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Multiple-Body Collision Algorithms for Computational Simulation of High-Speed Air-Delivered Systems

Published online by Cambridge University Press:  22 January 2015

Robert E. Harris ,
Peter A. Liever ,
Edward A. Luke  and
Jonathan G. Dudley
Robert E. Harris*
Affiliation:
Aerospace & Defense Division, CFD Research Corporation, Huntsville, AL 35806, USA
Peter A. Liever
Affiliation:
Aerospace & Defense Division, CFD Research Corporation, Huntsville, AL 35806, USA
Edward A. Luke
Affiliation:
Computer Science Department, Mississippi State University, Mississippi State, MS 39762, USA
Jonathan G. Dudley
Affiliation:
Air Force Research Laboratory, Eglin AFB, FL 32542-6810, USA
*
*Email addresses: [email protected] (R. E. Harris), [email protected] (P. A. Liever), [email protected] (E. A. Luke), [email protected] (J. G. Dudley)
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Abstract

Currently, there exists a lack of confidence in the computational simulation of multiple body high-speed air delivered systems. Of particular interest is the ability to accurately predict the dispersion pattern of these systems under various deployment configurations. Classical engineering-level methods may not be able to predict these patterns with adequate confidence due primarily to accuracy errors attributable to reduced order modeling. In the current work, a new collision modeling capability has been developed to enable multiple-body proximate-flight simulation in the Loci/CHEM framework. This approach maintains high-fidelity aerodynamics and incorporates six degrees of freedom modeling with collision response, and is well-suited for simulation of a large number of projectiles. The proposed simulation system is intended to capture the strong interaction phase early in the projectile deployment, with subsequent transfer of projectile positions and flight states to the more economical engineering-level methods. Collisions between rigid bodies are modeled using an impulse-based approach with either an iterative propagation method or a simultaneous method. The latter is shown to be more accurate and robust for cases involving multiple simultaneous collisions as it eliminates the need to sort and resolve the collisions sequentially. The implementation of both the collision detection methodology and impact mechanics are described in detail with validation studies to demonstrate the efficiency and accuracy of the developed technologies. The studies chronologically detail the findings for simulating simple impacts and collisions between multiple bodies with aerodynamic interference effects.

Keywords

76SubmunitionprojectileballisticCFD6-DoFsolid body collisioncontact forcehypersonicaerodynamicinterference effects
Type
Research Article
Information
Communications in Computational Physics , Volume 17 , Issue 2 , February 2015 , pp. 564 - 593
Copyright
Copyright © Global Science Press Limited 2015 

