October 13, 2026 Fall 2026 Faustin-Prinz Research Fellows Written By: Kassi Akers Departments: Mechanical Engineering Categories: Research | Students | Undergraduate Six undergrad students are joining the Faustin-Prinz Undergraduate Research Program as fellows, conducting independent research alongside Department of Mechanical Engineering faculty. With projects spanning robotics, aerospace, conceptual research, and more, these students are gaining valuable hands-on experience that complements their engineering education. Learn more about the impactful research they’re pursuing below. Fall 2026 Faustin Prinz Fellows Austin Brey Student: Austin Brey Research Advisor: Jennifer Franck Project: Machine Learning Models for Predicting Power Output of Oscillating Airfoils for Clean Energy Generation Marine energy generation, or harvesting energy from moving water, is a solution to renewable energy, and tandem oscillating airfoils will be explored in this project. The strokes of the airfoils can be computationally modeled, and their power and efficiency obtained. Machine learning will be used to analyze the flow over various oscillatory kinematics to find the most optimal configuration for power generation. The proposed research will quicken the current computational fluid dynamics (CFD) methodology, which will enable optimization procedures not currently possible due to the large time required for iteration. This will be accomplished by combining a predictive neural network of force and power with a secondary neural network that predicts the wake structure. Utilizing both will provide the information needed to predict the instantaneous power of the second foil, and thus the entire array configuration. Various optimization procedures will be explored, and the resulting code will be the backbone for a graphical user interface (GUI) so the research community can benefit from, and easily use, the tandem oscillating airfoil prediction models. Yao Buchl Student: Yao Buchl Research Advisors: Wei Wang Project: Kármán Robotic Trout: A Bio-Inspired Energy Harvesting System Using Kármán Vortices Hydrokinetic energy offers a promising renewable power source for distributed underwater sensing and marine systems. Most existing marine energy harvesters are large, complex, and ecologically disruptive—unlike fish, which naturally extract energy from vortices to reduce swimming effort. To bridge this gap, we propose the development of the Kármán Robotic Trout (KRT), a low-cost robotic fish that harvests energy from naturally shed vortices in flowing water. The robot converts vortex-induced boProposaldy oscillations into electrical energy using DC rectification; this project experimentally investigates how bluff body diameter, downstream spacing, flow velocity, and tail flexibility influence energy harvesting performance and vortex synchronization. This system combines bio-inspired locomotion, marine robotics, and renewable energy harvesting into a simple, manufacturable platform with a robot cost under $150. By leveraging naturally occurring flow structures instead of resisting them, this work introduces a new paradigm for eco-friendly hydrokinetic energy harvesting and provides a foundation for future autonomous underwater sensing and robotic systems. Laura Hansen Student: Laura Hansen Research Advisor: Harsh Sharma Project: Physics-preserving Adaptive Surrogate Models for Intelligent Soft Robots High-fidelity models are often computationally impractical for real-time control of soft robotic systems, which can exhibit high-dimensional, nonlinear, and strongly deformable behavior. While data-driven model reduction creates efficient reduced-order models (ROMs) for control, traditional ROMs constructed using batch methods are static and can become inaccurate when soft robots exhibit dynamics that were not represented in the batch training data. Recent developments in streaming Operator Inference (OpInf) allow for real-time updates as data streams in, yet these methods fail to enforce essential physical constraints, leading to unstable or unphysical predictions. This research proposes a physics-preserving framework that integrates OpInf with incremental singular value decomposition techniques. Specifically, we will investigate efficient streaming methods for adaptive basis updates and develop a recursive operator learning strategy that preserves the underlying physics during online updates. The proposed approach will be tested on a swimming soft robot benchmark, advancing adaptive surrogate models for embodied intelligence in soft robots. Student: Lily Margoshes Research Advisor: Scott Sanders Project: Design and Optimization of a Laser Speckle Reduction System for Advanced Hydrogen Sensing Laser speckle significantly degrades the signal-to-noise ratio of laser-based imaging systems, limiting the performance of sensing technologies used in critical applications. The Applied Optical Sensing Lab at UW-Madison has developed inverse Backscatter Absorption Gas Imaging (iBAGI), a novel method to visualize gas leaks so that the leaks can be stopped. However, iBAGI performance is limited by laser speckle noise. In this project I will design a device to reduce laser speckle effects and improve leak detection performance. Of particular interest for iBAGI are hydrogen (H₂) leaks. Although H2 combustion produces no carbon emissions, leaks contribute to greenhouse gas emissions and create dangerous environments for humans and structures, making precise detection essential. Laser speckle reduction (LSR) instruments will be evaluated through controlled experiments, with performance quantified via noise level. Based on these results, I will develop an LSR device for minimal noise, size, weight, power consumption, and cost to be integrated into an iBAGI system. This despeckler could also improve laser-based sensing used in medical imaging, quality control, and environmental applications. Student: Ryan Peplinski Research Advisor: Mark Anderson Project: Analysis of Reflood Quenching After a LOCA for a Silicon Carbide Heater Rod Following the Fukushima-Daiichi accident, the development of accident-tolerant fuel (ATF) cladding has become critical to improving nuclear safety. A key metric for these materials is their performance during a loss-of-coolant accident (LOCA). This is where the absence of coolant causes a rapid temperature increase in the fuel rods. As a safety procedure to lower rod temperatures, water is pumped through the system during a reflood event. During reflood, however, there is a delay between the bulk water front and surface rewetting, affecting the quenching dynamics and cooling speed. This study will investigate Silicon Carbide (SiC) as a cladding material, offering higher thermal conductivity, oxidation resistance, and high-temperature strength over the traditional Zircaloy cladding. This will involve the use of experiments, with high-speed imaging and temperature measurements to characterize the quench front velocity and wetting behavior during a reflood. The results will then be compared to prior experiments using 316 stainless steel cladding to evaluate the relative performance. The work aims to better understand the transient flow and heat behavior of cladding in nuclear systems. Luz Rodriguez Student: Luz Rodriguez Research Advisor: Kate Fu Project: Embedding Design Interventions to Support Theory-to-Practice Integration in Senior Design Mechanical engineering students often struggle to apply theoretical knowledge in open-ended design contexts, particularly within senior capstone courses where support is distributed and largely self-directed. Prior work has developed theory-to-practice learning modules to address this gap, yet preliminary data suggest persistent misalignment between students’ perceived abilities and their observed design behaviors, including limited use of prototyping and underutilization of lab resources. This project leverages an existing multi-modal dataset from a senior design course and design lab to design and evaluate embedded, context sensitive interventions that support student decision making within authentic design workflows. By integrating design supports directly into the lab environment and course structure, this work aims to improve theory-to-practice integration, prototyping behavior, and overall design performance, while also advancing undergraduate research through the involvement of Luz Rodriguez. Students interested in participating in the Faustin-Prinz Research Fellowship can find more information about the opportunity on the ME Intranet under research opportunities or can reach out to Mark Anderson, manderson@engr.wisc.edu, with questions.