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Research Papers: Codes and Standards

Challenges in Validation of Computational Weld Mechanics Code to Compute Residual Stress and Distortion in Welds

[+] Author and Article Information
John Goldak

e-mail: jgoldak@mrco2.carleton.ca

Mahyar Asadi

e-mail: masadi@connect.carleton.ca
Mechanical and Aerospace Engineering,
Carleton University,
Ottawa, ON K1S 5B6, Canada

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the Journal of Pressure Vessel Technology. Manuscript received October 21, 2010; final manuscript received January 20, 2013; published online October 23, 2013. Assoc. Editor: T. L. (Sam) Sham.

J. Pressure Vessel Technol 136(1), 011201 (Oct 23, 2013) (8 pages) Paper No: PVT-10-1158; doi: 10.1115/1.4024458 History: Received October 21, 2010; Revised January 20, 2013

Validation of a computational weld mechanics (CWM) code for a particular welding application requires an estimate of the difference between experimentally measured parameters and parameters computed by a computational model. This requires estimates of the uncertainty in both the experimental data and the computational data and this requires careful design of both the experiment and the CWM model. The authors experience in performing validation tests for a CWM code is summarized. An example of a validation test for a welding application that compares measured and computed transient temperatures, displacements and strains is described in detail that demonstrates that model can accurately predict this data. Challenges on both the experimental side and computational side are discussed but the greatest challenge is the limited availability of experimental data that has a measure of uncertainty.

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References

Figures

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Fig. 1

This figure is taken from Ref. [10]. It provides an over-view of the verification and validation process. The left side is devoted to the development of the model. The right side is devoted to the development of experimental data.

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Fig. 3

Comparison between computed thermal profiles and measured profiles for the four thermocouples. The first two curves from the top are experimental and computed temperatures for thermocouple 1, the second two curves are experimental and computed temperatures for thermocouple 2, the third two curves are experimental and computed temperatures for thermocouple 3, and the last two curves are experimental and computed temperatures for thermocouple 4. See Fig. 2 for the locations of the thermocouples.

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Fig. 2

Specimen dimensions and locations of thermocouples 1–4 located 12.7, 38.1, 76.2, and 144.7 mm below the upper edge of the specimen and strain gauges 1–4 located 7.6, 38.1, 76.2, and 114.3 mm above the lower edge of the specimen, a dial gauge and extensometers [5]

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Fig. 4

Comparison between the computed result for strain gauges positioned as described in Fig. 2 with the experimental data taken from Ref. [5]. Thermal strain is fully subtracted from the total strain. Curves with legend including “Fig 5.24_…” are experimental data for strain gauge 4, 3, 2, and 1, respectively. The other legends below the experimental ones “SG11-…” are computed results for strain gauge 4, 3, 2, and 1, respectively.

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Fig. 5

Sensitivity of strain gauge 4 to its position distanced from top edge by 4.13, 3.81, 3.49, 3.17, and 2.85 mm are shown with cross, star, blank square, solid square, and rhombic character, respectively. Experimental data are shown with cross character.

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Fig. 6

Comparison of experimental measured transient displacement, shown in top line, with the computed transient displacement. Cross character shows the setting of cut off temperature to 700 K, convergence criteria 0.001 and maximum number of NR iterations to 1. Star character shows the setting of cut off temperature to 700 K, convergence criteria 0.000001, maximum number of NR iterations to 10. Blank square character shows the setting of cut off temperature to 800 K, convergence criteria 0.000001, maximum number of NR iterations to 10. Solid square character shows the setting of cut off temperature to 840 K, convergence criteria 0.000001, maximum number of NR iterations to 10. Rhombic character shows the setting of cut off temperature to 840 K, convergence criteria 0.000001, maximum number of NR iterations to 10 and an increase in CTE of about 10%.

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Fig. 7

Comparison the measured mid-length deflection and computed mid-length deflection with setting of cut-off temperature to 850 K, convergence criteria to 1 × 10−6, maximum number of NR iterations to 10 and an increase in CTE by adding 2 × 10−6 to the original CTE values taken from Ref. [5].

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