Alice Ding

MIT Department: Nuclear Science and Engineering
Faculty Mentor: Prof. Emilio Baglietto
Undergraduate Institution: Vanderbilt University
Website: LinkedIn
Research Poster
Lightning Talks

Biography

My name is Alice Ding and I am a rising senior at Vanderbilt University majoring in Mechanical and Biomedical Engineering. Outside of academics, I serve as the chief financial officer of my residential community and am the upcoming president of Vanderbilt’s Chinese Association. My previous summers have been spent at my university working on research surrounding surgical modeling through the Institute of Surgery and Engineering and the SyBBURE Searle Program. Specifically, my past projects have included works on microwave ablation and vagus nerve stimulation. Currently, I am interested in applications of computational fluid dynamics and plan on pursuing future doctoral studies in this field. My hobbies including trying new foods, listening to music, and nonograms.


2021 Abstract

Extending the Performance of the STRUCT-e Hybrid Turbulence Approach Through Consistent Mesh Refinement

Alice Ding1, Emilio Baglietto2
1Department of Mechanical Engineering, Vanderbilt University
2Department of Nuclear Engineering, Massachusetts Institute of Technology

Computational fluid dynamics (CFD) has been used largely for the design of safety related issues such as identifying how unsteady turbulent flow in nuclear systems can limit the structural performance of critical components. Current methods such as direct numerical simulation (DNS), unsteady Reynolds averaged Navier-Stokes (URANS), and large eddy simulation (LES) are limited by numerous factors such as resolution, flow resolvable through Reynolds number, or computational cost. As a result, hybrid methods have been proposed to leverage the specific strengths of the different methods. One such method is the STRUCT-e model, which uses URANS in quasi-equilibrium regions, while adopting an LES-like method in regions where coherent turbulent structures appear. We propose to further this hybrid method by incorporating automatic mesh refinement (AMR) consistently with the hybrid activation, increasing the computation mesh resolution in regions of interest. Our method allows for improvements in predictions about the fluid flow in regions of high turbulence while keeping the mesh resolution low in other areas in order to reduce their impact to computational costs.