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NASA Jet Propulsion Laboratory · Current research

Lunar interior modelling

Lunar geophysics · Thermodynamic modelling · Mission-relevant observables

I develop and validate thermodynamic and geophysical models of the Moon to investigate how interior structure and composition may be constrained by future observations. The work connects scientific hypotheses to observable quantities and measurement needs.

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Scientific question

Different assumptions about the Moon’s bulk-silicate composition lead to different mantle mineralogies, thermodynamic properties, and interior structures. Comparing these scenarios helps identify which future geophysical observations could meaningfully distinguish between competing models.

Science focus
Bulk-silicate Moon composition, mantle structure, and planetary evolution
Mission connection
Relating model outputs to future geophysical measurements and end-user science needs

My contribution

I define and compare bulk-silicate Moon composition scenarios, develop and validate the modelling workflow, and connect changes in interior structure to quantities that future geophysical observations may constrain.

  • Develop thermodynamic and geophysical models of the lunar interior.
  • Apply and extend PlanetProfile and develop Planetary EOS Lab for reproducible Perple_X workflows.
  • Document assumptions, limitations, validation checks, and scientific traceability.

Modelling approach

  • Define and compare plausible bulk-silicate Moon composition scenarios.
  • Generate thermodynamic and geophysical interior models using reproducible Python workflows.
  • Evaluate how compositional assumptions affect mantle structure and observable properties.
  • Document assumptions, limitations, validation checks, and model traceability.

From models to measurements

The key objective is not modelling in isolation, but identifying how measurements can test a scientific hypothesis. Forward-modelled observables provide the bridge between interior scenarios and the performance expected from future investigations.

Scientific need
Discriminate between plausible lunar composition and structure scenarios
Measurement logic
Determine which observable quantities are sensitive to the parameters of interest

Scientific software

I apply and extend the open-source PlanetProfile framework and develop Planetary EOS Lab, a Python workflow that streamlines Perple_X calculations for planetary interior modelling and integration with PlanetProfile.

PythonPlanetProfilePerple_XThermodynamic modellingModel validationReproducible research

Scientific relevance

The work identifies which modelled properties are most sensitive to lunar composition and structure, and therefore which observations would be most informative for distinguishing between interior scenarios. This keeps the modelling anchored to measurable quantities and the interpretation of future geophysical data.