eagar-tsai 0.2.2

Eagar–Tsai moving heat source model for melt pool dimension estimation

eagar-tsai

eagar-tsai is a Python library implementing the Eagar–Tsai moving heat source model to estimate melt pool dimensions (length, width, depth) for a scanning laser over a semi-infinite solid. Temperature fields are computed via a 1D integral; melt pool dimensions are extracted from the liquidus isotherm.

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Installation

[!NOTE] A C compiler is required: GCC or Clang on Linux/macOS; MSVC Build Tools or MinGW-w64 on Windows. The C extension is compiled automatically during installation.

# Recommended — using uv
uv add eagar-tsai

# Alternative — using pip
pip install eagar-tsai

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Quick Start

For a single parameter set, use compute_single_point directly:

from eagar_tsai import BeamParameters, MaterialProperties, SimulationDomain, compute_single_point

beam = BeamParameters(
        beam_diameter=100e-6,
        power=200.0,
        velocity=0.5,
        absorptivity=0.35
)
mat = MaterialProperties(
        liquidus_temperature=1700.0,
        thermal_conductivity=30.0,
        density=7800.0,
        specific_heat=700.0
)
dom = SimulationDomain(
        x_length_um=1200.0,
        y_length_um=1200.0,
        z_depth_um=1000.0,
        spatial_resolution_um=1.0,
)

result = compute_single_point(beam, mat, dom)
print(f"Length: {result.length_um:.1f} µm")
print(f"Width:  {result.width_um:.1f} µm")
print(f"Depth:  {result.depth_um:.1f} µm")

To run multiple parameter sets in parallel, use compute_melt_pool with a DataFrame. workers, chunk_size, and output_dir are optional:

import pandas as pd
from eagar_tsai import compute_melt_pool

df = pd.DataFrame({
    "velocity_m_s":              [0.5],
    "power_w":                   [200.0],
    "beam_diameter_m":           [100e-6],
    "absorptivity":              [0.35],
    "liquidus_temperature_k":    [1700.0],
    "thermal_conductivity_w_mk": [30.0],
    "density_kg_m3":             [7800.0],
    "specific_heat_j_kgk":       [700.0],
})

result = compute_melt_pool(
    data=df,
    workers=4,                # parallel worker processes (default: None, serial)
    chunk_size=50,            # rows per worker chunk (default: 50)
    output_dir="CalcFiles/",  # write per-chunk CSVs (default: None)
)
print(result[["melt_length_um", "melt_width_um", "melt_depth_um"]])

Required Input Columns

Column	Unit	Description
velocity_m_s	m/s	Scan velocity
power_w	W	Laser power
beam_diameter_m	m	Beam diameter (2σ)
absorptivity	—	Absorptivity (0, 1]
liquidus_temperature_k	K	Liquidus temperature
thermal_conductivity_w_mk	W/(m·K)	Thermal conductivity at liquidus
density_kg_m3	kg/m³	Density
specific_heat_j_kgk	J/(kg·K)	Specific heat at liquidus

Output Columns Added to the DataFrame

Column	Unit
melt_length	m
melt_width	m
melt_depth	m
melt_length_um	µm
melt_width_um	µm
melt_depth_um	µm
peak_temperature	K
min_temperature	K

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Temperature Field Visualization

compute_single_point returns a MeltPoolResult that always includes the full TemperatureField and a built-in plot method:

from eagar_tsai import BeamParameters, MaterialProperties, SimulationDomain, compute_single_point

beam = BeamParameters(
        beam_diameter=100e-6,
        power=200.0,
        velocity=0.5,
        absorptivity=0.35
)

mat  = MaterialProperties(
        liquidus_temperature=1700.0,
        thermal_conductivity=30.0,
        density=7800.0,
        specific_heat=700.0
)

dom = SimulationDomain(
        x_length_um=320.0,
        y_length_um=110.0,
        z_depth_um=60.0,
        spatial_resolution_um=5.0,
)

result = compute_single_point(
        beam=beam,
        material=mat,
        domain=dom
)

print(result.length_um)                        # melt pool length in µm
print(result.temperature_field.T_xy.shape)     # (ny, nx) — surface plane in Kelvin
print(result.temperature_field.T_xz.shape)     # (nz, nx) — depth cross-section in Kelvin

fig = result.plot(output="temperature_field.png") # equivalently: result.temperature_field.plot(output="temperature_field.png")

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References

• T. W. Eagar and N.-S. Tsai, "Temperature Fields Produced by Traveling Distributed Heat Sources," Welding Journal (Research Supplement), December 1983, pp. 346-s–354-s.
• C integrand reformulation: Sasha Rubenchik, LLNL, 2015.

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License

This project is licensed under the GNU GPLv3 License. See the LICENSE file for details.

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Citation

We are currently preparing a preprint for publication. If you use eagar-tsai in your research, please cite the following:

Sarıtürk, D., & Vela, B. (2026). eagar-tsai. Zenodo. https://doi.org/10.5281/zenodo.19837225

BibTeX:

@software{sariturk_2026_19837225,
  author    = {Sarıtürk, Doğuhan and Vela, Brent},
  title     = {eagar-tsai},
  year      = 2026,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19837225},
  url       = {https://doi.org/10.5281/zenodo.19837225},
}