9 research outputs found
Uncertainty quantification to assess a reduced model for the remote heating of a polymer
This article studies the feasibility of a 1D radiative transfer model to compute the thermal source for a remote heating problem associated to the physics of the so-called plasmonic resonance (PR) in a synthetic polymeric material. The PR is responsible for converting the optical radiation from the incident laser beam into an equivalent thermal source and is achieved by embedding gold nanoparticles during the design of the synthetic polymer. Since the Radiative Transfer Equation cannot be analytically solved for a real experimental case, a two-staged simplified process is considered which requires the uncertainty quantification as a prior stage, in order to make an appropriate control of the resulting temperature profile. In this work, we include propagation errors for lattices of 1D, 2D and 3D geometries, due to the approximate laser source profile used, as well as those arisen from uncertainties in the thermal parameters and the ones derived from the variables involved in the design of the polymer. Computational simulations for a suitable experimental polymer are carried out using COMSOL®. Corresponding results show the scope of the reduced model in terms of a range of parameter values where it can be effectively used in practice.Publicado en: Mecánica Computacional vol. XXXV, no. 21Facultad de IngenierÃ
Uncertainty quantification to assess a reduced model for the remote heating of a polymer
This article studies the feasibility of a 1D radiative transfer model to compute the thermal source for a remote heating problem associated to the physics of the so-called plasmonic resonance (PR) in a synthetic polymeric material. The PR is responsible for converting the optical radiation from the incident laser beam into an equivalent thermal source and is achieved by embedding gold nanoparticles during the design of the synthetic polymer. Since the Radiative Transfer Equation cannot be analytically solved for a real experimental case, a two-staged simplified process is considered which requires the uncertainty quantification as a prior stage, in order to make an appropriate control of the resulting temperature profile. In this work, we include propagation errors for lattices of 1D, 2D and 3D geometries, due to the approximate laser source profile used, as well as those arisen from uncertainties in the thermal parameters and the ones derived from the variables involved in the design of the polymer. Computational simulations for a suitable experimental polymer are carried out using COMSOL®. Corresponding results show the scope of the reduced model in terms of a range of parameter values where it can be effectively used in practice.Publicado en: Mecánica Computacional vol. XXXV, no. 21Facultad de IngenierÃ
Uncertainty quantification to assess a reduced model for the remote heating of a polymer
This article studies the feasibility of a 1D radiative transfer model to compute the thermal source for a remote heating problem associated to the physics of the so-called plasmonic resonance (PR) in a synthetic polymeric material. The PR is responsible for converting the optical radiation from the incident laser beam into an equivalent thermal source and is achieved by embedding gold nanoparticles during the design of the synthetic polymer. Since the Radiative Transfer Equation cannot be analytically solved for a real experimental case, a two-staged simplified process is considered which requires the uncertainty quantification as a prior stage, in order to make an appropriate control of the resulting temperature profile. In this work, we include propagation errors for lattices of 1D, 2D and 3D geometries, due to the approximate laser source profile used, as well as those arisen from uncertainties in the thermal parameters and the ones derived from the variables involved in the design of the polymer. Computational simulations for a suitable experimental polymer are carried out using COMSOL®. Corresponding results show the scope of the reduced model in terms of a range of parameter values where it can be effectively used in practice.Publicado en: Mecánica Computacional vol. XXXV, no. 21Facultad de IngenierÃ
Polymerization-Induced Phase Separation:Â A Maximum in the Intensity of Scattered Light Associated with a Nucleation-Growth Mechanism
Light Scattering in the Course of a Polymerization-Induced Phase Separation by a Nucleation-Growth Mechanism
A rubber-modified thermoplastic where the morphology produced by phase-separation induced by polymerization disappears at high conversions
Bayesian approach to the inverse problem in a light scattering application
In this article, static light scattering (SLS) measurements are processed to estimate the particle size distribution of particle systems incorporating prior information obtained from an alternative experimental technique: scanning electron microscopy (SEM). For this purpose we propose two Bayesian schemes (one parametric and another non-parametric) to solve the stated light scattering problem and take advantage of the obtained results to summarize some features of the Bayesian approach within the context of inverse problems. The features presented in this article include the improvement of the results when some useful prior information from an alternative experiment is considered instead of a non-informative prior as it occurs in a deterministic maximum likelihood estimation. This improvement will be shown in terms of accuracy and precision in the corresponding results and also in terms of minimizing the effect of multiple minima by including significant information in the optimization. Both Bayesian schemes are implemented using Markov Chain Monte Carlo methods. They have been developed on the basis of the Metropolis–Hastings (MH) algorithm using Matlab® and are tested with the analysis of simulated and experimental examples of concentrated and semi-concentrated particles. In the simulated examples, SLS measurements were generated using a rigorous model, while the inversion stage was solved using an approximate model in both schemes and also using the rigorous model in the parametric scheme. Priors from SEM micrographs were also simulated and experimented, where the simulated ones were obtained using a Monte Carlo routine. In addition to the presentation of these features of the Bayesian approach, some other topics will be discussed, such as regularization and some implementation issues of the proposed schemes, among which we remark the selection of the parameters used in the MH algorithm.Fil: Otero, Fernando AgustÃn. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y TecnologÃa de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de IngenierÃa; ArgentinaFil: Barreto Orlande, Helcio R.. Universidade Federal Do Rio de Janeiro. Inst A.luiz Coimbra de Pos-graduacao E Pesquisa de Engenharia; BrasilFil: Frontini, Gloria Lia. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y TecnologÃa de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de IngenierÃa; ArgentinaFil: Elicabe, Guillermo Enrique. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Mar del Plata. Instituto de Investigación En Ciencia y TecnologÃa de Materiales (i); Argentina. Universidad Nacional de Mar del Plata. Facultad de IngenierÃa; Argentin