图书馆订阅: Guest
雾化与喷雾

每年出版 12 

ISSN 打印: 1044-5110

ISSN 在线: 1936-2684

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.2 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.8 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00095 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.28 SJR: 0.341 SNIP: 0.536 CiteScore™:: 1.9 H-Index: 57

Indexed in

ON THE USE OF DYNAMIC MODE DECOMPOSITION FOR LIQUID INJECTION

卷 29, 册 11, 2019, pp. 965-985
DOI: 10.1615/AtomizSpr.2020033203
Get accessGet access

摘要

Dynamic mode decomposition (DMD) and its algorithmic variants have become commonly used techniques in the analysis, prediction, modeling, and understanding of nonlinear dynamical systems. In particular, these techniques are of interest for liquid injection systems due to the inherent complexity of multiphase interactions, and extracting the underlying flow processes is desired. These methods extract spatio-temporally coherent modes, which serve as fundamental processes that govern the system, and have had demonstrated utility in reduced-order modeling and control. Although numerous works investigating flow processes have implemented DMD, and similarly proper orthogonal decomposition, the results are often highly interpretive with little to no validation of their physical meaning. Additionally, a common result of DMD-based analysis is in capturing modal structures that are higher harmonics of lower frequency fundamental modes but have not been discussed at length. This work provides a focused study into the extraction of modes, harmonic modes, and their physical interpretation for common liquid injection systems. It is shown empirically that, for systems with suitable temporal resolution, standard DMD will produce harmonic modes even if higher harmonics are not present in the system. Only in specific conditions do these harmonic modes provide additional spatio-temporal information, but are more often indicators of system motion. Furthermore, DMD modes may be unable to resolve the underlying physical spatial scales whose dynamics they represent. Reasons for the emergence of harmonic modes are given.

参考文献
  1. Arienti, M. and Soteriou, M.C., Time-Resolved Proper Orthogonal Decomposition of Liquid Jet Dynamics, Phys. Fluids, vol. 21, no. 11, p. 112104,2009.

  2. Bao, A., Gildin, E., Narasingam, A., and Kwon, J.S., Data-Driven Model Reduction for Coupled Flow and Geomechanics based on DMD Methods, Fluids, vol. 4, no. 3, pp. 1-22,2019.

  3. Bender, C.M. and Orszag, S.A., Advanced Mathematical Methods for Scientists and Engineers I: Asymptotic Methods and Perturbation Theory, Berlin, Germany: Springer Science & Business Media, 2013.

  4. Bradshaw, P., Turbulence: The Chief Outstanding Difficulty of Our Subject, Exp. Fluids, vol. 16, nos. 3-4, pp. 203-216,1994.

  5. Brunton, S.L. and Kutz, J.N., Data-Driven Science and Engineering: Machine Learning, Dynamical Systems,and Control, Cambridge, UK: Cambridge University Press, 2019.

  6. Chaudhary, K. and Maxworthy, T., The Nonlinear Capillary Instability of a Liquid Jet. Part 2. Experiments on Jet Behaviour before Droplet Formation, J. FluidMech, vol. 96, no. 2, pp. 275-286,1980.

  7. Chaudhary, K. and Redekopp, L., The Nonlinear Capillary Instability of a Liquid Jet. Part 1. Theory, J. Fluid Mech, vol. 96, no. 2, pp. 257-274,1980.

  8. Chen, H., Hung, D., Xu, M., and Zhong, J., Analyzing the Cycle-to-Cycle Variations of Pulsing Spray Characteristics by Means of the Proper Orthogonal Decomposition, Atomization Sprays, vol. 23, no. 7, pp. 623-641,2013.

  9. Chen, K.K., Tu, J.H., and Rowley, C.W., Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses, J. Nonlinear Sci., vol. 22, no. 6, pp. 887-915,2012.

  10. Dawson, S.T., Hemati, M.S., Williams, M.O., and Rowley, C.W., Characterizing and Correcting for the Effect of Sensor Noise in the Dynamic Mode Decomposition, Exp. Fluids, vol. 57, no. 42, pp. 1-19, 2016.

