2026-08-14 –, Muschel — N1
Computational Fluid Dynamics (CFD) has traditionally suffered from the "Two-Language Problem": researchers develop new physical models in high-level environments like Python or MATLAB, only to face an extensive rewrite in C++ or Fortran for production-scale runs. This "viscous" development cycle slows down innovation across the aerospace, automotive, and energy industries. In this talk, we present XCALibre.jl: a new Julia package designed to eliminate this friction. XCALibre.jl provides a "Laminar" workflow, allowing for rapid prototyping of complex Multiphysics, and a "Turbulent" runtime performance with native GPU acceleration. XCALibre.jl handles industry-relevant geometries and complex physical solvers, proving that in the Julia ecosystem, developer productivity and fast simulation runtime are not mutually exclusive.
XCALibre.jl achieves "Turbulent" performance by utilising Julia’s unique language features and composable ecosystem. The proposed structure for this talk is as follows (approximately 5 minutes per section):
• Motivation: Why XCALibre.jl? We discuss the gap this framework fills and how it complements existing Julia CFD packages like Trixi.jl, WaterLily.jl, and Oceananigans.jl, highlighting how XCALibre.jl contributes to this ecosystem.
• Development History: Released just over a year ago, XCALibre.jl has matured with surprising speed. We provide a brief timeline of its evolution, highlighting a key success story: much of the core development was driven by undergraduate and master’s students, a testament to the "Laminar" ease of prototyping in Julia.
• Technical Architecture: We dive into the dependencies that underpin XCALibre.jl, specifically KernelAbstractions.jl and the broader GPU ecosystem. We explore how we use (and perhaps "abuse") Julia’s type system, multiple dispatch, macros, and generated functions to achieve high-performance and to build our Domain Specific Language (DSL) for defining new physics.
• Features & Benchmarks: A showcase of current capabilities, performance benchmarks against legacy solvers, and CFD visualisations.
• Vision & Future Plans: An honest look at current limitations, ongoing work, and our roadmap for industrial-scale simulation.
Target Audience: This talk is for engineers, physicists, and HPC enthusiasts. Attendees will learn how Julia can modernize legacy engineering workflows without sacrificing efficiency. We hope to inspire developers in other scientific domains by sharing our experience and to foster new collaborations within the Julia CFD community.
Associate Professor in Aerodynamics how loves coding in Julia and advancing CFD simulation