hydrogym 0.1.1

A Reinforcement Learning Benchmarking Environment for Fluid Dynamics

HydroGym provides is an open-source library of challenge problems in data-driven modeling and control of fluid dynamics.

Features

• Hierarchical: Designed for analysis and controller design from a high-level black-box interface to low-level operator access
  • High-level: hydrogym.env.FlowEnv classes implement the OpenAI gym.Env interface
  • Intermediate: Typical CFD interface with hydrogym.FlowConfig and hydrogym.TransientSolver classes
  • Low-level: Access to linearized operators and sparse scipy or PETSc CSR matrices
• Differentiable: Adjoint-enabled for PDE-constrained optimization via pyadjoint (extensible to PyTorch, Jax, etc... planned for future)
• Modeling and anlysis tools: Global stability analysis (via SLEPc) and modal decompositions (via modred)
• Scalable: Since linear algebra backend is PETSc, fully parallelized with MPI (including pyadjoint, SLEPc, modred)

Quick Start

To begin using Hydrogym we need to first recursively clone the Hydrogym repository

git clone --recursive https://github.com/dynamicslab/hydrogym.git

After which we can build the package with its dependencies with

python old_setup.py build_ext

by default it will build with Firedrake as its simulation engine. With our wheel built, we then only need to install it

pip install .

with which we then have the latest version of Hydrogym, and all of its dependencies inside of the virtualenv, installed.

source $VENV/bin/activate

If you try to run something and get an error like "python: command not found" you probably missed this step.

Then you can get running in the interpreter as easy as:

import hydrogym as gym
env = gym.env.CylEnv(Re=100) # Cylinder wake flow configuration
for i in range(num_steps):
	action = 0.0   # Put your control law here
    (lift, drag), reward, done, info = env.step(action)

Or to test that you can run things in parallel, try to run the steady-state Newton solver on the cylinder wake with 4 processors:

cd /home/hydrogym/examples/cylinder
mpiexec -np 4 python solve-steady.py

For more detail, check out:

• A quick tour of features in notebooks/overview.ipynb
• Example codes for various simulation, modeling, and control tasks in examples

Flow configurations

There are currently a number of main flow configurations, the most prominent of which are:

• Periodic cyclinder wake at Re=100
• Chaotic pinball at Re=130
• Open cavity at Re=7500

with visualizations of the flow configurations available in the docs. For the time being the cylinder wake is the most well-developed flow configuration, although the pinball should also be pretty reliable. The cavity is in development (the boundary conditions are a little iffy and there's no actuation implemented yet) and the backwards-facing step is still planned.