Abstract
Numerical simulations of river bed evolution (morphodynamics) can be used for evaluating river engineering concepts and efficient operation of waterways. Reliability analysis is used to detect the origin of uncertainties, to track their propagation in the model and to evaluate their contribution to the result of the modeling. We have developed a new approach for the reliability analysis of morphohydrodynamical simulations, including a fast interpolation of simulation results with radial basis functions (RBF) and an efficient quantile estimator based on quasi-Monte Carlo sampling. Realistic examples are used to test the efficiency of the approach. An interactive 3D virtual environment has been employed for visual exploration of the results. The developed virtual environment application allows for a detailed inspection of the numerical model, including the evolution of the water level, river bed profiles and their quantiles. In this way one achieves a full immersion into the model space and an understanding of morphohydrodynamical processes in an intuitive way.
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References
M.D. Buhmann, Radial Basis Functions: Theory and Implementations (Cambridge University Press, Cambridge, 2003)
L. Cizelj, B. Mavko, H. Riesch-Oppermann, Application of first and second order reliability methods in the safety assessment of cracked steam generator tubing. Nucl. Eng. Des. 147, 359–368 (1994)
T. Clees, I. Nikitin, L. Nikitina, C.-A. Thole, Nonlinear metamodeling and robust optimization in automotive design, in Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications SIMULTECH 2011, Noordwijkerhout, July 29–31 (SciTePress, Setúbal, 2011), pp. 483–491
T. Clees, I. Nikitin, L. Nikitina, Nonlinear metamodeling of bulky data and applications in automotive design, in Progress in industrial mathematics at ECMI 2010, ed. by M. Günther et al. Mathematics in Industry, vol. 17 (Springer, Berlin, 2012), pp. 295–301
T. Clees, I. Nikitin, L. Nikitina, R. Kopmann, Reliability analysis of river bed simulation models, in CDROM Proceedings of EngOpt 2012, 3rd International Conference on Engineering Optimization, ed. by J.Herskovits. vol. 267. Rio de Janeiro, Brazil, July 1–5 (2012)
T. Clees, I. Nikitin, L. Nikitina, C.-A. Thole, Analysis of bulky crash simulation results: deterministic and stochastic aspects, in Simulation and Modeling Methodologies, Technologies and Applications: International Conference, ed. by N. Pina, J. Kacprzyk, J. Filipe. SIMULTECH 2011 Noordwijkerhout, The Netherlands, July 29–31, 2011 Revised Selected Papers (Springer, Berlin, 2013), pp. 225–237
T. Clees, I. Nikitin, L. Nikitina, S. Pott, Quasi-Monte Carlo and RBF metamodeling for quantile estimation in river bed morphodynamics, in Simulation and Modeling Methodologies, Technologies and Applications, ed. by M.S. Obaidat, S. Koziel, J. Kacprzyk, L. Leifsson, T. Ören. Advances in Intelligent Systems and Computing, vol. 319 (Springer International Publishing, Berlin, 2015), pp. 211–222
J.H. Halton, Algorithm 247: Radical-inverse quasi-random point sequence. Commun. ACM 7, 701–702 (1964)
F.E. Harrell, C.E. Davis, A new distribution-free quantile estimator. Biometrika 69, 635–640 (1982)
J.-M. Hervouet, Hydrodynamics of Free Surface Flows: Modelling with the Finite Element Method (Wiley, New York, 2007)
M.C. Jones, The performance of kernel density functions in kernel distribution function estimation. Stat. Probab. Lett. 9, 129–132 (1990)
R. Kopmann, A. Schmidt, Comparison of different reliability analysis methods for a 2D morphodynamic numerical model of River Danube, in Proceedings of River Flow 2010 - International Conference on Fluvial Hydraulics, Braunschweig, 8–10. Sept. 2010, ed. by A. Dittrich, K. Koll, J. Aberle, P. Geisenhainer (2010), pp. 1615–1620
W.H. Press, S. Teukolsky, W. T. Vetterling, B.P. Flannery, Numerical Recipes in C, Chap. 7.7 (Cambridge University Press, Cambridge, 1992)
B. Rhein, T. Clees, M. Ruschitzka, Robustness measures and numerical approximation of the cumulative density function of response surfaces. Commun. Stat. B-Simul. 43, 1–17 (2014)
J. Riehme, R. Kopmann, and U. Naumann, Uncertainty quantification based on forward sensitivity analysis in SISYPHE, in Proceedings of V European Conference on Computational Fluid Dynamics: ECCOMAS CFD 2010, Lisbon, Portugal,14–17 June 2010, ed. by J.C.F. Pereira, A. Sequeira, ECCOMAS (2010)
M.E. Sfakianakis, D.G. Verginis, A new family of nonparametric quantile estimators. Commun. Stat. Simul. Comput. 37, 337–345 (2008)
I.M. Sobol, On the distribution of points in a cube and the approximate evaluation of integrals. USSR Comput. Math. Math. Phys. 7, 86–112 (1967)
B. Sudret, Meta-models for structural reliability and uncertainty quantification, in Proceedings of 5th Asian-Pacific Symposium on Structural Reliability APSSRA 2012, Singapore, ed. by K. Phoon et al., (2012), pp. 53–76
G. van Bühren, T. Clees, N. Hornung, L. Nikitina, Aspects of adaptive hierarchical RBF metamodels for optimization. J. Comput. Methods Sci. Eng. 12, 5–23 (2012)
A.W. van der Vaart, Asymptotic Statistics (Cambridge University Press, Cambridge, 1998)
C. Villaret, Sisyphe 6.0 User Manual H-P73-2010-01219-FR, Department National Hydraulics and Environ-ment Laboratory, Electricité de France (2010)
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Clees, T., Nikitin, I., Nikitina, L., Pott, S., Klimenko, S. (2017). River Bed Morphodynamics: Metamodeling, Reliability Analysis, and Visualization in a Virtual Environment. In: Griebel, M., Schüller, A., Schweitzer, M. (eds) Scientific Computing and Algorithms in Industrial Simulations. Springer, Cham. https://doi.org/10.1007/978-3-319-62458-7_3
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DOI: https://doi.org/10.1007/978-3-319-62458-7_3
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