Browsing by Author "Syed, Adnan U."
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Item Open Access A microscopy study of nickel-based superalloys performance in type I hot corrosion conditions(Taylor and Francis, 2023-03-10) Waeytens, Manon; Syed, Adnan U.; Roberts, Tracey; Duarte Martinez, Fabian; Gray, Simon; Nicholls, John R.Alloy material selection for sustainable, efficient, and cost-effective use in components is a key requirement for both power generation and aerospace sectors. Superalloys are manufactured using a combination of different elements, selected carefully to balance mechanical performance and environmental resistance to be used in a variety of different service conditions. Therefore, a fundamental understanding of each element is critical to alloy design. In this paper, the interaction of alloy chemistry, particularly chromium as a corrosion-resistant element along with titanium and molybdenum, and their effect on alloys performance for the relevant gas turbine industries were discussed. Based on the findings, the single-crystal alloy is found to be a better corrosion resistant alloy exhibited higher corrosion resistance in comparison to polycrystal alloys and proved that microstructure has a significant impact on alloy performance. This study also established that molybdenum level in chromia former alloys can significantly enhance the corrosion damage.Item Open Access An SVM-based health classifier for offline Li-ion batteries by using EIS technology(IOP Publishing, 2023-03-24) Luo, Wei; Syed, Adnan U.; Nicholls, John R.; Gray, SimonThis paper presents an offline testing framework and simulation to measure the aging situation of Li-ion batteries within the battery management system or laddering use for maintenance activities. It presents the use of electrochemical impedance spectroscopy as a non-destructive inspection method to detect battery states. Multiple cycles (charge and discharge) were done to gain EIS results in different conditions, such as temperature. Results were captured and digitalised through a suitable circuit model and mathematical methods for fitting. The state of health values were calibrated, and data were reshaped as vectors and then used as input for support vector machine. These data were then used to create a machine learning model and analyse the aging mechanism of lithium-ion batteries. The machine learning model is established, and the decision boundaries are visualised in 2D graphs. The accuracy of these machine learning models can reach 80% in the test cases, and good fitting in lifetime tracking. The framework allows more reliable SOH estimation in electric vehicles and more efficient maintenance or laddering operations.