Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks

This study investigates the efficiency of artificial neural networks (ANNs) in health monitoring of pristine and damaged beam-like structures. Beam modeling is based on Timoshenko theory. Two commonly used network models, multilayer perceptron (MLP) and radial basis neural network (RBNN), are used. Beam material and geometrical properties, beam end conditions and dynamically obtained data are used as input to the neural networks. The combinations of these parameters yield umpteenth input data. Therefore, to examine the effectiveness of ANNs, the frequency of intact beams is first tried to be determined by the network models, given the material and geometrical characteristics of beam elements and support conditions. The methodology to compute the vibrational data utilized in training the networks is provided. Showing the robustness of network models, the second stage of the study is carried out. At this stage, the crack parameters (e.g. the location and severity of crack) are estimated by the ANNs using the beam properties, beam end conditions and vibrational data, which consist of natural frequencies and mode shape rotation values. Despite the multiplexed input data, no data reduction schemes or multistage computations are executed in training and validation of neural network models. As a result of analysis runs, the optimal MLP and RBNN models are determined. Comparison of these models shows that the optimal RBNN algorithm performs better. The effectiveness of optimal ANN models in the presence of noise is also presented. As a conclusion, the trained network can be used as a diagnosis method in structural health monitoring of beam-like structures.     

Adaptive kernels in approximate filtering of state‐space models

Standard Bayesian algorithms used for online filtering of states of hidden Markov models from noisy measurements assume an accurate knowledge of the measurement model in the form of a conditional probability density function. However, this knowledge is often unreachable in practice, and the used models are more or less misspecified, or it is too complex, making the resulting models intractable. This paper focuses on these issues from the particle filtering perspective. It adopts the principles of the approximate Bayesian filtering, where the particle weights are based on the (dis)similarity of the true measurements and the pseudo‐measurements obtained by plugging the state particles directly into the measurement equation. Specifically, a new robust method for online tuning of the weighting kernel is proposed. Copyright © 2016 John Wiley & Sons, Ltd.     

Counter flow membraneless microfluidic fuel cell

A counter flow membraneless microfluidic fuel cell is presented, where a non-reacting electrolyte separates the reacting streams. In this fuel cell design, vanadium reactants flow through porous carbon electrocatalysts. A sulfuric acid stream is introduced in the gap between the electrodes and diverts the reactants to opposite and independent outlets. This fuel cell differs from other membraneless designs in its ability to maintain a constant separation between the reactants without diffusive mixing.     

Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods

Clay bearing, weathered and other weak rocks cause major problems in engineering practice due to their interactions with water. The slake durability index (I _d2) is an important tool used to assess the resistance of these rocks to erosion and degradation, but sample preparation for this test is tedious. The paper reports an attempt to define I _d2 through statistical models using other parameters that are simpler to obtain. The main objective of this study was to define the best empirical relationship between the I _d2 and the point load strength index (I _s(50)), dry unit weight (γ _d) and fractal dimension (D) parameters of eight rock types by applying general multiple linear regression (GLM), artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The models obtained were evaluated using the R ², MSE, MARE and d parameters. The results indicate that the relationships between I _d2 and γ _d, I _s(50) and D were best obtained using ANN, followed by GLM and ANFIS. It is concluded that ANN modelling is a fast and practical method of establishing I _d2.     

Engine performance and optimum injection timing for 4-cylinder direct injection hydrogen fueled engine

