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Title: Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint

Abstract

A high-fidelity computational fluid dynamics (CFD)-based model for bubble nucleation of the refrigerant HFE7100 on micrometer-featured surfaces is presented in this work. The single-fluid incompressible Navier-Stokes equations, along with energy transport and natural convection effects are solved on a featured surface resolved grid. An a priori cavity detection method is employed to convert raw profilometer data of a surface into well-defined cavities. The cavity information and surface morphology are represented in the CFD model by geometric mesh deformations. Surface morphology is observed to initiate buoyancy-driven convection in the liquid phase, which in turn results in faster nucleation of cavities. Simulations pertaining to a generic rough surface show a trend where smaller size cavities nucleate with higher wall superheat. This local-scale model will serve as a self-consistent connection to larger device scale continuum models where local feature representation is not possible.

Authors:
 [1];  [1]; ORCiD logo [1];  [2];  [2];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Toyota Research Institute of North America
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
Toyota Research Institute of North America; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1378440
Report Number(s):
NREL/CP-2C00-68294
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the ASME Summer Heat Transfer Conference, 9-14 July 2017, Bellevue, Washington
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; high fidelity; computational; fluid dynamics; CFD; incompressible Navier-Stokes; profilometer; surface morphology

Citation Formats

Sitaraman, Hariswaran, Moreno, Gilberto, Narumanchi, Sreekant V, Dede, Ercan M., Joshi, Shailesh N., and Zhou, Feng. Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint. United States: N. p., 2017. Web. doi:10.1115/HT2017-4710.
Sitaraman, Hariswaran, Moreno, Gilberto, Narumanchi, Sreekant V, Dede, Ercan M., Joshi, Shailesh N., & Zhou, Feng. Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint. United States. doi:10.1115/HT2017-4710.
Sitaraman, Hariswaran, Moreno, Gilberto, Narumanchi, Sreekant V, Dede, Ercan M., Joshi, Shailesh N., and Zhou, Feng. Thu . "Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint". United States. doi:10.1115/HT2017-4710. https://www.osti.gov/servlets/purl/1378440.
@article{osti_1378440,
title = {Local-Scale Simulations of Nucleate Boiling on Micrometer Featured Surfaces: Preprint},
author = {Sitaraman, Hariswaran and Moreno, Gilberto and Narumanchi, Sreekant V and Dede, Ercan M. and Joshi, Shailesh N. and Zhou, Feng},
abstractNote = {A high-fidelity computational fluid dynamics (CFD)-based model for bubble nucleation of the refrigerant HFE7100 on micrometer-featured surfaces is presented in this work. The single-fluid incompressible Navier-Stokes equations, along with energy transport and natural convection effects are solved on a featured surface resolved grid. An a priori cavity detection method is employed to convert raw profilometer data of a surface into well-defined cavities. The cavity information and surface morphology are represented in the CFD model by geometric mesh deformations. Surface morphology is observed to initiate buoyancy-driven convection in the liquid phase, which in turn results in faster nucleation of cavities. Simulations pertaining to a generic rough surface show a trend where smaller size cavities nucleate with higher wall superheat. This local-scale model will serve as a self-consistent connection to larger device scale continuum models where local feature representation is not possible.},
doi = {10.1115/HT2017-4710},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Aug 03 00:00:00 EDT 2017},
month = {Thu Aug 03 00:00:00 EDT 2017}
}

Conference:
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