Mechanical engineering has always had a strong hold among all engineering disciplines over many decades, even in the face of vastly changing technological scenarios. To safeguard its pristine position, mechanical engineers must look beyond and make a landscape for sustaining their future technological careers for the coming decades. This article looks at the development of new courses that aim to cultivate desirable skill sets for mechanical engineers of the future. Specialised courses such as computational fluid dynamics (CFD), microelectromechanical systems (MEMS) and micro-nano fluidics, and non-equilibrium and quantum thermodynamics will enable mechanical engineers to meet the technological demands of future. Mechanical engineers have to use their intellectual competency to meet challenges posed by design, economic, environmental and social constraints. Engineers are required to get inspiration from nature so that they accomplish their goals working within these imposed constraints. All the tasks undertaken by engineers today or in the future should have a touch of innovation with creativity. Taking a technological approach that also takes cognisance of the need for sustainability is key to unlocking solutions. For a design methodology using invention and discovery through nature inspiration, engineers will have to use the same concepts as nature does in all their assignments. For this, a nature-inspired engineering ecosystem has to be developed. At least three tools or components will be needed within its framework, such as simulation, energy and quantum. The following sections describe the ingredients that enable to develop such an ecosystem. Simulation: It is needless to mention the role played by simulation in realising simulation-based or simulation-driven designs [1] and mechanical engineers are expected to work with superengineering skills. Today, simulations are carried out following the multiscale [2] and multiphysics modelling paradigm. This modelling approach considers models at different scales that shares efficiency of macroscopic models as well as accuracy of microscopic models by connecting engineering applications with basic science providing unified approach to modelling. Energy: This includes generation, conversion, transfer, storage, conservation, management and environmental pollution control of both conventional and renewable sources of energy. Energy needs are met for transport in air, water, road, space and terrestrial applications. Energy used for cooling electronics and computing needs conservation effort through thermoelectric conversion [3]. Fuel cell energy conversion and battery storage technologies need to be designed addressing economy, efficiency and energy density. Miniaturised devices are currently very important and will continue to remain so in near future. Quantum: Quantum studies enable to get fundamental understanding of the interplay of constituents of matter and forces. Designs based on quantum approach offer better working systems or supplement with existing design approaches. Thermoelectric [4] and electrochemical energy systems design can take full benefit from quantum-based design approaches. Traditional classical computing approaches when replaced by quantum counterpart offer considerable benefits in the near future when the technology of quantum computer becomes a reality.

Technological demands of the future

  • Computational Fluid Dynamics
Scope: Numerical treatment of the physics of fluid flows incorporating energy, species and momentum transport mechanisms [5]. Understanding of fundamental concepts through review of governing equations, numerical discretisation of finite difference, finite volume and finite elements methods covering the types of fluids flows namely incompressible, compressible, laminar and turbulence, newtonian and complex fluids. In CFD study, the simulation is carried at macro scales. A good grasp of continuum-based simulation tools is needed for numerical maths handling using the finite difference method (FDM), OpenFOAM [6,7] based on the finite volume method (FVM) and Elemer [8] based on the finite element method (FEM).
  • MEMS and Micro-Nano Fluidics
Scope: Physics at micro, nano and molecular scales manipulating very small volumes of fluid with handling of new phenomena. Fluid flows incorporating energy, species and momentum as well as charge transport mechanisms are covered. Understanding through fundamental concepts of surface tension driven capillary, electrokinetic and slip flow [9] phenomena are important. In MEMS and micro-nano fluidics study, the simulation is carried at micro-nano or atomic/molecular scales. Methods for simulation available are, particle-based molecular dynamics, direct simulation Monte Carlo, Lattice Boltzmann method, Boltzmann transport equation [10] with emphasis on multiphysics and multiscale modelling approaches.
  • Non-Equilibrium & Quantum Thermodynamics
Scope: Unified understanding of complex systems such as quantum energy transport, computing, biology and finance [11]. Understanding of fundamental concepts of kinetic gas theory, non-equilibrium or Irreversible thermodynamics, quantum mechanics are included here. Irreversible process and coupled phenomena can be studied [12]. In non-equilibrium and quantum thermodynamics study, the simulation is carried encompassing all the above-mentioned scales i.e. multiscale modelling including subatomic quantum scales. Available tools for exploring quantum simulation are ShengBTE [13] and QuTiP [14]. Table 1 provides summary features for the above.
Table 1: Summary features of courses for mechanical engineering
Design using invention and discovery through nature inspiration Computational Fluid Dynamics MEMS & Micro-Nano Fluidics Non-equilibrium & Quantum Thermodynamics
Understanding through fundamental concepts 1) Governing PDE equations 2) Numerical discretization using FDM, FVM and FEM 3) Compressible and Incompressible Flows 4) Laminar and Turbulent Flows 5) Newtonian and Complex fluids 1) Surface tension driven capillary flows 2) Electrokinetic flows 3) Slip flows 1) Kinetic Theory of Gases 2) Non-equilibrium or Irreversible Thermodynamics 3) Quantum Transport
Prediction through simulation 1) Numerical maths in FDM 2) OpenFOAM in FVM 3) Elmer in FEM 1) Molecular Dynamics 2) DSMC 3) LBM 4) BTE 5) VoF 6) SPH 1) Quantum simulation 2) ShengBTE 3) QuTiP
Scale of Modeling and other distinguishable feature Macro scale, continuum, mesh based Micro-nano, multi scale and multiphysics, continuum and non continuum, Particulate, meshless Nano, quantum, multi scale, particulate
Application Industry, Automobile, Aerospace Biotechnology, MEMS, Medicine, Lab on a Chip Thermoelectric, Computing, Biology, Finance
The article discussed the adaption of new courses in the study of mechanical engineering. Mechanical engineering graduates will be able to retain their competitive edge in the coming years by developing the desired skills, as discussed in the above course descriptions. Author: Auro Ashish Saha is with the Department of Mechanical Engineering, Pondicherry Engineering College, Pondicherry, India. Email: References 1. Auro Ashish Saha, Training guide on computer aided engineering and design, 2. E, Weinan, Principles of Multiscale Modeling, Cambridge University Press, New York, 2011. 3. Veljko Zlatic and Rene Monnier, Modern Theory of Thermoelectricity, Oxford University Press, Oxford, 2014. 4. Kamran Behnia, Fundamentals of Thermoelectricity, Oxford University Press, London, 2015. 5. H. Versteeg and W. Malalasekra, An Introduction to Computational Fluid Dynamics, Pearson, England, 2007. 6. OpenFOAM, 7. Auro Ashish Saha, Training Methodology for Competency Development in CFD, manuscript under preparation. 8. Elmer, 9. P, Abgrall and N. T. Nguyen, Nanofluidics, Artech House, Boston, 2009. 10. G, Karniadakis et al., Microflows and Nanoflows, Springer, New York, 2005. 11. Gunter Mahler, Quantum Thermodynamic Processes, CRC Press, Boca Raton, 2015. 12. Yasar Demirel, Nonequilibrium Thermodynamics, Elsevier, Oxford, 2014. 13. Wu Li et al., 'ShengBTE: A solver for the Boltzmann transport equation for phonons,' Comp. Phys. Commun., 185, pp. 1747-1758, 2014. 14. J. R. Johansson et al., 'QuTiP 2: A Python framework for the dynamics of open quantum systems,' Comp. Phys. Comm. 184, 1234, 2013.