NORCAN R&D Team is Focused on Modern Hydraulic Geometries for Higher Turbine Efficiencies which generate more kW Output and significantly increased Sustainable Annual Energy Production, Maximizing Revenue for each site along with Decades of Dependable Operations from Best Quality Made in Canada.
NORCAN R&D engineers rely on in-house Computational Fluid Dynamics CFD, CAD, FEA, and FMEA in collaboration with a world-leading turbine hydraulic fluid machinery dynamic laboratories located in Austria. The collaboration enhances and modernizes NORCAN‘s turbine range with the development and research optimization of modern Hydraulic Geometries for higher efficiencies across the turbine range, components, and complete systems operational range. NORCAN’s fundamental goal is to increase their turbine range efficiencies to benefit the hydropower plant owners with more power output and increased sustainable annual energy production while supporting building climate resilience and maximizing their generating station revenue with dependable operations for decades.
Numerical Simulation is successfully applied using the latest CFD tools benchmarked against laboratory research analysis on Homologous Runners on physical test rigs. In addition, experimental laboratory trials are performed in cooperation with a world-leading European turbine hydraulic fluid lab in Austria for acceptance and approval tests according to IEC 60193, ISO 9906, IEC 60534.
Core Competencies in Turbine Hydraulic Fluid Machinery Dynamic
NORCAN’s R&D team uses proprietary software tools to facilitate automated shape generation and simulation. The R&D team has developed in-house software to construct a digital twin of a turbine in a virtual environment, through which any turbine configuration can be constructed and simulated for a physical response. Any turbine parameter (be it physical dimension, blade shape parameter, or simulation setting) is available through a single interface for; optimization, sensitivity analysis, what-if scenarios, and physical response testing for any loading condition. This capability is coupled with optimization tools using Integrated Artificial Intelligence (AI) to leverage machine learning in order to reach better solutions in less time than conventional optimizers while working with more degrees of freedom.
NORCAN’s R&D team has developed a proprietary code to advance the parametric generation of turbine blade CAD, improving the accuracy, flexibility, and sensitivity of blade parameters. NORCAN captures a complete definition of a turbine runner in a blade card file, which is mathematically turned into the runner CAD surfaces and inserted into the turbine model. Blade shapes are defined explicitly by structured surfaces in parametric space, removing the need for lofting features through spline curves and avoiding interpolation error.
NORCAN’s CAD generator python application produces blade surfaces directly through the explicit definition of NURBS surfaces, with high data point resolution, and incorporation of blade fillets in a single seamless surface. The methods developed by NORCAN’s R&D team maximize the output CAD accuracy to the input card parameters, not available through conventional blade modeling. This improves the parameter sensitivity for design and optimization and increases the reliability of the blade CAD generation during automated optimization iterations.
NORCAN’s R&D capabilities allow for any existing runner from a customer’s hydroelectric site to be captured digitally, leading to the extraction of the equivalent surface definition parameters with exceptional accuracy. The parameters homologous to the existing turbine are then available immediately for design assessment and optimization of annual energy production.