Tri Nguyen

College of Engineering

Works (4)

Updated: August 7th, 2024 05:00

2024 journal article

NEAMS IRP challenge problem 1: Flexible modeling for heat transfer for low-to-high Prandtl number fluids for applications in advanced reactors

NUCLEAR ENGINEERING AND DESIGN, 428.

By: I. Bolotnov* , A. Iskhakov* , T. Nguyen*, C. Tai*, R. Wiser, E. Baglietto, N. Dinh* , D. Shaver, E. Merzari

author keywords: Mixed convection; DNS; Turbulence modeling; Turbulent heat transfer; Data driven modeling
Sources: Web Of Science, NC State University Libraries
Added: August 5, 2024

2023 article

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Iskhakov, A. S., Tai, C.-K., Bolotnov, I. A., Nguyen, T., Merzari, E., Shaver, D. R., & Dinh, N. T. (2023, March 16). NUCLEAR TECHNOLOGY, Vol. 3.

By: A. Iskhakov n , C. Tai n, I. Bolotnov n , T. Nguyen*, E. Merzari *, D. Shaver *, N. Dinh n 

Contributors: A. Iskhakov n , C. Tai n, I. Bolotnov n , T. Nguyen*, E. Merzari *, D. Shaver *, N. Dinh n 

author keywords: Machine learning; turbulence modeling; forced convection; low- and high-Prandtl fluids; data-driven modeling
Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 16, 2023

2022 article

A GPU-accelerated Data Transformation Framework Rooted in Pushdown Transducers

2022 IEEE 29TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING, DATA, AND ANALYTICS, HIPC, pp. 215–225.

By: T. Nguyen n & M. Becchi n

author keywords: Finite state transducers; Pushdown transducers; Data transformation; GPU acceleration
TL;DR: This work defines an extended pushdown transducer abstraction (effPDT) that allows expressing a wide range of data transformations in a memory-efficient fashion, and is thus amenable for GPU deployment, and extends it to also support finite state transducers (FSTs). (via Semantic Scholar)
Source: Web Of Science
Added: June 12, 2023

2021 article

PILOT: a Runtime System to Manage Multi-tenant GPU Unified Memory Footprint

2021 IEEE 28TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING, DATA, AND ANALYTICS (HIPC 2021), pp. 442–447.

By: J. Ravi n, T. Nguyen n, H. Zhou n  & M. Becchi n

TL;DR: A proposed three methods to transparently mitigate memory interference through kernel preemption and scheduling policies are proposed, which would enable new OS-managed scheduling policies to be implemented for GPU kernels to dynamically handle resource contention and offer consistent performance. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: May 2, 2022

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