Predictive Modeling and Simulation for Next Generation Naval Undersea Vehicles and Platforms
Abstract
The designs of Naval undersea systems constantly grow in complexity, scope, and sophistication. In addition,the harsh and complex op erating environments combine to make advanced predictive modeling and computational tools for such systems (e.g., vehicles or platfo rms) difficult to develop. Without validated modeling tools, underwater vehicleand platform development relies heavily on experiment ation which can significantly lengthen the design phase, result incostly system level rework, and restrict innovation. The proposed research project at Brown University plans to advancenaval warfare capabilities by providing focused research on experimentally vali dated, multi-physics, and multi-fidelitypredictive modeling to support undersea smart systems design. Augmented by machine learning capabilities the researchwill support applications such as digital twin, situational awareness and vessel health monitoring. The res earch willleverage the expertise of Brown University researchers in the fundamental areas of applied mechanics (solids, fluids,wave propagation, interface phenomena), computational mechanics (finite element, meshfree and isogeometricmethods), and computational sci ence and engineering (high-performance computing, reduced-order modeling, machinelearning, and autonomy) to carry out collaborative research with NUWC scientists and engineers. The research activitieswill be driven to advance the state-of-the art for both manned a nd autonomous undersea systems and enable the nextgeneration of digital strategy. The research will result in methods and tools that will be available to the entire underseavehicle community for future use.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 20, 2021
- Source ID
- N000142112670
Entities
People
- Yuri Bazilevs
Organizations
- Brown University
- Office of Naval Research
- United States Navy