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References

[1]Meakin, R., Composite Overset Structured Grids, Chapter 11, pp. 1–20, Thompson, J., Soni, B., and Weatherill, N., (Eds.), CRC Press, 1999.Google Scholar
[2]Dietz, W.E., Simulation and analysis of multiple-body dispense event, AIAA Paper 2004–1252.Google Scholar
[3]Luke, E., and Cinnella, P., Numerical simulations of mixtures of fluids using upwind algorithms, Computers and Fluids, Volume 36, December 2007, pp. 1547–1566.Google Scholar
[4]Luke, E., and George, T., Loci: A rule-based framework for parallel multidisciplinary simulation synthesis, Journal of Functional Programming, Volume 15, Issue 03, 2005, pp. 477–502.Google Scholar
[5]Luke, E.A., Loci: Automated synthesis for terascale computing systems, 27th Army Science Conference, Orlando, Fl, November 29th – December 2nd, 2010, CP–08.Google Scholar
[6]Luke, E.A., Loci: A deductive framework for graph-based algorithms, in: Matsuoka, S., Oldehoeft, R., and Tholburn, M., (Eds.), Third International Symposium on Computing in Object-Oriented Parallel Environments, number 1732 in Lecture Notes in Computer Science, pages 142153, Springer-Verlag, 1999.Google Scholar
[7]Luke, E.A., A rule-based specification system for computational fluid dynamics, PhD Thesis, Mississippi State University, Mississippi State, Mississippi, 1999.Google Scholar
[8]Luke, E.A., Loci: A rule-based framework for parallel multi-disciplinary simulation synthesis, Journal of Functional Programming, Special Issue on Functional Approaches to High-Performance Parallel Programming, 15(03):477502.Google Scholar
[9]Luke, E., On robust and accurate arbitrary polytope CFD solvers, AIAA Paper 2007–3956.Google Scholar
[10]Wilcox, D.C., Turbulence Modeling for CFD, DCW Industries, 2006.Google Scholar
[11]Veluri, S., Roy, C.J., Luke, E., Comprehensive code verification for an unstructured finite volume CFD code, AIAA Paper 2010–127.Google Scholar
[12]Chalasani, S., Senguttuvan, V., Thompson, D., and Luke, E., On the use of general elements in fluid dynamics simulations, Communications in Numerical Methods in Engineering, Vol. 24, No. 6, 2008, pp. 435–448.Google Scholar
[13]Tong, X-L., and Luke, E., Eulerian simulations of icing collection efficiency using a singularity diffusion model, AIAA Paper 2005–1246.Google Scholar
[14]Liu, Q., Luke, E., and Cinnella, P., Coupling heat transfer and fluid flow solvers for multi-disciplinary simulations, AIAA Journal of Thermophysics and Heat Transfer, Vol. 19, No. 4, 2005, pp. 417–427.Google Scholar
[15]Blades, E., Miskovish, S., Luke, E., Collins, E., and Kurkchubashe, A., Multiphysics simulation capability using the SIMULIA co-simulation engine, AIAA Paper 2011–3397.Google Scholar
[16]Rani, S., and Luke, E., Advanced non-gray radiation module in the Loci framework for combustion CFD, AIAA Paper 2008–5253.Google Scholar
[17]Keller, J.B., Impact with friction, ASME J. Appl. Mech., 53,1–4,1986.Google Scholar
[18]Stronge, W.J., Impact Mechanics, Cambridge University Press, 2000.CrossRefGoogle Scholar
[19]Wei, G., Three-dimensional collision modeling for rigid bodies and its coupling with fluid flow computation, Flow Science Technical Note (FSI-06-TN75).Google Scholar
[20]Meakin, R.L., A general simulation method for multiple bodies in proximate flight, AIAA-2003–3831.Google Scholar
[21]Meakin, R.L., Multiple-body proximate-flight simulation methods, AIAA-2005–4621.Google Scholar
[22]Baraff, D., Analytical methods for dynamic simulation of non-penetrating rigid bodies, Computer Graphics, Vol. 23, No. 3, 1989.Google Scholar
[23]Giang, T., Bradshaw, G., and O’Sullivan, C., Complementarity based multiple point collision resolution, Fourth Irish Workshop on Computer Graphics, 2003.Google Scholar
[24]Ermolin, E., and Kazakov, A., Impulse-based approach for rigid body collisions simultaneous resolution, International Conference Graphicon 2005, Novosibirsk Akademgorodok, Russia. www.graphicon.ru/.Google Scholar
[25]Ermolin, E., Private Communication.Google Scholar
[26]Watson, K., et al., Wind tunnel measurements of transonic aerodynamic loads on mine clearing darts, AIAA Paper 2008–0346.Google Scholar
[27]Carmichael, R.L., and Dillenius, M.F.E., Measurements of transonic aerodynamic interference between mine clearing darts, NEAR TR 638, Nielsen Engineering & Research, Mountain View, CA, Dec. 2007.Google Scholar
[28]Marcum, D.L., and Gaither, J.A., Mixed element type unstructured grid generation for viscous flow application, AIAA Paper 1999–3252.Google Scholar
[29]Watson, K.P., Neaves, M.D., Nguyen, T.C., Development of a 6-DoF model for mine clearing darts, AIAA Paper 2006–672.Google Scholar