Estimation of Inlet Conditions of Fluid Flow in a Thick Pipe Using Inverse Technique

  • Conference paper
  • First Online:
Fluid Mechanics and Fluid Power, Volume 6 (FMFP 2022)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

  • 127 Accesses

Abstract

A conjugate heat transfer problem of steady laminar flow in a circular pipe with a thick wall is considered for estimating inlet conditions of fluid flow in a thick pipe. An inverse method is used to estimate the unknown velocity and temperature of the fluid at the inlet of the pipe based on temperature data obtained on the outer surface along the pipe's length by subjecting the pipe's outer surface to uniform heat flux. Simulations are run for two-dimensional steady laminar flow in COMSOL for different data sets of inlet velocity and inlet temperature to obtain the temperature data along the length of the pipe. Then the curve is fitted using this data by using nonlinear regression analysis with the Bayesian framework. This fitted curve acts as a forward model for estimating the flow conditions at the inlet. The simulated and surrogated temperatures are compared using a Bayesian framework for the guess sample of parameters. To sample the parameter space of inlet conditions (vi, Ti) of fluid flow, Metropolis–Hastings algorithm has been used. Point estimates of v and T and their associated standard deviation (SD) are estimated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 143.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 179.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

h:

Heat transfer coefficient (w/m2 K)

k:

Thermal conductivity (w/m K)

MCMC:

Markov Chain Monte Carlo

MH:

Metropolis–Hastings

r:

Radius (m)

v:

Velocity (m/s)

T:

Temperature (K)

α:

Thermal diffusivity (m2/s)

i:

Inlet

in:

Inner

out:

Outer

References

  1. Huang C-H, Yan J-Y (1995) An inverse problem in simultaneously measuring temperature-dependent thermal conductivity and heat capacity. Int J Heat Mass Transfer 38(18):3433–3441

    Google Scholar 

  2. Znaidia S, Mzali F, Sassi L et al (2005) Inverse problem in a porous medium: estimation of effective thermal properties. Inverse Prob Sci Eng 13:581–593. https://doi.org/10.1080/17415970500098337

    Article  Google Scholar 

  3. Mota CAA, Orlande HRB, De Carvalho MOM et al (2010) Bayesian estimation of temperature-dependent thermophysical properties and transient boundary heat flux. Heat Transfer Eng 31:570–580. https://doi.org/10.1080/01457630903425635

    Article  Google Scholar 

  4. Ghosh S, Pratihar D, Maiti B et al (2011) Inverse estimation of location of internal heat source in conduction. Inverse Prob Sci Eng 19:337–361. https://doi.org/10.1080/17415977.2011.551876

    Article  Google Scholar 

  5. Beck JV, Arnold KJ (1977) Parameter estimation in engineering and science. Wiley series in probability and mathematical statistics. Wiley, New York (NY)

    Google Scholar 

  6. Reddy BK, Balaji C (2015) Bayesian estimation of heat flux and thermal diffusivity using liquid crystal thermography. Int J Therm Sci 87:31–48

    Google Scholar 

  7. Gnanasekaran N, Balaji C (2013) Markov Chain Monte Carlo (MCMC) approach for the determination of thermal diffusivity using transient fin heat transfer experiments. Int J Therm Sci 63:46–54

    Article  Google Scholar 

  8. Kaipio J, Somersalo E (2007) Statistical inverse problems: discretization, model reduction and inverse crimes. J Comput Appl Math 198:493–504

    Article  MathSciNet  Google Scholar 

  9. Siekmann I, Wagner LE II, Yule D, Fox C, Bryant D, Crampin EJ, Sneyd J (2011) MCMC estimation of Markov models for ion channels. Biophys J 100:1919–1929

    Article  Google Scholar 

  10. Kim SK, Lee II W (2002) An inverse method for estimating thermophysical properties of fluid flowing in a circular duct. Int Commun Heat Mass Transfer 29(8):1029–1036. https://doi.org/10.1016/S07351933(02)00431-1

  11. Lu T, Han WW, Jiang PX, Zhu YH, Wu J, Liu CL (2015) A two-dimensional inverse heat conduction problem for simultaneous estimation of heat convection coefficient, fluid temperature and wall temperature on the inner wall of a pipeline. Prog Nuclear Energ 81:161–168

    Google Scholar 

  12. Han WW, Chen HB, Lu T (2019) Estimation of the time-dependent convective boundary condition in a horizontal pipe with thermal stratification based on inverse heat conduction problem. Int J Heat Mass Transf 132:723–730

    Article  Google Scholar 

  13. Lih CW, Ching YY, ** CW, Long LH (2008) Inverse problem of estimating transient heat transfer rate on external wall of forced convection pipe. Energ Convers Manage 49(8):2117–2123

    Google Scholar 

  14. Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1):97–109

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Konda Reddy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dinesh Reddy, K., Konda Reddy, B. (2024). Estimation of Inlet Conditions of Fluid Flow in a Thick Pipe Using Inverse Technique. In: Singh, K.M., Dutta, S., Subudhi, S., Singh, N.K. (eds) Fluid Mechanics and Fluid Power, Volume 6. FMFP 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-5755-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5755-2_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5754-5

  • Online ISBN: 978-981-99-5755-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Navigation