needs to be a joint effort to help the energy user gain access to electronic information, so the data can be used to better manage conservation, sustainability and technology integration. Innovation in the energy sector continues to create data silos, as new technologies used don’t allow for the data to freely flow out of the systems to other solutions. Customers require easy access & a simplified understanding of electric information (not print or PDFs). For this to happen, the end customer requires data to be managed, simplified & shared, so information can be “open” to innovation and electronic data to be standardized.
The market needs Green practices and conservation to flourish in the private sector and public sector. Internationally and in Ontario, there are regulatory and political requirements to provide end-user access to electronic data, but without a simplified roadmap to interconnectivity how will it happen? The market needs linkages to the “Internet of Things” to learn, inform and conserve. In a few short years, there will be more than 25 billion devices generating data on every topic imaginable. The energy customer and building owner needs simplified data from multiple sources. Screaming has an opportunity to deliver this information cost-effectively to the utility, government & customer.
Next generation low-emission combustor technologies for high-efficiency compact aviation gas turbine engines
The primary objective of the proposed research is to develop next-generation combustor technologies for aviation gas turbine engines that produce extremely low emissions and higher fuel efficiency, while reducing development times/costs and maintenance requirements. Impacts will be realized in terms of local air quality/public health, climate change, sustainability, and commercial engine sales. The current design priorities of aviation gas turbine combustors are operability, efficiency, emissions, durability, compatibility with the main engine core, and safety. Today’s combustors for small aviation gas turbine engines provide reliable and dependable service.
However, increasingly stringent emissions regulations are being enacted (e.g. by the International Civil Aviation Organization) that impose tighter criteria for emissions. Moreover, combustor conditions are continuously becoming more extreme in terms of operating pressure and temperature in order to improve engine efficiency. These conditions result in increased maintenance requirements, and hence increased engine operating cost. Future combustors for gas turbine engines are therefore in need of far-reaching design changes to meet these emissions requirements, while simultaneously being more cost effective and providing better operability/durability.
These next generation combustor technologies are essential to help our main industrial partner, Pratt & Whitney Canada (P&WC), to maintain its market competitiveness in the small aviation gas turbine sector. P&WC is the leading manufacturer of small aviation gas turbine engines, and these engines are extensively used around the globe as well as in Ontario (e.g., all commercial aviation at Toronto’s Billy Bishop Airport). Together with the two industrial partners, P&WC and IBM Canada, we will design novel new combustors that have lower emissions per unit fuel consumed, less fuel consumption per unit thrust produced, and reduced maintenance requirements relative to current systems. This will be achieved by combining the state-of-the-art experimental, computational, and analytical capabilities of the university research team with the practical gas turbine design knowledge of P&WC engineers and the high-performance computing (HPC) expertise of IBM.
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.
Predicting and Reducing Pollutant Emissions in Gas Turbine Engines
Gas turbine engines are the primary propulsion device for today’s aircraft. These engines operate on liquid hydrocarbon-based fuels and as such can yield a range of undesirable pollutants including gaseous emissions such as nitrogen oxides (NOx), carbon monoxide (CO), green-house gases (GHG, largely CO2, really a combustion product) and unburned hydrocarbons (UHC), as well as nanometer-sized carbonaceous particulate matter or soot. Due to increasing concerns for the environment and causes of global climate change, the manufacturers of gas turbine engines are today facing increasingly more stringent governmental and/or environmental regulations pertaining to emissions. These increased regulations are in turn driving the need for significantly reduced engine emission strategies. Unfortunately, there are major scientific and technological challenges associated with designing robust, low-emission, gas turbine combustors. The physical processes governing the formation of emissions in the high-pressure and high-temperature turbulent flames of combustion systems in today’s gas turbines are extremely complex and remain poorly understood. This lack of understanding combined with the competing interactions between processes makes the simultaneously minimization of all pollutants very difficult in practice, particularly when constrained by the additional requirements that overall engine performance and efficiency should not be adversely impacted. The proposed two-year research project will therefore consider the development of a combination of new mathematical theory, combustion models, and more accurate and efficient numerical methods and tools with the goal of enabling improved predictions of emission processes in gas turbine engines. By bringing to bear new computational methods and advanced high-performance computing tools, in part through a strong collaboration with the proposed industrial partner, IBM Canada, the research is expected to lead to the new knowledge and improved understanding. as well as the new reactive flow and combustion modelling computational tools, needed for the design of next-generation gas turbine engines having reduced emissions of NOX, CO, UHC, and GHG.
Smart analytics for smart grid
How we use energy reveals vital information about our everyday behaviour – from our work and school schedules to when we are likely to be sleeping, eating and performing simple chores such as laundry or washing dishes. It even reveals when we are on vacation. While this information in the wrong hands could put us at risk, it also plays an important role in reducing our carbon footprint and ensuring the cost of energy is kept relatively low. Power generation companies use information derived from microclimate, small areas within general climate zones, to accurately predict power generation needs. Microclimate information is captured from smart meters, which wirelessly transmits data from our homes to utility companies. Considering that more than one million smart meters have been installed in Ontario homes since 2010 and that number continues to grow, it’s necessary to find a way to ensure the data captured by smart meters is protected and secure from threat.
Transition to turbulence on aircraft engine nacelles
Turbulence significantly affects the aerodynamic drag of aircrafts. Reducing its impact can result in substantial economic benefits to the aerospace industry and to the public, principally in terms of reduced fuel consumption and therefore, lower operational costs. One of the main causes of turbulence, on an engine nacelle, is the presence of surface imperfections: gaps between metal plates, roughness, ice formations, rivets. This project will examine how these surface imperfections affect the generation of turbulence, and what manufacturing tolerances are required to delay the onset of turbulence, thereby decreasing the aerodynamic drag. This will be achieved through a combination of high fidelity numerical models of the flow over the nacelle, combined with the development of simplified models to be transitioned to the industrial partner.
Watson #HERE4U Military Edition
Mental health issues affect a large percentage of the military population. The #HERE4U Military Edition will be an instant messaging smartphone application to connect military family members (adults only) who are dealing with mental health problems to a mental health counseling solution that uses the IBM Watson cognitive platform. Being able to engage with Watson in a secure, private and anonymous manner will encourage military personnel to reach out for help where they might not have in the past to obtain timely advice. Watson will engage with the client to identify a presenting problem and when clinically serious, engage a counselor for guidance and referral; otherwise, Watson will carry on a conversation with the client, giving advice, referring to mental health resources, including self-help materials, as appropriate. The key challenge for this project is to collect unstructured case data and best practice literature that Watson can use to manage conversations.