Dual-Model Architecture combining PINNs for density sensing with Surrogate Models for real-time CFD proxies to handle instability.
Read Time: 10 min read | Published: 2025-01-22
Slurry transport systems are prone to critical instabilities where density waves drift and amplify, potentially causing blockage or line failure (Terra et Aqua 166). Additionally, cavitation in dredging centrifugal pumps acts as a critical failure mode that is strongly influenced by operating parameters (Ramirez et al., 2020), yet difficult to predict in dynamic conditions.
We employ a Dual-Model Architecture to address both inverse and forward problems. A PINN handles the inverse problem, acting as a virtual sensor for real-time density estimation (Self-balancing physics-informed LSTM). Simultaneously, a DeepCFD surrogate model serves as a real-time proxy for the forward problem, predicting flow dynamics and impending cavitation events from operating conditions.
Surrogate models like DeepCFD have demonstrated orders-of-magnitude speedups over conventional CFD while maintaining sub-percent-level errors for complex flow fields. While multiphase cavitating flows present a higher challenge than single-phase benchmarks, this architecture enables predictive control speeds previously impossible with standard numerical solvers.
Deployment requires a robust strategy for multi-fidelity data assimilation. The system must periodically recalibrate against high-fidelity offline simulations to prevent model drift.
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