Low-Order Vessel Propulsion Power Prediction Method
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Solution Overview
Problem
The design and control of hybrid electric marine propulsion systems face challenges due to the complexity of predicting vessel propulsion power demands, which is labor-intensive and computationally demanding, especially in accurately modeling hull resistance and propulsor thrust, limiting the optimization of energy efficiency and emissions.
Innovation Solution
A low-order vessel propulsion power prediction method (LOPM) is introduced, integrating a propulsion resistance and thrust model with a propulsion system and control scheme model, using computational fluid dynamics and empirical equations to estimate hull drag and propulsor thrust, enabling accurate and efficient prediction of vessel performance and power demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational fluid dynamics simulation is used to accurately predict hull resistance and propulsor thrust, then measurement precision is improved, but device complexity and computational time increase significantly
Solution Approach 1:
The patent segments the hull resistance calculation into distinct components: hull drag (using CFD) and wind resistance (using empirical equations). This allows accurate prediction of the complex hull drag through CFD while using simpler empirical methods for wind resistance, thereby reducing overall computational complexity while maintaining precision.
Solution Approach 2:
The patent introduces an intermediary approach by using a hybrid model that combines CFD simulation for hull drag with empirical equations for wind resistance. This intermediary hybrid model acts as a mediator between the need for high accuracy in hull resistance prediction and the desire to reduce computational complexity.
2Measurement precision
If labor-intensive methods are used to model vessel performance, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent segments the prediction task into two parts: hull drag (computationally intensive but automated via CFD) and wind resistance (handled by quick empirical calculations). This segmentation allows the system to maintain high precision while improving productivity by avoiding labor-intensive manual calculations throughout the entire process.
Solution Approach 2:
The patent replaces manual, labor-intensive modeling methods with automated computational methods. Specifically, it substitutes manual empirical calculations with automated CFD simulations for hull drag and integrates these with automated empirical wind resistance calculations, thereby maintaining precision while significantly improving productivity.
3Device complexity
If a simplified low-order model is used to reduce computational complexity, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent applies segmentation by using a low-order empirical model for wind resistance (simplified) while reserving high-fidelity CFD simulation for hull drag (accurate). This segmented approach allows the overall system to maintain sufficient precision for propulsion power demand prediction while keeping the model complexity manageable through the use of simplified empirical equations where appropriate.
Data Source
AI summary
A low-order vessel propulsion power prediction method may be performed to determine factors, including power demand parameters, used in configuring a propulsion system for a marine vessel. The low-order method may receive stability data and vessel operation profile data, in addition to computational fluid dynamics simulation results to determine predicted vessel power profiles. The predicted vessel power profiles may be used to configure a powertrain system model for the marine vessel.