Jaime de Salas Juanas contributed to the PaSieg0/Ekranoplan-DSE repository by developing and refining advanced aircraft performance modeling and simulation tools over two months. He implemented maneuver planning, load and center of gravity analysis, and mission scenario modules, focusing on data-driven design using Python and JSON. His work included aerodynamic model updates, drag and fuel consumption calculations, and robust data handling for iterative optimization. By integrating visualization with Matplotlib and enhancing backend logic, Jaime improved reliability, maintainability, and decision support for aircraft design. The depth of his engineering addressed both technical accuracy and workflow efficiency across the project’s evolving requirements.
June 2025 monthly summary for PaSieg0/Ekranoplan-DSE: Delivered major aerodynamic model updates and robust data governance, enabling more reliable simulations and faster iteration. Key features included parameterized design3.json updates, enhanced drag estimation (Cd0) and Oswald factor, and performance-oriented refinements across main execution and plotting. These efforts reduce design risk, improve decision quality, and position the project for more accurate optimization in next cycles.
June 2025 monthly summary for PaSieg0/Ekranoplan-DSE: Delivered major aerodynamic model updates and robust data governance, enabling more reliable simulations and faster iteration. Key features included parameterized design3.json updates, enhanced drag estimation (Cd0) and Oswald factor, and performance-oriented refinements across main execution and plotting. These efforts reduce design risk, improve decision quality, and position the project for more accurate optimization in next cycles.
Summary for 2025-05 PaSieg0/Ekranoplan-DSE: Delivered a broad set of features and refactors across maneuver planning, load/CG modeling, and mission planning tooling, with targeted bug fixes to improve reliability and maintainability. The work emphasizes business value through more accurate planning, better visualization, and scalable design parameterization. Key achievements for May 2025: - Maneuver Planning Module Setup: initial setup and input configuration for maneuver planning with new design data files; includes multiple commits (New Files, Initial maneuver, Maneuver almost done, removed prints, design json files, changed inputs). - Load Diagram Plotting and Gust Load Calculations Enhancements: dynamic density integration, flapped load factor, and complete load diagram plotting; updated density sources and cruise/stall annotations for clearer design decisions. - AltitudeVelocity and Range Modeling Enhancements: comprehensive AltitudeVelocity refactor with power and climb calculations, type hints, improved Vx/Vy and AoC calculations; introduced RangeCalculator and improved PayloadRange/plotting; added OptimumClimb/Descent/Speeds modules and richer plotting. - Aircraft Data Access and CG Computation: refactor to use design attributes for MTOM/WS, and implement center of gravity calculation with visualization for easier trade studies. - PayloadRange and Mission Scenario Improvements: enhanced payload-range plotting with area fill and axis limits; added FERRY mission scenario and improved annotations; updated related classes for better accuracy and visuals. Overall impact: The month delivered improved accuracy in maneuver planning, load/gust analysis, CG/weight handling, and range-payload trade-offs. Visualizations and data access improvements enable faster design decisions and more reliable mission planning. The groundwork is in place for further optimization and automated decision support. Technologies/skills demonstrated: Python refactoring and OO design, data-driven design parameterization via JSON, dynamic density calculations, plotting enhancements, data visualization for CG and payload-range, and integration of optimization-oriented classes (RangeCalculator, OptimumClimb/Descent, AltitudeVelocity).
Summary for 2025-05 PaSieg0/Ekranoplan-DSE: Delivered a broad set of features and refactors across maneuver planning, load/CG modeling, and mission planning tooling, with targeted bug fixes to improve reliability and maintainability. The work emphasizes business value through more accurate planning, better visualization, and scalable design parameterization. Key achievements for May 2025: - Maneuver Planning Module Setup: initial setup and input configuration for maneuver planning with new design data files; includes multiple commits (New Files, Initial maneuver, Maneuver almost done, removed prints, design json files, changed inputs). - Load Diagram Plotting and Gust Load Calculations Enhancements: dynamic density integration, flapped load factor, and complete load diagram plotting; updated density sources and cruise/stall annotations for clearer design decisions. - AltitudeVelocity and Range Modeling Enhancements: comprehensive AltitudeVelocity refactor with power and climb calculations, type hints, improved Vx/Vy and AoC calculations; introduced RangeCalculator and improved PayloadRange/plotting; added OptimumClimb/Descent/Speeds modules and richer plotting. - Aircraft Data Access and CG Computation: refactor to use design attributes for MTOM/WS, and implement center of gravity calculation with visualization for easier trade studies. - PayloadRange and Mission Scenario Improvements: enhanced payload-range plotting with area fill and axis limits; added FERRY mission scenario and improved annotations; updated related classes for better accuracy and visuals. Overall impact: The month delivered improved accuracy in maneuver planning, load/gust analysis, CG/weight handling, and range-payload trade-offs. Visualizations and data access improvements enable faster design decisions and more reliable mission planning. The groundwork is in place for further optimization and automated decision support. Technologies/skills demonstrated: Python refactoring and OO design, data-driven design parameterization via JSON, dynamic density calculations, plotting enhancements, data visualization for CG and payload-range, and integration of optimization-oriented classes (RangeCalculator, OptimumClimb/Descent, AltitudeVelocity).
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