Methodological approach to efficient modeling and optimization of thermal processes taking place in a die: Application to pultrusion

Abstract

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A novel methodology for the rapid identification of the water diffusion coefficients of composite materials

“…Moreover it can deal with anisotropic materials. This idea has been proposed by Chinesta et al (see [14,13] and the references therein) and has been extended for other types of extra-coordinates as initial and boundary conditions [15,16], large number of process parameters [15] and more recently the length of the domain [17]. In [15], these authors have employed the parametric PGD solution for identifying and optimizing the thermal parameters involved in die thermal process: in this context, the computed temperature field can be directly compared to the measured temperature field which can be evaluated at several experimental points, allowing a robust identification.…”

Real-time simulation techniques for augmented learning in science and engineering

“…This technique has received a warm welcome in the scientific community, as proved by the large number of papers devoted to it in the very last years. Just to name a few, it is possible to find PGD applications to thermal problems [33,34], theories of plates and shells [17], gene regulatory networks [6], and structural mechanics [49] among many others [2,3,5,21,22,35,36]. The technique is beginning also to be understood from a mathematical point of view and its properties, convergence and error estimates have also been studied recently [4,48,51,59].…”

A manifold learning-based reduced order model for springback shape characterization and optimization in sheet metal forming

“…Meta-modeling has been widely used to approximate the physical fields associated with the design problem using a lower order meta-model i.e. output space [17,18,19], using the methods of Proper Orthogonal Decomposition [20,21,22,23] or Proper Generalized Decomposition [24,25,26,27] or hyper-reduction [28]. In the field of metal forming, [29] presented an approach for displacement field approximation using the Proper Orthogonal Decomposition (POD) combined with kriging interpolation of projection coefficients.…”