Simulation-Based Engineering

  • Melih CakmakciEmail author
  • Gullu Kiziltas Sendur
  • Umut Durak
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


Engineers, mathematicians, and scientists were always interested in numerical solutions of real-world problems. The ultimate objective within nearly all engineering projects is to reach a functional design without violating any of the performance, cost, time, and safety constraints while optimizing the design with respect to one of these metrics. A good mathematical model is at the heart of each powerful engineering simulation being a key component in the design process. In this chapter, we review role of simulation in the engineering process, the historical developments of different approaches, in particular simulation of machinery and continuum problems which refers basically to the numerical solution of a set of differential equations with different initial/boundary conditions. Then, an overview of well-known methods to conduct continuum based simulations within solid mechanics, fluid mechanics and electromagnetic is given. These methods include FEM, FDM, FVM, BEM, and meshless methods. Also, a summary of multi-scale and multi-physics-based approaches are given with various examples. With constantly increasing demands of the modern age challenging the engineering development process, the future of simulations in the field hold great promise possibly with the inclusion of topics from other emerging fields. As technology matures and the quest for multi-functional systems with much higher performance increases, the complexity of problems that demand numerical methods also increases. As a result, large-scale effective computing continues to evolve allowing for efficient and practical performance evaluation and novel designs, hence the enhancement of our thorough understanding of the physics within highly complex systems.


Engineering design cycle V-process Waterfall model Hardware-in-the-loop simulations Feature-in-the-loop simulations Component-in-the-loop simulations Continuum mechanics Computational electromagnetics Partial differential equations (PDE) Finite element method (FEM) Finite-difference method (FDM) Multi-scale methods Lumped parameter models Model-based control system design Vehicle dynamics models Networked control systems Discretized systems Quantization Observer models Iterative learning 


