aerosim-cfdmod 2.1.0

Tools for analysis of CFD cases

cfdmod

Post-processing and geometry-preparation tools for CFD wind-tunnel simulations: pressure (Cp), force (Cf), moment (Cm) and shape (Ce) coefficients; terrain loft and roughness elements; inflow analysis; climate / Weibull / Gumbel statistics; ParaView snapshot automation.

v2.0 redesigned the pressure pipeline around external consumers: an XDMF+H5 output contract end-to-end, no input mutation, embedded post-processing metadata, multi-format geometry (.lnas / .stl / .h5 / .xdmf), and flat output by default. See docs/source/release_notes.md for the full v2.0 changeset.

Install

Base install (Cp / Cf / Cm / Ce pipeline, IO helpers, CLI):

pip install aerosim-cfdmod

Optional extras:

Extras	When you need it
[vtk]	ParaView snapshot automation, VTK polydata writers, S1 probe-on-line
[geometry]	Altimetry section + loft helpers (trimesh)
[notebook]	jupyter / ipykernel for the worked-example notebook
[docs]	sphinx + shibuya theme + nbsphinx for make html
[legacy]	pandas-HDFStore compat readers (inflow, HFPI static, migrate)

Install several at once with pip install "aerosim-cfdmod[vtk,geometry,notebook]".

pymeshlab is intentionally not an extra (its GPL license would force GPL on downstream code). Code paths that genuinely need it expect the user to install it explicitly at their own license risk.

Quickstart

Install the package and run the pipeline against an existing body + probe XDMF+H5 pair (the layout produced by the AeroSim CFD solver):

from cfdmod import (
    BasicStatisticModel, BodyConfig, BodyDefinition, CpCaseConfig, CpConfig,
    CfCaseConfig, CfConfig, CmCaseConfig, CmConfig, MomentBodyConfig,
    ZoningModel, run_cp, run_cf, run_cm,
)
from cfdmod.io.geometry.transformation_config import TransformationConfig

cp_cfg = CpCaseConfig(
    pressure_coefficient={
        "default": CpConfig(
            statistics=[BasicStatisticModel(stats="mean")],
            timestep_range=(150.0, 260.0),
            simul_U_H=1.0,
            simul_characteristic_length=10.0,
            # Optional: defaults are 'pressure' and 'probe' respectively.
            # macroscopic_type: 'pressure' | 'rho'
            # reference_pressure: 'probe' (point above body) | 'average'
        )
    }
)

# 1) Cp from body + probe; geometry is read from body_h5 by default. Pass
#    mesh_path= to embed a single fixed-frame reference mesh into the cp_h5
#    output (useful when running several wind directions whose body H5s are
#    rotated copies of each other) -- run_cf / run_cm inherit it from cp_h5.
run_cp(
    body_h5="body.h5",
    probe_h5="probe.h5",
    cfg_path=cp_cfg,        # in-memory config -- YAML path also accepted
    output="output",
    # mesh_path optional; .lnas / .stl / .h5 / .xdmf all supported
)

# 2) Cf from the cp.time_series.h5 produced above. nominal_area is required:
#    cfdmod will not pick a tribute area for you (so the resulting Cf can be
#    converted back to Forces unambiguously).
run_cf(
    cp_h5="output/cp.default.time_series.h5",
    cfg_path=CfCaseConfig(
        bodies={"my_body": BodyDefinition(surfaces=[])},  # [] = whole mesh
        force_coefficient={
            "scan": CfConfig(
                statistics=[BasicStatisticModel(stats="mean")],
                bodies=[BodyConfig(name="my_body", sub_bodies=ZoningModel())],
                directions=["x", "y", "z"],
                nominal_area=100.0,  # m^2 -- e.g. building frontal area
                transformation=TransformationConfig(),
            )
        },
    ),
    output="output",
)

