Multi-layer Turbulent Mixing Network for Ocean Depth Analysis
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Solution Overview
Problem
Current methods for analyzing turbulent mixing in oceans face challenges due to nonlinearity and instability of data, requiring significant computational power and specific prior knowledge, limiting the study of multi-scale energy mixing and real-time processing of turbulence data across full ocean depth profiles.
Innovation Solution
A method and system that construct a multi-layer turbulent mixing network using topological attributes, where nodes are formed by fusion of time series, scale data, energy local intermittency measure, and phase, allowing for the representation of physical properties of multi-scale turbulent energy and recognition of cross-scale energy transfer processes, followed by parametric calculation of turbulent mixing intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spectral flux estimation methods are used to analyze turbulent mixing, then specific prior knowledge and physical formulas can be applied, but the method requires significant computational power and specific adjustments to maintain turbulent system balance
Solution Approach 1:
The patent replaces traditional mechanical/computational spectral flux estimation methods with a topology-based analytical approach. Instead of using complex numerical simulations and spectral analysis that require significant computational power, the invention uses topological attributes of turbulent flow structures to directly estimate mixing intensity, substituting computational mechanics with topological analysis.
Solution Approach 2:
The patent changes the fundamental parameters used for turbulent mixing analysis from spectral domain parameters (requiring Fourier transforms and spectral flux calculations) to topological domain parameters (connectivity, loops, and structural attributes of flow fields). This parameter transformation simplifies the computational requirements while maintaining reliability in assessing turbulent system balance.
2Loss of information
If numerical simulation methods are used to reflect multi-scale fluid interactive energy transfer, then the process can be visualized, but the computing power required increases sharply with scenario size
Solution Approach 1:
The patent extracts the essential topological features from complex multi-scale turbulent flows, separating the critical connectivity and loop structure information from the overwhelming computational detail. By focusing only on topological attributes rather than full numerical simulation, the method captures multi-scale energy transfer processes without requiring computational resources that increase sharply with scenario size.
Solution Approach 2:
Instead of using numerical simulation to infer topological properties from detailed flow data, the patent inverts the approach by using topological analysis to directly characterize energy transfer processes. This inversion allows capturing essential multi-scale interaction information while avoiding the computational burden of full numerical simulations.
3Ease of operation
If traditional algorithms are used to detect turbulent mixing, then the process can be analyzed, but the nonlinearity and instability of turbulent data with strongly transient flow velocity and direction greatly limit the research process
Solution Approach 1:
The patent applies preliminary topological processing to turbulent flow data before detailed analysis. By first identifying and characterizing the topological structure (connectivity, loops, and persistent features) of the flow field, the method creates a stable framework that can handle the nonlinearity and transient nature of turbulent data, making subsequent analysis more reliable and easier to operate.
Solution Approach 2:
The patent creates a topological copy or representation of the complex turbulent flow field that captures its essential structural features without requiring direct manipulation of the unstable, transient velocity and direction data. This topological copy serves as a stable model for analysis, preserving the critical information while eliminating the numerical instability of the original data.
Data Source
AI summary
It is provided a method and system for calculating a turbulent mixing intensity, a computer device and a storage medium. The method comprises: performing denoising preprocess on ocean spatiotemporal coupled shear profile data information obtained by a turbulence profiler; constructing a multi-layer turbulent mixing network with topological attributes, where the network includes nodes and connecting edges; and each node is formed by fusion of four physical properties: time series, scale data, energy local intermittency measure and phase; recognizing a cross-scale transfer process of the turbulent energy according to a presence state of the connecting edges in the network; and performing parametric calculation of the turbulent mixing intensity according to the topological attributes of the multi-layer turbulent mixing network. According to the method, quantitative calculation of a turbulent energy transport intensity in an observation area of a full ocean depth profile is implemented.