This paper presents the engine performance and optimum injection timing for 4-cylinder direct injection hydrogen fueled engine. The 4-cylinder direct injection hydrogen engine model was developed utilizing the GT-Power commercial software. This model employed one dimensional gas dynamics to represent the flow and heat transfer in the components of engine model. Sequential pulse injectors are adopted to inject hydrogen gas fuel within the compression stroke. Injection timing was varied from 110° before top dead center (BTDC) until top dead center (TDC) timing. Engine speed was varied from 2000 rpm to 6000 rpm, while the equivalence ratio was varied from 0.2 to 1.0. The validation was performed with the existing previous experimental results. The negative effects of the interaction between ignition timing and injection duration was highlighted and clarified. The results showed that optimum injection timing and engine performance are related strongly to the air fuel ratio and engine speed. The acquired results show that the air fuel ratio and engine speed are strongly influence on the optimum injection timing and engine performance. It can be seen that the indicated efficiency increases with increases of AFR while decreases of engine speed. The power and torque increases with the decreases of AFR and engine speed. The indicated specific fuel consumption (ISFC) decreases with increases of AFR from rich conditions to lean while decreases of engine speed. The injection timing of 60° BTDC was the overall optimum injection timing with a compromise.     

An experimental optimization study on a tube-in-shell latent heat storage

Thermal energy storage (TES) using phase change materials (PCMs) has recently received considerable attention in the literature, due to its high storage capacity and isothermal behaviour during the storage (melting or charging) and removal (discharging or solidification). In this study, a novel modification on a tube-in-shell-type storage geometry is suggested. In the proposed geometry, the outer surface of the shell is inclined and it is the objective of this study to determine the optimum range for the inclination angle of the shell surface. Paraffin with a melting temperature of 58.06°C, which is supplied by the Merck Company, is used as the PCM. The PCM is stored in the vertical annular space between an inner tube through which the heat transfer fluid (HTF), hot water, is flowing and a concentrically placed outer shell. At first, the thermophysical properties of this paraffin are determined through the differential scanning calorimeter (DSC) analysis. Temporal behaviour of the PCM undergoing a non-isothermal solid–liquid phase change during its melting or charging by the HTF are determined for different values of the inlet temperature and the mass flow rate of the HTF. The new geometry is shown to respond well with the melting characteristics of the PCM and to enhance heat transfer inside the PCM for a specific range of the shell inclination angle. Copyright © 2006 John Wiley & Sons, Ltd.     

Effect of the oat,horse chestnut,cowherb, soy,quinoa and soapwort extracts on skin-mimicking monolayers and cell lines

The main goal of the study was to compare the effect of aqueous extracts of oat (Avena sativa L.), horse chestnut (Aesculus hippocastanum L.), quinoa (Chenopodium quinoa Willd.), soapwort (Saponaria officinalis L.), cowherb (Vaccaria hispanica [P. Mill.] Rauschert) and soy (Glycine max L.) on model lipid monolayers mimicking the lipid membrane of keratinocytes and intercellular lipids of stratum corneum, and on human skin-related cell lines. Two lipid monolayers, consisting of a dipalmitoylphosphatidylcholine (DPPC) and cholesterol mixture in a molar ratio of 7:3 and Ceramide VI, stearic acid and cholesterol in a molar ratio of 1:1:0.7, and two cell lines (human keratinocyte HaCaT and human skin malignant melanoma A375) were employed. None of the extracts reduced surface pressure below the level achieved for bare monolayers. The strength of the effect on the lipid monolayers (horse chestnut > cowherb > soapwort > soy) points to the existence of some specific interactions responsible for the observed affinity of biosurfactants from the extracts to the lipids in the monolayers. The cytotoxicity tests performed with two model skin cell lines showed that all six plants extracts significantly reduced the cells' viability in a concentration-dependent way. The model lipid monolayers were not solubilized by the investigated surface-active extracts. The latter thus proved interesting candidates for application in mild cleansing cosmetic formulations. Penetration of the monolayers by surface-active components of some extracts, especially horse chestnut, cowherb and soapwort, opens new possibilities for topical delivery of active components.     

Combined EBSD and Computer-Assisted Quantitative Analysis of the Impact of Spark Plasma Sintering Parameters on the Structure of Porous Materials

Metallurgical and Materials Transactions A - The paper presents the experimental, numerical, and theoretical investigation of the microstructure of nickel aluminide samples manufactured by spark...