2022-Sustainable Industrial Processing Summit
SIPS2022 Volume 16 Intl. Symp on Mathematics, Modelling and Geomechanics

Editors:F. Kongoli, E. Aifantis, T. Vougiouklis, A. Bountis, P. Mandell, R. Santilli, A. Konstantinidis, G. Efremidis.
Publisher:Flogen Star OUTREACH
Publication Year:2022
Pages:235 pages
ISBN:978-1-989820-64-3(CD)
ISSN:2291-1227 (Metals and Materials Processing in a Clean Environment Series)
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    Dynamic System Analysis Using the Markov/Cell-To-Cell Mapping Technique

    Tunc Aldemir1;
    1THE OHIO STATE UNIVERSITY, Columbus, United States;
    Type of Paper: Plenary
    Id Paper: 75
    Topic: 38

    Abstract:

    The Markov/cell-to-cell mapping technique (CCMT) is a systematic procedure to describe the dynamics of both linear and non-linear systems in discrete time and in system state space previously partitioned into computational cells in a similar manner used by finite difference or finite element methods [1]. An important feature of the Markov/CCMT is its capability to model the long term dynamics of chaotic systems in a probabilistic format. Markov/CCMT has been used for the failure modeling of different types of control systems, as well as for state/parameter estimation and diagnostics, accident management and global analysis of reactor dynamics. Some example applications are provided in [1-5]. A continuous-time, discrete state-space version of Markov/CCMT has also been developed [6] and implemented for dynamic probabilistic risk/safety assessment [7]. An overview of the Markov/CCMT is presented, including computational tools for applications.

    Keywords:

    Mathematics;

    References:

    1. T. Aldemir. Utilization of the Cell-to-Cell Mapping Technique to Construct Markov Failure Models for Process Control Systems. In Probabilistic Safety Assessment and Management, 2, G. Apostolakis (Ed.), pp. 1431-1436, Elsevier Science Publishing Co., New York (1991). 2. T. Aldemir, P. Wang, D. W. Miller, Parameter and State Estimation Using DSD, Trans. Am. Nucl. Soc., 84, 109-110, (2001) 3. A. Burghelea, T. Aldemir, An Application of DSD with Recursive Partitioning Scheme to Constant Temperature Power Sensors. In Probabilistic Safety Assessment and Management: PSAM 7-ESREL’04, C. Spitzer, U. Schmocker, V. N. Dang (Eds.), 1821-1827, Springer – Verlag, London, U.K, (June 2004). 4. T. Aldemir, S. Guarro, D. Mandelli, J. Kirschenbaum, L.A. Mangan, P. Bucci, M. Yau, E. Ekici, D.W. Miller, X. Sun, S.A. Arndt, Probabilistic Risk Assessment Modeling of Digital Instrumentation and Control Systems Using Two Dynamic Methodologies, Reliab. Engng & System Safety, 95, 1011-1039 (2010) 5. M. Hejase, A. Kurt, T. Aldemir, U. Ozguner, S. B. Guarro, M. K. Yau, Matt. D. Knudson, “Quantitative and Risk-Based Framework for Unmanned Aircraft Control System Assurance”, Journal of Aerospace Information Systems, 15, 55-71 (2018). 6. B. Tombuyses, T. Aldemir, "Continuous Cell-to-Cell Mapping", J. Sound and Vibration, 202, 395-415 (1997). 7. B. Tombuyses, T. Aldemir. Dynamic PSA of Process Control Systems via Continuous Cell-to-Cell Mapping. In Probabilistic Safety Assessment and Management, P. C. Cacciabue, I. A. Papazoglou (Eds.), 1541-1546, Springer-Verlag, New York, N.Y. (1996)

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    Aldemir T. (2022). Dynamic System Analysis Using the Markov/Cell-To-Cell Mapping Technique. In F. Kongoli, E. Aifantis, T. Vougiouklis, A. Bountis, P. Mandell, R. Santilli, A. Konstantinidis, G. Efremidis. (Eds.), Sustainable Industrial Processing Summit SIPS2022 Volume 16 Intl. Symp on Mathematics, Modelling and Geomechanics (pp. 69-78). Montreal, Canada: FLOGEN Star Outreach