Trung Le

Associate Professor, Department of Civil, Construction and Environmental Engineering
North Dakota State University

The impact of ice on momentum transfer in rivers of cold regions

January 15, 2026

River ice is a common phenomenon in cold regions during winter. Its presence alters hydrodynamics of rivers from the ice boundary to the river bed. In this project, we developed new methodologies to study ice-covered flows in small and medium-sized rivers using Unmanned Aerial Vehicle (UAV), pointwise measurements, and Large Eddy Simulation. First, we developed a theoretical model for the lateral momentum transfer and depth-averaged velocity profiles in ice-covered rivers using the depth-integrated Reynolds-Averaged Navier–Stokes equations. Second, a series of field measurement campaigns (Acoustic Doppler Current Profiler- ADCP and Acoustic Doppler Velocimetry) were conducted to validate our theoretical model in the Red River of the North in Fargo, North Dakota and Buffalo River, Minnesota in United States under fully ice-coverage from 2020 to 2025. Our results show that secondary flows and Reynolds stresses both contribute to the lateral momentum load. Also, the ice cover suppresses the development of coherent secondary cells. A term-by-term analysis of streamwise momentum budget demonstrates that lateral gradients of momentum load are closely tied to variations in bed shear stress, highlighting the coupling between momentum load and near-bed dynamics. Finally, a Large-Eddy Simulation is carried out with inputs from field measurements for the Red River reach. Our results show a significant impact of ice cover in changing the spatial arrangement of the turbulent statistics and the secondary flow structures. Specifically, the Turbulent Kinetic Energy is altered significantly near the bend apex as the result of the interaction between the turbulent jet and the ice cover in regions near both banks. Our results suggest that ice coverage promote the redistribution of momentum and sustain turbulence near banks. Our work reveals a hidden role of ice coverage in moderating the bed shear stresses near river banks in winter. This work proposes a fundamental framework to study river ice dynamics and also provide practical tools for monitoring and predicting flow profiles under ice conditions in rivers of cold regions, which are under pressure of a changing cryosphere globally. This study is supported by National Science Foundation (NSF) CAREER Award No. 2239799.

Biosketch

Dr. Trung Le is an Associate Professor at the Department of Civil, Construction and Environmental Engineering at North Dakota State University, United States. His research interest focuses on computational methods and data analytics for complex flows in bioengineering and environmental problems. He received a number of national and international awards including the Gallery of Fluid Motion (American Physical Society Division of Fluid Dynamics) and National Science Foundation CAREER Award.

Kelly Huang

Assistant Professor, Department of Mechanical and Aerospace Engineering
University of Houston

Measuring, modeling, and mimicking atmospheric turbulent processes

January 22, 2026

Atmospheric turbulence is at the core of many weather phenomena such as rain, fog, storms, and tornadoes, but despite its ubiquity, the role of turbulence in these atmospheric surface layer (ASL) processes remains unclear, largely due to the complexity of the ASL and the wide range of temporal and spatial scales present. Resolving all these scales continues to be a daunting challenge to field and laboratory settings and to numerical simulations. To address this issue, I present measurement platforms and experimental facilities that are tailored for capturing, replicating, and modeling atmospheric turbulent processes. First, I detail a unique and economically scalable measurement system leveraging nano-scale sensors that was deployed over the salt flats of Utah, and show that the resulting data allowed for an examination of high Reynolds number turbulent boundary layer processes. Then, I show that relevant fog–turbulence interaction mechanisms are uncovered by the "super combo probe,"" where simultaneous velocity, temperature, and droplet size measurements were made down to the microscales.

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Dr. Kelly Huang is the Kalsi Assistant Professor of Mechanical and Aerospace Engineering at the University of Houston. She received her B.S. in Mechanical Engineering from Cornell University, and her M.A. and Ph.D. in Mechanical and Aerospace Engineering from Princeton University. Prior to arriving at UH, she completed a postdoctoral appointment at the University of Notre Dame in the department of Civil and Environmental Engineering. Her research focuses on the turbulent processes that drive the atmospheric surface layer and the development of novel and high-resolution sensing techniques.

