Modeling of material behavior data in a functional form suitable for neural network representation

Decision making through connectionist expert systems is getting adopted in various disciplines. For such systems the subject knowledge has to be represented by neural networks. A simple and fast method is developed for modeling of material behavior data in a functional form suitable for neural network representation. Complete algorithm for the method is given. The algorithm is used for modeling of material behavior data for several examples, taken from handbooks, etc. The error of approximation is very small in all the cases considered. The algorithm deals with cases where the property to be modeled is a function of one variable or its variation is measured with change of one parameter, keeping other parameters constant. The algorithm can also be used as a curve fitting technique, preserving the shape as desired by subject expert, even in presence of errors in the measurement data.     

Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks

Forecasting of intermittent stream flow is necessary for water resource planning and management at catchment scale. Forecasting of extreme events and events outside the range of training data used for artificial neural network (ANN) model development has been a major bottleneck in their generalization capabilities till date. Despite of several studies using wavelet analysis in water resource modelling, no study has yet been conducted to explore capabilities of hybrid ANN modelling techniques for extreme events outside the training range. In this study a wavelet based ANN model (WANN) is proposed for intermittent streamflow forecasting and extreme event modelling. This study is carried out in a watershed in semi arid middle region of Gujarat, India. 6 years of hydro-climatic data are used in this study. 4 years of data are used for model training, 1 year for cross-validation and remaining 1 year data are used to evaluate the effectiveness of the WANN model. Two different approaches of data arrangement are considered in this study, in one approach testing data are within the range of training dataset, whereas in another approach testing data are outside the training dataset range. Performance of four different training algorithms and different types of wavelet functions are also evaluated for WANN model development. In this study it is found that WANN model performed significantly better than standard ANN models. It is observed in this study that different wavelet functions have different role in modelling complexities of normal and extreme events. WANN model simulated peak values very well and it shows that WANN model has the potential to be applied successfully for intermittent streamflow forecasting even for the data outside the training range and for extreme events.     

Squeeze infiltration processing of nickel coated carbon fiber reinforced Al-2014 composite

Ni-coated fibers are widely used to synthesize carbon fiber reinforces Al based composites. It is observed that during the pressure infiltration process, significant amount of damage takes place in the nickel (Ni) coating due to a difference in the thermal expansion coefficients of fibers and Ni-coating. A modified squeeze infiltration set up is studied in the present work to minimize the damage to the Ni-coating during the infiltration process. This process resulted in significant improvement in retention of the Ni-coating compared to the unmodified process. The solidification pattern in the matrix was established by Energy Dispersive Spectroscopic analysis, which suggests that solidification of primary α-Al started in the interfiber region. The Vickers hardness values for the unreinforced portion of the sample, the matrix regions within the fiber tows and at the fiber matrix interface were found to be 55, 72 and 87, respectively.     

Traffic planning for non-homogeneous traffic

Traffic on Indian roads (both urban and inter-urban) consists of a variety of vehicles. These vehicles have widely different static and dynamic characteristics. The traffic is also very different from homogeneous traffic which primarily consists of motorized vehicles. Homogeneous traffic follows strict lane discipline as compared to non-homogeneous traffic. Western traffic planning methodologies mostly address the concerns of homogeneous traffic and therefore often prove inadequate in solving problems involving non-homogeneous traffic conditions as found in Indian cities. This paper presents studies conducted on non-homogeneous traffic. Section 1 presents a methodology to verify the continuity equation, the basic block of any traffic planning analysis. In § 2, the methodology developed is applied to modify the Highway Capacity Manual (HCM) 2000 density method to derive passengercar equivalencies (PCEs) or units (PCUs) for heavy vehicles and recreational vehicles. These PCUs appear as ‘ET’ and ‘ER’ in HCM tables. The density method assumes motorized, four-wheeler traffic, i.e., homogeneous traffic, and does not include motorized three-wheelers, motorized two-wheelers, and non-motorized traffic often present on Indian highways. By modifying the density method to represent non-homogeneous traffic, which includes significant percentages of motorized, three-wheelers, motorized two-wheelers, and non-motorized traffic entities, one can derive more accurate passenger car units for Indian conditions. Transport professionals can use these PCU values for accurate capacity, safety, and operational analysis of highways carrying non-homogeneous traffic.     

Synthesis of Fe–SiC Nanowires via Precursor Route

Magnetic SiC nanowires were obtained by the pyrolysis of iron containing polycarbosilane (PCS) which in turn was synthesized by the reaction of polycarbosilane and iron (III) acetylacetonate [Fe(acac)₃] in solvent. Characterization of synthesized polycarbosilane and Fe–SiC were performed with Fourier transformation infrared spectroscopy, energy dispersive X-ray spectrometry, powder X-ray diffraction, thermogravimetric analysis, scanning electron microscopy, electron spin resonance and vibrating sample magnetometer.     

