Intelligently Linking Sensor Data and Simulation
The CAURUS Technologies sensor platform is mounted above the extinguishing water bucket and uses HD and infrared cameras as well as position sensors to provide real-time data on the fire situation. The data can be georeferenced, i.e., localized on a map, thus providing a comprehensive picture of the fire situation. Furthermore, the sensor data will now be used in the »Forest Shield« project to provide real-time predictions on suppressant drops. This enables response management to see the precise benefit from each drop.
However, many datasets are needed to quickly and correctly train the prediction systems for the complex aerial water drops. And this is precisely where the MESHFREE simulation software comes into play. The sensor and image data from the CAURUS sensor platform enable the creation of a digital twin of the drop area and to precisely simulate and analyze water drops with simulation models. The MESHFREE software generates physically accurate simulations by incorporating climatic factors such as wind and forest structure in the planning process. The ability to subsequently modify drop conditions and other parameters and to process alternative scenarios provides added value. This generates significantly more information from each deployment, directly contributing to improving predictive models in the field.
»The real-time forecasting system is based on the simulation data from our MESHFREE software. MESHFREE simulates the path of the water droplets from the bucket to the fire on the ground, accounting for all environmental factors«, explains Fraunhofer ITWM research scientist Isabel Michel. Machine learning surrogate models, i.e., simplified models that approximate complex simulations and run significantly faster, learn from simulation data and statistically capture the complex aerial drop dynamics. These provide rapid predictions directly at the deployment site. The recorded real-world drop data is then fed back into the simulation system, continuously improving the predictive models. This results in a cloud/edge system that continuously learns and refines its predictions on each new deployment.