Skip to main content

2022 | OriginalPaper | Buchkapitel

5. On Predicting Uncertainties in the Dynamic Response of a Welded Structure

verfasst von : A. Muraleedharan, R. J. Barthorpe, K. Worden

Erschienen in: Dynamic Substructures, Volume 4

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Present-day engineering projects are highly dependent on numerical models; thanks to the improvements in computing capabilities that have contributed significantly in this area. Although models these days are far better representations of engineering structures than before, every model is limited by its mathematical representation and the knowledge about the underlying physics. Validating numerical models involves obtaining test or performance data, which may not be practical in the case of many engineering structures. In such cases where data for the full structural model are not available, the subsystems or components can be tested and the associated models calibrated and validated separately. Inferring response at system level from these subsystem validation results is not straightforward and needs a proper uncertainty propagation strategy. Furthermore, the response of a structure depends not only on the components that it is made of, but also equally on the joints. Joints determine how the components interact within a structure, making validating the models for joints as important as validating the subcomponents. A joint does not physically exist on its own but co-exists with the components it connects, which makes it difficult to define a joint model. Isolated models, where the same type of joints is employed to connect ‘simple’ components with well-established numerical models can serve such a joint model for the purpose of validation. This paper describes a probabilistic approach to dealing with joints via such isolated models, where the uncertainties related to a welded joint are quantified and propagated into a target model of a welded structure to predict the dynamic response.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Oberkampf, W.L., DeLand, S.M., Rutherford, B.M., Diegert, K.V., Alvin, K.F.: Error and uncertainty in modeling and simulation. Reliab. Eng. Syst. Saf. 75(3), 333–357 (2002)CrossRef Oberkampf, W.L., DeLand, S.M., Rutherford, B.M., Diegert, K.V., Alvin, K.F.: Error and uncertainty in modeling and simulation. Reliab. Eng. Syst. Saf. 75(3), 333–357 (2002)CrossRef
2.
Zurück zum Zitat Richards, S.A.: Completed Richardson extrapolation in space and time. Commun. Numer. Methods Eng. 13(7), 573–582 (1997)MathSciNetCrossRef Richards, S.A.: Completed Richardson extrapolation in space and time. Commun. Numer. Methods Eng. 13(7), 573–582 (1997)MathSciNetCrossRef
3.
Zurück zum Zitat Sankararaman, S., Mahadevan, S.: Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems. Reliab. Eng. Syst. Saf. 138, 194–209 (2015)CrossRef Sankararaman, S., Mahadevan, S.: Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems. Reliab. Eng. Syst. Saf. 138, 194–209 (2015)CrossRef
4.
Zurück zum Zitat Mestrovic, M.: An application of Richardson extrapolation on FEM solutions. Int. J. Math. Comput. Method. 1, 351–354 (2016) Mestrovic, M.: An application of Richardson extrapolation on FEM solutions. Int. J. Math. Comput. Method. 1, 351–354 (2016)
5.
Zurück zum Zitat Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 63(3), 425–464 (2001) Kennedy, M.C., O’Hagan, A.: Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 63(3), 425–464 (2001)
6.
Zurück zum Zitat Schwer, L.E.: An overview of the PTC 60/V&V 10: guide for verification and validation in computational solid mechanics. Eng. Comput. 23(4), 245–252 (2007)CrossRef Schwer, L.E.: An overview of the PTC 60/V&V 10: guide for verification and validation in computational solid mechanics. Eng. Comput. 23(4), 245–252 (2007)CrossRef
7.
Zurück zum Zitat Roache, P.J.: Perspective: validation – What does it mean? J. Fluid. Eng. 131(3) (2009) Roache, P.J.: Perspective: validation – What does it mean? J. Fluid. Eng. 131(3) (2009)
8.
Zurück zum Zitat Roache, P.J.: Fundamentals of Verification and Validation. Hermosa Publishers, Socorro (2009) Roache, P.J.: Fundamentals of Verification and Validation. Hermosa Publishers, Socorro (2009)
9.
Zurück zum Zitat Dvurecenska, K., Graham, S., Patelli, E., Patterson, E.A.: A probabilistic metric for the validation of computational models. R. Soc. Open Sci. 5(11), 180687 (2018)MathSciNetCrossRef Dvurecenska, K., Graham, S., Patelli, E., Patterson, E.A.: A probabilistic metric for the validation of computational models. R. Soc. Open Sci. 5(11), 180687 (2018)MathSciNetCrossRef
10.
