CFD Wind Models for UAS Bridge Inspection Safety
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Solution Overview
Problem
Unmanned Aerial Systems (UAS) face challenges during bridge inspections due to limited visibility, weakened wireless signals, and safety concerns related to wind turbulence and GPS signal obstruction.
Innovation Solution
Generating computational fluid dynamics (CFD) models of bridge structures and surrounding areas to provide UAS operators with 3D models and wind flow data, enabling improved navigation and hazard identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UAS operators rely on GPS navigation during bridge inspections, then navigation accuracy is improved, but GPS signal obstruction by bridge structures causes loss of positioning information
Solution Approach 1:
The patent introduces CFD wind flow models as an intermediary information source to compensate for lost GPS positioning data. When GPS signals are obstructed by bridge structures, the pre-computed wind flow models provide alternative navigational reference data, allowing operators to maintain accurate positioning through wind pattern recognition and model-based prediction rather than direct GPS signals.
Solution Approach 2:
The patent performs CFD simulations and generates wind flow models in advance before actual bridge inspection operations. This preliminary action creates a database of expected wind patterns and flow characteristics around the bridge structure, which can be referenced during GPS-denied operations to maintain navigation accuracy without real-time external signals.
2Reliability
If UAS operators gain more training experience to handle challenging bridge inspection conditions, then operational confidence and safety are improved, but training time and resource requirements increase
Solution Approach 1:
The patent creates virtual copies of bridge structures with associated CFD wind flow models that replicate real-world conditions. These digital twins serve as training environments where operators can practice bridge inspections without requiring extensive real-world flight time, thereby reducing training duration while maintaining safety standards through realistic scenario simulation.
3Adaptability or versatility
If UAS systems operate in areas with significant wind turbulence and shear around bridge structures, then inspection capability is improved, but wind velocity changes and shear locations create navigation challenges
Solution Approach 1:
The patent implements a feedback mechanism where pre-computed CFD wind flow models provide continuous reference information about expected wind conditions at different locations around the bridge. During operations, operators can compare actual sensor readings with model predictions to detect changes in wind velocity and shear locations, enabling proactive navigation adjustments and maintaining operational stability despite turbulent conditions.
Data Source
AI summary
Methods of generating computational models representative of bridges include receiving a first user input representative of a bridge, receiving a second user input representative of the bridge, generating a three-dimensional (3D) bridge model based upon the second user input, generating a computation fluid dynamics (CFD) model representative of an area surrounding the bridge, and performing a CFD analysis on the mesh model to generate output results. The first user input includes a bridge type selection from a plurality of stored bridge types. Each of the plurality of stored bridge types correlates to a plurality of bridge parameters. The second user input includes a plurality of bridge parameters correlating to the first user input.