Andrea Ianiro

Event-based sensing offers a promising alternative to conventional frame-based measurement in turbulent flows, where intermittency, multiscale dynamics, and stringent latency constraints can make dense uniform sampling both expensive and redundant. Rather than acquiring less data, the goal is to acquire more-relevant data: measurements triggered by dynamically meaningful changes that reduce uncertainty in control-relevant flow coordinates. We leverage ideas from reduced-order modeling, sparse observability, biological mechanosensors, and event-based signal processing to outline a conceptual framework in which turbulent flows are sensed through asynchronous measurements, encoded at the hardware level, and processed by hybrid observers or neuromorphic architectures. A simple analytical argument shows that the estimation-error advantage of event-based encoding over uniform sampling scales directly with the temporal intermittency of the observable. Experimental demonstrations remain limited but encouraging. This perspective reframes the sensing bottleneck in turbulence as a problem of information relevance rather than sample count, and sets out a research agenda covering reduced-state targets, trigger policies, asynchronous observers, uncertainty quantification, and reproducible benchmarking.

We investigate the drag reduction on a heavy-duty vehicle using dielectric-barrier discharge plasma actuators located on the A-pillars. An experimental campaign is carried out on a generalized truck model, the Ground Transportation System (GTS), which is known for its lateral separation bubbles on both sides of the truck's cabin. Measurements are performed for several yaw angles up to 7.5°. Actuation is applied individually on the leeward and windward sides as well as simultaneously. Load cell measurements show that the plasma actuators effectively reduce the axial force on the GTS, with symmetric actuation achieving the highest reduction. Leeward actuation demonstrates greater control authority than the windward one; at large yaw angles the latter has a negligible effect on the axial force. Regarding side force, the leeward actuation produces a drop in its magnitude while windward actuation produces an increase. Interestingly, actuating symmetrically also augments the side force. Particle image velocimetry reveals that the plasma actuator causes a reduction in the length and width of the separation bubble on the cabin side, reducing the apparent frontal area of the truck and thus its drag. Under crosswind conditions, the stronger authority of the leeward actuator is explained by the larger separation bubble. The side force variation is driven by the net lateral suction force, which correlates with the size of the lateral recirculation regions controlled by the actuators.

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.

A method is proposed to obtain full-domain spatial modes based on proper orthogonal decomposition (POD) of particle image velocimetry (PIV) measurements taken at different (overlapping) spatial locations. This situation occurs when large domains are covered by multiple non-simultaneous measurements and yet the large-scale flow field organization is to be captured. The proposed methodology leverages the definition of POD spatial modes as eigenvectors of the spatial correlation matrix, where local measurements, even when not obtained simultaneously, provide each a portion of the latter, which is then analyzed to synthesize the full-domain spatial modes. The measurement domain coverage is found to require regions overlapping by 50–75% to yield a smooth distribution of the modes. The procedure identifies structures twice as large as each measurement patch. The technique, referred to as Patch POD, is applied to planar PIV data of a submerged jet flow where the effect of patching is simulated by splitting the original PIV data. Patch POD is then extended to 3D robotic measurement around a wall-mounted cube. The results show that the patching technique enables global modal analysis over a domain covered with a multitude of non-simultaneous measurements.