Abstract
The principle of causality constrains the real and imaginary parts of the complex modulus \(G^{*} = G^{\prime } + i G^{\prime \prime }\) via Kramers–Kronig relations (KKR). Thus, the consistency of observed elastic or storage (\(G^{\prime }\)) and viscous or loss (\(G^{\prime \prime }\)) moduli can be ascertained by checking whether they obey KKR. This is important when master curves of the complex modulus are constructed by transforming a number of individual datasets; for example, during time-temperature superposition. We adapt a recently developed statistical technique called the ‘Sum of Maxwell Elements using Lasso’ or SMEL test to assess the KKR compliance of linear viscoelastic data. We validate this test by successfully using it on real and synthetic datasets that follow and violate KKR. The SMEL test is found to be both accurate and efficient. As a byproduct, the parameters inferred during the SMEL test provide a noisy estimate of the discrete relaxation spectrum. Strategies to improve the quality and interpretability of the extracted discrete spectrum are explored by appealing to the principle of parsimony to first reduce the number of parameters, and then to nonlinear regression to fine tune the spectrum. Comparisons with spectra obtained from the open-source program pyReSpect suggest possible tradeoffs between speed and accuracy.
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References
Tschoegl NW (1989) The phenomenological theory of linear viscoelastic behavior: an introduction, 1st edn. Springer, Munich
Ferry JD (1980) Viscoelastic properties of polymers, 3rd edn. Wiley, New York
Cho KS (2016) Viscoelasticity of polymers: theory and numerical algorithms. Springer, Dordrecht
de L. Kronig R (1926) On the theory of dispersion of X-rays. J Opt Soc Am 12(6):547–557. https://doi.org/10.1364/JOSA.12.000547
Kramers H A (1927) La diffusion de la lumiere par les atomes. Atti Cong Intern Fisica Como 2:545–557
Booij HC, Thoone GPJM (1982) Generalization of Kramers–Kronig transforms and some approximations of relations between viscoelastic quantities. Rheol Acta 21(1):15–24. https://doi.org/10.1007/BF01520701
Peiponen K-E, Vartiainen EM (1991) Kramers–Kronig relations in optical data inversion. Phys Rev B 44:8301–8303. https://doi.org/10.1103/PhysRevB.44.8301
Lucarini V, Saarinen JJ, Peiponen K-E, Vartiainen EM (2005) Kramers–Kronig relations in optical materials research, vol 110, 1st edn. Springer, Berlin. https://doi.org/10.1007/b138913
Gross B (1941) On the theory of dielectric loss. Phys Rev 59:748–750. https://doi.org/10.1103/PhysRev.59.748
Boukamp BA (2004) Electrochemical impedance spectroscopy in solid state ionics: recent advances. Solid State Ionics 169(1):65–73. https://doi.org/10.1016/j.ssi.2003.07.002. Proceedings of the Annual Meeting of International Society of Electrochemistry
Bode HW (1945) Network analysis and feedback amplifier design. D. Van Nostrand Company, Princeton. https://books.google.com/books?id=fDv0tQEACAAJ
Lucarini V (2009) Evidence of dispersion relations for the nonlinear response of the Lorenz 63 system. J Stat Phys 134(2):381–400. https://doi.org/10.1007/s10955-008-9675-z
Lembo V, Lucarini V, Ragone F (2020) Beyond forcing scenarios: predicting climate change through response operators in a coupled general circulation model. Sci Rep 10(1):8668. https://doi.org/10.1038/s41598-020-65297-2
Rouleau L, Deü J-F, Legay A, Le Lay F (2013) Application of Kramers–Kronig relations to time-temperature superposition for viscoelastic materials. Mech Mater 65:66–75. https://doi.org/10.1016/j.mechmat.2013.06.001
Gupta R, Baldewa B, Joshi YM (2012) Time temperature superposition in soft glassy materials. Soft Matter 8:4171–4176. https://doi.org/10.1039/C2SM07071E
Shukla A, Shanbhag S, Joshi YM (2020) Analysis of linear viscoelasticity of aging soft glasses. J Rheol 64(5):1197–1207. https://doi.org/10.1122/8.0000099
