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Identification of the Heat Losses at the Jaws of a Tensile Testing Machine

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Abstract

In the present paper, an application of infrared thermography to inverse heat conduction problems is presented. The study involves the identification of conductive losses at the jaws of a tensile testing machine, which must be assessed in situ, because of variable fastening conditions of the specimen, inducing variable thermal resistances at the jaws. For this purpose, a photo-thermal technique has been developed to identify all the heat exchanges of the sample. An optical excitation, based on a halogen lamp projector, illuminates the specimen in situ on the tensile machine, while the radiative flux excitation is measured using a photodiode. The conductive fluxes are identified, transient and steady, then the equivalent exchange coefficients at the jaws. For this purpose, a direct numerical model by finite differences and an inverse procedure allow to estimate these exchange coefficients. The resolution of the inverse problem is based on the optimization by the conjugate gradient method, of a least square criterion between the temperatures measured by infrared thermography and the temperatures calculated from the direct model.

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Abbreviations

d :

descent direction

e :

thickness (m)

h a :

heat transfer coefficient (W. m − 2 K − 1)

L :

length (m)

m :

fin coefficient (m −1)

P :

perimeter (m)

q(x):

heat flux (W. m − 2)

S :

error functional

S C :

transverse section (m 2)

t :

time coordinate (s)

t f :

final time (s)

x, y :

spatial coordinates (m)

Y i (t):

transient measured temperature, obtained by infrared thermography (°C)

α :

thermal diffusivity (m 2 ⋅ s − 1)

β :

conjugate coefficient

θ(x, t):

temperature (°C)

θ a :

room temperature (°C)

δθ(x, t):

sensitivity of temperature to the x and t parameters

λ :

thermal conductivity (W ⋅ m − 1 ⋅ K − 1)

ξ(θ, Φ1, Φ2, ψ):

Lagrange functional

Φ1, Φ2 :

heat fluxes at the ends of the sample

ψ(x, t):

Lagrange multiplier

N :

iteration index

References

  1. Luong MP (1998) Fatigue limit evaluation of metals using an infrared thermographic technique. Mech Mater 28:155–163

    Article  Google Scholar 

  2. La Rosa G, Risitano A (2000) Thermographic methodology for rapid determination of the fatigue limit of materials and mechanical components. Int J Fatigue 22:65–73

    Article  Google Scholar 

  3. Yang B, Liaw PK, Wang H, Jiang L, Huang JY, Kuo RC, Huang JG (2001) Thermographic investigation of the fatigue behavior of reactor pressure vessel steels. Mater Sci Eng A314:131–139

    Article  Google Scholar 

  4. Doudard C, Calloch S (2009) Influence of hardening type on self-heating of metallic materials under cyclic loadings at low amplitude. Eur J Mech A28:233–240

    Article  Google Scholar 

  5. Poncelet M, Doudard C, Calloch S, Hild F, Weber B, Galtier A (2007) Prediction of self-heating measurements under proportional and non-proportional multiaxial cyclic loadings. C R Mécanique 335:81–86

    Article  MATH  Google Scholar 

  6. Doudard C, Poncelet M, Calloch S, Boue C, Hild F, Galtier A (2007) Determination of a HCF criterion by thermal measurements under biaxial cyclic loading. Int J Fatigue 29:748–757

    Article  Google Scholar 

  7. Chrysochoos A, Louche H (2000) An infrared image processing to analyse the calorific effects accompanying strain localisation. Int J Eng Sci 38:1759–1788

    Article  Google Scholar 

  8. Boulanger T, Chrysochoos A, Mabru C, Galtier A (2004) Calorimetric analysis of dissipative and themoelastic effects associated with the fatigue behavior of steel. Int J Fatigue 26:221–229

    Article  Google Scholar 

  9. D Fraux (2010) Caractérisation thermomécanique par thermographie infrarouge du comportement d’éprouvettes en acier sollicitées en fatigue. PhD., Reims

  10. Fraux D, Pron H, Laloue P, Bissieux C, Maitournam H, Rota L (2010) Characterization by infrared thermography of the fatigue behavior of steel samples. Rev Metall Cah Inf Technol 107(2–3):69–74

    Google Scholar 

  11. Meneghetti G (2007) Analysis of the fatigue strength of a stainless steel bases on the energy dissipation. Int J Fatigue 29:81–94

    Article  Google Scholar 

  12. Berthel B, Watrisse B, Chrysochoos A, Galtier A (2007) Thermographic analysis of fatigue dissipation properties of steel sheets. Strain 43:273–279

    Article  Google Scholar 

  13. Berthel B, Chrysochoos A, Watrisse B, Galtier A (2008) Infrared image processing for the clorimetric analysis of fatigue phenomena. Exp Mech 48:79–90

    Article  Google Scholar 

  14. Maquin F, Pierron F (2009) Heat dissipation measurements in low stress cyclic loading of metallic materials: from internal friction to micro-plasticity. Mech Mater 41:928–942

    Article  Google Scholar 

  15. Alifanov OM (1994) Inverse Heat Transfer Problems. Springer, Berlin

    Book  MATH  Google Scholar 

  16. Ascher UM, Haber E (2003) A multigrid method for distributed parameter estimation problems. Electron Trans Numer Anal 15:1–17

    MathSciNet  MATH  Google Scholar 

  17. Engl HW, Hanke M, Neubauer A (1996) Regularization of inverse problems. Kluwer Academic Publishers, Dordrecht

    Book  MATH  Google Scholar 

  18. Nocedal J, Wright SJ (1999) Numerical Optimization. Springer, Berlin

    Book  MATH  Google Scholar 

  19. G Allaire (2005) Analyse numérique et optimisation, Ed de l’Ecole Polytechnique, Paris, 2nd ed 2012, ISBN 2-7302-1255-8

  20. Sacadura JF (1978) Initiation aux transferts thermiques, éd. Technique et documentation, Paris, ISBN 2-85206-033-7

  21. Bergheau JM, Fortunier R (2004) Simulation numérique des transferts thermiques par éléments finis, éd. Hermes-Lavoisier, ISBN 2-7462-0976-4

  22. Larsen EW, Nelson P (1982) Finite-difference approximations and super-convergence for the discrete-ordinate equations in slab geometry. SIAM J Numer Anal 19:334–348

    Article  MathSciNet  MATH  Google Scholar 

  23. Banoczi JM, Kelley CT (1999) A fast multilevel algorithm for the solution of nonlinear systems of conductive-radiative heat transfer equations in two space dimensions. SIAM J Sci Comput 20(4):1214–1228

    Article  MathSciNet  MATH  Google Scholar 

  24. Moussawi A et al (2013) The constitutive compatibility method for identification of material parameters based on full-field measurements. Comput Methods Appl Mech Eng 265:1–14

    Article  MathSciNet  MATH  Google Scholar 

  25. Lubineau G (2009) A goal-oriented field measurement filtering technique for the identification of material model parameters. Comput Mech 44(5):591–603

    Article  MATH  Google Scholar 

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Acknowledgments

This work has been realised with the support of the French National Agency for Research.

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Correspondence to H. Pron.

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Bouache, T., Pron, H. & Caron, D. Identification of the Heat Losses at the Jaws of a Tensile Testing Machine. Exp Mech 56, 287–295 (2016). https://doi.org/10.1007/s11340-015-0096-z

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