Engineering topics
Publications and source records attributed to Inalpolat, Murat.
This research aims to advance the field of vanadium redox flow batteries (VRFBs) by introducing a pioneering approach to optimize the microstructural characteristics of carbon cloth electrodes.
Vanadium redox flow batteries (VRFBs) have shown to be a promising technology for integrating intermittent renewable energy sources into the existing electrical grid. Incorporation of carbon cloth electrodes into VRFB is an area of interest for their enhanced electrochemical performance, however, issues with performance degradation throughout the duration of the experiment persist. This study investigates the performance evolution of carbon cloth electrodes during VRFB cycling to build a hypothesis on possible reasons for the declining performance. Electrochemical impedance spectroscopy and polarization curve measurements are used in conjunction to monitor the electrode degradation and shed light on the effectiveness of carbon cloth electrodes during extended cycling experiments. A detailed investigation into the structure of the carbon cloth electrodes before and after cycling, via several material characterization tests, provides insight needed to determine an explanation for the increasing resistance. The structural integrity and surface morphology of the carbon cloth electrodes are evaluated to compare the electrode before and after cycling, displaying any changes to the electrode due to cycling. Durability of hydrophilicity during RFB cycling is found to be a key feature for future carbon cloth electrode design efforts.
This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.