  11. Donnelly, R.J. and Glaberson, W., Experiments on the Capillary Instability of a Liquid Jet, Proc. R. Soc. London Ser. A., vol. 290, no. 1423, pp. 547-556,1966.

  12. Duke, D., Honnery, D., and Soria, J., Experimental Investigation of Nonlinear Instabilities in Annular Liquid Sheets, J. Fluid Mech, vol. 691, pp. 594-604,2012.

  13. Feeny, B. and Liang, Y., Interpreting Proper Orthogonal Modes of Randomly Excited Vibration Systems, J. Sound Vib, vol. 265, no. 5, pp. 953-966,2003.

  14. Herrmann, F.J., Friedlander, M.P., and Yilmaz, O., Fighting the Curse of Dimensionality: Compressive Sensing in Exploration Seismology, IEEE Signal Process. Mag., vol. 29, no. 3, pp. 88-100,2012.

  15. Higham, J., Brevis, W., and Keylock, C., Implications of the Selection of a Particular Modal Decomposition Technique for the Analysis of Shallow Flows, J. Hydraul. Res, vol. 56, no. 6, pp. 796-805,2018.

  16. Hua, J.C., Gunaratne, G.H., Talley, D.G., Gord, J.R., and Roy, S., Dynamic-Mode Decomposition based Analysis of Shear Coaxial Jets with and without Transverse Acoustic Driving, J. Fluid Mech, vol. 790, pp. 5-32,2016.

  17. Kerschen, G. and Golinval, J.C., Physical Interpretation of the Proper Orthogonal Modes Using the Singular Value Decomposition, J. Sound Vib, vol. 249, no. 5, pp. 849-865,2002.

  18. Kevorkian, J. and Cole, J.D., Perturbation Methods in Applied Mathematics, Vol. 34, Berlin, Germany: Springer Science & Business Media, 2013.

  19. Koizumi, H., Tsutsumi, S., and Haga, T., Sparsity-Promoting Dynamic Mode Decomposition Analysis on Aeroacoustics of a Clustered Supersonic Jet, 23rd AIAA Computational Fluid Dynamics Conf, pp. 1-19, 2017.

  20. Krolick, W. and Owkes, M., Primary Atomization Instability Extraction Using Dynamic Mode Decomposition, Atomization Sprays, vol. 28, no. 12, pp. 1061-1079,2018.

  21. Kutz, J.N., Brunton, S.L., Brunton, B.W., and Proctor, J.L., Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, Philadelphia, PA: SLAM, 2016a.

  22. Kutz, J.N., Fu, X., and Brunton, S.L., Multiresolution Dynamic Mode Decomposition, SIAM J. Appl. Dyn. Syst., vol. 15, no. 2, pp. 713-735,2016b.

  23. Leask, S.B., Li, A.K., McDonell, V.G., and Samuelsen, S., Preliminary Development of a Measurement Reference Using a Research Simplex Atomizer, J. Fluids Eng., vol. 141, no. 12, pp. 1-11,2019b.

  24. Leask, S.B., McDonell, V.G., and Samuelsen, S., Critical Evaluation of Momentum Flux Ratio Relative to a Liquid Jet in Crossflow, Atomization Sprays, vol. 28, no. 7, pp. 599-620,2018.

  25. Leask, S.B., McDonell, V.G., and Samuelsen, S., Emulsion Jet in Crossflow Atomization Characteristics and Dynamics, J. Eng. Gas Turbines Power, vol. 141, no. 4, p. 041025,2019a.

  26. Lefebvre, A.H. and McDonell, V.G., Atomization Sprays, Boca Raton, FL: CRC Press, 2017.

  27. Li, Q., Dietrich, F., Bollt, E.M., and Kevrekidis, I.G., Extended Dynamic Mode Decomposition with Dictionary Learning: A Data-Driven Adaptive Spectral Decomposition of the Koopman Operator, Chaos, vol. 27, no. 10, p. 103111,2017.

  28. Lusch, B., Kutz, J.N., and Brunton, S.L., Deep Learning for Universal Linear Embeddings of Nonlinear Dynamics, Nat. Commun., vol. 9, no. 1,pp. 1-10,2018.

