Skip to main content

Sensitivity Analysis

  • Chapter
  • First Online:
  • 498 Accesses

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

Any deterministic process can be represented by a mathematical function, a model, mapping a set of input values to an output. Sensitivity analysis is the study of how variations of its inputs affect the output of a model. This generic principle covers a set of techniques of disparate aims and complexity. Local sensitivity analysis focuses on the response of the model around a given reference point, which is somehow related to gradient determination. In the present case, the aim of the sensitivity analysis is to assess the relative impact of clogging depending on its localisation in the steam generator. Therefore, the whole range of variation of clogging ratios must be considered which calls for a global sensitivity analysis technique.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Archer GEB, Saltelli A, Sobol’ IM (1997) Sensitivity measures, ANOVA-like techniques and the use of bootstrap. J Stat Comput Simul 58:99–120

    Article  MATH  Google Scholar 

  • Campbell K, McKay MD, Williams BJ (2006) Sensitivity analysis when model outputs are functions. Reliab Eng Syst Saf 91(10–11):1468–1472

    Article  Google Scholar 

  • Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall, New York

    Google Scholar 

  • Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliab Eng Syst Saf 52(1):1–17

    Article  Google Scholar 

  • Iooss B (2011) Revue sur l’analyse de sensibilité globale de modèles numériques. Journal de la Société Française de Statistique 152(1):3–25

    MathSciNet  Google Scholar 

  • Jansen MJ (1999) Analysis of variance designs for model output. Comput Phys Commun 117 (1–2):35–43

    Google Scholar 

  • Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New York

    Google Scholar 

  • Kucherenko S (2012) Monte Carlo and quasi-Monte Carlo methods. In: SAMO summer school 2012, J.R.C

    Google Scholar 

  • Lamboni M, Monod H, Makowski D (2010) Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliab Eng Syst Saf 96:450–459

    Article  Google Scholar 

  • Lebart L, Piron M, Morineau A (2006) Statistique exploratoire multidimensionnelle, 4th edn. Dunod, Sciences Sup

    Google Scholar 

  • Marrel A, Iooss B, Laurent B, Roustant O (2009) Calculations of Sobol indices for the gaussian process metamodel. Reliab Eng Syst Saf 94(3):742–751

    Article  Google Scholar 

  • Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33(2):161–174

    Google Scholar 

  • Robert CP, Casella G (2004) Monte Carlo statistical methodes. Springer, New York

    Google Scholar 

  • Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145(2):280–297

    Google Scholar 

  • Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270

    Article  MATH  MathSciNet  Google Scholar 

  • Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis: the primer. Wiley Online Library

    Google Scholar 

  • Saporta G (2006) Probabilités, analyse des données et statistique. Editions Technip

    Google Scholar 

  • Sobol’ IM (1993) Sensitivity estimates for nonlinear mathematical models, in Matem. Modelirovanie 2 (1)(1990):112–118. English Transl: MMCE 1(4)

    Google Scholar 

  • Sobol’ IM (1998) On quasi-Monte Carlo integrations. Math Comput Simul 47:103–112

    Article  MathSciNet  Google Scholar 

  • Sobol’ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55:271–280

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sylvain Girard .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 The Author(s)

About this chapter

Cite this chapter

Girard, S. (2014). Sensitivity Analysis. In: Physical and Statistical Models for Steam Generator Clogging Diagnosis. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-09321-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09321-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09320-8

  • Online ISBN: 978-3-319-09321-5

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics