Abstract: Multiphase and reacting flows such as the fuel spray and combustion in an internal combustion engine are extremely complex processes. Computational fluid dynamics simulations have become a key tool to understand their behavior and help design more efficient engines or develop engines that can run on renewable bio-derived fuels. However, Current simulations suffer from uncertain predictive accuracy and relatively high computational cost. These limitations mean many different expensive and time-consuming validation experiments are required to confirm the simulation results, and CFD has limited direct utility in design optimization studies. This presentation will cover several on-going projects in the Multiphase and Reacting Flows Laboratory at the University of Massachusetts Lowell that are addressing each of these issues, including: quantifying the mesh discretization error in engine-like flows; the convergence properties of stochastic Lagrangian-Eulerian spray models (a collaboration with Prof. David Schmidt at UMass Amherst); using machine learning to accelerate chemical kinetics calculations in engine simulations. The first two projects are part of efforts to quantify individual sources of numerical error in engine simulations, rather than just the aggregate validation error, so the uncertainty in numerical simulations can be estimated and used to identify the best opportunities to reduce the simulation error. The third project attempts to accelerate individual engine simulations by computing the chemical reactions a priori, then using a neural network to tabulate the results. This is similar to efforts such as flamelet generated manifolds to reduce computational cost, but makes no assumptions about the structure of the flame or combustion mode, and so should be better suited for simulating modern high-efficiency mixed-mode combustion strategies.
Biosketch: Dr. Noah Van Dam is an assistant professor of Mechanical Engineering at the University of Massachusetts Lowell where he leads the Multiphase and Reacting Flows Laboratory. His research focuses on quantifying and improving the predictive accuracy of simulations of complex flows, focusing on applications in energy and transportation. Current projects reflect a wide range of subjects, including steam pipe flows for nuclear energy, conversion of biomass to bio-oil in a flow reactor, use of bio-derived alternative fuels for internal combustion engines, and spray and combustion models for ground and aerospace propulsion. Dr. Van Dam received his Ph.D. from the University of Wisconsin-Madison in 2015 where his research focused on simulating spray and flow variability in internal combustion engines and introduced Uncertainty Quantification methods to engine spray simulations. He then worked as a postdoctoral appointee at Argonne National Laboratory where he worked on simulations of 2nd- and 3rd-generation biofuels in advanced combustion strategies for internal combustion engines before joining UML in the Fall of 2018.