Advection-Diffusion Grid Refinement for Forecast-Based Load Balancing
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Solution Overview
Problem
Current forecasting practices for advection-diffusion processes in meteorology and physical oceanography are inefficient due to the fixed or reactive nature of grid refinement, leading to high computational costs and limited precision in predicting dynamic events across wide geographic areas.
Innovation Solution
A method for targeted, iterative refinement of advection-diffusion model grid resolution based on predefined event forecasts, which dynamically allocates high-performance computing resources and adjusts grid resolution in response to significant events, ensuring optimal computational efficiency and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed or reactive grid refinement is used for forecasting advection-diffusion processes, then computational resources are allocated uniformly across the domain, but computational costs become excessively high and precision is limited in predicting dynamic events
Solution Approach 1:
The patent applies local quality by refining grid resolution dynamically in specific regions where dynamic events are detected or forecasted, rather than uniformly across the entire domain. The system identifies regions of interest based on event criteria (such as areas with significant advection-diffusion activity) and applies higher resolution only to those localized areas, thereby improving prediction precision where needed while maintaining computational efficiency in other regions.
Solution Approach 2:
The patent implements dynamics by making grid refinement adaptive and time-dependent. The grid resolution is not fixed but dynamically adjusted based on forecasted or detected dynamic events. The system continuously monitors the computational domain, identifies regions experiencing significant changes or events, and refines the grid in those areas accordingly, allowing the simulation to adapt its precision to the actual physical processes occurring at different times and locations.
2Measurement precision
If high-resolution grid refinement is applied across the entire domain, then prediction precision is improved, but computational costs increase significantly
Solution Approach 1:
The patent applies local quality by refining grid resolution dynamically in specific regions where dynamic events are detected or forecasted, rather than uniformly across the entire domain. The system identifies regions of interest based on event criteria (such as areas with significant advection-diffusion activity) and applies higher resolution only to those localized areas, thereby improving prediction precision where needed while maintaining computational efficiency in other regions.
Solution Approach 2:
The patent applies partial action by performing grid refinement only in the portions of the domain where it is actually needed - specifically in regions where dynamic events are detected or forecasted. Rather than applying full high-resolution refinement across the entire domain (excessive action), the system selectively refines only the necessary sub-regions, reducing overall computational cost while maintaining sufficient precision for predicting dynamic events.
3Ease of operation
If uniform grid resolution is used across the domain, then computational simplicity is maintained, but precision in predicting dynamic events in specific regions is limited
Solution Approach 1:
The patent implements dynamics by making grid refinement adaptive and time-dependent. The grid resolution is not fixed but dynamically adjusted based on forecasted or detected dynamic events. The system continuously monitors the computational domain, identifies regions experiencing significant changes or events, and refines the grid in those areas accordingly, allowing the simulation to adapt its precision to the actual physical processes occurring at different times and locations.
Solution Approach 2:
The patent applies feedback by using the results of event detection and forecasting to guide subsequent grid refinement decisions. The system monitors the computational domain for dynamic events, uses this information to identify regions requiring higher resolution, refines the grid in those areas, and continues monitoring to detect further events. This closed-loop feedback mechanism ensures that computational resources are dynamically allocated to regions where they are most needed for accurate event prediction.
Data Source
AI summary
A mechanism is provided for targeted, iterative refinement of advection-diffusion model grid resolution. A simulation is executed of advection-diffusion processes for an area associated with a user-defined location. Responsive to detecting an event within a subset of cells of a first set of cells, the subset of cells is refined such that each of the subset of cells is gridded to comprise a second set of cells with dimensions less than the first set of cells. A number of run-time floating-point operations per second (FLOPS) of the model is computed for each of the first set of cells and the second set of cells. Based on a collective time associated with a computed number of FLOPS failing to exceed the user-defined constraint, the simulation continues to execute with the first set of cells and the second set of cells. The process is repeated until a predetermined resolution value is met.