Data-Driven Modeling for On-Demand Flow Prescription in Fan-Array Wind Tunnels
Alejandro A. Stefan-Zavala, Isabel Scherl, Ioannis Mandralis, Steven, L. Brunton, Morteza Gharib

TL;DR
This paper develops a surrogate linear model to predict and control flow profiles in fan-array wind tunnels, enabling more precise and constrained flow design in complex turbulent environments.
Contribution
It introduces a linear surrogate model for fan-array flow prediction and demonstrates its use in an open-loop control scheme for flow profile design.
Findings
Model achieved a mean prediction error of 1.02 m/s.
Control scheme had a mean tracking error of 1.05 m/s.
Physics are dominated by linear dynamics for constant fan speeds.
Abstract
Fan-array wind tunnels are an emerging technology to design bespoke wind fields through grids of individually controllable fans. This design is especially suited for the turbulent, dynamic, non-uniform flow conditions found close to the ground, and has enabled applications from entomology to flight on Mars. However, due to the high dimensionality of fan-array actuation and the complexity of unsteady fluid flow, the physics of fan arrays are not fully characterized, making it difficult to prescribe arbitrary flow fields. Accessing the full capability of fan arrays requires resolving the map from time-varying grids of fan speeds to three-dimensional unsteady flow fields, which remains an open problem. This map is unfeasible to span in a single study, but it can be partitioned and studied in subsets. In this paper, we study the special case of constant fan-speeds and time-averaged…
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Taxonomy
TopicsTraffic Prediction and Management Techniques · Simulation Techniques and Applications · Vehicle emissions and performance
