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Este artículo se encuentra disponible en la página web de la revista en la siguiente URL: https://www.frontiersin.org/articles/10.3389/frai.2021.761123/full Este artículo pertenece a la sección "AI in Business". The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the “shape” of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis.

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/2227-7390/9/6/634 En este artículo de investigación también participan: Manyong Han y Jean-Louis Duval. Este artículo pertenece al número especial " Numerical simulation in biomechanics and biomedical engineering". 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 model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

Turbulent mixing of hot and cold streams in T-junction geometries is a critical safety issue for power plants, as temperature fluctuations have the potential to lead to high cycle thermal fatigue failures. Due to strong flow deformation in the mixing region, the time-scale separation assumption is not locally respected, and unsteady Reynolds-averaged Navier-Stokes (URANS) models fail to provide sufficient accuracy in predicting the temperature variations. Turbulence resolving methods such as large eddy simulation (LES) can in contrast provide reliable results, while being computationally overly expensive for industrial application. A STRUCTure-based second-generation URANS (2G-URANS) model was recently proposed at MIT, which aims at locally resolving unsteady flow structures; its applicability was demonstrated on a collection of classic flow tests. In the present work, the performance of the STRUCT model is assessed against low Reynolds number DNS data for a squared section T-junction, produced at the THTLAB (Fukushima et al., 2003). The mean and root mean square of the temperature and velocity predictions from the STRUCT solution are evaluated, confirming consistent predictions to the reference DNS results as well as the high-resolution LES simulations. The STRUCT turbulence model demonstrates LES-like accuracy on a URANS-like computational mesh. Spectral analysis of the velocity and temperature variation is also presented and provides consistent prediction of the frequencies of the large turbulent eddies.

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/2076-3417/11/14/6554 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.