Luc Deike

Assistant Professor

Princeton University
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About

Luc Deike is an Assistant Professor at Princeton University, USA. His research focuses on air-sea gas exchange, turbulence, and ocean carbon uptake, particularly through the study of wave-induced variability and bubble dynamics. He has published extensively on topics including thin liquid films, droplet nucleation, and direct numerical simulations of interfacial flows.

Articles (27)

Year:

2025

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2025

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2025

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2025

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2025

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2025

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2025

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2025

Due to the multitude of scales present in realistic oceanic conditions, resolving the surface stress is computationally intensive, motivating modeling approaches. In this work, a dynamic wave drag model is developed for large eddy simulation (LES) to quantify the effects of multiscale dynamically rough surfaces on the atmospheric boundary layer. The waves are vertically unresolved, and the total drag due to the horizontally resolved portion of the wave spectrum is computed through a superposition of the force from each mode. As LES can only resolve the horizontal wind–wave interactions to the filter scale Δ, the effects of the horizontally unresolved, subfilter waves are modeled by specifying a roughness length scale characterizing the unresolved wave energy spectrum. This subfilter roughness is set proportional to the subfilter root mean square of the wave height distribution, and the constant of proportionality is evaluated dynamically during the simulation based on the assumption that the total drag force at the wave surface is independent of the filter scale. The dynamic approach is used to simulate the airflow over a spectrum of moving waves, and the results are validated against high-fidelity phase-resolved simulations. The dynamic approach combined with the wave spectrum drag model is then used to study flow through a fixed-bottom offshore wind farm array, equivalent to an infinite farm, with each turbine represented using an actuator disk model. The dynamic model accurately adapts to the changing velocity field and accurately predicts the mean velocity profiles and power produced from the offshore wind farm. Furthermore, the effect of the wind–wave interactions on the mean velocity profiles, power production, and kinetic energy budget is quantified.

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (10)

Leonel Romero

Assistant Professor

University of Connecticut

UNITED STATES
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Martin Erinin

Assistant Professor

University of Michigan–Ann Arbor

UNITED STATES
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Amir Pahlavan

Assistant Professor

Yale University

UNITED STATES
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Wouter Mostert

Associate Professor

University of Oxford

UNITED KINGDOM
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Aditya Aiyer

Assistant Professor

Lehigh University

UNITED STATES
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Howard A. Stone

Princeton University

UNITED STATES
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Rodney Fox

Anson Marston Distinguished Professor in Engineering

Iowa State University

UNITED STATES
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Xinan Liu

Research Professor

University of Maryland at College Park

UNITED STATES
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James H Duncan

Professor

University of Maryland

UNITED STATES
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M. E. Mueller

-

UNITED STATES
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