Traditional CFD tools predominantly solve the non-linear governing equations of fluid flow, capturing the temporal evolution of a mean flow state and various levels of turbulence, from Reynolds-Averaged Navier-Stokes (RANS) to Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS). Our code offers a unique approach by focusing on the linearized governing equations, enabling a deeper exploration of flow dynamics. This allows users to directly analyze the energy pathways within the flow and efficiently identify opportunities for flow control.
Designed to address a wide range of flow scenarios, our code can simulate diverse configurations, from incompressible, laminar flows to turbulent, compressible, and even chemically reacting flows. This versatility supports both foundational research and advanced applications in fluid dynamics.
Though initially developed as an academic tool, our aim is to democratize linear hydrodynamic analysis—a field with a rich academic heritage but limited adoption in industrial engineering. Today, we are collaborating with esteemed industry partners to bring this powerful analysis approach into practical use.
With built-in adjoint-based optimization, our code is capable of pinpointing the most effective system changes to optimize specific objectives. Additionally, it integrates with LES-based surrogate modeling to significantly enhance model performance in complex simulations.
Our code is written in Python, built on the FEniCS finite element framework, and leverages the high-performance linear algebra libraries SLEPc and PETSc for efficient computations. While the code is currently proprietary, we aim to release its core functionality for public use by 2025, fostering wider access to advanced linear flow analysis.