Zurück zum Zitat Liu, Y., Chen, W., Arendt, P., Huang, H.Z.: Toward a better understanding of model validation metrics. J. Mech. Des. 133(7) (2011) Liu, Y., Chen, W., Arendt, P., Huang, H.Z.: Toward a better understanding of model validation metrics. J. Mech. Des. 133(7) (2011)
11.
Zurück zum Zitat Gardner, P., Lord, C., Barthorpe, R.J.: A unifying framework for probabilistic validation metrics. J. Verification Validation Uncertain. Quantif. 4(3) (2019) Gardner, P., Lord, C., Barthorpe, R.J.: A unifying framework for probabilistic validation metrics. J. Verification Validation Uncertain. Quantif. 4(3) (2019)
12.
Zurück zum Zitat Ferson, S., Oberkampf, W.L., Ginzburg, L.: Model validation and predictive capability for the thermal challenge problem. Comput. Method. Appl. Mech. Eng. 197(29), 2408–2430 (2008)CrossRef Ferson, S., Oberkampf, W.L., Ginzburg, L.: Model validation and predictive capability for the thermal challenge problem. Comput. Method. Appl. Mech. Eng. 197(29), 2408–2430 (2008)CrossRef
13.
Zurück zum Zitat Rebba, R., Huang, S., Liu, Y., Mahadevan, S.: Statistical validation of simulation models. Int. J. Mat. Product Technol. 25(1–3), 164–181 (2006)CrossRef Rebba, R., Huang, S., Liu, Y., Mahadevan, S.: Statistical validation of simulation models. Int. J. Mat. Product Technol. 25(1–3), 164–181 (2006)CrossRef
14.
Zurück zum Zitat Sindo, K.: Welding Metallurgy. John Wiley and Sons, Inc., New Jersey (2003) Sindo, K.: Welding Metallurgy. John Wiley and Sons, Inc., New Jersey (2003)
15.
Zurück zum Zitat American Welding Society. Structural Welding Code – Steel. AWS, Miami (1994) American Welding Society. Structural Welding Code – Steel. AWS, Miami (1994)
16.
Zurück zum Zitat Kreye, H.: Melting phenomena in solid state welding processes. Weld. J. 56(5), 154–158 (1977) Kreye, H.: Melting phenomena in solid state welding processes. Weld. J. 56(5), 154–158 (1977)
17.
Zurück zum Zitat Benaroya, H., Nagurka, M., Han, S.: Mechanical Vibration: Analysis, Uncertainties, and Control. CRC Press, Boca Raton (2017)CrossRef Benaroya, H., Nagurka, M., Han, S.: Mechanical Vibration: Analysis, Uncertainties, and Control. CRC Press, Boca Raton (2017)CrossRef
18.
Zurück zum Zitat Bauchau, O.A., Craig, J.I.: Euler-Bernoulli beam theory. In: Structural Analysis, pp. 173–221. Springer, Berlin (2009) Bauchau, O.A., Craig, J.I.: Euler-Bernoulli beam theory. In: Structural Analysis, pp. 173–221. Springer, Berlin (2009)
19.
Zurück zum Zitat Nashif, A.D., Jones, D.I.G., Henderson, J.P.: Vibration Damping. John Wiley & Sons, Hoboken (1985) Nashif, A.D., Jones, D.I.G., Henderson, J.P.: Vibration Damping. John Wiley & Sons, Hoboken (1985)
20.
Zurück zum Zitat Orban, F.: Damping of materials and members in structures. In: Journal of Physics – Conference Series, vol. 268, pp. 012–022. IOP Publishing, Bristol (2011) Orban, F.: Damping of materials and members in structures. In: Journal of Physics – Conference Series, vol. 268, pp. 012–022. IOP Publishing, Bristol (2011)
21.
Zurück zum Zitat Peeters, B., De Roeck, G.: Reference-based stochastic subspace identification for output-only modal analysis. Mech. Syst. Signal Proces. 13(6), 855–878 (1999)CrossRef Peeters, B., De Roeck, G.: Reference-based stochastic subspace identification for output-only modal analysis. Mech. Syst. Signal Proces. 13(6), 855–878 (1999)CrossRef
22.
Zurück zum Zitat Brincker, R., Andersen, P.: Understanding stochastic subspace identification. In: Proceedings of the 24th IMAC, St. Louis, vol. 126 (2006) Brincker, R., Andersen, P.: Understanding stochastic subspace identification. In: Proceedings of the 24th IMAC, St. Louis, vol. 126 (2006)
23.
Zurück zum Zitat Ching, J., Chen, Y.: Transitional Markov Chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J. Eng. Mech. 133(7), 816–832 (2007)CrossRef Ching, J., Chen, Y.: Transitional Markov Chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J. Eng. Mech. 133(7), 816–832 (2007)CrossRef
24.
Zurück zum Zitat Betz, W., Papaioannou, I., Straub, D.: Transitional Markov Chain Monte Carlo: observations and improvements. J. Eng. Mech. 142(5), 04016016 (2016)CrossRef Betz, W., Papaioannou, I., Straub, D.: Transitional Markov Chain Monte Carlo: observations and improvements. J. Eng. Mech. 142(5), 04016016 (2016)CrossRef
25.
Zurück zum Zitat Ferson, S., Oberkampf, W.L.: Validation of imprecise probability models. Int. J. Reliab. Saf. 3(1/2/3), 3–22 (2009) Ferson, S., Oberkampf, W.L.: Validation of imprecise probability models. Int. J. Reliab. Saf. 3(1/2/3), 3–22 (2009)
Metadaten
Titel
On Predicting Uncertainties in the Dynamic Response of a Welded Structure
verfasst von
A. Muraleedharan
R. J. Barthorpe
K. Worden
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-75910-0_5