Airborne Hazard Evaluation System Using Distributed Sensor Modules
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Solution Overview
Problem
Existing systems fail to accurately and rapidly diagnose real-time wind flow and the spread of airborne hazards, leading to inadequate protection measures for soldiers and civilians in emergency situations.
Innovation Solution
A system comprising distributed sensor modules that collect weather data on wind speed, direction, temperature, humidity, and barometric pressure, connected through a network to a model module for real-time wind flow calculations, and a display module for visualizing wind conditions and plume impact, utilizing models like 3DWF and ALOHA, with quality control and archiving capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to predict airborne hazard spread, then system complexity is reduced, but accuracy and speed of diagnosis deteriorate
Solution Approach 1:
The system divides the monitoring area into multiple zones with distributed sensor modules, each independently measuring local weather conditions. This segmentation allows parallel data collection across the area, improving both accuracy through multiple measurement points and managing complexity by modularizing the system architecture.
Solution Approach 2:
A central processing system acts as an intermediary that receives data from distributed sensor modules, integrates the information, and generates comprehensive wind flow and hazard spread predictions. This intermediary approach enables accurate diagnosis by consolidating data from multiple sources while keeping individual sensor modules simple.
2Speed
If real-time sensor data collection is implemented across the area, then speed of assessment is improved, but device complexity increases
Solution Approach 1:
The system uses multiple distributed sensor modules that independently collect local weather data simultaneously. This segmentation enables parallel data acquisition across the entire monitoring area, dramatically improving assessment speed while keeping each individual sensor module relatively simple in design.
Solution Approach 2:
Each sensor module is designed as a universal, multi-functional unit capable of measuring multiple weather parameters (wind speed, wind direction, temperature, humidity, barometric pressure). This universality allows the system to achieve comprehensive real-time monitoring through standardized modules, improving speed without proportionally increasing complexity.
3Measurement precision
If multiple sensor modules are distributed throughout the area, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The monitoring area is divided into multiple zones with sensor modules distributed throughout, with each module providing precise local measurements. This spatial segmentation improves overall measurement precision by capturing local variations in weather conditions while managing system complexity through modular, repeatable sensor units.
Solution Approach 2:
The system measures multiple atmospheric parameters (wind speed, wind direction, temperature, humidity, barometric pressure) simultaneously at each sensor location. By changing from single-parameter to multi-parameter measurement at each distributed point, the system achieves comprehensive precision improvement without requiring a proportional increase in the number of sensor modules.
Data Source
AI summary
A system to evaluate airborne hazards having at least one sensor module which detects atmospheric conditions and generates output signals representative of those atmospheric conditions. A model module receives the output from the sensor and generates a model output signal representative of a calculated wind flow and plume footprint, when applicable, over an area of interest. A display module receives the model output signal and visually displays the calculated wind flow and its effect on a plume if present in near real-time. The final system output is provided to authorized end users in near real-time.