Sensitivity-based analysis of the k-ϵ model for the turbulent flow between two plates

Chemical Engineering Science

Volumn 63, Issue 19, 2008, Pages 4763-4775

  Bardow, André ^a   Bischof, Christian H ^a   Martin Bucker H ^a   Dietze, Georg ^a   Kneer, Reinhold ^a   Leefken, Ansgar ^a   Marquardt, Wolfgang ^a   Renz, Ulrich ^a   Slusanschi, Emil ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

Automatic differentiation; Computational fluid dynamics; Experimental design; Fluid mechanics; Inverse problem; Parameter identification; Robustness; Turbulence

Indexed keywords

APPLICATION PROGRAMS; COMPUTATIONAL FLUID DYNAMICS; DESIGN OF EXPERIMENTS; DOPPLER EFFECT; FLUID MECHANICS; IDENTIFICATION (CONTROL SYSTEMS); INVERSE PROBLEMS; ROBUSTNESS (CONTROL SYSTEMS); SENSITIVITY ANALYSIS; TURBULENCE; TURBULENCE MODELS;

AUTOMATIC DIFFERENTIATIONS; ENGINEERING APPLICATIONS; LASER DOPPLER VELOCIMETRY; QUANTITATIVE MODELING; SENSITIVITY COEFFICIENT; SENSITIVITY COMPUTATION; SENSITIVITY-BASED ANALYSIS; TURBULENCE PARAMETERS;

PARAMETER ESTIMATION;

EID: 52149101604     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2007.12.029     Document Type: Article

References (40)

