Data-Driven, Physics-Based, and Equation-Free Multi-Fidelity Reduced Order Models for Fast and Accurate Predictions of Naval Ship Transient Maneuvers in Calm Water and Waves (DAPHNE)
Abstract
The objective of the proposed effort is to develop and apply to test cases of interest to ONR and AVT research task groups data-driven, physics-based and equation-free MF ROMs for fast and accurate predictions of naval ship transient maneuvers in calm water and waves, extending the current steady-state maneuvering prediction capabilities and physical understanding. The project will aim at: (1) supporting the development of the MMG model for transient maneuvers of naval ships in calm water and waves via MF CFD-based training; (2) advancing ROMs capabilities to the prediction of load distribution; (3) advancing ROMs capabilities to the uncertainty estimate of computed solutions; (4) extending the use of ROMs to physical knowledge extraction for transient maneuvers of naval ships in calm water and waves; (5) supporting companion University of Iowa (#Global and Local Flow Experiments for Free Running ONRT and KCS Transient Maneuvering in Calm Water and Waves#) and Hiroshima University (#Development of a Generalized MMG-model for KCS Transient Maneuvering in Calm Water and Waves#) efforts; supporting AVT-399 research; and complementing AVT-368 and AVT-382 activities. The proposed research will be conducted in collaboration with the University of Iowa and Hiroshima University, along with research task groups AVT-399, AVT-368, and AVT-382. Tight collaboration with Iowa and Hiroshima PIs is anticipated including weekly online meetings and yearly in person meetings in the United States and Japan. The research supports the Naval Engineering focus area, in that reduced-order maneuvering models are key enablers to ship operations. Codes employing these reduced-order models allow executions much faster than real time and are invaluable in predicting vessel response in autopilots, simulators, and digital twins.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N629092412102
Entities
People
- Matteo Diez
Organizations
- Consiglio Nazionale delle Ricerche
- Office of Naval Research
- United States Navy