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A software environment for effective reliability management for pulsed power design

Author

Listed:
  • Robinson, Allen C.
  • Drake, Richard R.
  • Swan, M. Scot
  • Bennett, Nichelle L.
  • Smith, Thomas M.
  • Hooper, Russell
  • Laity, George R.

Abstract

The reliable design of magnetically insulated transmission lines (MITLs) for very high current pulsed power machines must be accomplished in the future by utilizing a variety of sophisticated modeling tools. The complexity of the models required is high and the number of sub-models and approximations large. The potential for significant analyst error using a single tool is large, with possible reliability issues associated with the plasma modeling tools themselves or the chosen approach by the analyst to solve a given problem.

Suggested Citation

  • Robinson, Allen C. & Drake, Richard R. & Swan, M. Scot & Bennett, Nichelle L. & Smith, Thomas M. & Hooper, Russell & Laity, George R., 2021. "A software environment for effective reliability management for pulsed power design," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:reensy:v:211:y:2021:i:c:s0951832021001307
    DOI: 10.1016/j.ress.2021.107580
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    1. Borg, Audun & Paulsen Husted, Bjarne & Njå, Ove, 2014. "The concept of validation of numerical models for consequence analysis," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 36-45.
    2. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    3. Mathias, Donovan L. & Mattenberger, Christopher J. & Go, Susie, 2016. "Engineering Risk Assessment of a dynamic space propulsion system benchmark problem," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 316-328.
    4. Karanki, D.R. & Rahman, S. & Dang, V.N. & Zerkak, O., 2017. "Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: Tradeoff between accuracy and accident simulations," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 91-102.
    5. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    6. Allaire, Douglas & Noel, George & Willcox, Karen & Cointin, Rebecca, 2014. "Uncertainty quantification of an Aviation Environmental Toolsuite," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 14-24.
    7. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Nagel, Joseph B. & Rieckermann, Jörg & Sudret, Bruno, 2020. "Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    9. Qian, Gengjian & Massenzio, Michel & Brizard, Denis & Ichchou, Mohamed, 2019. "Sensitivity analysis of complex engineering systems: Approaches study and their application to vehicle restraint system crash simulation," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 110-118.
    10. McClarren, Ryan G. & Ryu, D. & Paul Drake, R. & Grosskopf, Michael & Bingham, Derek & Chou, Chuan-Chih & Fryxell, Bruce & van der Holst, Bart & Paul Holloway, James & Kuranz, Carolyn C. & Mallick, Ban, 2011. "A physics informed emulator for laser-driven radiating shock simulations," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1194-1207.
    11. Pilch, Martin & Trucano, Timothy G. & Helton, Jon C., 2011. "Ideas underlying the Quantification of Margins and Uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 965-975.
    12. Burgazzi, Luciano & Pierini, Paolo, 2007. "Reliability studies of a high-power proton accelerator for accelerator-driven system applications for nuclear waste transmutation," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 449-463.
    13. Cheung, Sai Hung & Oliver, Todd A. & Prudencio, Ernesto E. & Prudhomme, Serge & Moser, Robert D., 2011. "Bayesian uncertainty analysis with applications to turbulence modeling," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1137-1149.
    14. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    15. Kelly, Dana L. & Smith, Curtis L., 2009. "Bayesian inference in probabilistic risk assessment—The current state of the art," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 628-643.
    16. Worrell, Clarence & Luangkesorn, Louis & Haight, Joel & Congedo, Thomas, 2019. "Machine learning of fire hazard model simulations for use in probabilistic safety assessments at nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 128-142.
    17. Cooke, Roger M. & Goossens, Louis L.H.J., 2008. "TU Delft expert judgment data base," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 657-674.
    18. Schoefs, Franck, 2008. "Sensitivity approach for modelling the environmental loading of marine structures through a matrix response surface," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1004-1017.
    19. Zhao, Yunfei & Smidts, Carol, 2019. "A method for systematically developing the knowledge base of reactor operators in nuclear power plants to support cognitive modeling of operator performance," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 64-77.
    20. Bodda, Saran Srikanth & Gupta, Abhinav & Dinh, Nam, 2020. "Enhancement of risk informed validation framework for external hazard scenario," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    21. Weirs, V. Gregory & Kamm, James R. & Swiler, Laura P. & Tarantola, Stefano & Ratto, Marco & Adams, Brian M. & Rider, William J. & Eldred, Michael S., 2012. "Sensitivity analysis techniques applied to a system of hyperbolic conservation laws," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 157-170.
    22. Trucano, T.G. & Swiler, L.P. & Igusa, T. & Oberkampf, W.L. & Pilch, M., 2006. "Calibration, validation, and sensitivity analysis: What's what," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1331-1357.
    23. Francesco, Di Maio & Matteo, Fumagalli & Carlo, Guerini & Federico, Perotti & Enrico, Zio, 2021. "Time-dependent reliability analysis of the reactor building of a nuclear power plant for accounting of its aging and degradation," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
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