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    Effect of adherend recessing on bi-adhesively bonded single-lap joints with spew fillet
    (Indian Acad Sciences, 2017) Calik, Ahmet; Yildirim, Sefa
    The effect of adherend recessing on the strength of full (spew)-fillet-formed bi-adhesively bonded single-lap joint (SLJ) was investigated using the finite-element (FE) method under pure tension (i.e., 20 dissimilar recess length and depth and two-type bi-adhesive bond). A three-dimensional (3D) FE model was developed for bi-adhesively bonded SLJ, which had fillet and recessed adherend, assuming that both adhesive and adherend have geometrical non-linearity and exhibit linear material behaviour. The novelty of present study is the application of recessing process on the fillet-formed bi-adhesively bonded SLJs. The bondline characteristics of bi-adhesively bonded joints with the effect of adherend recessing have been investigated by examining the distributions of the peel and maximum principle stresses (MPS) at the mid-plane of the bondline. The results from the FE simulations in which varying geometric parameters are used reveal that the combined effects of adherend recessing spew fillet and bi-adhesive bondline led to a major decrease in the peak values of the peel stress, which is the governing failure stress and MPS. A novel design that may be beneficial to improve the strength characteristics of aluminium SLJ is presented.
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    Estimation of crack propagation in polymer electrolyte membrane fuel cell under vibration conditions
    (Pergamon-Elsevier Science Ltd, 2017) Calik, Ahmet; Yildirim, Sefa; Tosun, Erdi
    In transportation applications, the main reasons of mechanical damage in polymer electrolyte membrane fuel cell (PEMFC) are road-induced vibrations and impact loads. The most vulnerable place of these cells is the interface between membrane and catalyst layer in the membrane electrode assembly (MEA). Hence, studies on mechanical strength of PEMFC should focus on that interface. The objective of present study lies in the fact that employing a prediction method to investigate the damage propagation behavior of vibration applied PEMFC using artificial neural network (ANN). The data available in the literature are used to constitute an ANN model. Three-layer model; input, hidden and output, are used for construction of ANN structure. Initial delamination length (a), amplitude (A), frequency (omega) and time (t) are used as input neurons whereas delamination length is output. Levenberg-Marquardt algorithm is selected as learning algorithm. On the other hand, number of hidden layer neuron is decided with the use of different neuron, numbers by trial and error method. It is concluded that prediction capability of ANN model is in allowable limits and model can be suggested as efficient way of delamination length estimation. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Evaluation of vibration characteristics of a hydroxyl (HHO) gas generator installed diesel engine fuelled with different diesel-biodiesel blends
    (Pergamon-Elsevier Science Ltd, 2017) Uludamar, Erinc; Tosun, Erdi; Tuccar, Gokhan; Yildizhan, Safak; Calik, Ahmet; Yildirim, Sefa; Serin, Hasan
    There are two main reasons of alternative fuel search of scientists: environmental problems resulted from combustion of fossil fuels and limited reserves of crude oil. Biodiesel and Hydrogen (H-2) are two of the most promising alternative fuels with their environmental friendly combustion profiles. The aim of this study was to evaluate vibration level of a hydroxyl (HHO) gas generator installed and diesel engine using different kinds of biodiesel fuels. In this study, at different flow rates, the effect of HHO gas addition on engine vibration performance was investigated with a Mitsubishi Canter 4D34 -2A diesel engine. HHO gas introduced to the test engine via its intake manifold with 2, 4 and 6 L per minute (LPM) flow rates when the engine was fuelled with sunflower, canola, and corn biodiesels. The vibration data was collected between 1200 and 2400 rpm engine speeds by 300 rpm intervals. Finally, artificial neural network (ANN) approach was conducted in order to predict the effect of fuel properties and HHO amount on engine vibration level. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.