Cong Wang

Assistant Professor, Department of Mechanical Engineering
University of Iowa

Physics and control of surface-piercing turbulent wake flow

January 29, 2026

Turbulent free-surface wakes generated by surface-piercing bodies during transient maneuvers represent a technically complex and operationally critical domain in naval hydrodynamics. The unsteady transport of mass, momentum, and energy across the dynamic air–water interface gives rise to highly anisotropic, vortical flows characterized by inherent instabilities that significantly impact the performance and safety of naval platforms. In this study, we characterize the high unsteady and 3D turbulent wake flows of representative surface-piercing bluff bodies using a combination of qualitative and quantitative techniques, including fluorescent dye visualization, synchronized multi-plane Particle Image velocimetry (PIV), and volumetric 3D defocusing PIV. It was found that free-surface wake flows are stabilized at high Froude numbers, with the canonical vortex shedding process suppressed. Consequently, the near-surface wake exhibits reduced mixing and momentum transfer process. In addition, the shedding vortices exhibit a strong spatio-temporal misalignment, causing depth-varying dynamic loadings on the surface-piercing structure. Based on the discoveries, we apply surface-distributed fluid actuators to stabilize the coupled fluid–structure system. The finding results lay the foundation for high-fidelity modeling of anisotropic turbulent shear flows and future intelligent naval systems.

Biosketch

Dr. Cong Wang is currently assistant professor in the Department of Mechanical Engineering at the University of Iowa. Before joining the University of Iowa, he was postdoc associate and research scientist at Caltech. Dr. Wang received his B.Eng. degree in Engineering Science from the National University of Singapore in 2013, and his M.S. and Ph.D. degrees in Aeronautics from Caltech in 2014 and 2019. His current research interests lie in the general areas of physics and control of turbulent multi-phase flow, as well as developing advanced flow diagnosis techniques. He is a recipient of the Ernest E. Sechler Memorial Award in Aeronautics in 2018 and the Donald Coles Prize in Aeronautics in 2019 from Caltech.

Fotis Sotiropoulos

Executive Vice President and Provost
Professor, Department of Mechanical Engineering
Pennsylvania State University

The evolution of computational fluid dynamics: High-fidelity simulation, digital twins, and the rise of AI

February 5, 2026

Over the past twenty years, computational fluid dynamics (CFD) has evolved from a discipline focused on idealized configurations to a powerful, predictive tool for tackling real-world problems of societal importance. This seminar presents an overview of my research program during this period, using it to trace the broader evolution of CFD from early high-fidelity simulations to today's emerging era of AI-augmented modeling.

I will begin by describing foundational work on numerical methods for turbulent flows in complex geometries, which led to the development of the Virtual Flow Simulator (VFS)—a high-fidelity framework based on immersed boundary methods, large-eddy simulation, and multi-physics coupling. These advances enabled predictive simulations of fluid–structure interaction, free-surface and multiphase flows, and coupled hydro-morphodynamics in applications ranging from aquatic locomotion and cardiovascular flows to river flooding, sediment transport, and wind and marine energy systems.

As simulations grew in scale and complexity, the central challenge shifted from accuracy alone to computational efficiency and scalability. I will highlight recent efforts that integrate machine learning with physics-based LES to dramatically reduce computational cost while retaining near-LES accuracy. These AI-augmented approaches point toward a new paradigm for CFD—one that combines first-principles modeling, high-performance computing, and data-driven methods to enable digital twins for engineering design, optimization, and control.

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Dr. Fotis Sotiropoulos is the Executive Vice President and Provost of The Pennsylvania State University. Reporting directly to the President, Dr. Sotiropoulos serves as the University’s chief academic officer, overseeing all colleges, campuses, and academic support units. He works closely with university leadership, faculty, staff, and students to set academic priorities and advance Penn State's mission of excellence in education, research, and community impact.

Dr. Sotiropoulos most recently served as Provost and Senior Vice President for Academic Affairs at Virginia Commonwealth University (2021–2025). He previously held several leadership and faculty positions, including: Interim Provost and Senior Vice President for Academic Affairs (2020–2021), Dean of the College of Engineering and Applied Sciences and SUNY Distinguished Professor (2015–2021) at Stony Brook University; James L. Record Professor of Civil Engineering and Director of the St. Anthony Falls Laboratory at the University of Minnesota, Twin Cities (2006–2015); and Faculty member in the School of Civil and Environmental Engineering, with a joint appointment in the G. W. Woodruff School of Mechanical Engineering, at the Georgia Institute of Technology (1995–2005).

An internationally recognized scholar in fluid mechanics, Dr. Sotiropoulos's research focuses on simulation-based engineering science for addressing complex, societally relevant problems in renewable energy, environmental hydrodynamics, human health, and biological systems. He has authored over 220 peer-reviewed journal articles and book chapters, with a Google Scholar h-index of 77, and his work has been featured on the covers of several leading scientific journals.

