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.
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
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.
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.
Mining microbiome community structure and biomarker identification through data intensive biology, machine learning, and high throughput technologies
Microbial ecosystems such as those associated with the mining industry are complex networks of interacting species and biochemical dependencies. Modeling responses to different or changing environmental conditions is a considerable computational and statistical challenge. Current approaches largely identify important features through differential abundance, ignoring the disparate influence some species or functions exert on the community, greatly simplifying biological complexity. Additionally, these approaches can ignore poorly annotated features (e.g., hypothetical genes, microbial dark matter, ORFans). Excluding these known unknowns and unknown unknowns reduces the resolution and sensitivity of these analyses. Metagenomic feature selection using machine learning has been most widely applied to the human microbiome, which currently has more extensive data than other systems. We will apply a superficially similar but much higher resolution approach to less studied, more dynamic industrial microbiomes, such as mining.
Real-time cloud-based hydrologic risk assessment platform development for watershed scale applications
There is a growing recognition within the hydrologic modeling community that the results from real-time hydrologic modeling will need to be analyzed and presented via standardized web/cloud-based tools in a manner that facilitates expeditious interpretation of what could be considered unwieldy large scientific data-sets. Furthermore, there is also growing recognition that best-in-class weather forecast data can potentially add significant value to the resultant hydrologic simulation results.
To support on-going research into real-time hydrologic modeling at Aquanty, the post-doc will develop a pilot application for HGS real-time modeling at a spatial scale relevant to groundwater and surface water management professionals (i.e. >1000 km2) who have interests across agriculture, urban, and industrial water issues. However, there are still technical challenges that must be overcome before real time fully-integrated hydrologic modeling can become operational at a scale large enough to attract significant end user commercial interest. In the project herein, data assimilation methodology will be developed in order to facilitate using high resolution, spatially distributed weather forecast data for multiple time frames (i.e. 1 d, 3d, 7d, 10d), as the principle driver for watershed scale (~4000 km2) fully integrated real-time hydrologic modeling. Data analytics and visualization methodology will also be developed so that large (>1 TB) model output data-sets can be readily interpreted via a cloud hosted dashboard platform. The outcome from the effort will be in the form of a pilot demonstration of a cloud based hydrologic forecasting system.
Visual breadcrumbs for emergency return of unmanned aerial vehicles
Unmanned Aerial Vehicle (UAV) technology has advanced rapidly in the last decade, making wide-ranging applications (e.g., automated remote sensing, precision agriculture, emergency response, reconnaissance & surveillance) a near-term reality. A key building block that is yet to be developed is a solution that enables safe beyondline-of-sight flight, preventing uncontrolled vehicle behaviour in the event of failures such as GPS signal loss. The goal of this project is to develop a vision-based navigation solution that enables UAVs to safely return home when they lose their means of localization (i.e., GPS).
The University of Toronto (UofT) has been developing visual route-following techniques for ground vehicles for the past decade and gained extensive experience in practical deployments during field trials. This project will require major extensions including the adaptation of the ground-based visual navigation framework to aerial applications and the development of new techniques for path planning and localization that guarantee robust path-tracking and safe return. Moreover, safety solutions as proposed here will help to accelerate the development of a regulatory framework for the commercial use of UAVs in beyond-line-of-sight applications.
Water quality analytics, reporting and forecasting using mobile water kit
Real-time monitoring of water quality for bacterial contamination is difficult with present technologies in the market. Assessing water quality for bacterial contamination takes a long time, costly, tiresome, laboratory-based, and mostly not available to where testing and results are needed most. Water samples are tested by municipalities at specified locations and one or twice in a year. This is mainly due to time, cost and complexity involved in testing water samples for bacterial contamination.
The data collected by the regulators is not sufficient to estimate the future trends of water quality. Clearly, there remains a need for a rapid and reliable drinking water quality monitoring for more remote communities and mitigating future illness and outbreak risks in these and other rural or remote communities. Mobile Water Kit (MWK) is a rapid and low-cost test kit that can detect indicator bacteria (E. coli) in water samples within one hour. MWK is a simple method and it will be an optimal solution for testing water samples for bacterial contamination on daily basis.
The proposed project will utilize the functionality of MWK for creating water quality data management for bacterial contamination. We will develop m-Water APP for retrieving the water quality data from MWK and web-console for analyzing the retrieved data over cloud platform. We use the data collected with MWK for analyzing the trends in water quality over a time period. In addition, we will map the water quality data and forecast the E. coli outbreaks with the help of stored data. This project will provide an early warning signal about water quality to the communities, regulators, and/or municipalities. This kind of real-time monitoring of water quality not only empowers the communities with access to clean water but also delivers much-needed intervention for public health.