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Simulation Manager: "Git for Simulations"

Source files: Architechture & Research/Autonomous CAE/Concepts & Problem Briefs/Simulation Manager Problem Brief.md Last synthesized: March 2026

Executive Summary

Build a lightweight desktop application that helps mechanical engineers track, organize, and manage their CAE simulation jobs. Think of it as version control for simulations: a single dashboard to see all analysis runs, their status, and results history.

Target Users: Mechanical design engineers, stress analysts, CFD engineers Market: 100,000+ CAE professionals globally who run simulations daily Problem: Engineers lose track of simulation runs, waste time searching for old results, have no centralized view of analysis history

The Problem

Current Workflow (Broken)

  1. Engineer creates 3D model in CAD software
  2. Sets up simulation parameters (loads, materials, boundary conditions)
  3. Launches simulation job from command line or software GUI
  4. Job runs for 2-30 hours on local workstation or HPC cluster
  5. Engineer manually checks if job completed (opens folders)
  6. Results scattered across directories with cryptic names
  7. No history of what was run, when, or why

Pain Points

  • "Where did I save that analysis from last month?"
  • "Which version of the model was this result from?"
  • "My computer is locked up running this 8-hour simulation"
  • "Did that job finish overnight? Need to check manually"
  • "What were the parameters I used for that successful run?"
  • Real cost: Engineers waste 30-60 minutes/day organizing and finding old simulations

Market Opportunity

Target Segments

1. Freelance / Consultant Engineers (20,000+ globally)

  • Run simulations for multiple clients
  • Need to track billable analysis hours
  • No budget for enterprise PLM
  • Price tolerance: $15-30/month SaaS or $199 one-time

2. Small Engineering Firms (5-50 employees)

  • Mechanical design, aerospace suppliers, medical device startups
  • 3-10 simulation engineers per company
  • Need team visibility but can't afford Siemens Teamcenter ($50K+/year)
  • Price tolerance: $50-150/user/year

3. Mid-Size Manufacturers (50-500 employees)

  • Automotive suppliers, industrial equipment, appliances
  • Have CAD/CAE tools but no simulation data management system
  • Price tolerance: $500-2000/year per team (5-10 users)

Why Now?

  • Remote work: Engineers running simulations from home; need better organization
  • AI integration: Auto-summarize results, suggest optimizations (differentiator!)
  • No good solutions: HPC job schedulers (SLURM, PBS) require IT infrastructure; native solver GUIs only track jobs from their interface; enterprise PLM is overkill and expensive

Commercial Model

Freemium

  • Free tier: Single user, unlimited jobs, local storage
  • Monetization: Upsell to Pro/Enterprise

Pro ($20/month)

  • Team collaboration (5 users)
  • Cloud sync of results
  • AI summaries of analysis (5 jobs/month)
  • 6-month result history
  • Email notifications

Enterprise ($100/user/year)

  • Multi-site deployment
  • Unlimited AI summaries
  • Team hierarchy and permissions
  • 2-year result history
  • Custom integration (Teamcenter, Windchill, etc.)
  • Priority support

Estimated TAM: 100,000 CAE professionals × $100 average annual = $10M/year addressable

Technical Concept

Architecture

Lightweight desktop app that passively monitors the filesystem and maintains a searchable database of simulation runs.

Key innovation: Auto-detects when engineers launch jobs, regardless of which tool they use (OptiStruct, Abaqus, Nastran, LS-DYNA, etc.). No native tool integration needed.

Core components: 1. File Watcher Service (background process) - Monitors standard simulation directories - Detects when job launches (identifies .inp, .bdf, .fem, .k files being written) - Detects when jobs complete (looks for output files, .log status) - Records timestamp, parameters, file paths

  1. Local Database (SQLite or similar)
  2. Job metadata: name, status, start time, end time, solver type, model file, parameters
  3. Result summary: element count, max stress, runtime, convergence status

  4. Tags and notes (user-added context)

  5. UI Dashboard

  6. List all jobs: status (running, completed, failed), runtime, date
  7. Search and filter: by date range, solver type, result status, tags
  8. Drill down: click job → see full parameters, log, output summary

  9. Comparison: side-by-side result view for two runs

  10. Result Parsing

  11. Extract key metrics from solver output (.log, .out, .h3d files)
  12. Max/min stress, run time, iteration count, convergence

