Sensor optimization for urban wind estimation with cluster-based probabilistic framework
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912615356170240 |
|---|---|
| author | Liang, Yutong Hou, Chang Maceda, Guy Y. Cornejo Ianiro, Andrea Discetti, Stefano Meilán-Vila, Andrea Sornette, Didier Lera, Sandro Claudio Chen, Jialong He, Xiaozhou Noack, Bernd R. |
| author_facet | Liang, Yutong Hou, Chang Maceda, Guy Y. Cornejo Ianiro, Andrea Discetti, Stefano Meilán-Vila, Andrea Sornette, Didier Lera, Sandro Claudio Chen, Jialong He, Xiaozhou Noack, Bernd R. |
| contents | We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulations at many wind conditions. The outputs are velocity and uncertainty estimates for a target domain and subsequent sensor optimization for minimal uncertainty. The framework has three innovations compared to traditional flow estimators. First, the algorithm scales proportionally to the domain complexity, making it suitable for flows that are too complex for any monolithic reduced-order representation. Second, the framework extrapolates beyond the training data, e.g., smaller and larger wind velocities. Last, and perhaps most importantly, the sensor location is a free input, significantly extending the vast majority of the literature. The key enablers are (1) a Reynolds number-based scaling of the flow variables, (2) a physics-based domain decomposition, (3) a cluster-based flow representation for each subdomain, (4) an information entropy correlating the subdomains, and (5) a multi-variate probability function relating sensor input and targeted velocity estimates. This framework is demonstrated using drone flight paths through a three-building cluster as a simple example. We anticipate adaptations and applications for estimating complete cities and incorporating weather input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensor optimization for urban wind estimation with cluster-based probabilistic framework Liang, Yutong Hou, Chang Maceda, Guy Y. Cornejo Ianiro, Andrea Discetti, Stefano Meilán-Vila, Andrea Sornette, Didier Lera, Sandro Claudio Chen, Jialong He, Xiaozhou Noack, Bernd R. Machine Learning Robotics Fluid Dynamics We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulations at many wind conditions. The outputs are velocity and uncertainty estimates for a target domain and subsequent sensor optimization for minimal uncertainty. The framework has three innovations compared to traditional flow estimators. First, the algorithm scales proportionally to the domain complexity, making it suitable for flows that are too complex for any monolithic reduced-order representation. Second, the framework extrapolates beyond the training data, e.g., smaller and larger wind velocities. Last, and perhaps most importantly, the sensor location is a free input, significantly extending the vast majority of the literature. The key enablers are (1) a Reynolds number-based scaling of the flow variables, (2) a physics-based domain decomposition, (3) a cluster-based flow representation for each subdomain, (4) an information entropy correlating the subdomains, and (5) a multi-variate probability function relating sensor input and targeted velocity estimates. This framework is demonstrated using drone flight paths through a three-building cluster as a simple example. We anticipate adaptations and applications for estimating complete cities and incorporating weather input. |
| title | Sensor optimization for urban wind estimation with cluster-based probabilistic framework |
| topic | Machine Learning Robotics Fluid Dynamics |
| url | https://arxiv.org/abs/2509.25222 |