Ocean Surface Material Collection via Deformation Tensor Dilation Maps
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Solution Overview
Problem
Current methods for gathering materials on the ocean surface lack efficiency in directing resources to areas of higher material density due to limited forecasting capabilities, leading to suboptimal collection strategies.
Innovation Solution
Utilizing an ocean model forecast to compute deformation tensors and generate dilation maps, which predict area density changes, allowing for informed resource allocation to maximize material collection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional detection methods are used to locate material on the ocean surface, then material distribution can be identified, but resource allocation efficiency is suboptimal due to inability to predict future material density
Solution Approach 1:
The system performs preliminary computation of deformation tensors and generation of dilation maps before resource allocation decisions are made. By calculating velocity gradients and predicting future material density distributions in advance, the system enables proactive resource positioning rather than reactive response, directly improving collection efficiency
Solution Approach 2:
The dilation map serves as an intermediary information product that translates complex ocean model data and velocity gradients into actionable predictions about future material density. This intermediary representation enables efficient resource allocation by providing intuitive guidance on where materials will concentrate, bridging the gap between raw model data and operational decision-making
2Ease of operation
If resources are directed based on current material distribution only, then resource allocation is simple, but collection efficiency is reduced by missing areas of future high density
Solution Approach 1:
The system transforms the basis for resource allocation from static current distribution parameters to dynamic predicted density parameters derived from deformation tensors and dilation maps. By changing the reference parameter from present-state observations to future-state predictions, the system maintains operational simplicity while dramatically improving collection efficiency
Solution Approach 2:
The system incorporates feedback loops where dilation map predictions guide resource allocation, which then updates material distribution observations, which in turn refine future deformation tensor calculations and dilation maps. This continuous feedback mechanism enables adaptive resource allocation that maintains simplicity through automated decision rules while improving productivity through learning from observed outcomes
Data Source
AI summary
Embodiments relate to gathering materials on an ocean surface. Initially, an initial distribution of material is determined based on observational sources, and the material is represented by particles in a numerical ocean model. Trajectories for the numerical ocean model are determined based on modeled surface currents data, and velocity gradients are computed along a corresponding trajectory of the trajectories for each of the particles based on the initial distribution. At this stage, deformation tensors are computed for each of the particles based on the velocity gradients, and a dilation map for the particles is generated based on a time step tensor of the plurality of deformation tensors for each of the particles. Collection of the material is monitored based on the dilation map.