# 3) Cm with per-region overturning moments about each container's footprint
#    base. lever_strategy="region_bbox_corners_xy" expands every body into
#    four independent runs (xmin_ymin, xmin_ymax, xmax_ymin, xmax_ymax).
#    nominal_volume is required (same rationale as nominal_area for Cf).
run_cm(
    cp_h5="output/cp.default.time_series.h5",
    cfg_path=CmCaseConfig(
        bodies={"my_body": BodyDefinition(surfaces=[])},
        moment_coefficient={
            "scan": CmConfig(
                statistics=[BasicStatisticModel(stats="mean")],
                bodies=[
                    MomentBodyConfig(
                        name="my_body",
                        sub_bodies=ZoningModel(),
                        lever_strategy="region_bbox_corners_xy",
                    )
                ],
                directions=["x", "y", "z"],
                nominal_volume=1000.0,  # m^3 -- e.g. building bounding-box volume
                transformation=TransformationConfig(),
            )
        },
    ),
    output="output",
)

output/ afterwards contains, flat:

File	Contents
cp.{label}.time_series.{h5,xdmf}	Cp animation on the full mesh
Cf.{label}.{body}.time_series.{h5,xdmf}	Cf animation per body (3 directional Attributes per timestep)
Cm.{label}.{body}[.{case}].time_series.{h5,xdmf}	Cm animation per body / case
Ce.{label}.time_series.{h5,xdmf}	Ce animation on the sliced regions mesh
Ce.{label}.regions.stl	Cut regions mesh as STL
stats.{h5,xdmf}	Combined statistics with one Grid per leaf group

Every output H5 carries the post-processing config under /processing_metadata/; read it back with cfdmod.io.read_processing_metadata(path, group).

Filtering between coefficients

Filters are an opt-in pipeline step: take any *.time_series.h5, apply a chain in order, and write a new *.time_series.h5 with the applied chain recorded under /processing_metadata. Cf / Cm / Ce then consume the filtered file in place of the raw Cp.

from cfdmod import MovingAverageFilter, apply_filters

apply_filters(
    input_h5="output/cp.default.time_series.h5",
    output_h5="output/cp.default.smoothed.time_series.h5",
    filters=[MovingAverageFilter(window=3.0)],   # in input time units
    group="cp",
)
# Then point run_cf / run_cm at the smoothed file.

MovingAverageFilter.window is in the same units as the input file's time axis (raw solver time when CpConfig.normalize_time=False, the default; convective time when True) -- the filter performs no implicit unit conversion.

Worked example notebook

notebooks/process_container_pack.ipynb runs the full Cp/Cf/Cm pipeline on a multi-container body, auto-detects container partition from triangle centroids using a >1 m gap rule, and produces a four-corner overturning moment scan per container -- without authoring any surfaces or sub-bodies. Drop a body H5 and a probe H5 next to the repo root and execute the notebook top-to-bottom.

CLI

The same pipeline is reachable via python -m cfdmod pressure (or cfdmod pressure after install):

python -m cfdmod pressure cp \
    --body body.h5 --probe probe.h5 \
    --config cp.yaml --output output

python -m cfdmod pressure cm \
    --cp output/cp.default.time_series.h5 \
    --config cm.yaml --output output

--mesh is optional; it accepts .lnas / .stl / .h5 / .xdmf and defaults to the geometry embedded in --body / --cp.

Development

Tests

The suite is grouped by pytest markers:

uv run pytest                          # full default suite (excludes -m perf)
uv run pytest -m unit                  # pure-function tests
uv run pytest -m integration           # end-to-end pipeline tests
uv run pytest -m perf                  # opt-in synthetic big-data benchmarks
uv run pytest tests/io tests/pressure  # the v2 pipeline scope

The perf run writes a markdown + JSON report (Python heap peak + RSS per scale) to output/perf/perf_report.{md,json} so regressions across releases are tracked over time.

Tox

uv run tox

Memory profiling

pip install -U memory-profiler
mprof run -C -M python path_to_script.py
mprof plot

Schemas

Generate JSON Schema for every config model:

uv run python -m scripts.generate_schemas

In VSCode, point yaml.schemas at the generated file:

"yaml.schemas": {
    "file:///path/to/cfdmod/output/schema-cfdmod.json": "**/cfdmod/**/*.yaml"
}