
Developed a Turbopump System Visualization and RPM Prediction feature for the PURPL-Purdue/Turbopump repository, focusing on modeling enthalpy rise under varying pressure and flow conditions. Leveraging Python for scientific computing and data visualization, the work introduced a 3D regression approach to predict RPM based on volumetric flow and head, enabling a visual performance dashboard to support design optimization. The implementation emphasized modular architecture and maintainable code, with a clear commit history to facilitate future enhancements. This feature provided a scalable pipeline for numerical modeling, allowing for faster design iterations and improved performance forecasting without reported bugs during the development period.
March 2026 (2026-03) monthly summary for PURPL-Purdue/Turbopump: Delivered a Turbopump System Visualization and RPM Prediction feature that enables enthalpy rise visualization under varying pressure and flow, and models the relationship between volumetric flow and head using 3D regression to predict RPM. This work provides a visual performance dashboard and a foundation for design optimization of turbopump systems. No major bugs were reported this month; all work focused on feature delivery and code quality. The feature was implemented with a clear commit trail (510469c8ba1d36c701cb8f76e050ee6cc367f124; 08daf0d110a187864288a21031251f4b07aa2a91) to support future extensions. Overall impact includes faster design iterations, improved performance forecasting, and a scalable modeling pipeline. Technologies demonstrated include numerical modeling of enthalpy rise, regression-based RPM prediction, data visualization, and modular software architecture.
March 2026 (2026-03) monthly summary for PURPL-Purdue/Turbopump: Delivered a Turbopump System Visualization and RPM Prediction feature that enables enthalpy rise visualization under varying pressure and flow, and models the relationship between volumetric flow and head using 3D regression to predict RPM. This work provides a visual performance dashboard and a foundation for design optimization of turbopump systems. No major bugs were reported this month; all work focused on feature delivery and code quality. The feature was implemented with a clear commit trail (510469c8ba1d36c701cb8f76e050ee6cc367f124; 08daf0d110a187864288a21031251f4b07aa2a91) to support future extensions. Overall impact includes faster design iterations, improved performance forecasting, and a scalable modeling pipeline. Technologies demonstrated include numerical modeling of enthalpy rise, regression-based RPM prediction, data visualization, and modular software architecture.

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