  29. Mezic, I., Spectral Properties of Dynamical Systems, Model Reduction and Decompositions, Nonlinear Dyn., vol. 41, nos. 1-3, pp. 309-325,2005.

  30. Mohan, A.T., Visbal, M.R., and Gaitonde, D.V., Model Reduction and Analysis of Deep Dynamic Stall on a Plunging Airfoil Using Dynamic Mode Decomposition, 53rd AIAA Aerospace Sciences Meeting, pp. 1-20,2015.

  31. Noack, B.R., Stankiewicz, W., Morzynski, M., and Schmid, P. J., Recursive Dynamic Mode Decomposition of Transient and Post-Transient Wake Flows, J. Fluid Mech, vol. 809, pp. 843-872,2016.

  32. Pan, C., Yu, D., and Wang, J., Dynamical Mode Decomposition of Gurney Flap Wake Flow, Theor. Appl. Mech. Lett, vol. 1,no. 1, p. 012002,2011.

  33. Rowley, C.W., Mezic, I., Bagheri, S., Schlatter, P., and Henningson, D.S., Spectral Analysis of Nonlinear Flows, J. Fluid Mech, vol. 641, pp. 115-127,2009.

  34. Roy, S., Hua, J.C., Barnhill, W., Gunaratne, G.H., and Gord, J.R., Deconvolution of Reacting-Flow Dynamics Using Proper Orthogonal and Dynamic Mode Decompositions, Phys. Rev. E, vol. 91, no. 1, p. 013001,2015.

  35. Schmid, P. J., Dynamic Mode Decomposition of Numerical and Experimental Data, J. Fluid Mech., vol. 656, pp. 5-28,2010.

  36. Schmid, P.J., Li, L., Juniper, M., and Pust, O., Applications of the Dynamic Mode Decomposition, Theor. Comput. Fluid Dyn, vol. 25, nos. 1-4, pp. 249-259,2011.

  37. Sieber, M., Paschereit, C.O., and Oberleithner, K., Spectral Proper Orthogonal Decomposition, J. Fluid Mech., vol. 792, pp. 798-828,2016.

  38. Tu, J.H., Rowley, C.W., Luchtenburg, D.M., Brunton, S.L., and Kutz, J.N., On Dynamic Mode Decomposition: Theory and Applications, J. Comput. Dyn, vol. 1, no. 2, pp. 391-421,2014.

  39. van der Maaten, L., Postma, E., and van den Herik, J., Dimensionality Reduction: A Comparative Review, Tech. Rep. TiCC-TR 2009-005, Tilburg University, 2009.

  40. Vashahi, F., Rezaei, S., and Lee, J., Large Eddy Simulation and Dynamic Mode Decomposition of Internal Flow Structure of Pressure Swirl Atomizer, 21st Australasian Fluid Mechanics Conf, 2018.

  41. Wiggins, S., Introduction to Applied Nonlinear Dynamical Systems and Chaos, Vol. 2, Berlin, Germany: Springer Science & Business Media, 2003.

  42. Williams, M.O., Kevrekidis, I.G., and Rowley, C.W., A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition, J. Nonlinear Sci., vol. 25, no. 6, pp. 1307-1346,2015.

  43. Wu, P.K., Kirkendall, K.A., Fuller, R.P., and Nejad, A.S., Breakup Processes of Liquid Jets in Subsonic Crossflows, J. Propuls. Power, vol. 13, no. 1, pp. 64-73,1997.

  44. Yeung, E., Kundu, S., and Hodas, N., Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems, 2019 American Control Conf., pp. 4832-4839,2019.

  45. Yuen, M.C., Non-Linear Capillary Instability of a Liquid Jet, J. Fluid Mech., vol. 33, no. 1, pp. 151-163, 1968.

  46. Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O., Understanding Deep Learning Requires Rethinking Generalization, arXiv preprint: 1611.03530,2016.

对本文的引用
  1. Leask S. B., McDonell V. G., Samuelsen S., Modal extraction of spatiotemporal atomization data using a deep convolutional Koopman network, Physics of Fluids, 33, 3, 2021. Crossref

Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集 订购及政策 Begell House 联系我们 Language English 中文 Русский Português German French Spain