His honors include the American Society of Mechanical Engineers (ASME) Fluids Engineering Award (2023), the American Geophysical Union (AGU) Borland Lecture Hydrology Days Award (2019), the American Society of Civil Engineers (ASCE) Hunter Rouse Hydraulic Engineering Award (2017), and a CAREER Award from the U.S. National Science Foundation. Dr. Sotiropoulos is an elected Fellow of both the American Physical Society and ASME, and his research has twice been recognized in the APS Division of Fluid Dynamics Gallery of Fluid Motion (2009, 2011).

John Brigham

Professor, Department of Civil and Environmental Engineering
University of Pittsburgh

Computational approaches to evaluate and design wellbore fluid processes for zonal isolation

February 12, 2026

Properly cementing new oil and gas wells and plugging unprofitable and unproductive abandoned wells is critical for environmental protection and enhances overall operational efficiency, while preventing interference with active operations through fluid migration. As a result, considerable work has focused on improving the placement and performance of isolation materials, such as cement slurries and clay-based materials (e.g., bentonite clay gel). However, the success of these operations is often affected by complex fluid behaviors, material variability, and operational uncertainties that are difficult to capture using current toolsets.

This talk will present strategies actively being investigated to aid in the development of materials and fluid placement processes to ensure effective wellbore zonal isolation. These strategies primarily utilize the Lattice Boltzmann Method, a mesoscale statistical mechanics approach, that is well suited for modeling multi-component, multi-phase, and non-Newtonian fluid processes with complex boundary conditions. Examples will also be provided, demonstrating the applicability and effectiveness of these strategies to evaluate isolation fluid placement and gas migration processes within wellbore systems, and the potential for use within a design optimization framework.

Biosketch

Dr. John Brigham received a B.E. from Vanderbilt University and an M.S. and Ph.D. from Cornell University. He joined the University of Pittsburgh in 2008 in the Department of Civil and Environmental Engineering and with a secondary appointment in the Department of Bioengineering. John then departed Pitt to join the Department of Engineering at Durham University in the United Kingdom in 2016. John was subsequently appointed as a Deputy Executive Dean for the Faculty of Science overseeing the 8 Departments of Science and Engineering at Durham, but decided to return to Pitt in the Fall of 2020. John's Computational Diagnostics & Inverse Mechanics (CDIM) research group maintains the overall objective of the prediction of life in the broadest sense, encompassing manmade, natural, and biological phenomena. The CDIM group has been and continues to be actively involved in a number of projects covering a diverse array of applications, including novel design concepts and optimal design strategies for adaptive structures, evaluation of wellbore cement integrity, efficient and effective computational nondestructive material characterization algorithms, reduced-order modeling for simulating multi-physics behaviors, and shape and kinematic analysis of medical imaging data for disease diagnosis.

Parvin Bayati

Assistant Research Professor, Department of Chemistry
Pennsylvania State University

Physics-informed neural networks (PINNs) reveal non-uniform slip velocity in active colloids

February 19, 2026

The motion of active colloidal particles originates from non-uniform surface properties, such as spatial variations in phoretic activity or interfacial tension, which remain difficult to measure directly in experiments. We present a physics-informed neural network (PINN) framework for inferring these hidden surface characteristics from indirect observations of the surrounding flow field. Unlike purely data-driven approaches, our method combines experimental or simulated velocity data with the Stokes equations and boundary conditions, ensuring physically consistent predictions even with sparse velocity data. We validate the approach on fundamental flow problems, where the model recovers velocity and pressure fields in excellent agreement with analytical solutions, even beyond the training region. We further demonstrate the framework on squirmer micro-swimmer and self-phoretic Janus micromotors models, showing that the PINN accurately recovers slip velocities. By enabling the estimation of slip velocity from flow data, this work provides a pathway toward programmable colloidal dynamics and microscale fluid control, with potential applications in microfluidics, drug delivery, and active matter engineering.

Biosketch

Dr. Parvin Bayati is a Assistant Research Professor in the Department of Chemistry at Pennsylvania State University, where she initially joined as a postdoctoral researcher in 2022. Her current research focuses on physics of soft and active matter systems. Prior to her time at Penn State, she held a postdoctoral position at Paris-Saclay University in France and received a postdoctoral fellowship from the Iranian National Science Foundation. Her academic background includes a Guest Researcher Fellowship at the Max Planck Institute in Germany, as well as a Ph.D. and bachelor's degree in physics from the University of Zanjan and a master's degree from the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran.

