Gas Plume Estimation via Multi-Point Spatial Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for estimating gas plume emissions are either overly complex due to detailed atmospheric modeling or require direct access to the leak, leading to biases and inefficiencies.
Innovation Solution
The approach involves simultaneous concentration measurements at spatially separated points to estimate the lateral spatial extent of the plume, using a time-independent gas plume model with random variables for position and width, allowing for estimation of emission rates without needing detailed atmospheric knowledge or direct access to the leak.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed atmospheric modeling is used to estimate plume emissions, then measurement precision can be improved, but device complexity increases significantly
Solution Approach 1:
The patent extracts only the essential correlation information from multi-point concentration measurements, separating the plume's spatial structure from the complex atmospheric conditions. By focusing on the correlation between measurements at different locations rather than modeling entire atmospheric flows, the solution achieves accurate plume emission estimates without requiring detailed atmospheric modeling.
Solution Approach 2:
The patent creates a simplified representation of the plume's spatial structure by measuring concentration correlations at multiple fixed points. Instead of directly modeling the complex atmospheric plume dynamics, the method copies the essential spatial correlation pattern from the measurements and uses this simplified pattern to estimate emissions, avoiding the need for complex atmospheric models.
2Measurement precision
If multi-point measurements combined with detailed atmospheric modeling are used, then plume emission estimates can be obtained, but the approach becomes labor intensive and complex
Solution Approach 1:
The patent extracts only the essential correlation information from multi-point concentration measurements, separating the plume's spatial structure from the complex atmospheric conditions. By focusing on the correlation between measurements at different locations rather than modeling entire atmospheric flows, the solution achieves accurate plume emission estimates without requiring detailed atmospheric modeling.
Solution Approach 2:
The patent replaces complex atmospheric modeling computations with a simpler statistical correlation analysis of concentration measurements. Instead of solving complex fluid dynamics equations to model plume behavior, the method uses correlation statistics from fixed-point measurements to infer plume emission rates, significantly improving computational efficiency.
3Measurement precision
If direct measurement of emission rate by enclosing the leak is used, then measurement precision is improved, but ease of operation deteriorates due to physical access requirements
Solution Approach 1:
The patent introduces fixed concentration measurement points as intermediaries between the leak source and the analyst. Instead of requiring direct physical access to enclose the leak, the method uses these intermediary measurement points to infer plume emission rates through correlation analysis, maintaining measurement precision while eliminating the need for direct leak access.
Solution Approach 2:
The patent creates a simplified representation of the plume's spatial structure by measuring concentration correlations at multiple fixed points. Instead of directly modeling the complex atmospheric plume dynamics, the method copies the essential spatial correlation pattern from the measurements and uses this simplified pattern to estimate emissions, avoiding the need for complex atmospheric models.
Data Source
AI summary
Repeated simultaneous concentration measurements at spatially separated points are used to provide information on the lateral spatial extent of a gas plume. More specifically the spatial correlations in this data provide this information. Fitting a gas plume model directly to this multi-point data can provide good estimates of total plume emission. The distance between the plume source and the measurement points does not need to be known to provide these estimates. It is also not necessary to perform any detailed atmospheric modeling.


