cfdtwin 0.1.0

Neural-network surrogate models for ANSYS Fluent simulations.

Build neural-network surrogate models from ANSYS Fluent simulations. Two ways to drive the same pipeline:

• GUI — wizard-based desktop app, click through Setup → DOE → Simulate → Train → Analyze.
• API — Python library, the same pipeline as ten method calls in a script.

Docs: https://uark-ned3.github.io/CFDTwin/ GUI walkthrough (video): coming soon

Install

Requires Python 3.10+ and a working ANSYS Fluent installation.

pip install cfdtwin                  # from PyPI (released)
pip install -e .[dev]                # from a clone, with test deps

Use the GUI

Three ways to launch, pick whichever you prefer:

cfdtwin-gui                          # any terminal, after pip install
python -m gui                        # from a repo clone, no install needed
double-click scripts/launch_gui.bat  # Windows, no terminal pops up

To put a launcher on your Desktop (Windows): right-click scripts/launch_gui.bat, Copy, then right-click your Desktop and Paste shortcut. Optionally right-click the shortcut → Properties → Change Icon → browse to gui/assets/logo_icon.png for the CFDTwin logo.

On launch, select or create a project. The sidebar steps unlock as prerequisites are met:

1. Setup — pick .cas file, set Fluent options, define inputs and outputs
2. DOE — generate LHS/factorial samples
3. Simulate — batch-run Fluent with live progress
4. Train — transfer-list filter, live loss curves, per-output NN
5. Analyze — metrics dashboard, predictions, Fluent comparison with caching

Use the API

Same pipeline from a Python script — useful for automation, parameter sweeps, or building bigger surrogate libraries:

import cfdtwin

project = cfdtwin.Project.create("./elbow_study", name="elbow_v1")
project.set_case_file("mixing_elbow.cas.h5")

project.set_inputs({
    "cold-inlet|momentum > velocity_magnitude": (0.2, 0.6),
    "hot-inlet|momentum > velocity_magnitude":  (0.4, 1.2),
})
project.set_outputs([
    {"name": "outlet", "category": "Surface",
     "field_variables": ["temperature"]},
])

project.generate_doe(n=20, method="lhs")
project.connect_fluent(precision="single")    # mixing_elbow is a single-precision case
project.run_simulations()
project.train(model_name="run1")

pred = project.predict("run1", {
    "cold-inlet|momentum > velocity_magnitude": 0.4,
    "hot-inlet|momentum > velocity_magnitude":  0.8,
})
print(pred.values.shape)

Runnable scripts live in docs/examples/ — quickstart, full workflow, training tuning, and a "what BCs / parameters does my case expose?" discovery script.

• API reference — every method, every argument
• Tutorials — narrative walk-throughs
• Quickstart — smallest end-to-end script

License

MIT — see LICENSE.