Depth-Averaged Flow Simulation Using Parameterized Templates for Vertical Profiling
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Solution Overview
Problem
Current depth-averaged flow simulations in reservoir and basin modeling fail to accurately represent vertical variations in flow velocity and sediment concentrations, leading to significant errors due to the loss of variability in these parameters over time and space, which is crucial for simulating the geometries and properties of deposited materials.
Innovation Solution
A dynamically depth-averaged flow simulation system employing parameterized templates for depth profiling, which reconstructs 3D flow and sediment concentration fields by calculating flux and updating sediment concentrations, allowing for accurate representation of vertical variations and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full 3D fluid flow simulations are used, then accuracy of flow velocity and sediment concentration representation is improved, but computational cost becomes prohibitive
Solution Approach 1:
The 3D flow domain is segmented into vertical columns at each horizontal grid point, with depth-averaged equations solving for horizontal variations while template functions represent vertical variations. This segmentation allows the complex 3D problem to be divided into manageable 2D depth-averaged calculations combined with analytical vertical profile representations.
Solution Approach 2:
Template functions serve as intermediaries that bridge the gap between simplified depth-averaged equations and full 3D vertical variations. These templates parameterize the vertical structure of flow velocity and sediment concentration, allowing the model to capture essential 3D effects without solving complete 3D equations.
2Productivity
If 2D depth-averaged flow equations are used, then computational advantage is gained, but vertical variations in flow velocity and sediment concentrations are lost
Solution Approach 1:
The model transforms the depth-averaged variables into parameterized vertical profiles using template functions. By introducing vertical structure parameters (such as power-law exponents for velocity profiles and exponential decay parameters for sediment concentrations), the model recovers vertical variation information from the depth-averaged solutions.
Solution Approach 2:
The model effectively adds the vertical dimension back to the 2D depth-averaged equations through parameterized template functions. These templates describe vertical distributions of flow velocity and sediment concentration, allowing the model to represent 3D structures while solving only 2D equations.
3Device complexity
If reduced physics strategies with heuristic rules are used, then computational simplicity is achieved, but artifacts and numerical noise limit utility
Solution Approach 1:
The model replaces complex mechanical 3D fluid dynamics calculations with depth-averaged equations combined with analytical template functions. This substitution maintains physical realism by solving simplified but physically-based equations rather than using purely heuristic rules, thereby reducing numerical noise while preserving essential physics.
Data Source
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AI summary
Depth-averaged flow simulation systems and methods provided herein employ parameterized templates for dynamical depth profiling for at least one step of a simulation. In one illustrative computer-based embodiment, the simulation method includes, for each map point at one given time step: determining a flow template and a sediment concentration template based on depth-averaged flow velocity and depth-averaged sediment concentrations of different classes of grain size for that map point; employing the templates to construct a vertically-distributed flow velocity profile and vertically-distributed sediment concentration profiles for associated classes of grain size for that map point, thereby obtaining 3D flow velocity and 3D sediment concentration fields; using the 3D fields to calculate fluid and sediment fluxes; updating the flow velocity and sediment concentration profiles based on the divergence of the fluxes; integrating the profiles to compute updated depth-averaged flow velocity and sediment concentrations and center of gravity; and solving the depth-averaged flow equations for the next time step.