Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T07:36:41.743Z Has data issue: false hasContentIssue false

Continuous and Discrete Adjoint Approach Based on Lattice Boltzmann Method in Aerodynamic Optimization Part I: Mathematical Derivation of Adjoint Lattice Boltzmann Equations

Published online by Cambridge University Press:  03 June 2015

Mohamad Hamed Hekmat*
Affiliation:
Center of Excellence for Design & Simulation of Space Systems, K. N. Toosi University of Technology, Tehran, 891567, Iran
Masoud Mirzaei*
Affiliation:
Center of Excellence for Design & Simulation of Space Systems, K. N. Toosi University of Technology, Tehran, 891567, Iran
*
Corresponding author. Email: [email protected]
Get access

Abstract

The significance of flow optimization utilizing the lattice Boltzmann (LB) method becomes obvious regarding its advantages as a novel flow field solution method compared to the other conventional computational fluid dynamics techniques. These unique characteristics of the LB method form the main idea of its application to optimization problems. In this research, for the first time, both continuous and discrete adjoint equations were extracted based on the LB method using a general procedure with low implementation cost. The proposed approach could be performed similarly for any optimization problem with the corresponding cost function and design variables vector. Moreover, this approach was not limited to flow fields and could be employed for steady as well as unsteady flows. Initially, the continuous and discrete adjoint LB equations and the cost function gradient vector were derived mathematically in detail using the continuous and discrete LB equations in space and time, respectively. Meanwhile, new adjoint concepts in lattice space were introduced. Finally, the analytical evaluation of the adjoint distribution functions and the cost function gradients was carried out.

Type
Research Article
Copyright
Copyright © Global-Science Press 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Hekmat, M. H., Aerodynamic Optimization of an Airfoil Using Adjoint Equations Approach, M. S. Thesis, K. N. Toosi University of Technology, 2009.Google Scholar
[2]Nadarajah, S., A comparison of the discrete and continuous adjoint approach to automatic aero-dynamic optimization, AIAA paper 2000-0667, 38th Aerospace Sciences Meeting and Exhibit, Reno, Nevada, January 2000.Google Scholar
[3]Mittal, R. and Iaccarino, G., Immersed boundary methods, Ann. Rev. Fluid Mech., 37 (2005), pp. 239261.Google Scholar
[4]Jameson, A., Aerodynamic design via control theory, J. Sci. Comput., also ICASE Report No. 88-64, 3 (1988), pp. 233260.Google Scholar
[5]Jameson, A., Computational aerodynamics for aircraft design, Science, 245 (1989), pp. 361371.Google Scholar
[6]Jameson, A., Automatic design of transonic airfoils to reduce the shock induced pressure drag, In Proceedings of the 31st Annual Conference on Aviation and Aeronautics, 517, 1990.Google Scholar
[7]Elliot, J. and Peraire, J., Aerodynamic design using unstructured meshes, AIAA paper 961941, 1996.Google Scholar
[8]Nadarajah, S., The Discrete Adjoint Approach to Aerodynamic Shape Optimization, Ph.D. Thesis, Standford University, 2003.Google Scholar
[9]Hekmat, M. H., Mirzaei, M. and Izadpanah, E., Constrained and non-constrained aerodynamic optimization using the adjoint equations approach, J. Mech. Sci. Tech., 23 (2009), pp. 19111923.Google Scholar
[10]Hekmat, M. H., Mirzaei, M. and Izadpanah, E., Numerical investigation of adjoint method in aerodynamic optimization, Mech. Aerospace Eng. J., 5(1) (2009), pp. 7586.Google Scholar
[11]Tonomura, O., Kano, M. and Hasebe, S., Shape optimization of microchannels using CFD and adjoint method, 20th European Symposium on Computer Aided Process Engineering-ESCAPE20, 2010.Google Scholar
[12]Jacques, , Peter, E. V. and Dwight, R. P., Numerical sensitivity analysis for aerodynamic optimization: a survey of approaches, J. Comput. Fluids, 39 (2010), pp. 373391.Google Scholar
[13]Hicken, J. E. and Zingg, D. W., Aerodynamic optimization algorithm with integrated geometry parameterization and mesh movement, AlAA Journal, 48(2) (2010), pp. 400414.Google Scholar
[14]Freund, J. B., Adjointt-based optimization for understanding and suppressing jet noise, Procedia IUTAM J., 1 (2010), pp. 5463.Google Scholar
[15]Tekitek, M. M., Bouzidi, M., Dubois, F. and Lallemand, P., Adjoint lattice Boltzmann equation for parameter identification, Comput. Fluids, 35 (2006), pp. 805813.Google Scholar
[16]Pingen, G., Evgrafov, A. and Maute, K., Topology optimization of flow domains using the lattice Boltzmann method, Struct Multidisc Optim J., 34 (2007), pp. 507524.Google Scholar
[17]Pingen, G., Evgrafov, A. and Maute, K., Parameter Sensitivity analysis for the hydrodynamic lattice Boltzmann method with application, J. Comput. Fluids, 38 (2009), pp. 910923.Google Scholar
[18]Krause, M. J., Thater, G. and Heuveline, V., Adjoint-based fluid flow control and optimization with lattice Boltzmann methods, Comput. Math. Appl., 65 (2013), pp. 945960.Google Scholar
[19]Gladrow, W. and Dieter, A., Lattice-Gas Cellular Automata and Lattice Boltzmann Models: an Introduction, Heidelberg, Springer, 2000.Google Scholar
[20]Bhatnagar, P. L., Gross, E. P. and Krook, M., A model for collision processes in gases I. small amplitude processes in charged and neutral one-component systems, J. Phys. Rev., 94(3) (1954), pp. 511525.Google Scholar
[21]Sh. Luo, L., The Lattice-Gas and Lattice Boltzmann Methods: Past, Present, and Future, in Wu, J.-H. and Zhu, Z.-J., editors, International Conference on Applied Computational Fluid Dynamics, 5283, 2000.Google Scholar
[22]Guo, Z., Zheng, C. and Shi, B., Discrete lattice effects on the forcing term in the lattice Boltzmann method, Phys. Rev. E, 65(4) (2002), pp. 16.Google Scholar