Case Study: AMC filter digital twin for fast life-cycle prediction
An ML-powered digital twin built from validated physics models and synthetic CFD training data to predict filter performance and remaining useful life in seconds.
- Problem: Experimental evaluation of filter performance is costly and slow, especially at low contaminant concentrations.
- Approach: Generate synthetic datasets using CFD; extract reduced-order models (ROMs) for real-time operation; run what-if scenarios across inlet concentration (10–100 ppb) and flow (≈78–591 cfm).
- Outcome: Instant predictions of outlet concentration and replacement timing, enabling preventative maintenance and faster iteration without months-long test cycles.
“Reduced-order models make real-time digital twins practical—without sacrificing physics.”