Robert Martinuzzi

Professor, Department of Mechanical and Manufacturing Engineering
University of Calgary

Towards modelling scale interactions in wake-induced laminar-turbulent transition in separating boundary layers

February 26, 2026

Wake-induced laminar-to-turbulent bypass transition in a separated laminar boundary layer (SLB) is investigated downstream of a cylinder of diameter D mounted in a constant free stream near a smooth flat plate. Direct numerical simulation (DNS) is conducted for a moderate gap of 0.9D with Re_D = 3900 and Re_θ = 150, based on the momentum thickness. The transition process is driven by coherent vortex dynamics along the wall synchronized with Kármán shedding behind the cylinder. The transition process is identified from the mean velocity and Reynolds stress fields and then characterized from the spatial evolution of the velocity field spectra. Turbulence emerges at such remarkably low Re_θ because the transition process follows a hybrid pathway combining SLB instability with wake-driven near-wall Λ-vortex formation and their interaction with periodically shed vortices. The transition unfolds in distinct stages: linear disturbance amplification; nonlinear saturation via super-harmonic resonance; and turbulent breakdown. Unlike classical SLB transitions, the wake's periodicity imposes unique spectral signatures on the transition dynamics. An approach based on weakly nonlinear theory in proposed to investigate the spectral energy transfer between scales. A combination of orthogonal wavelet decomposition and proper orthogonal decomposition (WPOD) is introduced for separating scales. The approach is effective in separating linear amplification of instabilities due to linear processes and isolating triadic interactions responsible for spectral redistribution. The findings characterize wake-boundary interactions inherent to turbomachinery cascades or slotted-wing aerodynamic systems, offering insights for potential flow control strategies.

Biosketch

Dr.-Ing. Robert Martinuzzi is a Fellow of the ASME, a Pratt & Whitney Canada Research Fellow and Professor of Fluid Mechanics at the University of Calgary (Canada). He is an expert in experimental fluid mechanics, optical diagnostics, bluff-body aerodynamics and separated, turbulent flows. His fundamental work addresses rapidly evolving turbulence in wakes and separated flows. His research addresses reduced-order dynamical models, low-dimensional system representations, nonlinear dynamics, data-driven sensor-based estimation and flow control. Over the last 30 years, industry applications of his research include: high-speed compressor aerodynamics, flow-induced vibrations in exposed pipelines and on aircraft components, and propulsion systems. Dr. Martinuzzi is ditor-in-Chief of the International Journal of Heat & Fluid Flow and Associate Editor of the Journal of Turbulence.

Rana Zakerzadeh

Associate Professor, Department of Biomedical Engineering
Duquesne University

Multiphysics fluid–structure interactions in biofluids: From cardiovascular flow to phonation

March 19, 2026

Fluid–structure interactions (FSI) play a central role in the mechanics and function of many biofluid systems. This talk focuses on the development and application of predictive computational models to investigate the coupled behavior of biological flows and deformable tissues, highlighting how tissue mechanics, fluid dynamics, and transport processes interact in both healthy and pathological states. Many soft tissues also exhibit poroelastic behavior due to their fluid-saturated microstructure. Incorporating porous media mechanics and mass transport into FSI frameworks enables the study of oxygenation, perfusion, and interstitial flow as functional indicators of tissue health.

These modeling tools have been applied to two primary biofluid applications: cardiovascular flow and vocal fold dynamics. In the cardiovascular context, we have examined conditions such as supravalvular aortic stenosis and abdominal aortic aneurysms, incorporating realistic vessel mechanics and permeable tissue effects. In parallel, we have developed biologically informed models of phonation to investigate the interaction between glottal airflow and deformable vocal fold tissue, including the impact of lesions, hydration, and oxygen transport on vibration and voice function. This talk will present the computational frameworks underlying these studies, highlight selected findings, and discuss emerging directions in multiphysics FSI modeling of biofluid systems, with a focus on how tissue alterations impact cardiovascular and phonation mechanics.

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Dr. Rana Zakerzadeh is an Associate Professor in the Biomedical Engineering Department at Duquesne University. She received her Ph.D. in Mechanical Engineering from the University of Pittsburgh in 2016 and was a Postdoctoral Fellow at the Oden Institute, The University of Texas at Austin. At Duquesne, she has received the Dean's Award for Excellence in Scholarship and Teaching, the Presidential Scholarship Award, and the Samuel & Emma Winters Foundation Biomedical Research Award. Her research focuses on computational modeling of fluid–structure interactions in biological systems, integrating poroelastic tissue mechanics, fluid dynamics, and transport processes. Applications include cardiovascular flows and vocal fold biomechanics in both healthy and pathological states. She has also received the National Mentoring Award from the Council for Undergraduate Research (CUR) and an NSF CAREER award.