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SUMMARY:From Nanochannels to Oscillating Heat Pipes: Understanding and Eng
 ineering Liquid-Vapor Interfaces for Passive Thermal Management
DTSTART:20260930T110000
DTEND:20260930T120000
DTSTAMP:20260919T084323Z
UID:9b70315349b0fab3433c139c5884fffbd5f4032acf84620946fcf870
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Shalabh C. Maroo\, Syracuse University\nAbstract\nTherm
 al management of high-power electronics increasingly hinges on how we unde
 rstand and engineer liquid-vapor interfaces\, from the molecular scale to 
 the device scale. This talk presents an experimental and computational bod
 y of work spanning three connected threads. First\, I will show how the di
 sjoining pressure of water\, a near-surface molecular interaction\, can be
  quantified from wicking experiments in silicon-dioxide nanochannels\, rea
 ching pressures as high as ~1.5 MPa. Implemented in continuum CFD\, this r
 elation predicts bubble nucleation temperatures that match independent exp
 eriments\, bridging nanoscale physics and continuum simulation. Second\, I
  will describe a route to durable\, regenerative superhydrophobic surfaces
  built on porous-nanochannel geometry\, which sustain a stable air plastro
 n that survives high-speed water jets\, saltwater\, chemicals\, freeze-tha
 w cycling\, and month-long submersion. Third\, I will turn to oscillating 
 heat pipes (OHPs) as passive two-phase cooling devices\, using a glass adi
 abatic section to isolate the working fluid's true contribution\, measurin
 g equivalent thermal conductivities for multiple working fluids\, and intr
 oducing predictive criteria for OHP operational failure. Finally\, I will 
 present a machine-learning (ML) framework that links flow visualization di
 rectly to OHP thermal transport. These ML models detect liquid slugs and v
 apor plugs across 84\,000 frames at 90-96% accuracy\, and\, together with 
 slug-population frequency analysis\, produce two thermal-conductivity corr
 elations that collapse distinct working fluids onto a single trend. These 
 tools advance the long-sought but still-unrealized goal of predictive OHP 
 design\, a capability increasingly important for managing AI and data-cent
 er thermal loads.\n\nAbout the speaker\nShalabh C. Maroo is a Professor in
  the Department of Mechanical and Aerospace Engineering at Syracuse Univer
 sity\, where he directs the Multiscale Research and Engineering Laboratory
 . His group works at the intersection of multiscale transport phenomena\, 
 thermal management\, energy conversion\, and water desalination\, and usin
 g the fundamentals of nanoscience to build efficient macroscale systems\, 
 with support from the DoD\, NSF\, DOE and industry. Before joining Syracus
 e in 2011\, he earned his B.Tech. from IIT Bombay\, his M.S. and Ph.D. fro
 m University of Florida\, and was a postdoctoral associate at MIT. He is t
 he recipient of the NSF CAREER award\, and his group’s recent research h
 as appeared in broad-ranging journals such as Nano Letters\, Chemical Engi
 neering Journal\, ACS Applied Materials & Interfaces\, Journal of Colloid 
 and Interface Science\, Cell Reports Physical Science\, and Applied Therma
 l Engineering.
LOCATION:MED 0 1618 https://plan.epfl.ch/?room==MED%200%201618
STATUS:CONFIRMED
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