Samuel da Silva

• Abstract

  Ensuring operational safety and monitoring the lifespan of infrastructures, such as bridges and wind turbines, requires continuous monitoring combined with structural inspections, primarily through structural health monitoring (SHM) methods. However, despite significant advances in machine learning algorithms and hardware, such as new sensors, finding generalized, cost-effective, and effi…

  Contributions to Digital Shadows Through Virtual Sensing for Population-Based Structural Health Monitoring of Infrastructure, BP.DR

• Abstract

  The integration of active vibration control and real-time assessment of structural integrity has become increasingly important in reducing the impact that unwanted vibration and damage can have on a structure. As structural damage can be characterized by adverse changes in the structure's parameters, monitoring the system's input-output signals in real time can assist in detecting damage …

  Damage-tolerant active control in real-time of flexible structures, BP.PD

• Abstract

  Typically, the engineers who perform inspections in structures are not the same as those that design them. Consequently, the model-based approaches are limited for Structural Health Monitoring (SHM) due to the high cost associated with obtaining an adequate model. On the other hand, it is fundamental to have a model to reach a higher hierarchy in SHM procedures. Thus, this Ph.D. project i…

  Digital Twin for Structural Health Monitoring in Bolted Joints using Physics-Informed Machine Learning, BP.DR

• Abstract

  A novel approach is proposed for structural health monitoring (SHM) using nanocomposites designed explicitly to detect torque variations in bolted joints and crack propagation in metallic structures. Two resistive nanocomposite sensors were developed and tested to correlate electrical resistance changes with the torque applied to bolted joints and crack growth. This allows for static meas…

  Development and Application of Nanocomposites Sensors for Structural Health Monitoring, BP.DR

• Abstract

  The continuous monitoring of heat exchanger health in the field is crucial to ensure optimal performance and safety in industrial plant operations. In this context, various state diagnostic techniques are available in the literature, especially those utilizing information from installed sensors for control actions involving variables such as pressure, flow, and temperature. However, for m…

  Transfer Learning Among Data-Driven Models for Heat Exchangers Monitoring, BP.MS

• Abstract

  Heat exchangers are essential pieces of equipment for many industrial processes and, in general, must be monitored in the field with sensors in order to diagnose their operating status with safety and energy efficiency. However, it is vital to build numerical replicas from this data that communicate with actions control in the physical plant, allowing simulations in a virtual environment,…

  Data-Driven Bayesian Modeling for Heat Exchanger Monitoring, BP.IC

(Only some records are available in English at this moment)

• Abstract

  Calibrating parameters and selecting suitable models to describe hysteresis effects in bolted joints is a highly challenging task. First, it requires defining parameters that govern hysteretic and memory behavior in dynamic systems based on controlled-vibration data, while acknowledging that several existing models can describe these effects. Moreover, performing this identification withi…

  Neural Parameter Calibration of Digital Shadow Models for Hysteresis in Bolted Joint Assemblies, BE.EP.DR

• Abstract

  Eduardo Preto's ongoing thesis proposes a novel SHM approach using nanocomposites to detect torque variations in bolted joints and crack propagation in metallic structures. Two resistive sensors were developed: a flexible rubber-based composite with carbon nanotubes and carbon black for torque detection via piezoresistivity, and a rigid PVB film with ITO nanowires for crack monitoring, wh…

  Development of a constitutive model for nanocomposite sensor applications, BE.EP.DR

• Abstract

  Fouling is one of the main factors degrading the performance of thermal systems, particularly boilers, heat exchangers, and piping networks. Monitoring such effects is typically carried out using plant control signals, with models developed through physics-based approaches or data-driven techniques. While the application of artificial intelligence has been extensively explored in the lite…

  Data-Driven Bayesian Modeling Using Approximate Bayesian Computation for Heat Exchanger Monitoring, BE.EP.IC

• Abstract

  In addition to mitigating unwanted vibration in a structure, damage-tolerant active control (DTAC) seeks to provide appropriate performance even in cases where there is damage. Prior knowledge of the characteristics of the damage that may be imposed on the structure allows the design of controllers that take the effects of the damage into account, but this can be a very limiting assumptio…

  Koopman Model Predictive Control applied in a Damage-tolerant Active strategy, BE.EP.PD

• Abstract

  This project aims to present a method for fault detection in heat exchangers based on monitoring temperature data that are measured and used in control processes in industrial environments. In this process, it is essential to have a numerical model as a digital replica that communicates with the process control. Bayesian calibration tools oriented by this data are a welcome way to carry o…

  Data-Driven Bayesian Modeling Using Approximate Bayesian Computation (ABC) for Heat Exchanger Monitoring, BE.EP.IC

(Only some records are available in English at this moment)