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The wind propulsion industry is expanding rapidly, yet it still lacks standardized tools and methodologies for accurately predicting the power performance of Wind-Assisted Ship Propulsion (WASP) systems on board commercial vessels. There is still a lot of uncertainty surrounding many PPP tools and methodologies.
Current practices rely heavily on simplified modelling approaches that cannot capture aerodynamic interactions, coupling effects, sea state penalties, or operational routing variability. AppWind’s goal is to provide confidence by establishing a robust, high-fidelity reference workflow that enables reliable power performance predictions that support regulatory compliance, competitive benchmarking and investment decision-making.
Drawing on our experience from the America’s Cup, we approached the problem from a high-fidelity perspective, focusing on accurately capturing aero-hydrodynamic flow behavior and interactions effects. Our methodology is built around full-scale 3D RANS CFD simulations of the complete ship–system configuration and smart DOE sampling strategies.
These high-fidelity simulations, used to train response surface models, are incorporated into our in-house Power Performance Prediction (PPP) tool. This tool computes the force equilibrium and optimal sailing conditions simultaneously, enabling the generation of accurate polars that represent the ship’s behavior across a wide range of wind conditions. Finally, the resulting polars feed into our route optimization program, a graph-based solver implementing the Dijkstra algorithm to determine the most energy-efficient route at a resolution of a few nautical miles.
The resulting methodology and tools provide a robust, science-based framework that supports technology developers, shipowners, classification societies and regulators with credible, traceable performance metrics, including direct quantification of CII and ROI. By bridging high-fidelity physics with computationally efficient surrogate models, AppWind enables affordable trade-off studies across technologies, routes and operating profiles, reducing uncertainty in investment-grade decision processes.
This high-fidelity pipeline lays the groundwork for future industrial standardisation of WASP performance evaluation and supports the digital-twin roadmap for decarbonised maritime transport.
The wind propulsion industry is expanding rapidly, yet it still lacks standardized tools and methodologies for accurately predicting the power performance of Wind-Assisted Ship Propulsion (WASP) systems on board commercial vessels. There is still a lot of uncertainty surrounding many PPP tools and methodologies.
Current practices rely heavily on simplified modelling approaches that cannot capture aerodynamic interactions, coupling effects, sea state penalties, or operational routing variability. AppWind’s goal is to provide confidence by establishing a robust, high-fidelity reference workflow that enables reliable power performance predictions that support regulatory compliance, competitive benchmarking and investment decision-making.
Drawing on our experience from the America’s Cup, we approached the problem from a high-fidelity perspective, focusing on accurately capturing aero-hydrodynamic flow behavior and interactions effects. Our methodology is built around full-scale 3D RANS CFD simulations of the complete ship–system configuration and smart DOE sampling strategies.
These high-fidelity simulations, used to train response surface models, are incorporated into our in-house Power Performance Prediction (PPP) tool. This tool computes the force equilibrium and optimal sailing conditions simultaneously, enabling the generation of accurate polars that represent the ship’s behavior across a wide range of wind conditions. Finally, the resulting polars feed into our route optimization program, a graph-based solver implementing the Dijkstra algorithm to determine the most energy-efficient route at a resolution of a few nautical miles.
The resulting methodology and tools provide a robust, science-based framework that supports technology developers, shipowners, classification societies and regulators with credible, traceable performance metrics, including direct quantification of CII and ROI. By bridging high-fidelity physics with computationally efficient surrogate models, AppWind enables affordable trade-off studies across technologies, routes and operating profiles, reducing uncertainty in investment-grade decision processes.
This high-fidelity pipeline lays the groundwork for future industrial standardisation of WASP performance evaluation and supports the digital-twin roadmap for decarbonised maritime transport.
30% more efficient sport yacht developed with Bluegame / Sanlorenzo.
Multiphase and multi degrees of freedom CFD simulation setup for optimization and validation of performance predictions related to sailing yachts and commercial vessels with WASP devices
Accurate Wind Performance Prediction: A high-fidelity Ship Digital Twin for Wind-Assisted Power Performance Prediction and Voyage Optimisation.
During the 33rd America’s Cup cycle, Mario Caponnetto contributed to hydrodynamic assessment workstreams aligned with the BMW Oracle wing-sail platform, the configuration that ultimately won the Match. This milestone marked the shift toward aero-hydrodynamic integration in Cup design culture.
BMW Oracle Racing
America’s Cup / Aero-Hydro Integration / Performance Engineering
In 2021, Caponnetto Hueber led the CFD, foil design, and hydrodynamic engineering for the AC75 of Luna Rossa Challenge, the eventual Prada Cup winner. We deployed multiscale CFD and aero-hydro coupling to ensure optimum lift and control. Rapid iteration delivered performance gains under tight competition timelines.
Luna Rossa Challenge
Racing Concept / CFD / Foil Design