Empowering Advanced Parametric Modes Clustering from Topological Data Analysis
Article dans une revue avec comité de lecture
Modal analysis is widely used for addressing NVH—Noise, Vibration, and Hardness—in automotive engineering. The so-called principal modes constitute an orthogonal basis, obtained from the eigenvectors related to the dynamical problem. When this basis is used for expressing the displacement field of a dynamical problem, the model equations become uncoupled. Moreover, a reduced basis can be defined according to the eigenvalues magnitude, leading to an uncoupled reduced model, especially appealing when solving large dynamical systems. However, engineering looks for optimal designs and therefore it focuses on parametric designs needing the efficient solution of parametric dynamical models. Solving parametrized eigenproblems remains a tricky issue, and, therefore, nonintrusive approaches are privileged. In that framework, a reduced basis consisting of the most significant eigenmodes is retained for each choice of the model parameters under consideration. Then, one is tempted to create a parametric reduced basis, by simply expressing the reduced basis parametrically by using an appropriate regression technique. However, an issue remains that limits the direct application of the just referred approach, the one related to the basis ordering. In order to order the modes before interpolating them, different techniques were proposed in the past, being the Modal Assurance Criterion—MAC—one of the most widely used. In the present paper, we proposed an alternative technique that, instead of operating at the eigenmodes level, classify the modes with respect to the deformed structure shapes that the eigenmodes induce, by invoking the so-called Topological Data Analysis—TDA—that ensures the invariance properties that topology ensure.
Showing items related by title, author, creator and subject.
Article dans une revue avec comité de lectureFRAHI, Tarek; CHINESTA, Francisco; FALCO, Antonio; BADIAS, Alberto; CUETO, Elias; CHOI, Hyung Yun; HAN, Manyong; DUVAL, Jean-Louis (MDPI, 2021)We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive ...
Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes Article dans une revue avec comité de lectureGHNATIOS, Chady; CUETO, Elias; FALCO, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco (Springer Science and Business Media LLC, 2020)Non-intrusive approaches for the construction of computational vademecums face different challenges, especially when a parameter variation affects the physics of the problem considerably. In these situations, classical ...
Article dans une revue avec comité de lectureFRAHI, Tarek; ARGERICH, Clara; YUN, Minyoung; FALCO, Antonio; BARASINSKI, Anais; CHINESTA, Francisco (AIMS Press, 2020)The aim of this paper is to leverage the main surface topological descriptors to classify tape surface profiles, through the modelling of the evolution of the degree of intimate contact along the consolidation of pre-impregnated ...
Structural health monitoring by combining machine learning and dimensionality reduction techniques Article dans une revue avec comité de lectureQUARANTA, Giacomo; LOPEZ, Elena; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; HUERTA, Antonio; CHINESTA, Francisco (Universitat politecnica de Catalunya, 2019)Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate ...
Article dans une revue avec comité de lectureIBÁÑEZ PINILLO, Rubén; AMMAR, Amine; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco (Wiley, 2019)Solutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The ...