Sensitivity Analysis in Predictive Models for assessing the Level of β-Glucan in Oats and Barley Cultivars Using Meta-Models

Oats and barley β-glucans are well-known for their many health benefits; this has encouraged the food industry to develop new functional foods containing oats and barley. This study aims to develop an advanced sensitivity analysis to analyse and evaluate the most significant model inputs contributing to uncertainty in assessing the level of β-glucan content in harvested oat and barley grains. Two methodologies, nominal value and regression method sensitivity analysis, were adopted. The nominal sensitivity analysis highlighted that cultivar selection is the predominant factor with a correlation coefficient 0.66 for hulled oats and barley cultivars, whereas the correlation was 0.80 and 0.77 for naked oats and hull-less barley, respectively. Advanced sensitivity analysis using regression modelling highlighted that cultivar selection, storage days and germination time (days) were the most important parameters in both the oats and barley model. Regression analysis using the response surface methodology shows that prediction models were found to be significant (P < 0.0001) with low standard errors and high coefficients of determination (R ²  > 0.94). This study shows that regression modelling is an effective tool to highlight the effect of key input variables and their interactive effects on the predictive response of β-glucan in harvested oats and barley cultivars.     

Characterization of radio frequency heating of fresh fruits influenced by dielectric properties

Because of its fast and volumetric nature, radio frequency (RF) heating has been looked upon as a way to overcome the problems associated with conventional heating methods used for disinfestation of fruits. But non-uniform heating within fruits is a major obstacle in adaptation of this technology. In this study, RF heating patterns influenced by dielectric properties (DPs) of fruits were investigated both experimentally and mathematically. A computer simulation model was developed using FEMLAB 3.4, a commercial software for solving Maxwell’s electromagnetic and Fourier’s heat transfer equations. Orange, apple, grapefruit, peach, and avocado fruits, selected for these studies were subjected to RF heating in a water filled container equipped with a mechanism to keep fruits rotating and moving during RF heating in a 27.12 MHz, 12 kW parallel plate RF unit. DPs of constitutional parts of the selected fruits were measured by open-ended coaxial probe method. The study showed that dissimilarity in peel and pulp DPs greatly influenced the RF heating behavior of the fruits. Core heating was prominent in apple, peeled orange and grapefruit; whereas subsurface/peripheral heating in whole oranges and grapefruit, and avocado. The computer model was an effective tool in characterizing and explaining the heating patterns in the fruits based on DPs. The study helped in better understanding the complex RF heating characteristics of fruits, which may be useful in assessing the design feasibility of product specific RF energy based treatment protocol.     

Evaluation of some essential oils as botanical fungitoxicants for the protection of stored food commodities from fungal infestation

Essential oils from different parts of 18 plants belonging to 11 families were extracted and tested against two toxigenic strains of Aspergillus flavus Link through the poisoned food technique. The oil of Mentha arvensis was found to be effective against both strains of A. flavus and completely stopped the radial mycelial growth of A. flavus at 0.10 mg mL^?1. It was found to be superior over the synthetic fungicides tested and showed a broad fungitoxic spectrum against A. niger, A. fumigatus, Botryodiplodia theobromae, Cladosporium cladosporioides, Fusarium oxysporum, Helminthosporium oryzae, Macrophomina phaseolina and Sclerotium rolfsii at 0.10 mg mL^?1. The oil completely inhibited the aflatoxin B1 production by the toxigenic strain of A. flavus at 0.05 mg mL^?1. Moreover, the Mentha oil also exhibited potent antioxidant activity in 2,2′‐azino‐bis‐3‐ethylbenzothiazoline‐6‐sulphuric acid (ABTS) bioassay. Keeping in mind the side effects of synthetic pesticides and the global interest in botanical pesticides for plant protection due to their biodegradable nature, M. arvensis oil may be used as a botanical fungitoxicant against fungal attack to stored food commodities. The antiaflatoxigenic and antioxidant nature of the oil suggest the possibility of its exploitation for enhancing the shelf life of stored food commodities. Copyright © 2007 Society of Chemical Industry     

Performance evaluation of photovoltaic thermal solar air collector for composite climate of India

The objective of present study is to evaluate the performance of the photovoltaic (PV) module integrated with air duct for composite climate of India. In this case, thermal energy is produced along with electrical energy generated by a PV module with higher efficiency. An analytical expression for an overall efficiency (electrical and thermal) has been derived by using energy balance equation for each component. Experimental validation of thermal model of hybrid photovoltaic/thermal (PV/T) system has also been carried out. It has been observed that there is a fair agreement between theoretical and experimental observations. Further it is concluded that an overall thermal efficiency of PV/T system is significantly increased due to utilization of thermal energy in PV module.