Methane Source Localization Using UAV Back-Trajectory Analytics
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Solution Overview
Problem
Current technologies lack efficient methods for accurately measuring and locating methane concentration sources, particularly in large areas such as natural gas production and distribution infrastructure, where methane leaks can occur.
Innovation Solution
The use of an Unmanned Aerial System (UAS) equipped with gas concentration sensors to measure methane concentrations along a flight path, combined with meteorological data and stochastic particle trajectory modeling, to determine the probability of gas source locations and generate a spatial map of methane concentration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional ground-based methods are used to measure methane concentration, then measurement cost is reduced, but measurement coverage area and detection efficiency are limited
Solution Approach 1:
The patent transitions from ground-based two-dimensional measurement to three-dimensional aerial measurement using UAVs. The gas concentration sensor is mounted on an unmanned aerial vehicle that flies at predetermined altitudes and positions, enabling volumetric sampling of the atmosphere and dramatically expanding the measurement coverage area while maintaining detection efficiency through automated flight paths and real-time data transmission to the ground control station.
2Productivity
If aerial measurement systems are deployed to increase coverage area, then detection efficiency improves, but system complexity and operational cost increase
Solution Approach 1:
The ground control station serves multiple functions: it transmits flight path information to the UAV, receives gas concentration measurements from the sensor, processes the measurement data through the back-trajectory model, generates the probability map of source locations, and displays results. This multi-functional integration reduces the need for separate specialized equipment and simplifies the overall system architecture.
Solution Approach 2:
The system uses freely available meteorological data from public sources to drive the back-trajectory model, eliminating the need for expensive dedicated weather monitoring equipment. The UAV autonomously follows predetermined flight paths and automatically transmits data to the ground control station, reducing the need for manual operation and interpretation.
3Measurement precision
If point source gas concentration measurements are collected without meteorological data integration, then data collection is simplified, but source location accuracy deteriorates
Solution Approach 1:
The back-trajectory model acts as an intermediary that connects the observed gas concentration measurements with the potential source locations. By inputting meteorological data (wind speed, wind direction, temperature, pressure) into the model, the system calculates the probable path that gas plumes would have taken to reach the measurement point, thereby identifying the most likely source locations with high accuracy.
Data Source
AI summary
Systems, devices, and methods for receiving, by a ground control station (GCS) having a processor with addressable memory, a plurality of point source gas concentration measurements; receiving, by the GCS, a meteorological data corresponding to each point source concentration gas measurement; determining, by the GCS, if each point source gas concentration measurement is an elevated ambient gas concentration; generating, by the GCS, a back trajectory for each elevated ambient gas concentration; storing, by the GCS, the position of each generated back trajectory in a grid; determining, by the GCS, a probability of a gas source location corresponding to the stored positions in the grid; and generating, by the GCS, an overlay showing the probability of the gas source location.