1. Albrecht H.E., Borys M., Damaschke N., and Tropea C. Laser Doppler and Phase Doppler Measurement Techniques (2003), Springer, Berlin
2. Atkinson A.C., and Donev A.N. Optimum Experimental Designs (1992), Oxford University Press, Oxford
3. Bard Y. Nonlinear Parameter Estimation (1974), Academic Press, New York
4. Berz, M., Bischof, C., Corliss, G., Griewank, A. (Eds.), 1996. Computational Differentiation: Techniques, Applications, and Tools. SIAM, Philadelphia.
5. Biegler, L.T., Ghattas, O., Heinkenschloss, M., Bloemen Waanders, B.v. (Eds.), 2003. Large-Scale PDE-Constrained Optimization. Springer, Berlin.
6. Bischof C., Carle A., Khademi P., and Mauer A. ADIFOR 2.0: Automatic differentiation of Fortran 77 programs. IEEE Computational Science & Engineering 3 3 (1996) 18-32
7. Bischof C.H., Bücker H.M., Lang B., and Rasch A. An interactive environment for supporting the transition from simulation to optimization. Scientific Programming 11 4 (2003) 263-272
8. Bischof C.H., Bücker H.M., Lang B., and Rasch A. Solving large-scale optimization problems with EFCOSS. Advances in Engineering Software 34 10 (2003) 633-639
9. Bischof, C.H., Bücker, H.M., Rasch, A., Slusanschi, E., 2003c. Evaluation of a computer model for wavy falling films using EFCOSS. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L'Ecuyer, P. (Eds.), Computational Science and Its Applications-ICCSA 2003. In: Proceedings of the International Conference on Computational Science and its Applications, Montreal, Canada, May 18-21, 2003. Lecture Notes in Computer Science, vol. 2668, Part II. Springer, Berlin, pp. 78-84.
10. Bischof C.H., Bücker H.M., and Rasch A. Sensitivity analysis of turbulence models using automatic differentiation. SIAM Journal on Scientific Computing 26 2 (2005) 510-522
11. Bischof C.H., Bücker H.M., Rasch A., Slusanschi E., and Lang B. Automatic differentiation of the general-purpose computational fluid dynamics package FLUENT. Journal of Fluids Engineering 129 5 (2007) 652-658
12. Brun R., Reichert P., and Kunsch H.R. Practical identifiability analysis of large environmental simulation models. Water Resource Research 37 4 (2001) 1015-1030
13. Bücker, H.M., Corliss, G.F., Hovland, P.D., Naumann, U., Norris, B. (Eds.), 2005. Automatic Differentiation: Applications, Theory, and Implementations. In: Lecture Notes in Computational Science and Engineering, vol. 50. Springer, Berlin.
14. Chen B.H., and Asprey S.P. On the design of optimally informative dynamic experiments for model discrimination in multiresponse nonlinear situations. Industrial & Engineering Chemistry Research 42 7 (2003) 1379-1390
15. Chien K.Y. Predictions of channel and boundary-layer flows with a low-Reynolds-number turbulence model. AIAA Journal 20 1 (1982) 33-38
16. Corliss, G., Faure, C., Griewank, A., Hascoët, L., Naumann, U. (Eds.), 2002. Automatic Differentiation of Algorithms: From Simulation to Optimization. Springer, New York.
17. Gasser T., Sroka L., and Jennen-Steinmetz C. Residual variance and residual pattern in nonlinear-regression. Biometrika 73 3 (1986) 625-633
18. Gay, D.M., 1990. Usage summary for selected optimization routines. Computing Science Technical Report 153, AT&T Bell Laboratories, Murray Hill.
19. Griewank A. Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (2000), SIAM, Philadelphia
20. Griewank, A., Corliss, G. (Eds.), 1991. Automatic Differentiation of Algorithms. SIAM, Philadelphia.
21. Grimstad A.-A., Kolltveit K., Mannseth T., and Nordtvedt J.-E. Assessing the validity of a linearized accuracy measure for a nonlinear parameter estimation problem. Inverse Problems 17 (2001) 1373-1390
22. Hjemfelt A., and Mockros L. Motion of discrete particles in a turbulent fluid. Applied Scientific Research 16 (1966) 149
23. Hrenya C.M., Bolio E.J., Chakrabarti D., and Sinclair J.L. Comparison of low Reynolds number k-ε{lunate} turbulence models in predicting fully developed pipe flow. Chemical Engineering Science 50 12 (1995) 1923-1941
24. Jones W.P., and Launder B.E. The prediction of laminarization with a two-equation model of turbulence. International Journal of Heat and Mass Transfer 15 (1972) 301-314
25. Lam C.K.G., and Bremhorst K.H. Modified form of k-ε model for predicting wall turbulence. Journal of Fluids Engineering 103 (1981) 456-460
26. Launder B., and Spalding D.B. The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering 3 (1974) 269-289
27. Launder B.E., and Sharma B.I. Application of the energy dissipation model of turbulence to the calculation of flow near a spinning disc. Letters in Heat and Mass Transfer 1 2 (1974) 131-138
28. Mansour N.N., Kim J., and Moin P. Reynolds-stress and dissipation-rate budgets in a turbulent channel flow. Journal of Fluid Mechanics 194 (1988) 15-44
29. Marquardt W. Model-based experimental analysis of kinetic phenomena in multi-phase reactive systems. Chemical Engineering Research & Design 83 A6 (2005) 561-573
30. Pahlevani F. Sensitivity computations of eddy viscosity models with an application in drag computation. International Journal for Numerical Methods in Fluids 52 4 (2006) 381-392
31. Press W.H., Teukolsky S.A., Vetterling W.T., and Flannery B.P. Numerical Recipes in C: The Art of Scientific Computing (1992), Cambridge University Press, Cambridge
32. Rall, L.B., 1981. Automatic differentiation: techniques and applications. Lecture Notes in Computer Science, vol. 120. Springer, Berlin.
33. Seifert B., Gasser T., and Wolf A. Nonparametric estimation of residual variance revisited. Biometrika 80 (1993) 373-383
34. Shiebener P., Straub J., Levelt Sengers J., and Gallagher J. Refractive index of water and steam as function of wavelength, temperature and density. Journal of Physical and Chemical Reference Data 19 3 (1990) 677-717
35. Sokolichin A., and Eigenberger G. Applicability of the standard k-ε{lunate} turbulence model to the dynamic simulation of bubble columns. Part I. Detailed numerical simulations. Chemical Engineering Science 54 13-14 (1999) 1923-1941
36. Speziale, C.G., Abid, R., Anderson, E.C., 1990. A critical evaluation of two-equation models for near wall turbulence. In: AIAA. No. 90-1481. Seattle.
37. Turgeon E., Pelletier D., and Borggaard J. A general continuous sensitivity equation formulation for the kappa-epsilon model of turbulence. International Journal of Computational Fluid Dynamics 18 (2004) 29-46
38. Vaidyanathan R., Senocak I., Wu J.Y., and Shyy W. Sensitivity evaluation of a transport-based turbulent cavitation model. Journal of Fluids Engineering-Transactions of the ASME 125 (2003) 447-458
39. Walter, E. (Ed.), 1987. Identifiability of Parametric Models. Pergamon Press, Oxford.
40. Walter E., and Pronzato L. Qualitative and quantitative experiment design for phenomenological models-a survey. Automatica 26 2 (1990) 195-213