  1. Ashby, M. F. (1996). Modelling of materials problems. Journal of Computer-Aided Materials Design, 3(1–3), 95–99.Google Scholar
  2. Automotive Suspension - MATLAB & Simulink Example. (n.d.). Retrieved February 15, 2017, from
  3. Bathe, K.-J. (2006). Finite element procedures. Klaus-Jurgen Bathe.Google Scholar
  4. Bensoussan, A., Lions, J.-L., & Papanicolaou, G. (1978). Asymptotic analysis for periodic structures (Vol. 5). North-Holland Publishing Company Amsterdam.Google Scholar
  5. Cakmakci, M., & Ulsoy, A. G. (2009). Improving Component-Swapping Modularity Using Bidirectional Communication in Networked Control Systems. IEEE/ASME Transactions on Mechatronics, 14(3), 307–316.
  6. Cakmakci, M., Li, Y., & Liu, S. (2011). Model-in-the-loop Development for a Fuel Cell Vehicle. In Proceedings of the American Control Conference (pp. 2462–2467).Google Scholar
  7. Chen, C.-T. (1995). Linear system theory and design. Oxford University Press, Inc.Google Scholar
  8. Chew, W. C. (1995). Waves and Fields in Inhomogeneous Media (Vol. 522). New York: IEEE Press.Google Scholar
  9. Chew, W. C., Michielssen, E., Song, J. M., & Jin, J.-M. (2001). Fast and efficient algorithms in computational electromagnetics. Artech House, Inc.Google Scholar
  10. Dhatt, G., Lefrançois, E., & Touzot, G. (2012). Finite element method. John Wiley & Sons.Google Scholar
  11. Dokuyucu, H. I. H. I., & Cakmakci, M. (2016). Concurrent Design of Energy Management and Vehicle Traction Supervisory Control Algorithms for Parallel Hybrid Electric Vehicles. IEEE Transactions on Vehicular Technology, 65(2), 555–565.
  12. El-Kahlout, Y., & Kiziltas, G. (2011). Inverse synthesis of electromagnetic materials using homogenization based topology optimization. Progress In Electromagnetics Research, 115, 343–380. doi:10.2528/PIER10081603
  13. Franklin, G. F., Powell, J. D., & Emami-Naeini, A. (2009). Feedback Control of Dynamic Systems (6th ed.). Prentice Hall. Retrieved from
  14. Fung, Y.-C. (1965). Foundations of solid mechanics. Prentice Hall.Google Scholar
  15. Gurel, L., & Ergul, O. (2007). Fast and accurate solutions of extremely large integral-equation problems discretised with tens of millions of unknowns. Electronics Letters, 43(9), 499–500.Google Scholar
  16. Harrington, R. F. (2001). Time-Harmonic Electromagnetic Fields. New York. IEEE Press.Google Scholar
  17. Jategaonkar, R. V, Fischenberg, D., & Gruenhagen, W. (2004). Aerodynamic modeling and system identification from flight data-recent applications at dlr. Journal of Aircraft, 41(4), 681–691.Google Scholar
  18. Kamadan, A. (2016). Development of Co-design frameworks for optimal variable compliant actuation. Sabanci University.Google Scholar
  19. Karnopp, D. C., Margolis, D. L., & Rosenberg, R. C. (2000). System Dynamics- Modeling and Simulation of Dynamic Systems (Third Edit). Wiley-Interscience.Google Scholar
  20. Kazachkov, I. V, & Kalion, V. A. (2002). Numerical Continuum Mechanics. Lecture notes. KTH.Google Scholar
  21. Khurmi, R. S., & Gupta, J. K. (1976). Theory of machines. Eurasia.Google Scholar
  22. Kiziltas, G., Psychoudakis, D., Volakis, J. L., & Kikuchi, N. (2003). Topology design optimization of dielectric substrates for bandwidth improvement of a patch antenna. IEEE Transactions on Antennas and Propagation, 51(10), 2732–2743.Google Scholar
  23. Lian, F., Moyne, J., & Tilbury, D. (2002). Network Design Consideration for Distributed Control Systems. IEEE Transactions on Control Systems Technology, 10(2), 297–307.Google Scholar
  24. Malvern, L. E. (1969). Introduction to the Mechanics of a Continuous Medium. Google Scholar
  25. Martins, J. R. R. A., & Lambe, A. B. (2013). Multidisciplinary Design Optimization: A Survey of Architectures. AIAA Journal, 51(9), 2049–2075.
  26. Milton, G. W. (2002). The theory of composites (Cambridge monographs on applied and computational mathematics).Google Scholar
  27. Muntean, A. (2015). Continuum Modeling: An Approach Through Pratical Examples. Springer.Google Scholar
  28. Ogata, K. (1990). Modern Control Engineering. Prentice Hall.Google Scholar
  29. Ogata, K. (1995). Discrete-Time Control Systems (2nd ed.). Prentice Hall. Retrieved from
  30. Patil, R., Filipi, Z., & Fathy, H. (2010). Computationally Efficient Combined Design and Control Optimization using a Coupling Measure. IFAC Proceedings Volumes, 43(18), 144–151.
  31. Rajamani, R., Choi, S. B., Law, B. K., Hedrick, J. K., Prohaska, R., & Kretz, P. (2000). Design and Experimental Implementation of Longitudinal Control for a Platoon of Automated Vehicles. Journal of Dynamic Systems, Measurement, and Control, 122(3), 470–476.
  32. Rajamani, R., & Hedrick, J. K. (1995). Adaptive observers for active automotive suspensions: theory and experiment. IEEE Transactions on Control Systems Technology, 3(1), 86–93.
  33. Ristevski, S., & Cakmakci, M. (2015). Mechanical design and position control of a modular mechatronic device (MechaCell). In 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (Vol. 2015–Augus, pp. 725–730). IEEE.
  34. Schetz, J. A., & Fuhs, A. E. (2013). Fundamentals of fluid mechanics. John Wiley & Sons.Google Scholar
  35. Sokolowski, J. A., & Banks, C. M. (2010). Modeling and simulation fundamentals: theoretical underpinnings and practical domains. John Wiley & Sons.Google Scholar
  36. Topçu, O., Durak, U., Oğuztüzün, H., & Yilmaz, L. (2016). Distributed Simulation. Cham: Springer International Publishing.
  37. Tureyen, E. B., Karpat, Y., & Cakmakci, M. (2016). Development of an iterative learning controller for polymer based micro-stereolithography prototyping systems. In 2016 American Control Conference (ACC) (pp. 852–857). IEEE.
  38. Ulsoy, A. G., Peng, H., & Çakmakci, M. (2012). Automotive Control Systems [Hardcover]. Cambridge University Press. Retrieved from
  39. Volakis, J. L., Mumcu, G., Sertel, K., Chen, C.-C., Lee, M., Kramer, B., … Kiziltas, G. (2006). Antenna miniaturization using magnetic-photonic and degenerate band-edge crystals. IEEE Antennas and Propagation Magazine, 48(5).Google Scholar
  40. Walsh, G. C., Ye, H., & Bushnell, L. (2002). Stability Analysis of Networked Control Systems. IEEE Transactions on Control Systems Technology, 10(3), 438–446.Google Scholar
  41. Wang, G. G., & Shan, S. (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design, 129(4), 370–380.Google Scholar
  42. Widder, D. V. (1976). The heat equation (Vol. 67). Academic Press.Google Scholar
  43. World-Record Algorithm from Jülich Calculates Over Three Trillion Particles - Research in Germany. (2011). Retrieved February 15, 2017, from
  44. Zienkiewicz, O. C., & Taylor, R. L. (2005). The finite element method for solid and structural mechanics. Butterworth-heinemann.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Melih Cakmakci
    • 1
    Email author
  • Gullu Kiziltas Sendur
    • 2
  • Umut Durak
    • 3
  1. 1.Department of Mechanical EngineeringBilkent UniversityAnkaraTurkey
  2. 2.Mechatronics Engineering Program, Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey
  3. 3.Institute of Flight SystemsGerman Aerospace Center (DLR)BraunschweigGermany

Personalised recommendations