Heat and Mass Transfer

, Volume 55, Issue 2, pp 327–340 | Cite as

Non-intrusive measurement of thermal contact conductance at polymer-metal two dimensional annular interface

  • Samarjeet ChandaEmail author
  • C. Balaji
  • S. P. Venkateshan


This work reports the development of a measurement technique for the estimation of thermal contact conductance at a polymer (Nylon)-metal(Copper) two dimensional interface having an annular contact under low external loading. The experimental setup consists of a vacuum chamber capable of achieving a pressure of 10−6 mbar. The chamber is fitted with electrical, thermocouple and fluid feed-throughs. Two dimensional temperature measurements are performed on the top surface of the polymer sample away from the boundary interface and are used to estimate the thermal contact conductance at the interface. The estimation process is accomplished by solving an inverse heat conduction problem using artificial neural networks coupled with Genetic algorithm. The actual pressure distribution at the annular Nylon-Copper interface is measured using a pressure sensitive film and the variation and the distribution of contact area between the mating surfaces is shown. The estimated values of thermal contact conductance obtained using the developed non-intrusive technique are found to be in good agreement with those reported in literature.


English Symbols


mean thermal contact conductance over the annular interface, W/m2K


thermal conductivity of sample, W/m K


number of samples used for testing the network


length of the nylon sample and copper frame (along x), mm


number of temperature measurement points


thickness of the nylon sample (along z), mm


computed temperature, C or K


width of the nylon sample and copper frame (along y), mm


Cartesian coordinates

Greek Symbols




uncertainty in measurement



actual value obtained from numerical solution of direct problem


physical dimension of heater (mm)


value at the copper frame surface


experimental value


maximum value


minimum value


predicted value obtained from the trained artificial neural network


simulated value



artificial neural network


genetic algorithm


mean squared error


mean relative error


mean absolute error


probability density function

SS 304

stainless steel of grade 304 designated by American Iron and Steel Institute



The authors thank IITM-ISRO Space Technology Cell for funding this research work.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology MadrasChennaiIndia

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