chaos-polymtl/lethe
Jul 2025 – Aug 2025
2 Months active
Languages Used
C++GeoMarkdownPythonRST
Technical Skills
C++CFD-DEM CouplingCFD-DEM SimulationComputational Fluid DynamicsConfiguration ManagementDEM Simulation
flo-op
PROFILE
Flo-op
Florence Caron contributed to the chaos-polymtl/lethe repository by developing and enhancing CFD-DEM simulation features, focusing on gas-solid spouted bed modeling and dynamic particle insertion. She implemented unresolved CFD-DEM examples validated against experimental data, integrated Python scripting for post-processing, and improved data logging for particle-wall collisions. Using C++ and MPI, Florence addressed a segmentation fault in multi-core simulations by refining particle sorting and synchronized parallel logging to ensure data integrity. Her work emphasized robust test coverage and documentation, resulting in more reliable, scalable workflows and facilitating easier onboarding for users and developers working with computational fluid dynamics simulations.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
4
Lines of code
4,471
Activity Months2
Your Network
6 peopleShared Repositories
6
Work History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025: Delivered stability and observability improvements to the Lethe CFD-DEM workflow (chaos-polymtl/lethe). Key bug fix resolved a segmentation fault in multi-core simulations by ensuring correct particle sorting after insertion. Implemented MPI-parallel collision statistics logging to synchronize writes across processes and capture particle diameter, mass, and time, improving data integrity and traceability. These changes enhance reliability, scalability, and post-run analysis, delivering business value through reduced crashes, faster debugging, and richer simulation metrics. Technologies demonstrated include MPI synchronization, parallel logging, and particle management within a multi-core CFD-DEM context.
2 Commits • 1 Features
Aug 1, 2025August 2025: Delivered stability and observability improvements to the Lethe CFD-DEM workflow (chaos-polymtl/lethe). Key bug fix resolved a segmentation fault in multi-core simulations by ensuring correct particle sorting after insertion. Implemented MPI-parallel collision statistics logging to synchronize writes across processes and capture particle diameter, mass, and time, improving data integrity and traceability. These changes enhance reliability, scalability, and post-run analysis, delivering business value through reduced crashes, faster debugging, and richer simulation metrics. Technologies demonstrated include MPI synchronization, parallel logging, and particle management within a multi-core CFD-DEM context.
August 2025
July 2025
4 Commits • 3 Features
Jul 1, 20252025-07 monthly summary for chaos-polymtl/lethe highlighting key features delivered, major bug fixes, and overall impact. This period focused on expanding CFD-DEM capabilities, enhancing validation, and improving test coverage and documentation to accelerate adoption and reduce integration risk. Key contributions: - Gas-Solid Spouted Bed CFD-DEM Example: Delivered an unresolved CFD-DEM spouted bed example in a rectangular configuration, including detailed parameter files and Python post-processing scripts, with experimental validation against Yue et al. data. Commit: 0527ef1da4814cfaa379b0ba2ba95949db3be17a. - Dynamic Particle Insertion in CFD-DEM: Implemented dynamic particle insertion by reusing existing DEM insertion methods, added a new test case, and updated documentation to confirm functionality. Commits: 80d255540808dce5d089a1976fc6382cd4843679; 9f4ca0a1076af2ebec5cb8764a66c58e1dabcb08. - Log Statistics for Particle-Wall Collisions in DEM: Implemented logging of particle-wall collision events (IDs, timestamps, velocity data) with configurability via model configuration and added a dedicated test case. Commit: eeaca70c972db244b549e1087681f26ffb578cc2. Impact and accomplishments: - Increased CFD-DEM fidelity and usability by adding a validated spouted bed example, dynamic insertion, and collision logging. - Strengthened reliability through tests and documentation, enabling easier validation and onboarding for users and developers. - Demonstrated end-to-end capability: model-config-driven features, Python-based post-processing, and alignment with experimental data for credible benchmarking. Technologies/skills demonstrated: - CFD-DEM integration, Python scripting for post-processing, version-controlled feature development, test-driven development, and documentation updates.
July 2025
4 Commits • 3 Features
Jul 1, 20252025-07 monthly summary for chaos-polymtl/lethe highlighting key features delivered, major bug fixes, and overall impact. This period focused on expanding CFD-DEM capabilities, enhancing validation, and improving test coverage and documentation to accelerate adoption and reduce integration risk. Key contributions: - Gas-Solid Spouted Bed CFD-DEM Example: Delivered an unresolved CFD-DEM spouted bed example in a rectangular configuration, including detailed parameter files and Python post-processing scripts, with experimental validation against Yue et al. data. Commit: 0527ef1da4814cfaa379b0ba2ba95949db3be17a. - Dynamic Particle Insertion in CFD-DEM: Implemented dynamic particle insertion by reusing existing DEM insertion methods, added a new test case, and updated documentation to confirm functionality. Commits: 80d255540808dce5d089a1976fc6382cd4843679; 9f4ca0a1076af2ebec5cb8764a66c58e1dabcb08. - Log Statistics for Particle-Wall Collisions in DEM: Implemented logging of particle-wall collision events (IDs, timestamps, velocity data) with configurability via model configuration and added a dedicated test case. Commit: eeaca70c972db244b549e1087681f26ffb578cc2. Impact and accomplishments: - Increased CFD-DEM fidelity and usability by adding a validated spouted bed example, dynamic insertion, and collision logging. - Strengthened reliability through tests and documentation, enabling easier validation and onboarding for users and developers. - Demonstrated end-to-end capability: model-config-driven features, Python-based post-processing, and alignment with experimental data for credible benchmarking. Technologies/skills demonstrated: - CFD-DEM integration, Python scripting for post-processing, version-controlled feature development, test-driven development, and documentation updates.
Activity
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Quality Metrics
Correctness93.4%
Maintainability86.6%
Architecture88.4%
Performance78.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++GeoMarkdownPythonRST
Technical Skills
Bug FixingC++CFD-DEMCFD-DEM CouplingCFD-DEM SimulationComputational Fluid DynamicsConfiguration ManagementDEM SimulationData LoggingData VisualizationDiscrete Element MethodDocumentationMPIMeshingParallel Computing
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