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Predicting Aggregation Rates of Polycyclic Aromatics Through Machine Learning

Saldinger, J. C., Elvati, P., Alrawi, K., & Violi, A. (2024). Predicting aggregation rates of polycyclic aromatics through machine learning. Fuel, 364, 131031. https://doi.org/10.1016/j.fuel.2024.131031

Participants

  • Georgia Institute of Technology

Projects

  • Predictive Simulation of nvPM Emissions in Aircraft Combustors

Lead Investigators

  • Angela Violi

    FAA Center of Excellence for Alternative Jet Fuels & Environment

    • Washington State University
    • Massachusetts Institute of Technology
    • Federal Aviation Administration
    © Washington State University