Abstract
Systems engineering is the interdisciplinary engineering field that focuses on the design of complex physical systems to optimize the system’s performance over its life-cycle. To support such optimization efforts a number of computational modeling methods are required: ontological modeling, stochastic modeling, and process simulation modeling. Despite this need, the field of systems engineering has mainly focused on the development and discussion of managerial methods. This paper tries to provide a first starting point for a discussion about a framework to understand how the above mentioned computational methods can support system engineers. The paper introduces a first set of important methods and tries to integrate them in an overall framework for analysing engineered systems from different points of view. For each of the methods we also provide a simple illustrative example from our ongoing systems engineering teaching efforts at the TU Berlin.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farlow, S.J.: Partial differential equations for scientists and engineers. Courier Corporation, North Chelmsford (1993)
Flager, F., Welle, B., Bansal, P., Soremekun, G., Haymaker, J.: Multidisciplinary process integration and design optimization of a classroom building. J. Inf. Technol. Constr. (ITcon) 14(38), 595–612 (2009)
Forrester, A., Keane, A., et al.: Engineering Design Via Surrogate Modelling: A Practical Guide. Wiley, Hoboken (2008)
Geyer, P.: Multidisciplinary grammars supporting design optimization of buildings. Res. Eng. Design 18(4), 197–216 (2008)
Kapurch, S.J.: NASA Systems Engineering Handbook. Diane Publishing, Collingdale (2010)
Krötzsch, M., Simancik, F., Horrocks, I.: A description logic primer. arXiv preprint arXiv:1201.4089 (2012)
Lantz, B.: Machine Learning with R. Packt Publishing Ltd., Birmingham (2013)
Luhmann, N.: Soziale Systeme, vol. 478. Suhrkamp, Frankfurt am Main (1984)
Luzeaux, D., et al.: Systems of Systems. Wiley, Hoboken (2013)
Matloff, N.: From Algorithms to Z-scores: Probabilistic and Statistical Modeling in Computer Science. Creative Commons License (2009)
Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: A guide to creating your first ontology (2001)
Pahl, G., Beitz, W.: Engineering Design: A Systematic Approach. Springer Science & Business Media, London (2013)
Ropohl, G.: Allgemeine Technologie: eine Systemtheorie der Technik. KIT Scientific Publishing, Karlsruhe (2009)
Ropohl, G.: Allgemeine Systemtheorie: Einführung in transdisziplinäres Denken. edition sigma, Berlin (2012)
Saltelli, A., Chan, K., Scott, E.M., et al.: Sensitivity Analysis, vol. 1. Wiley, New York (2000)
Wainer, G.A., Mosterman, P.J.: Discrete-Event Modeling and Simulation: Theory and Applications. CRC Press, Boca Raton (2016)
Ziari, H., Sobhani, J., Ayoubinejad, J., Hartmann, T.: Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. Int. J. Pavement Eng. 17(9), 776–788 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Hartmann, T. (2018). Engineering Informatics to Support Civil Systems Engineering Practice. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10864. Springer, Cham. https://doi.org/10.1007/978-3-319-91638-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-91638-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91637-8
Online ISBN: 978-3-319-91638-5
eBook Packages: Computer ScienceComputer Science (R0)