A Single-Chip Wideband Digital Receiver for the Australia Telescope Compact Array Upgrade

This week Duy Nguyen will present on “Modernizing Legacy: Wrapping a 25+ Year Computational Fluid Dynamics Codebase“.

Modernizing Legacy: Wrapping a 25+ Year Computational Fluid Dynamics Codebase

Authors: Duy Nguyen, Tisham Dhar, Peter Wang, Jean-Michel Perraud, Klaus Joehnk.

Computational Fluid Dynamics (CFD) is the science of using numerical analysis and data structures to solve many processes influencing fluid behavior. CFD requires many complex mathematical representations through various ODE and PDE solvers, and has traditionally been written in Fortran or C++, mostly for their speed in doing massively parallel computations, such as handling array operations and object-oriented features. As such, those codebases have a long-standing legacy in scientific computing, and many established CFD codes are written in them. However, as time goes on, we witness the popularity of a new generation of codebases such as Python, which stems from its simplicity, versatility, and strong community. Extensive libraries and frameworks make Python a popular choice for many developers and scientists alike. It is, however, not a preferred language for core CFD code due to performance limitations and its difficulties with parallelization.
 
This talk explores modernizing a 25+ year-old C++ CFD model, essential for simulating lake conditions, particularly for predicting temperature variations and algal bloom dynamics, by wrapping it with [C-interop] for seamless Python interoperability.
 
Beyond integration, we leverage [Optuna], a powerful hyperparameter optimization framework, to fine-tune models efficiently, transitioning from manual parameter tuning on a laptop to a distributed, scalable workflow powered by Dask Distributed and JupyterHub. This transformation enables automated hyperparameter optimization across many lakes in Australia, helping researchers investigate trends in tuning parameters and derive deeper environmental insights.

Attendees will gain insights into:
– Wrapping legacy C++ water quality models with CFFI for Python-driven analysis.
– Automating and scaling hyperparameter tuning using Optuna across multiple lake ecosystems.
– Utilizing Dask Distributed and JupyterHub to accelerate environmental simulations.
– Extracting insights from hyperparameter trends to refine large-scale water quality predictions.
 
For researchers, engineers, and data scientists working in environmental modeling, this session provides practical strategies for modernizing legacy systems, scaling model optimization, and enhancing predictive accuracy without sacrificing scientific integrity.

Please let me know if you’re willing to present at a future co-learnium or have requests for specific talks, thanks!

Kind regards,

Samuel (On behalf of the co-learnium organisers)

Teams meeting link below:

Organiser