Application of Data Analytics in Industrial CFD

Due to the potential for significant cost-savings, many companies are turning their attention to digital simulations which produce an enormous amount of data. For companies to realize the benefits of having access to this data they need tools that allow efficient and accurate extraction of information from the data set. The goal of this project is to conduct the research required to develop a framework for the implementation of machine learning algorithms to provide engineering predictions for industrial applications. With its mandate to develop state-of-the-art tools for physics-based engineering applications, SOTAES is well-positioned to develop this innovative product.

Intern: 
Emanuel Raad;Guanjie Lyu;Maziar Mosavati;Akindolu Dada
Faculty Supervisor: 
Mohamed Belalia;Christopher Houser
Province: 
Ontario
Partner: 
Partner University: 
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