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Marco

Original OneNote page: Survey and research - Marco

Route: meeting/person note

Categories: people_meetings, product_technical_or_learning

Source

  • Word export: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Marco.docx
  • MHT export: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Marco.mht

Extracted Notes

These notes are extracted from the Word export so the source is visible in the wiki without moving or deleting the original file.

  • Marco
  • Here’s a clear, polished, and structured version of your interview notes with Marco Losurdo (CFD Expert) — formatted so it reads like a professional research/interview summary and is ready to feed into your AI-in-CFD or CAD-LLM idea documentation:
  • Interview Notes – Marco Losurdo
  • Role: CFD Expert
  • Tools Used: Siemens STAR-CCM+ for ESU (Energy Storage Unit) thermal simulations
    1. Main Issues and Pain Points
  • 1.1 Watertight Geometry
  • The most frustrating issue: non-watertight CAD models.
  • For simulation, geometry must be perfectly closed — no gaps or overlaps.
  • Official CAD models contain built-in tolerances (by design intent), which must be manually fixed.
  • Engineers often have to extend or merge surfaces and solids to create one continuous volume before meshing.
  • 1.2 Naming and Surface Management
  • When importing from STEP or STL, all surface names and attributes are lost.
  • This makes it harder to assign boundary conditions, materials, or regions consistently.
  • Requires manual re-naming and re-assignment each time → repetitive and time-consuming.
    1. Meshing Challenges
  • Meshing time heavily depends on the geometry complexity.
  • For a full ESU model, meshing can take 12 hours to a full day.
  • Possibility of batch meshing:
  • STAR-CCM allows creation of macros for automating meshing sequences.
  • Still requires manual setup and monitoring — not fully autonomous.
    1. Simulation and Solver Performance
  • Typical simulation runs take several days to complete.
  • Biggest wish: reduce solver time.
  • Geometry preparation is a one-time task.
  • Once geometry is finalized, most of the workflow time is consumed by solving.
  • Physics-based ML could potentially reduce runtime, but:
  • It only works if the geometry and boundary conditions are similar to the training data.
  • Otherwise, results are unreliable (“otherwise it’s bullshit”).
    1. Desired Improvements / Wishlist
  • Solver Time Reduction
  • The top priority for productivity gains.
  • Even small reductions in solver iteration time translate into major savings.
  • Intelligent Post-Processing
  • Ability to define requirements and constraints (e.g., temperature limits, pressure drops).
  • Solver should automatically evaluate results against those requirements and generate graded reports (pass/fail or performance score).
  • Pre-Processing Enhancements
  • Major issues during pre-processing:
  • Identifying and fixing contacts and gaps between parts.
  • No current robust automatic solution in mainstream CFD tools.
  • Contact algorithms rely on manually defined tolerances or parameters, often tuned by trial and error.
    1. Summary of Key Opportunities for AI Integration
  • Workflow Stage
  • Current Problem
  • AI / Automation Opportunity
  • Geometry Cleaning
  • Manual fixing of tolerances, closing gaps
  • AI-based geometry repair or feature recognition
  • Surface Naming
  • Loss of metadata after import
  • Smart surface re-labeling using context (e.g., geometry + prior runs)
  • Meshing
  • Long, repetitive
  • Batch meshing automation with adaptive learning
  • Solving
  • Days of computation
  • Physics-informed neural nets to accelerate convergence
  • Post-Processing
  • Manual checks vs. requirements
  • Automated evaluation + report generation
  • ✅ Key Insight:
  • Marco emphasized that once geometry is ready, simulation time dominates — hence AI tools that reduce solver or post-processing time would bring the highest real-world value.
  • Would you like me to now summarize this into a short “Interview Insight” paragraph (like for a market research section of your business plan or pitch deck)? It would read like: “According to Marco Losurdo, a CFD expert at … the most time-consuming step in CFD is …”
  • From https://chatgpt.com/c/68d11b92-4fd4-832a-bb58-42235673c2f3
  • Interview with Marco Losurdo
  • CFD Expert
  • Please structure the notes
  • Notes from tht chat
  • Interview with MArco
  • STartCCM -- ESU thermal simulation
  • ISSUES
    1. Most annoying issue
  • water tight geometry: no gaps in the model, contact in the model
  • connecting stuff from CAD
  • official cad has tolerances by design, those must be removed
  • have to extend, either one volume or another
    1. naming surfaces
  • Everything is lost, when importing from Step or stl
  • MESHING TIME
  • Depends on what you are meshing

URLs Found

  • https://chatgpt.com/c/68d11b92-4fd4-832a-bb58-42235673c2f3

Next Curation Action

Promote this into a person page, link it to the relevant company/account, and create/update the EspoCRM contact if this relationship is still active.

Sources

  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Marco.docx
  • C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Marco.mht