Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T07:51:55.080Z Has data issue: false hasContentIssue false

Bridging simulation granularity in system-of-systems: conjunct application of discrete element method and discrete event simulations in construction equipment design

Published online by Cambridge University Press:  16 May 2024

Mubeen Ur Rehman*
Affiliation:
Blekinge Institute of Technology, Sweden
Raj Jiten Machchhar
Affiliation:
Blekinge Institute of Technology, Sweden
Alessandro Bertoni
Affiliation:
Blekinge Institute of Technology, Sweden

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The paper addresses a critical challenge in System-of-Systems (SoS) simulations arising from the different granularity levels in SoS simulations, integrating non-coupled Discrete Element Method results into SoS-level Discrete Event Simulations using surrogate modeling. Illustrated with a wheel loader bucket use-case in mining, it enhances early design decision-making and lays the groundwork for improving SoS simulations in construction equipment design. This paves the way for broader research and application across diverse engineering design domains.

Type
Systems Engineering and Design
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Avison, D. E., Lau, F., Myers, M. D., & Nielsen, P. A. (1999). Action research. Communications of the ACM, 42(1), 9497. https://doi.org/10.1145/291469.291479CrossRefGoogle Scholar
Bertoni, A., Larsson, T., Wall, J., & Askling, C. J. (2021). Model-Driven Product Service Systems Design: The Model-Driven Development and Decision Support (MD3S) Approach. Proceedings of the Design Society, 1, 21372146. https://doi.org/10.1017/pds.2021.475CrossRefGoogle Scholar
Bhalode, P., & Ierapetritou, M. (2020). Discrete element modeling for continuous powder feeding operation: Calibration and system analysis. International Journal of Pharmaceutics, 585, 119427. https://doi.org/10.1016/j.ijpharm.2020.119427CrossRefGoogle ScholarPubMed
Blessing, L. T., & Chakrabarti, A. (2009). DRM: A design research methodology. Springer.CrossRefGoogle Scholar
Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research, 278(3), 721737. https://doi.org/10.1016/j.ejor.2018.10.025CrossRefGoogle Scholar
Collopy, P. D., & Hollingsworth, P. M. (2011). Value-Driven Design. Journal of Aircraft, 48(3), 749759. https://doi.org/10.2514/1.C000311CrossRefGoogle Scholar
Filla, R. (2015). Evaluating the efficiency of wheel loader bucket designs and bucket filling strategies with non-coupled DEM simulations and simple performance indicators. Schriftenreihe Der Forschungsvereinigung Bau-Und Baustoffmaschinen: Baumaschinentechnik 2015–Maschinen, Prozesse, Vernetzung, 49, 273292. https://doi.org/10.13140/RG.2.1.1507.1201Google Scholar
Filla, R., Obermayr, M., & Frank, B. (2014). A study to compare trajectory generation algorithms for automatic bucket filling in wheel loaders. 588605.Google Scholar
Frank, B., Kleinert, J., & Filla, R. (2018). Optimal control of wheel loader actuators in gravel applications. Automation in Construction, 91, 114. https://doi.org/10.1016/j.autcon.2018.03.005CrossRefGoogle Scholar
Greasley, A. (2009). A comparison of system dynamics and discrete event simulation. Proceedings of the 2009 Summer Computer Simulation Conference, 8387.Google Scholar
Henderson, K., & Salado, A. (2021). Value and benefits of model-based systems engineering (MBSE): Evidence from the literature. Systems Engineering, 24(1), 5166. https://doi.org/10.1002/sys.21566CrossRefGoogle Scholar
INCOSE. (2015). INCOSE Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities. John Wiley & Sons.Google Scholar
Isaksson, O., Larsson, T. C., & Rönnbäck, A. Ö. (2009). Development of product-service systems: Challenges and opportunities for the manufacturing firm. Journal of Engineering Design, 20(4), 329348. https://doi.org/10.1080/09544820903152663CrossRefGoogle Scholar
Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., & Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), 113. https://doi.org/10.1016/j.ejor.2009.06.004CrossRefGoogle Scholar
Ketterhagen, W. R., am Ende, M. T., & Hancock, B. C. (2009). Process Modeling in the Pharmaceutical Industry using the Discrete Element Method. Journal of Pharmaceutical Sciences, 98(2), 442470. https://doi.org/10.1002/jps.21466CrossRefGoogle ScholarPubMed
Maier, J. F., Eckert, C. M., & Clarkson, P. J. (2016). Model granularity and related concepts (Marjanović, D., Štorga, M., Pavković, N., Bojčetić, N., & Škec, S., Eds.; pp. 13271336). https://www.designsociety.org/publication/38943/model_granularity_and_related_conceptsGoogle Scholar
Moon, Y. B. (2017). Simulation modelling for sustainability: A review of the literature. International Journal of Sustainable Engineering, 10(1), 219. https://doi.org/10.1080/19397038.2016.1220990CrossRefGoogle Scholar
Papageorgiou, A., Ölvander, J., Amadori, K., & Jouannet, C. (2020). Multidisciplinary and multifidelity framework for evaluating system-of-systems capabilities of unmanned aircraft. Journal of Aircraft, 57(2), 317332. Scopus. https://doi.org/10.2514/1.C035640CrossRefGoogle Scholar
Sobek, D. K. II, Ward, A. C., & Liker, J. K. (1999). Toyota's Principles of Set-Based Concurrent Engineering. MIT Sloan Management Review. https://sloanreview.mit.edu/article/toyotas-principles-of-setbased-concurrent-engineering/Google Scholar
Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks (arXiv:1909.09586). arXiv. https://doi.org/10.48550/arXiv.1909.09586CrossRefGoogle Scholar
Tomiyama, T., Lutters, E., Stark, R., & Abramovici, M. (2019). Development capabilities for smart products. CIRP Annals, 68(2), 727750. https://doi.org/10.1016/j.cirp.2019.05.010CrossRefGoogle Scholar
Verhagen, W. J. C., Bermell-Garcia, P., van Dijk, R. E. C., & Curran, R. (2012). A critical review of Knowledge-Based Engineering: An identification of research challenges. Advanced Engineering Informatics, 26(1), 515. https://doi.org/10.1016/j.aei.2011.06.004CrossRefGoogle Scholar
Wiesner, S., & Thoben, K.-D. (2017). Cyber-Physical Product-Service Systems. In Biffl, S., Lüder, A., & Gerhard, D. (Eds.), Multi-Disciplinary Engineering for Cyber-Physical Production Systems: Data Models and Software Solutions for Handling Complex Engineering Projects (pp. 6388). Springer International Publishing. https://doi.org/10.1007/978-3-319-56345-9_3CrossRefGoogle Scholar
Yin, R. K. (2009). Case Study Research: Design and Methods. SAGE.Google Scholar
Yondo, R., Andrés, E., & Valero, E. (2018). A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses. Progress in Aerospace Sciences, 96, 2361. https://doi.org/10.1016/j.paerosci.2017.11.003CrossRefGoogle Scholar
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7), 12351270. https://doi.org/10.1162/neco_a_01199Google Scholar