loganoz/horses3d
Jan 2026 – Apr 2026
4 Months active
Languages Used
FortranMarkdownPythonShellYAMLCMakefile
Technical Skills
FortranFortran programmingacoustic modelingdocumentationfluid dynamicsnumerical methods
Albert Jimenez Ramos
PROFILE
Albert Jimenez Ramos
Albert Jimenez Ramos contributed to the loganoz/horses3d repository by developing and enhancing features for acoustic and fluid dynamics simulations, focusing on Lamb vector interpolation, mesh analytics, and parallel I/O. He applied Fortran and C++ with OpenMP to implement time-accurate interpolation frameworks, parallelized data processing, and robust input validation, enabling reproducible and scalable simulations. His work included refactoring I/O routines, expanding solver outputs for post-processing, and strengthening CI/CD pipelines with automated testing and documentation. By integrating dynamic configuration, mesh-based analytics, and performance optimizations, Albert delivered solutions that improved simulation accuracy, data interoperability, and reliability for scientific computing workflows.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
61Total
Bugs
6
Commits
61
Features
20
Lines of code
6,315,987
Activity Months4
Your Network
8 peopleShared Repositories
8
Abbas BalloutMember
DhueperMember
Eduardo JanéMember
loganozMember
Himpu MarbonaMember
hmarbonaMember
Oscar MarinoMember
Esteban FerrerMember
Work History
April 2026
19 Commits • 5 Features
Apr 1, 2026April 2026 monthly summary for loganoz/horses3d: Delivered a set of Lamb Vector enhancements and supporting infrastructure aimed at improving time-accurate acoustic simulations, data interoperability, and production reliability. The work focused on features with clear business value: more accurate physics, easier post-processing, and a sturdier build/test pipeline.
19 Commits • 5 Features
Apr 1, 2026April 2026 monthly summary for loganoz/horses3d: Delivered a set of Lamb Vector enhancements and supporting infrastructure aimed at improving time-accurate acoustic simulations, data interoperability, and production reliability. The work focused on features with clear business value: more accurate physics, easier post-processing, and a sturdier build/test pipeline.
April 2026
March 2026
17 Commits • 2 Features
Mar 1, 2026March 2026 summary for loganoz/horses3d focused on delivering high-value features for mesh analytics and acoustics, improving reliability through CI automation, and increasing performance via parallelization. Key outcomes include a new Stats Mesh Interpolation module with Lamb vector statistics integration, documentation, refactoring, OpenMP parallelization, and a testing framework; and Acoustic Simulation Enhancements with base flow handling, input integrity checks, mandatory keyword validation, updated documentation, and CI/test coverage. Notable reliability improvements include replacing risky object-pointer usage with safe key-based access (containsKey) and hardening input/config checks. Outcome: stronger cross-mesh analytics, more reliable acoustics simulations, faster interpolation, and reproducible builds. Technologies demonstrated: C++, OpenMP, parallel computing, test-driven development, CI workflows, and comprehensive documentation.
March 2026
17 Commits • 2 Features
Mar 1, 2026March 2026 summary for loganoz/horses3d focused on delivering high-value features for mesh analytics and acoustics, improving reliability through CI automation, and increasing performance via parallelization. Key outcomes include a new Stats Mesh Interpolation module with Lamb vector statistics integration, documentation, refactoring, OpenMP parallelization, and a testing framework; and Acoustic Simulation Enhancements with base flow handling, input integrity checks, mandatory keyword validation, updated documentation, and CI/test coverage. Notable reliability improvements include replacing risky object-pointer usage with safe key-based access (containsKey) and hardening input/config checks. Outcome: stronger cross-mesh analytics, more reliable acoustics simulations, faster interpolation, and reproducible builds. Technologies demonstrated: C++, OpenMP, parallel computing, test-driven development, CI workflows, and comprehensive documentation.
February 2026
21 Commits • 11 Features
Feb 1, 2026February 2026 summary for loganoz/horses3d: Implemented parallel I/O (position-based reads/writes) with optional stats_and_gradients, centralized I/O by moving reading to the time integration function, and added a new APE source term module. Introduced an acoustic flag to control acoustics behavior and enabled Lamb vector stats to be supplied as a uniform field. Added multiphase solver enhancements (initialization of base variables and statistics saving) and improved test coverage for acoustics, plus CI workflow setup. Achievements include refactors to simplify I/O, key feature delivery, and reliability improvements through targeted bug fixes and tests.
21 Commits • 11 Features
Feb 1, 2026February 2026 summary for loganoz/horses3d: Implemented parallel I/O (position-based reads/writes) with optional stats_and_gradients, centralized I/O by moving reading to the time integration function, and added a new APE source term module. Introduced an acoustic flag to control acoustics behavior and enabled Lamb vector stats to be supplied as a uniform field. Added multiphase solver enhancements (initialization of base variables and statistics saving) and improved test coverage for acoustics, plus CI workflow setup. Achievements include refactors to simplify I/O, key feature delivery, and reliability improvements through targeted bug fixes and tests.
February 2026
January 2026
4 Commits • 2 Features
Jan 1, 2026January 2026 (Month: 2026-01) – Two core feature deliveries on loganoz/horses3d focused on usability, data interoperability, and reproducibility: (1) Qbase Initialization Flexibility with usage documentation; (2) Lamb Vector IO Extensions for the Acoustics Solver. No major bugs fixed this month. Overall impact: easier configuration, faster experimentation, and improved data reuse. Technologies used: Fortran, file-based IO, and documentation practices to boost adoption and maintainability. Business value: reduces setup time, accelerates analysis pipelines, and supports reproducible experiments.
January 2026
4 Commits • 2 Features
Jan 1, 2026January 2026 (Month: 2026-01) – Two core feature deliveries on loganoz/horses3d focused on usability, data interoperability, and reproducibility: (1) Qbase Initialization Flexibility with usage documentation; (2) Lamb Vector IO Extensions for the Acoustics Solver. No major bugs fixed this month. Overall impact: easier configuration, faster experimentation, and improved data reuse. Technologies used: Fortran, file-based IO, and documentation practices to boost adoption and maintainability. Business value: reduces setup time, accelerates analysis pipelines, and supports reproducible experiments.
Activity
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Quality Metrics
Correctness87.2%
Maintainability85.2%
Architecture85.0%
Performance86.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
CFortranMakefileMarkdownPythonShellYAML
Technical Skills
Acoustics SimulationC programmingC/C++ developmentCI/CDCode MaintenanceCode RefactoringCode refactoringFortranFortran programmingMakefile scriptingNumerical methodsOpenMPParallel ComputingPython scriptingSoftware Development
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