Kyle Niemeyer
Sessions
Aviation accounts for 2% of global greenhouse gas emissions, and reliance on liquid petroleum-based fuels makes this sector challenging to decarbonize. We seek to accelerate the development of sustainable aviation fuels using an early-stage design tool with a data-driven approach. We developed our strategy using the Python-based optimization packages BoTorch and Ax, and also rely on Pandas. We will discuss how to down-select from many possible fuel components to a specified number of chemical species and identify which combinations are most promising for a novel sustainable aviation fuel. We will also present its integration in our open-source web tool supporting biofuel research.
Monte Carlo / Dynamic Code (MC/DC) is a performant and scalable Monte Carlo radiation transport simulation package with GPU and CPU support. It is written entirely in Python and uses Numba to accelerate Python code to CPU and GPU targets. This allows MC/DC to be a portable, easily installable, single language source code ideal for rapid numerical methods exploration at scale. We will discuss the benefits and drawbacks of such a scheme and make comparisons to traditionally compiled codes as well as those written using other modern high-level languages (i.e., Julia).