Winter HH (1997) Analysis of dynamic mechanical data: inversion into a relaxation time spectrum and consistency check. J Non-Newton Fluid Mech 68(2):225–239. https://doi.org/10.1016/S0377-0257(96)01512-1. Papers presented at the Polymer Melt Rheology Conference
Fuoss RM, Kirkwood JG (1941) Electrical properties of solids. VIII. Dipole moments in polyvinyl chloride-diphenyl systems. J Am Chem Soc 63(2):385–394. https://doi.org/10.1021/ja01847a013
Malkin AY, Masalova I (2001) From dynamic modulus via different relaxation spectra to relaxation and creep functions. Rheol Acta 40(3):261–271. https://doi.org/10.1007/s003970000128
Baumgaertel M, Winter HH (1992) Interrelation between continuous and discrete relaxation time spectra. J Non-Newton Fluid Mech 44:15–36. https://doi.org/10.1016/0377-0257(92)80043-W
Giesekus H (1982) A simple constitutive equation for polymer fluids based on the concept of deformation-dependent tensorial mobility. J Non-Newton Fluid Mech 11(1):69–109. https://doi.org/10.1016/0377-0257(82)85016-7
Larson RG (1988) Constitutive equations for polymer melts and solutions. Butterworth-Heinemann, Stoneham. https://doi.org/10.1016/C2013-0-04284-3
McLeish TCB, Larson RG (1998) Molecular constitutive equations for a class of branched polymers: the pom-pom polymer. J Rheol 42(1):81–110. https://doi.org/10.1122/1.550933
Inkson NJ, McLeish TCB, Harlen OG, Groves DJ (1999) Predicting low density polyethylene melt rheology in elongational and shear flows with “pom-pom’’ constitutive equations. J Rheol 43(4):873–896. https://doi.org/10.1122/1.551036
Stadler F, Bailly C (2009) A new method for the calculation of continuous relaxation spectra from dynamic-mechanical data. Rheol Acta 48(1):33–49. https://doi.org/10.1007/s00397-008-0303-2
Cho KS (2010) A simple method for determination of discrete relaxation time spectrum. Macromol Res 18(4):363–371. https://doi.org/10.1007/s13233-010-0413-4
Cho KS, Park GW (2013) Fixed-point iteration for relaxation spectrum from dynamic mechanical data. J Rheol 57(2):647–678. https://doi.org/10.1122/1.4789786
McDougall I, Orbey N, Dealy JM (2014) Inferring meaningful relaxation spectra from experimental data. J Rheol 58:779
Bae J-E, Cho KS (2015) Logarithmic method for continuous relaxation spectrum and comparison with previous methods. J Rheol 59:1081
Cho KS (2013) Power series approximations of dynamic moduli and relaxation spectrum. J Rheol 57:679
Ankiewicz S, Orbey N, Watanabe H, Lentzakis H, Dealy J (2016) On the use of continuous relaxation spectra to characterize model polymers. J Rheol 60(6):1115–1120. https://doi.org/10.1122/1.4960334
Kedari SR, Atluri G, Vemaganti K (2022) A hierarchical Bayesian approach to regularization with application to the inference of relaxation spectra. J Rheol 66(1):125–145. https://doi.org/10.1122/8.0000232
Provencher SW (1976) An eigenfunction expansion method for the analysis of exponential decay curves. J Chem Phys 64(7):2772–2777. https://doi.org/10.1063/1.432601
Takeh A, Shanbhag S (2013) A computer program to extract the continuous and discrete relaxation spectra from dynamic viscoelastic measurements. Appl Rheol 23(2):24628
Shanbhag S (2019) pyReSpect: a computer program to extract discrete and continuous spectra from stress relaxation experiments. Macromol Theory Simul, 1900005. https://doi.org/10.1002/mats.201900005
Shanbhag S (2020) Relaxation spectra using nonlinear Tikhonov regularization with a Bayesian criterion. Rheol Acta 59(8):509–520. https://doi.org/10.1007/s00397-020-01212-w
Provencher SW (1982) CONTIN: a general purpose constrained regularization program for inverting noisy linear algebraic and integral equations. Comput Phys Commun 27(3):229–242. https://doi.org/10.1016/0010-4655(82)90174-6
Honerkamp J, Weese J (1989) Determination of the relaxation spectrum by a regularization method. Macromolecules 22(11):4372–4377. https://doi.org/10.1021/ma00201a036
Weese J (1992) A reliable and fast method for the solution of Fredholm integral equations of the first kind based on Tikhonov regularization. Comput Phys Commun 69(1):99–111. https://doi.org/10.1016/0010-4655(92)90132-I
