Tidal Front Estimation via Numerical Mixing Discriminant

The patent creates a numerical model that copies and simulates the physical ocean environment, allowing virtual observation of tidal front characteristics without actual field measurements. The model replicates water temperature distribution, current velocity, and mixing conditions to identify tidal fronts computationally, eliminating the need for time-consuming in-situ temperature profiling.

The patent replaces the mechanical/physical observation system (temperature sensors, CTD instruments, and manual sampling) with a computational numerical model. The model uses governing equations of fluid dynamics and heat transfer to simulate ocean conditions, substituting physical measurement infrastructure with mathematical computation to achieve the same identification goal.

The numerical model creates a virtual copy of the ocean environment, allowing repeated simulations and analyses without the need for expensive field expeditions, specialized equipment, and trained personnel required for actual temperature observations. The model can be executed multiple times with different parameters at minimal computational cost.

The patent employs a computational model that can be executed repeatedly at very low cost compared to field observations. The numerical simulations use standard computer hardware and mathematical algorithms, representing a form of disposable, low-cost analysis tool that can be run as many times as needed without incurring additional observational costs.

The patent substitutes complex physical observation systems with a numerical model that solves governing equations computationally. The model incorporates conservation of mass, momentum, and energy equations along with boundary conditions to simulate ocean mixing processes, providing reliable identification without requiring complex in-situ measurement infrastructure.

The numerical model acts as an intermediary between the complex physical ocean processes and the analysis requirements. It mediates by translating physical conditions (current velocity, water depth, friction coefficients) into simulated temperature distributions and mixing characteristics, simplifying the observation process while maintaining reliability through physically-based equations.