Cross-sector thermal digital twin — battery thermal management, industrial heat exchangers, and process thermal systems. One platform, three regimes. CFD + energy-conservation PINNs where geometry is clean, physics-guided ML where it isn't.
Pre-pilot with EV OEM & process-heating partner. GA rollout planned for 2027.
Each pillar uses the right method — CFD-grounded PINNs where energy conservation can be encoded, physics-guided ML where data must drive the model.
Cell-level heat generation, pack thermal runaway risk, and active cooling system optimization. The twin predicts where hot-spots will form before they damage cells.
Thermal-performance prediction and fouling detection across plate, shell-and-tube, and air-cooled exchangers. The twin learns from process sensors and stays current.
Temperature-field prediction in manufacturing processes: drying ovens, reactors, calenders, kilns. Live state estimation from limited sensor data.
Battery and exchanger thermal regimes have clean enough geometry for CFD + energy-conservation PINNs. Process thermal lives in complex geometries — physics-guided ML wins there.
We don't force a single method across all three pillars. Battery and exchanger thermal regimes are well-suited to PINNs anchored by CFD. Process thermal needs physics-guided ML because real-world manufacturing geometry breaks the PINN assumption set.
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/ Battery Mfg · In developmentEnd-to-end battery electrode manufacturing digital twin. Mixing, coating, calendering — one twin, line-rate inference.