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[1] Oliveira, H.L. and Leonel, E.D., Constitutive relation error formalism applied to the solution of inverse problems using the BEM. Engineering Analysis with Boundary Ele- ments, 108, pp. 30–40, 2019.
[2] Rasheed, A., San, O. & Kvamsdal, T., Digital twin: Values, challenges and enablers. arXiv preprint arXiv:1910.01719. 2019.
[3] Barricelli, B.R., Casiraghi, E. & Fogli, D. A survey on digital twin: Definitions, char- acteristics, applications, and design implications. IEEE Access, 7(Ml), pp. 167653– 167671, 2019. [Crossref]
[4] Wright, L. & Davidson, S. How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(1), pp. 1–3, 2020. [Crossref]
[5] Chinesta, F., Cueto, E., Abisset-Chavanne, E., Duval, J.L. & El Khaldi, F., Virtual, digi- tal and hybrid twins: a new paradigm in data-based engineering and engineered data. Archives of computational methods in engineering, 27(1), pp. 105–134, 2020.
[6] Abdelmegid, M.A., González, V.A., O’Sullivan, M., Walker, C.G., Poshdar, M. & Ying, F., The roles of conceptual modelling in improving construction simulation studies: A comprehensive review. Advanced Engineering Informatics, 46. https://doi. org/10.1016/j.aei.2020.101175 [Crossref]
[7] Robinson, S., Arbez, G., Birta, L.G., Tolk, A. & Wagner, G., Conceptual modeling: definition, purpose and benefits. In 2015 Winter Simulation Conference (WSC) (pp. 2812–2826). IEEE, 2015.
[8] Brynjarsdóttir, J. & O’Hagan, A. Learning about physical parameters: The impor- tance of model discrepancy. Inverse Problems, 30(11), p. 114007, 2014. https://doi. org/10.1088/0266-5611/30/11/114007
[9] Law, A.M., Kelton, W.D. & Kelton, W.D., Simulation Modeling and Analysis. New York: McGraw-Hill; 2000.
[10] Papalambros, P.Y. & Wilde, D.J., Principles of Optimal Design: Modeling and Compu- tation. Cambridge University Press, 2000.
[11] Venter, G., Review of optimization techniques. Encyclopedia of Aerospace Engineer- ing. 2010.
[12] Whitley, D., Rana, S., Dzubera, J. & Mathias, K.E., Evaluating evolutionary algorithms. Artificial Intelligence, 85(1–2), pp. 245–276, 1996. 3702(95)00124-7 [Crossref]
[13] Han, Z.H. & Zhang, K.S., Surrogate-based optimization. Real-World Applications of Genetic Algorithms, 7, p. 343, 2012.
[14] Boschert, S. & Rosen, R., Digital twin—the simulation aspect. In Mechatronic Futures (pp. 59–74). Springer, Cham, 2016.
[15] Schleich, B., Answer, N., Mathieu, L. & Wartzack, S., Shaping the digital twin for design and production engineering. CIRP Annals, 66(1), pp. 141–144, 2017. https://doi. org/10.1016/j.cirp.2017.04.040
[16] Tuegel, E.J., Ingraffea, A.R. Eason, T.G. & Spottswood, S.M., Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engi- neering, 2011, 2011.
[17] Giunta, A., Wojtkiewicz, S. & Eldred, M., Overview of modern design of experiments methods for computational simulations. In 41st Aerospace Sciences Meeting and Exhibit (p. 649). 2003.
[18] Rajaram, D., Methods for Construction of Surrogates For Computationally Expensive High-Dimensional Problems (Doctoral dissertation, Georgia Institute of Technology) 2020.
[19] Sapkota, M.S., Apeh, E., Hadfield, M., Adey, R. & Baynham, J., Design of experiments platform for online simulation model validation and parameter updating within digital twinning. WIT Transactions on Engineering Sciences, 130, pp. 3–14, 2020.
[20] Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S. & Escaleira, L.A., Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76(5), pp. 965–977, 2008.
[21] Myers, R.H., Montgomery, D.C. & Anderson-Cook, C.M., Response Surface Method- ology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons; 2016.
[22] MATLAB and Statistics Toolbox Release, TheMathWorks, Inc., Natick, Massachusetts, United States, 2012b.
[23] Adey, R.A., Modelling of Cathodic Protection Systems. United Kingdom, WIT, 2006.
[24] Adey, R., Peratta, C. & Baynham, J., Corrosion Data Management Using 3D Visualisa- tion and a Digital Twin. In NACE International Corrosion Conference Proceedings (pp. 1–13). NACE International, 2020.
[25] Alizadeh, R., Allen, J.K. & Mistree, F., Managing computational complexity using sur- rogate models: a critical review. Research in Engineering Design, 31(3), pp. 275–298, 2020. [Crossref]
[26] Montgomery, D.C. Design and Analysis of Experiments. John Wiley & Sons, 2017.
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Acadlore takes over the publication of IJCMEM from 2025 Vol. 13, No. 3. The preceding volumes were published under a CC BY 4.0 license by the previous owner, and displayed here as agreed between Acadlore and the previous owner. ✯ : This issue/volume is not published by Acadlore.

