A Modular, Object-Oriented Python Framework for Advanced SAF Microphysics Modelling

Sustainable aviation fuels (SAF) offer a promising pathway to mitigate the climate impacts of aviation by reducing contrail formation. The VOLCAN project utilises ONERA’s advanced 1D microphysical model MoMiE^{1, 2} (Modèle Microphysique pour Effluents) to assess SAF's effects on exhaust plumes and contrail characteristics across various fuel types and combustion modes. Written in FORTRAN, MoMiE offers exceptional computational efficiency and a robust set of libraries suitable for high-performance scientific computing. However, its rigidity can slow down development cycles and make the code less adaptable to the rapid iteration often required in research and development settings, such as in the testing of new types of sustainable aviation fuels. Currently, MoMiE supports the modelling of heterogeneous freezing on soot particles activated by sulfuric acid and organic species, homogeneous freezing of liquid sulphate and organic droplets, and the competition between these modes. Additional features include the representation of chemiionisation, Brownian coagulation, and the growth and sublimation of ice crystals. However, these processes are hardcoded into the code base, making it cumbersome to run a variety of experiments with supported species and difficult to expand to cover novel fuel scenarios.

To address these challenges, we discuss a modern, object-oriented, and modular Python-based approach tailored for emerging numerical modelling experiments of SAFs. We aim to retain the above functionality while providing a robust framework for future code development. Core components, including aerosol and molecular species distributions, scientific models (nucleation mechanisms), and thermodynamic calculations, are encapsulated within distinct Python classes, ensuring a clear separation of concerns and facilitating focused updates and development. The model objects are configured a priori with user-defined configuration files. These files specify molecular species relevant to specific fuel or burn scenarios, as well as different scientific models, nucleation schemes, experimental data sets etc., ensuring the code remains extensible without disrupting its foundational framework. In addition, all operational parameters and physical constants are externalised, making the code more flexible and encouraging maintainable, transparent coding practices. Python classes are instantiated dynamically during runtime, rather than being pre-defined at model start-up. This approach avoids unnecessary dependencies and ensures that objects are created only when needed, optimising memory usage and maintaining computational efficiency. We will explore preliminary results of quantitative scientific comparison with MoMiE in a few test cases and performance benchmarking.

The proposed development aims to replicate and extend the capabilities of 1D simulations in MoMiE, employing a design philosophy that supports scalability and adaptability. This approach aligns with the evolving scientific and operational demands of SAF research, enabling detailed and flexible modelling of complex microphysical processes.

¹Vancassel X. et al., Numerical simulation of aerosols in an aircraft wake using a 3D LES solver and a detailed microphysical model, International Journal of Sustainable Aviation, 2014

²Rojo C. et al., Impact of alternative jet fuels on aircraft-induced aerosols, Fuel, 2014