Browsing by Author "Figueiredo, Alisson A. A."

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    Exploring the potentialities of thermal asymmetries in composite wind turbine blade structures via numerical and thermographic methods: a thermophysical perspective
    (Springer , 2024) Figueiredo, Alisson A. A.; D’Alessandro, G.; Perilli, S.; Sfarra, Stefano; Fernandes, Henrique
    Using composite materials in turbine blades has become common in the wind power industry due to their mechanical properties and low mass. This work aims to investigate the effectiveness of the active infrared thermography technique as a non-destructive inspection tool to identify defects in composite material structures of turbine blades. Experiments were carried out by heating the sample and capturing thermographic images using a thermal camera in four different scenarios, changing the heating strategy. Such a preliminary experiments are prodromic to build, in future, the so-called optimal experiment design for thermal property estimation. The experimental results using two heaters arranged symmetrically on the sample detected the presence of the defect through temperature curves extracted from thermal images, where temperature asymmetries of 25% between the regions with and without defect occurred. Moreover, when only a larger heater was used in transmission mode, the defect was detected based on differences between normalized excess temperatures on the side with and without the defect in the order of 20%. Additionally, numerical simulations were carried out to present solutions for improving defect detection. It was demonstrated that active infrared thermography is an efficient technique for detecting flaws in composite material structures of turbine blades. This research contributes to advancing knowledge in inspecting composite materials.
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    Influence of thermal contrast and limitations of a deep-learning based estimation of early-stage tumour parameters in different breast shapes using simulated passive and dynamic thermography
    (Elsevier, 2025-04) Moraes, Mateus Felipe Benicio; Sfarra, Stefano; Fernandes, Henrique; Figueiredo, Alisson A. A.
    To enhance diagnostic sensitivity compared to passive thermography, thermal stress can be applied to the breast surface with the temperatures being measured in the thermal recovery phase, a process called dynamic thermography. This study aims to evaluate the limitations of both passive and dynamic thermography in estimating early-stage tumour parameters across different breast shapes and how to improve the results. Three breast models with thermoregulation were solved numerically using COMSOL Multiphysics®. A neural network developed in PyTorch was used to estimate breast tumour location and size. The estimates obtained using each approach were compared, and the effects of thermal contrast, noise, and tumour depth range were analysed. Dynamic thermography provided the most accurate estimates compared to passive thermography, with mean error reductions that reached up to 33.25%. Additionally, the number of estimates with errors higher than 10% was up to 48.42% lower. Tumour radius showed the lowest noise threshold, providing the highest estimations errors. Adding deeper tumours to the datasets caused mean error increases of up to 51.27%. Thus, this work contributes by comparing both types of thermography, analysing thermal aspects of the temperature data that influences the neural network's estimation process, and suggesting alternatives to improve its accuracy.