Prediction of Low-Altitude Turbulence for Drone Operations

Turbulence models designed for large aircrafts are not suitable for small aerial drones flying low and slow. Small drones are more sensitive to turbulence than large aircrafts, and fly in a flow field that is strongly affected by topographic and thermodynamic variations of the underlying terrain.

The goal of this research project is to advance the current understanding of near-surface turbulence in urban environments, and to derive improved predictive models for the safe design and certification of aerial vehicles.

Funding: Amazon.com Inc. - Prime Air Program

Uncertainty Quantification for Flow in Urban Areas

Computational fluid dynamics models have become highly sophisticated in terms of physical processes that they can accurately represent. Yet, their predictive capabilities still largely depend on the quality of input parameters. These include surface geometry and related material properties, which cannot be measured exactly in urban areas.

This project aims at developing an efficient framework for the quantification of uncertainty in surface model parameters, combining light detection and ranging surface data, atmospheric observations, and large-eddy simulations.

Funding: Army Research Office, Earth Materials and Processes

Characterization of Unsteady Flow for Wind Hazard Mitigation

Non-equilibrium turbulence is the rule in urban environments. Fundamental questions remain unanswered regarding the structural changes of turbulence in urban areas under unsteady flow conditions, challenging our ability to comprehend and model flow phenomena across a wide range of scenarios.

This research combines wind tunnel measurements and simulations to characterize fundamental mechanisms responsible for momentum and kinetic energy transport in unsteady turbulent flow over urban areas and associated wind loads on structures. Work is conducted in collaboration with Prof. Kurtis Gurley and Dr. Ryan Catarelli from the University of Florida.

Funding: National Science Foundation NSF-ECI-2340755

Secondary Circulations

Counter-rotating vortices are observed in the mean flow in urban canopies when the cross-stream heterogeneity in the canopy elements is of the order of the boundary layer height. These mean flow advection patterns change the flow system from a turbulent transport-dominated system to an advection-dominated system, but are not accounted for in existing surface-flux parameterizations.

This research project aims to identify the key parameters governing the structure (strength, polarity, etc.) of roughness-induced secondary circulations and develop novel surface flux parameterizations that account for advection-induced dynamics.

Hurricane Boundary-Layer Turbulence

Hurricanes account for a significant portion of damage, injury, and loss of life that is attributed to natural hazards and are the costliest natural catastrophes in the US. Yet, considerable uncertainties remain on the air-sea interaction process, which in turn affects wind loads on off-shore and on-shore structures.

This research project leverages direct and large-eddy simulations along with measurements to elucidate fundamental mechanisms supporting air-sea exchanges in hurricane boundary layer turbulence.

Funding: Computing Research Association (CRA) and National Institute for Standards and Technology (NIST)

Data-Driven Modeling of Vegetation-Airflow Interaction

Land surface models used in climate simulations to describe exchange processes between plant canopies and the atmosphere are based on simplistic phenomenological assumptions. Related errors represent a major source of uncertainty in climate model predictions.

This project aims at developing a multi-scale data-physics driven model of vegetation-airflow interaction to accurately capture exchange processes between plant canopies and the atmosphere under a range of realistic ambient conditions.

Funding: LEAP Center, Columbia University

Air-Sea Interaction

Surface wave dynamics within the air-sea boundary layer regulate exchanges of mass, momentum, and energy between the sea surface and the atmosphere – pivotal determinants for weather patterns and climate variability. Contemporary parameterizations of wind stress over the ocean have inadequately addressed the sea state’s influence on drag, neglecting key factors such as wave height, age, slope, and wind-wave alignment.

This research area comprises multiple projects aiming at developing improved sea-state-aware physics and data-based models for evaluating sea-surface drag. Work is conducted in collaboration with Prof. Zappa from Columbia and Prof. Yousefi from UT Dallas.

Funding: Office of Naval Research, Marine Meteorology and Space Weather Program and National Science Foundation CBET-2404369

Spatial Reconstruction of Turbulence via Machine Learning

Machine learning provides an attractive complement to classical physics- and math-based methods for forward and inverse problems in turbulence, given its inherent ability to learn non-linear relations between variables directly from measurements.

This research project combines machine learning with direct numerical simulations to reconstruct 3-D turbulent flow fields from sparse and possibly corrupt measurements thereof.

Funding: Data Science Institute, Columbia University

Snow Transport in Katabatic Winds

An understanding of the surface mass balance of the ice sheets is critical for predicting climate change, future sea level rise, and for interpreting ice core records. Yet, the evolution of the ice sheets through snow deposition, erosion, transport, and sublimation in katabatic winds (which are persistent across much of the Antarctic) remains poorly understood.

The goal of this project is to advance the present understanding of these processes leveraging direct numerical simulations and in-situ measurements in Antarctica.

Funding: National Science Foundation NSF-OPP-2035078

Towards a Mechanistic Epidemiological Modeling Framework

Existing epidemiological models are based on simplistic relations to determine how individuals contract an infection, and do not account for microscale processes (droplets and aerosol dispersion) delineating virus transmission opportunities.

The goal of this project is to bridge this knowledge gap by formulating the first individual-based, mechanistic epidemiological model, which captures the dynamics of pathogen-laden droplet dispersion and aerosolization under a range of ambient conditions and interactions between individuals.

Funding: Office of the Dean, The Fu Foundation School of Engineering and Applied Science, Columbia University and National Science Foundation NSF-CMMI-2218809