Wind Profile Model Accuracy for Trace Gas Detection
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Solution Overview
Problem
Current technologies face challenges in accurately detecting and quantifying trace gas emissions, particularly in complex environments where wind measurements vary significantly with altitude.
Innovation Solution
The system and method involve adjusting parameters in a wind model to account for differences between actual wind measurements and modeled wind speeds using an unmanned aerial vehicle (UAV) equipped with a trace gas sensor. This is achieved by generating multiple wind models based on key parameters and secondary wind measurements from various sources, and then iteratively refining these models to minimize errors across different altitudes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single wind model is used for trace gas detection, then the device complexity is low, but the measurement precision deteriorates due to altitude variations in wind speed
Solution Approach 1:
The patent divides the atmospheric boundary layer into multiple altitude segments, each with its own wind model parameters. By segmenting the vertical profile into distinct layers (surface layer, elevated layer, etc.), the system can apply different modeling approaches to each segment, improving overall measurement precision while managing complexity through modular parameter adjustment.
Solution Approach 2:
The wind model parameters are made dynamic rather than static, allowing continuous adjustment based on measured wind speeds at different altitudes. The system dynamically updates parameters such as surface roughness length and displacement height to match observed conditions, enabling the model to adapt to varying atmospheric stability and wind regimes.
2Measurement precision
If wind model parameters are adjusted for multiple altitudes, then the measurement precision improves, but the ease of operation deteriorates due to iterative optimization requirements
Solution Approach 1:
The system implements feedback loops where measured wind speeds from UAVs and stationary anemometers are continuously compared against model predictions. The discrepancies feed back into the optimization algorithm, which automatically adjusts parameters to minimize errors. This closed-loop feedback mechanism maintains high measurement precision while automating the parameter adjustment process.
Solution Approach 2:
The wind model performs self-calibration through automated optimization algorithms that adjust parameters without manual intervention. The system uses its own measurement data to iteratively refine parameters, effectively serving itself in the parameter tuning process. This self-service capability eliminates the need for operators to manually adjust complex parameters.
3Reliability
If multiple wind models are generated and compared, then the reliability of emission measurements improves, but the loss of time increases due to iterative optimization processes
Solution Approach 1:
The system performs preliminary actions by pre-calculating wind model parameters during periods when UAVs are not actively measuring trace gases. Background wind profiles are established in advance, and parameter optimization is performed during idle periods or using historical data. This preliminary preparation reduces the time required during actual emission measurement events while maintaining reliability.
Solution Approach 2:
The wind modeling process operates continuously rather than in discrete batches. Multiple wind models are generated and compared in real-time as UAVs collect data, with parameter optimization occurring continuously throughout the measurement campaign. This continuous operation maximizes the use of available data and reduces total processing time while improving reliability through ongoing validation.
Data Source
AI summary
Systems, devices, and methods for generating a first wind model, wherein the first wind model is based on at least one or more key parameters; generating a second wind model, wherein the second wind model is based on a secondary wind measurement device, from at least one of: a second stationary anemometer, an aerial-based data from an onboard anemometer, a control-system derived wind vector during a flight of an unmanned aerial vehicle, and a third-party meteorological data service; adjusting the second wind model based on a comparison of two or more altitudes; and adjusting the one or more key parameters to achieve a solution convergence, where the solution convergence is achieved when at least one of: a determined error between a received wind data and the second wind model is minimized to within an accepted tolerance range and a number of minimization attempts exceeds a threshold.