  13. Detect failures: divergence, memory issues, input errors

  14. Optional: AI Summary (Pro tier)

  15. Use LLM to summarize .log file
  16. Generate simple report: "Ran 50,000-element FEA, 12 hours, max stress 250 MPa, converged"
  17. Flag anomalies: unusual runtimes, failed iterations

Why It Works

Advantages over existing solutions: - HPC schedulers (SLURM, PBS): Require IT infrastructure; overkill for desktop/small-team users - Native solver GUIs: Only track jobs launched through their interface; miss scripts/command-line launches - Manual Excel tracking: Engineers literally maintain spreadsheets; error-prone, no search - Enterprise PLM: $50K-500K/year; too expensive and complex for small teams

Our approach: Passive, non-invasive file monitoring + lightweight database + clean UI. Works with any solver, any workflow.

Competitive Landscape

Solution Cost Scope Team Collaboration AI Features
HPC Schedulers (SLURM) Free Cluster only Yes No
Native Solver GUIs Free (bundled) Single solver Limited No
Siemens Teamcenter $50K+/year Full PLM Yes No
PTC Windchill $40K+/year Full PLM Yes No
Excel tracking Free Manual No No
Simulation Manager (our product) $20-100/user/year Simulation-focused Yes (Pro) Yes (Pro)

Positioning: The "lightweight, team-friendly middle" between manual organization and enterprise PLM.

Minimum Viable Product (MVP)

Phase 1: Local Desktop Only - File watcher for standard simulation directories - SQLite database of job metadata - Basic UI: list jobs, search by date/name/solver - Result parsing for one solver (e.g., Abaqus) - Runs on Windows (most CAE engineers use Windows)

Timeline: 3-4 months Cost to build: $50K-80K (one mid-level engineer)

Phase 2: Team & Cloud (6-12 months after Phase 1) - Multi-user support (local network or cloud sync) - Result file cloud backup - Web UI for result viewing - AI summaries (partner with OpenAI or Anthropic) - Support for 3+ solvers

Phase 3: Deep Integration (12+ months) - CAD model version tracking (link job to CAD version) - Optimization suggestions (use AI to recommend parameter changes) - Performance benchmarking (compare runtimes across machine configurations) - Integration with CAE knowledge base (see related past runs)

Go-to-Market

Early Access (Phase 1)

  • Target: 100 freelance engineers + small design firms
  • Channel: Engineering forums (r/engineering, CAD blogs), LinkedIn
  • Offer: Free MVP for 6 months in exchange for feedback
  • Goal: Validate product-market fit

Phase 2 Launch (Pro tier)

  • Price: $20/month
  • Target: Small firms (expand from Phase 1 users)
  • Marketing: Word-of-mouth + industry newsletters (NAFEMS, AIAA, etc.)
  • Partnerships: CAD/CAE tool vendors (sell through their app stores)

Phase 3 Enterprise

  • Price: $100/user/year
  • Sales model: Direct to mid-size manufacturers
  • Focus: Automation/aerospace/automotive suppliers

Success Metrics

Metric Year 1 Target Year 2 Target
Active users 500 5,000
Monthly recurring revenue $2K $50K
Jobs tracked 100K 5M
Average job runtime 8 hours 8 hours
User retention 70% 80%

Relationship to Autonomous CAE

Simulation Manager is not directly related to Autonomous CAE research, but they are complementary products:

  • Autonomous CAE solves: "How do I automate the pre-mesh setup?"
  • Simulation Manager solves: "How do I organize and learn from all my past runs?"

Together: - Autonomous CAE makes engineers more productive (2-4 hours saved per model) - Simulation Manager helps them track and reuse those runs - Simulation Manager's data becomes training data for Autonomous CAE (physics-in-the-loop learning on recorded runs)

Risk & Mitigation

Risk Severity Mitigation
Market too small Medium Start with niches (consultants, small firms); expand upmarket
Enterprise PLM vendors undercut price Medium Position as lightweight alternative; emphasize ease of use
Difficult to parse solver output Medium Partner with solver vendors; start with one solver (Abaqus)
Engineers don't change workflow High Free Phase 1; integrate with existing directories (no setup)
AI summaries don't add value Low Make optional; focus on core tracking first

Next Steps

  1. Validate market: Survey 20 freelance engineers and small firm leads
  2. Prototype Phase 1 MVP: File watcher + SQLite + basic UI (4 weeks)
  3. Pilot with 10 beta users: Gather feedback on workflow integration
  4. Decide: Proceed to Phase 2 or pivot?

This is a product opportunity, not a technology research project. It solves a real pain point with straightforward technology and clear commercial model.