Real-Time Wave Forecasting With IMU Sensor Fusion
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Solution Overview
Problem
Current deterministic sea wave prediction methods are limited by their inability to accurately and efficiently predict ocean waves in real-time, particularly in complex and dynamic marine environments, due to reliance on multiple probes and lack of real-time capability, which hinders the optimization of marine renewable energy technologies and safe marine operations.
Innovation Solution
A wave forecasting framework that utilizes XYZ motion data from a reduced number of measurement probes, combines sensor measurements through fusion approaches, and employs physics-based models for wave propagation, enabling continuous and accurate prediction of wave properties at target locations within seconds to minutes into the future.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple probes are used to accurately identify directional wave components, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines the functions of multiple probes into a single probe equipped with an inertial measurement unit (IMU) that can measure three-dimensional acceleration. This single probe integrates the capabilities of multiple separate measurement devices, reducing system complexity while maintaining the ability to identify directional wave components through sensor fusion algorithms that process acceleration data from multiple axes.
Solution Approach 2:
The patent replaces the mechanical system of multiple physical probes with a single probe using inertial sensors and computational algorithms. Instead of using multiple mechanical measurement points, the system uses an IMU to capture motion data and applies signal processing techniques to reconstruct wave characteristics, substituting physical complexity with computational complexity.
2Measurement precision
If physics-based wave propagation models are used to predict wave evolution, then prediction accuracy is improved, but computational time increases
Solution Approach 1:
The patent transforms the wave prediction problem from solving complex physics-based partial differential equations to using a spectral representation approach. By representing waves as a sum of sinusoidal components with specific frequencies, directions, and amplitudes, the system changes the mathematical parameters from spatial-gridded physical fields to spectral coefficients, enabling faster computation while maintaining prediction accuracy.
Solution Approach 2:
The patent discards the computationally intensive physics-based wave propagation models and recovers the essential wave prediction capability through a simplified spectral model. The system identifies wave parameters (frequency, direction, amplitude) from measurements and uses these to reconstruct and predict wave surfaces, recovering the predictive function without the computational burden of full physics-based models.
3Productivity
If real-time wave prediction is implemented, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary identification of wave parameters (spectral decomposition into frequency, direction, and amplitude components) from measurement data before prediction is needed. By pre-processing the raw acceleration data into meaningful wave characteristics, the system prepares the information in advance, enabling rapid real-time prediction without complex computations during the actual prediction phase.
Solution Approach 2:
The patent segments the wave field into discrete spectral components, each characterized by specific frequency, direction, and amplitude. This segmentation allows the complex wave field to be represented as a sum of simpler sinusoidal components, making the prediction process more manageable and computationally efficient while maintaining accuracy.
Data Source
AI summary
A method and system for prediction of wave properties include collecting time-series data streams from one or more wave measurement devices and processing the data to identify data parameters to establish boundary conditions of a numerical model. The numerical model may be used to compute a predicted wave field of time-series data for a variety of wave properties at a target location.