Open Access
Research article

Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning

madhu sudan sapkota1,
edward apeh1,
mark hadfield1,
roya haratian1,
robert adey2,
john baynham2
1
Faculty of Science and Technology, Bournemouth University Poole, United Kingdom
2
CM BEASY Ltd, Ashurst Lodge, UK
International Journal of Computational Methods and Experimental Measurements
|
Volume 10, Issue 2, 2022
|
Pages 158-171
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
View Full Article|Download PDF

Abstract:

The process of developing a virtual replica of a physical asset usually involves using the best available values of the material and environment-related parameters essential to run the predictive simulation. The parameter values are further updated as necessary over time in response to the behaviour/conditions of physical assets and/or environment. This parametric calibration of the simulation models is usually made manually with trial-and-error using data obtained from sensors/manual survey readings of designated parts of the physical asset. Digital twining (DT) has provided a means by which validating data from the physical asset can be obtained in near real time. However, the process of calibration is time-consuming as it is manual, and as with each parameter guess during the trial, a simulation run is required. This is even more so when the running time of a single simulation is high enough, like hours or even days, and the model involves a significantly high number of parameters. To address these shortcomings, an experimental platform implemented with the integration of a simulator and scientific software is proposed. The scientific software within the platform also offers surrogate building support, where surrogates assist in the estimation/update of design parameters as an alternative to time-consuming predictive models. The proposed platform is demonstrated using BEASY, a simulator designed to predict protection provided by a cathodic protection (CP) system to an asset, with MATLAB as the scientific software. The developed setup facilitates the task of model validation and adaptation of the CP model by automating the process within a DT ecosystem and also offers surrogate-assisted optimisation for parameter estimation/updating.

Keywords: BEASY, Cathodic-protection, Digital twin, Model adaptation, Software integration

1. Introduction

2. Digital Twin Concept with Self Adaptation Potential

3. Platform and Response Surrogates in Dt Realisation

4. Case Study – Simulator-Based Cathodic Protection Digital Twin Realisation

5. Conclusion

Acknowledgments

This work has been undertaken as part of a match-funded PhD research project between Computational Mechanics International Limited and Bournemouth University, UK.

