������ƵAPP

The Department of Mechanical, Materials, and Aerospace Engineering presents its fall 2025 seminar series featuring Samuel Grauer, assistant professor in the Department of Mechanical Engineering at Penn State, who will present “What Can Particle Tracks Tell Us About Turbulence?” This seminar is open to the public and will take place on Wednesday, October 1, from 12:45–1:45 p.m. in room 104 of the Rettaliata Engineering Center

Abstract

Tracking particles through turbulent flows offers a rich but incomplete picture of the underlying motion. In Lagrangian particle tracking (LPT), a leading tool for time-resolved, volumetric velocimetry, particles are imaged, localized, and tracked over time to produce a set of scattered Lagrangian trajectories. A central task in analyzing LPT data is converting those trajectories into dense Eulerian fields such as velocity, pressure, and even temperature or density. This process, called “flow reconstruction,” enables detailed analysis of flow physics: velocity gradients reveal energy transfer across scales, pressure and wall shear stress indicate forces on lifting surfaces, and so forth. Reconstruction becomes difficult when particles are too far apart, their positions are noisy, or they “slip” relative to the flow because of their size or density. These difficulties can be overcome, however, by leveraging suitable governing equations to “fill in the gaps.” In this talk, we will explore the limits of such reconstructions, i.e., the fundamental observability of turbulence from Lagrangian data, using a novel physics-based neural approach that incorporates governing equations for both the fluid dynamics (Navier–Stokes equations) and particle dynamics (extended Maxey–Riley equation). Examples in incompressible turbulence, supersonic flows with shocks, and inertial particle transport will illustrate when turbulent flows can be recovered from particle data, when they cannot, and what those limits reveal about the flow itself.

Biography

Samuel Grauer is an assistant professor in the Department of Mechanical Engineering at Penn State. His research group studies inverse problems related to optical diagnostics. This work involves data assimilation for flow reconstruction, limited-data non-destructive testing and evaluation, and large-scale non-linear uncertainty quantification.