Real-time haptic interactions occur under two exploration modes: active and passive. In this paper, we present a series of experiments that evaluate the main perceptual characteristics of both exploration modes. In particular, we focus on haptic shape recognition as it represents a fundamental task in many applications using haptic environments. The results of four experiments conducted with a group of 10 voluntary subjects show that the differences in motor activity between active and passive haptics ease the perception of surfaces for the first case and the perception of pathways for the latter. In addition, the guidance nature of passive haptics makes the pathway direction easy to recognize. This work shows that this last observation could find application in more challenging tasks such as navigation in space.

The problem of vibrations in civil structures is common; nevertheless, its negative effects can be significantly reduced using structural control methods with intention of maintaining structural welfare as much as possible. This work deals with the study of structural vibration control in a model of a civil-like structure, which consists of three-level building with a tuned mass damper implemented as a passive vibration absorber, mounted on the top of the structure, to attenuate the harmonic vibrations provided by an electromagnetic actuator connected at the base of the primary system. The action of the tuned mass damper is evaluated from an energy approach. The dissipation of energy in the overall system is conducted in an experimental way, where the passive control technique is designed to minimize the undesirable forced dynamic response of the main structure via the tuned mass damper. Experimental results are provided to show the effective performance of the proposed passive vibration absorption scheme to suppress resonant frequency harmonic excitations disturbing the primary system, evaluating the performance energy and contribution of the dissipative device for the energy release in the overall system.

This paper studies erosion at the tip of wind turbine blades by considering aerodynamic analysis, modal analysis and predictive machine learning modeling. Erosion can be caused by several factors and can affect different parts of the blade, reducing its dynamic performance and useful life. The ability to detect and quantify erosion on a blade is an important predictive maintenance task for wind turbines that can have broad repercussions in terms of avoiding serious damage, improving power efficiency and reducing downtimes. This study considers both sides of the leading edge of the blade (top and bottom), evaluating the mechanical imbalance caused by the material loss that induces variations of the power coefficient resulting in a loss in efficiency. The QBlade software is used in our analysis and load calculations are preformed by using blade element momentum theory. Numerical results show the performance of a blade based on the relationship between mechanical damage and aerodynamic behavior, which are then validated on a physical model. Moreover, two machine learning (ML) problems are posed to automatically detect the location of erosion (top of the edge, bottom or both) and to determine erosion levels (from 8% to 18%) present in the blade. The first problem is solved using classification models, while the second is solved using ML regression, achieving accurate results. ML pipelines are automatically designed by using an AutoML system with little human intervention, achieving highly accurate results. This work makes several contributions by developing ML models to both detect the presence and location of erosion on a blade, estimating its level and applying AutoML for the first time in this domain.