References
[1] Oliveira, H.L. and Leonel, E.D., Constitutive relation error formalism applied to the solution of inverse problems using the BEM. Engineering Analysis with Boundary Ele- ments, 108, pp. 30–40, 2019.
[2] Rasheed, A., San, O. & Kvamsdal, T., Digital twin: Values, challenges and enablers. arXiv preprint arXiv:1910.01719. 2019.
[3] Barricelli, B.R., Casiraghi, E. & Fogli, D. A survey on digital twin: Definitions, char- acteristics, applications, and design implications. IEEE Access, 7(Ml), pp. 167653– 167671, 2019. [Crossref]
[4] Wright, L. & Davidson, S. How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(1), pp. 1–3, 2020. [Crossref]
[5] Chinesta, F., Cueto, E., Abisset-Chavanne, E., Duval, J.L. & El Khaldi, F., Virtual, digi- tal and hybrid twins: a new paradigm in data-based engineering and engineered data. Archives of computational methods in engineering, 27(1), pp. 105–134, 2020.
[6] Abdelmegid, M.A., González, V.A., O’Sullivan, M., Walker, C.G., Poshdar, M. & Ying, F., The roles of conceptual modelling in improving construction simulation studies: A comprehensive review. Advanced Engineering Informatics, 46. https://doi. org/10.1016/j.aei.2020.101175 [Crossref]
[7] Robinson, S., Arbez, G., Birta, L.G., Tolk, A. & Wagner, G., Conceptual modeling: definition, purpose and benefits. In 2015 Winter Simulation Conference (WSC) (pp. 2812–2826). IEEE, 2015.
[8] Brynjarsdóttir, J. & O’Hagan, A. Learning about physical parameters: The impor- tance of model discrepancy. Inverse Problems, 30(11), p. 114007, 2014. https://doi. org/10.1088/0266-5611/30/11/114007
[9] Law, A.M., Kelton, W.D. & Kelton, W.D., Simulation Modeling and Analysis. New York: McGraw-Hill; 2000.
[10] Papalambros, P.Y. & Wilde, D.J., Principles of Optimal Design: Modeling and Compu- tation. Cambridge University Press, 2000.
[11] Venter, G., Review of optimization techniques. Encyclopedia of Aerospace Engineer- ing. 2010.
[12] Whitley, D., Rana, S., Dzubera, J. & Mathias, K.E., Evaluating evolutionary algorithms. Artificial Intelligence, 85(1–2), pp. 245–276, 1996. 3702(95)00124-7 [Crossref]
[13] Han, Z.H. & Zhang, K.S., Surrogate-based optimization. Real-World Applications of Genetic Algorithms, 7, p. 343, 2012.
[14] Boschert, S. & Rosen, R., Digital twin—the simulation aspect. In Mechatronic Futures (pp. 59–74). Springer, Cham, 2016.
[15] Schleich, B., Answer, N., Mathieu, L. & Wartzack, S., Shaping the digital twin for design and production engineering. CIRP Annals, 66(1), pp. 141–144, 2017. https://doi. org/10.1016/j.cirp.2017.04.040
[16] Tuegel, E.J., Ingraffea, A.R. Eason, T.G. & Spottswood, S.M., Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engi- neering, 2011, 2011.
[17] Giunta, A., Wojtkiewicz, S. & Eldred, M., Overview of modern design of experiments methods for computational simulations. In 41st Aerospace Sciences Meeting and Exhibit (p. 649). 2003.
[18] Rajaram, D., Methods for Construction of Surrogates For Computationally Expensive High-Dimensional Problems (Doctoral dissertation, Georgia Institute of Technology) 2020.
[19] Sapkota, M.S., Apeh, E., Hadfield, M., Adey, R. & Baynham, J., Design of experiments platform for online simulation model validation and parameter updating within digital twinning. WIT Transactions on Engineering Sciences, 130, pp. 3–14, 2020.
[20] Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S. & Escaleira, L.A., Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76(5), pp. 965–977, 2008.
[21] Myers, R.H., Montgomery, D.C. & Anderson-Cook, C.M., Response Surface Method- ology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons; 2016.
[22] MATLAB and Statistics Toolbox Release, TheMathWorks, Inc., Natick, Massachusetts, United States, 2012b.
[23] Adey, R.A., Modelling of Cathodic Protection Systems. United Kingdom, WIT, 2006.
[24] Adey, R., Peratta, C. & Baynham, J., Corrosion Data Management Using 3D Visualisa- tion and a Digital Twin. In NACE International Corrosion Conference Proceedings (pp. 1–13). NACE International, 2020.
[25] Alizadeh, R., Allen, J.K. & Mistree, F., Managing computational complexity using sur- rogate models: a critical review. Research in Engineering Design, 31(3), pp. 275–298, 2020. [Crossref]
[26] Montgomery, D.C. Design and Analysis of Experiments. John Wiley & Sons, 2017.
Cite this:
APA Style
IEEE Style
BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Sapkota, M. S., Apeh, E., Hadfield, M., Haratian, R., Adey, R., & Baynham, J. (2022). Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning. Int. J. Comput. Methods Exp. Meas., 10(2), 158-171. https://doi.org/10.2495/CMEM-V10-N2-158-171
M. S. Sapkota, E. Apeh, M. Hadfield, R. Haratian, R. Adey, and J. Baynham, "Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning," Int. J. Comput. Methods Exp. Meas., vol. 10, no. 2, pp. 158-171, 2022. https://doi.org/10.2495/CMEM-V10-N2-158-171
@research-article{Sapkota2022Surrogate-AssistedPC,
title={Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning},
author={Madhu Sudan Sapkota and Edward Apeh and Mark Hadfield and Roya Haratian and Robert Adey and John Baynham},
journal={International Journal of Computational Methods and Experimental Measurements},
year={2022},
page={158-171},
doi={https://doi.org/10.2495/CMEM-V10-N2-158-171}
}
Madhu Sudan Sapkota, et al. "Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning." International Journal of Computational Methods and Experimental Measurements, v 10, pp 158-171. doi: https://doi.org/10.2495/CMEM-V10-N2-158-171
Madhu Sudan Sapkota, Edward Apeh, Mark Hadfield, Roya Haratian, Robert Adey and John Baynham. "Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning." International Journal of Computational Methods and Experimental Measurements, 10, (2022): 158-171. doi: https://doi.org/10.2495/CMEM-V10-N2-158-171
SAPKOTA M S， APEH E， HADFIELD M， et al. Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning[J]. International Journal of Computational Methods and Experimental Measurements， 2022， 10(2)： 158-171. https://doi.org/10.2495/CMEM-V10-N2-158-171