Skip to content

README.md

Latest commit

 

History

History
110 lines (81 loc) · 4.68 KB

README.md

File metadata and controls

110 lines (81 loc) · 4.68 KB
Raw
Nefes

NEtwork solver for Flow, Eigenmodes, and Stability

License python Documentation DOI

Tests codecov pre-commit Code style: black Linting: flake8

Nefes — Turkish for breath — models a fluid system as a directed graph of lumped elements. It solves the steady mean flow and the linear perturbations about that mean (acoustics, entropy, and composition) without resolving the full three-dimensional field. The perturbation layer is the linearization of the same mean-flow residual, so the two stay consistent by construction.

image

The companion browser UI Nemo builds Nefes networks interactively.

Capabilities | Installation | Documentation | Minimal example: a reacting Rijke tube | Contributing |

Capabilities

  • Reacting compressible mean flows in the subsonic regime, including choking at a sonic throat
  • Chemical-equilibrium thermochemistry
  • Entropy and compositional (indirect) noise
  • Linear stability analysis: eigenmode search (Beyn contour-integral method) and real-frequency Nyquist criterion
  • Forced-response analysis
  • Scattering and transfer matrices
  • Identification of an unknown element's dynamic response (e.g. a flame) given a model of the rest

Installation

Python 3.11 or newer. The core package depends only on NumPy, SciPy, Numba, and PyYAML:

pip install -e .

For the examples and notebooks, install the jupyter extra (pulls in Plotly and a notebook stack):

pip install -e ".[jupyter]"

Plotting alone is available as pip install -e ".[viz]".

Documentation

See the documentation and the examples for a quick start. For AI assistants building or analyzing cases with Nefes, the project ships a nefes-user skill that steers them to the documented public workflow.

Minimal example: a reacting Rijke tube

import nefes
from nefes.chem import equivalence_ratio_mixture
from nefes.elements import n_tau_flame

mix = equivalence_ratio_mixture({"CH4": 1.0}, {"O2": 0.21, "N2": 0.79}, phi=0.8)
area = 0.01; length = 1.0; mdot = 0.02

net = nefes.Network(
    nefes.equilibrium(),
    nodes=[
        nefes.cat.mass_flow_inlet(mdot, 300.0, composition=mix, name="inlet"),
        nefes.cat.duct(0.25 * length, name="cold"),
        nefes.cat.equilibrium_flame(name="flame"),
        nefes.cat.duct(0.75 * length, name="hot"),
        nefes.cat.pressure_outlet(1.0e5, 300.0, name="outlet"),
    ],
    edges=[(0, 1, area), (1, 2, area), (2, 3, area), (3, 4, area)],
)
net.set_dynamic_source("flame", n_tau_flame(1.0, 4e-3, ref_edge=net.edge_between("cold", "flame")))

sol = net.solve()
assert sol.converged
modes = sol.eigenmodes(freq_band=(40.0, 400.0), growth_band=(-300.0, 300.0), isentropic=True)
print(modes.summary()[0])  # ~141 Hz, growth ~ +90 1/s

The full walkthrough is in examples/thermoacoustics/rijke_tube.ipynb.

Contributing

pip install -e ".[dev]"

That pulls in the tooling used to develop and test the package. See CONTRIBUTING.md for guidelines. When changing the package itself (kernels, solver, tests, docs), use the companion nefes-dev skill so AI assistants follow the same contracts as human contributors.