arXiv subjects

Daniel Quinn

Publications and source records attributed to Daniel Quinn.

3 recordsLinked to original sources

arXiv2026

Rather than drafting, vortex capture dictates efficiency in three-hydrofoil schools

Three-dimensional experiments are presented on a school of three pitching hydrofoils. Two side-by-side leader foils maintain the same relative positions while the location of a third follower foil is varied. Force and flow measurements detail the mechanisms that drive the school to achieve collective thrust and efficiency that are 58% and 24% higher than isolated foils, respectively. Traditional drafting involves positioning yourself in the wake of an upstream object. In wakes with a net momentum deficit, drafting reduces drag by lowering oncoming flow speed. By contrast, wakes from oscillatory swimmers feature strong momentum surplus regions, which increases drag by increasing the oncoming flow. Despite that, our results show that the best performance benefits occur for compact schools where the follower is directly in the vortex wake of a leader, whereas regions of reduced mean flow do not improve performance. The thrust and efficiency benefits are shown to be driven by vortex-body interactions that increase the thrust and efficiency of the follower and by body-to-body upstream interactions that reduce the power of the leaders. There is an optimal spatial phase to maximize the thrust and efficiency of the follower that depends upon the actual wake wavelength rather than the estimated wavelength used in previous literature. Moreover, wake breakdown, and its associated elimination of vortex-body performance benefits, is not observed within at least three chord lengths downstream of the leaders. Lastly, measurements of the cross-stream stability of the downstream foil indicate that compact, high-performance formations may require active control strategies in order to maintain their organization and maximise the hydrodynamic benefits of schooling.

physics.flu-dyn↗

arXiv2025

Conditioning in Generative Quantum Denoising Diffusion Models

Quantum denoising diffusion models have recently emerged as a powerful framework for generative quantum machine learning. In this work, we extend these models by introducing a conditioning mechanism that enables the generation of quantum states drawn from multiple target distributions. By sharing parameters across distinct classes of quantum states, our approach avoids the need to train separate models for each distribution. We validate our method through numerical simulations that span single-qubit generation tasks, entangled state preparation, and many-body ground state generation. Across these tasks, conditioning significantly reduced the error of targeted state generation by more than an order of magnitude. Finally, we perform an ablation study to quantify the effect of key hyperparameters on the model performance.

quant-ph↗

arXiv2018

How lovebirds fly in crosswinds based on minimal visual information

Flying birds navigate effectively through crosswinds, even when wind speeds are as high as flight speeds. What information birds use to sense crosswinds and compensate is largely unknown. We found that lovebirds can navigate 45-degree crosswinds similarly well in forest, lake, and cave-like visual environments. They navigate effectively using only a dim point light source as a beacon, despite being diurnal and raised in captivity. To maintain their heading, the lovebirds turn their bodies into the wind mid- flight, while orienting their heads towards the goal with neck angles up to 30 degrees. We show how this wind compensation can be achieved using a combination of passive aerodynamics and active control informed by muscle proprioception, a sensory input previously thought to be unimportant in detecting wind.

physics.bio-ph↗