Sustainable Energy Modelling and Simulation

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Combustion-generated soot particles are a major contributor to climate change and disease. They have been linked to respiratory illnesses such as lung cancer and emphysema, cancer-causing genotoxicity, and adverse pregnancy outcomes. Research toward the reduction and eventual elimination of particle emissions is needed to support the health of our planet and species. The main objectives of Prof. Dworkin's research program are to i) develop detailed numerical models to characterize the formation and emissions of combustion-generated particles, ii) leverage machine learning towards the generation of new algorithmic methods to estimate soot particle properties and concentrations, iii) widely disseminate knowledge and numerical tools for use in industry design and academic research, and iv) advance sustainable technology development in building energy, power generation, and transportation, through numerical modelling and experimentation. The research approach will involve Computational Fluid Dynamics calculations that incorporate chemical and physical phenomena of combustion, and statistical particle dynamics modelling. These computations will utilize parallel computing on large compute clusters to generate datasets that relate flow conditions to particle properties. The research will use computed and measured data to inform machine learning methods towards the generation of artificial intelligence algorithms that can predict soot particle phenomena (such as particle nucleation), properties (such as size and shape), and concentration, in a variety of systems. Prof. Dworkin's research will advance the field of combustion-generated particulate modelling through high-performance parallel computing, and the novel application of artificial intelligence methods. His novel use of machine learning will lead to major advances in computational efficiency and predictive capability. His research will permit the creation of models and algorithms that predict or estimate aspects of soot particle formation, which will be used to gain a better understanding of particle physics, and contribute to the design of lower-emitting devices in the transportation and power generation sectors. His work will have a major impact on these industries, providing new insights, and advanced numerical tools. Finally, his research will directly support the reduction of harmful pollutants present in our atmosphere, especially in cities where anthropogenic activities are concentrated.