Honerkamp J, Weese J (1993) A nonlinear regularization method for the calculation of relaxation spectra. Rheol Acta 32(1):65–73. https://doi.org/10.1007/BF00396678
Weese J (1993) A regularization method for nonlinear ill-posed problems. Comput Phys Commun 77(3):429–440. https://doi.org/10.1016/0010-4655(93)90187-H
Roths T, Marth M, Weese J, Honerkamp J (2001) A generalized regularization method for nonlinear ill-posed problems enhanced for nonlinear regularization terms. Comput Phys Commun 139(3):279–296. https://doi.org/10.1016/S0010-4655(01)00217-X
Hansen S (2008) Estimation of the relaxation spectrum from dynamic experiments using Bayesian analysis and a new regularization constraint. Rheol Acta 47:169–178. https://doi.org/10.1007/s00397-007-0225-4
Baumgaertel M, Winter HH (1989) Determination of discrete relaxation and retardation time spectra from dynamic mechanical data. Rheol Acta 28(6):511–519. https://doi.org/10.1007/BF01332922
Shanbhag S, Joshi YM (2022) Kramers–Kronig relations for nonlinear rheology. Part II: validation of medium amplitude oscillatory shear (MAOS) measurements. J Rheol 66(5):925–936. https://doi.org/10.1122/8.0000481
Shanbhag S, Joshi YM (2022) Kramers–Kronig relations for nonlinear rheology. Part I: general expression and implications. J Rheol 66(5):973–982. https://doi.org/10.1122/8.0000480
Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J R Stat Soc Ser B Stat Methodol 58(1):267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B Stat Methodol 73(3):273–282. https://doi.org/10.1111/j.1467-9868.2011.00771.x
Lawson CL, Hanson RJ (1995) Solving least squares problems. Classics in Applied Mathematics, vol 15. Society for Industrial and Applied Mathematics (SIAM), Philadelphia
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1–22
Kim SJ, Koh K, Lustig M, Boyd S, Gorinevsky D (2008) An interior-point method for large-scale l1-regularized least squares. IEEE J Sel Top Signal Process 1(4):606–617
Watanabe H, Ishida S, Matsumiya Y, Inoue T (2004) Viscoelastic and dielectric behavior of entangled blends of linear polyisoprenes having widely separated molecular weights: test of tube dilation picture. Macromolecules 37(5):1937–1951. https://doi.org/10.1021/ma030443y
Davies AR, Anderssen RS (1997) Sampling localization in determining the relaxation spectrum. J Non-Newton Fluid Mech 73(1):163–179. https://doi.org/10.1016/S0377-0257(97)00056-6
Larson RG, Goyal S, Aloisio C (1996) A predictive model for impact response of viscoelastic polymers in drop tests. Rheol Acta 35(3):252–264. https://doi.org/10.1007/BF00366912
Goyal S, Larson RG, Aloisio CJ (1999) Quantitative prediction of impact forces in elastomers. J Eng Mater Technol 121(3):294–304. https://doi.org/10.1115/1.2812378
Singh PK, Soulages JM, Ewoldt RH (2018) Frequency-sweep medium-amplitude oscillatory shear (MAOS). J Rheol 62(1):277–293. https://doi.org/10.1122/1.4999795
Lennon KR, McKinley GH, Swan JW (2022) A data-driven method for automated data superposition with applications in soft matter science. https://doi.org/10.48550/ARXIV.2204.09521
Mason TG, Weitz DA (1995) Optical measurements of frequency-dependent linear viscoelastic moduli of complex fluids. Phys Rev Lett 74:1250–1253. https://doi.org/10.1103/PhysRevLett.74.1250
Xu J, Viasnoff V, Wirtz D (1998) Compliance of actin filament networks measured by particle-tracking microrheology and diffusing wave spectroscopy. Rheol Acta 37(4):387–398. https://doi.org/10.1007/s003970050125
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This work is based in part upon work supported by the National Science Foundation under Grant no. NSF DMR-1727870.
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Poudel, S., Shanbhag, S. Efficient test to evaluate the consistency of elastic and viscous moduli with Kramers–Kronig relations. Korea-Aust. Rheol. J. 34, 369–379 (2022). https://doi.org/10.1007/s13367-022-00041-y
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DOI: https://doi.org/10.1007/s13367-022-00041-y