
Contributed to the lanl/singularity-eos repository by expanding equation of state (EOS) capabilities and improving numerical robustness. Integrated the Simple MACAW EOS model into the library, implementing core thermodynamic property calculations and ensuring seamless framework integration with dedicated unit tests for correctness and stability. Addressed a critical issue in the Carnahan-Starling EOS by refining parameter validation, tuning root-finding tolerances, and enhancing edge-case handling, particularly for zero covolume scenarios. Updated and expanded test coverage to validate these changes. Demonstrated strong skills in C++ development, numerical methods, and software engineering, resulting in broader EOS coverage and more reliable property predictions for simulations.
2025-09 monthly summary for lanl/singularity-eos. Expanded EOS capabilities and improved robustness. Key outcomes include integrating the Simple MACAW EOS model into the library, with core EOS calculations, thermodynamic property derivations, framework integration, and dedicated unit tests to verify correctness and stability. Fixed critical Carnahan-Starling EOS DensityFromPressureTemperature issues by refining parameter checks, tuning root-finding tolerances/bounds for numerical stability, and improving edge-case handling (e.g., zero covolume); updated tests accompany the fix. Overall impact: broader EOS coverage, more reliable property predictions, and a stronger regression test suite enabling more accurate simulations in production. Technologies demonstrated include numerical methods, EOS modeling, unit testing, and framework integration.
2025-09 monthly summary for lanl/singularity-eos. Expanded EOS capabilities and improved robustness. Key outcomes include integrating the Simple MACAW EOS model into the library, with core EOS calculations, thermodynamic property derivations, framework integration, and dedicated unit tests to verify correctness and stability. Fixed critical Carnahan-Starling EOS DensityFromPressureTemperature issues by refining parameter checks, tuning root-finding tolerances/bounds for numerical stability, and improving edge-case handling (e.g., zero covolume); updated tests accompany the fix. Overall impact: broader EOS coverage, more reliable property predictions, and a stronger regression test suite enabling more accurate simulations in production. Technologies demonstrated include numerical methods, EOS modeling, unit testing, and framework integration.

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