Modeling Flow and Heat Transfer During Freezing of Foods in Forced Airstreams

ABSTRACT: Mathematical modeling of food freezing has been limited to the modeling of the internal heat transfer where the external convective heat-transfer coefficients are assumed or empirically estimated. Previous procedures followed to solve the external boundary layer in tandem with the internal heat transfer were constrained by numerical complexities due to the transient nature of the heat transfer, requiring unsteady formulation for the flow. In this article, attempts have been made to decouple the flow and heat transfer equations for the external boundary layer flow over a food product being frozen. The flow equations have been solved as a steady-state problem using Falker-Skan transformations of the boundary layer equation. The heat-transfer equation for fluid flow is solved as an unsteady-state problem in conjunction with the internal heat transfer and phase change inside the product undergoing freezing. The model is validated for a case of air-impingement freezing.     

Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model

Multimedia Tools and Applications - Breast cancer, the most common invasive cancer, causes deaths of thousands of women in the world every year. Early detection of the same is a remedy to lessen...     

Comments on the erratum to “Supply planning model for remanufacturing system in reverse logistics environment” [Comput. Ind. Eng. 51 (2006) 279–287]

To maximize the total cost of savings, a mixed-integer programming model for remanufacturing proposed by Kim et al. [Kim, K., Song, I., Kim, J., & Jeong, B. (2006). Supply planning model for remanufacturing system in reverse logistics environment. Computers & Industrial Engineering, 51, 279–287] was published in Computers & Industrial Engineering journal to decide optimally the quantity of parts to be processed at each remanufacturing facilities, the number of purchased parts from subcontractor. In this work, comments and suggestions are given to the published model of Kim, Song, Kim, and Jeong (2006). Comments like partial and complete disassembly, disassembly cost, optimum level of disassembly and finally, the modification of constraints are suggested in this work.     

Design of compact patch type curved frequency selective surface

Miniaturized FSS is very useful in communication system. It is obtained by lowering the resonant frequency. Many good research works are reported on compact planar FSS, but it is challenging to decrease the resonant frequency in case of curved FSS. This article deals with the design and fabrication of compact patch type curved FSSs. Here maximum 50.31% compactness is achieved in semicylindrical curved FSS with square slit. Also the parametric studies have been analyzed by introducing same slit in circular and hexagonal patch elements of the curved FSSs. The proposed design has been fabricated. Simulated result has been ratified with experiment result.     

Morphology tuning of bismuth oxychloride nano-crystals by citric acid variation: Application in visible light-assisted dye degradation and hydrogen evolution by electrochemical method

Bismuth Oxychloride (BiOCl) is a p-type indirect bandgap semiconductor with gaps in the range of 3.2–3.5 eV. In this work, BiOCl was synthesized by a simple hydrothermal method along with variation in the concentration of the capping agent citric acid to perform the tuning of the morphology of the samples. The samples were analyzed by various techniques for insight into phase formation, morphology, chemical composition etc. The as-synthesized nanomaterials exhibited extensive variation in crystal structures due to variation in the citric acid ratio. Finally the as-synthesized samples were utilized for effective photocatalytic degradation of toxic textile dyes like Rhodamine B under visible light irradiation and also for hydrogen evolution by electrochemical method. The structural tuning resulted in a noticeable increment in hydrogen evolution observed in an acidic medium for a low overpotential. The as-synthesized material can thus be utilized for waste-water remediation and various electrochemical water-splitting related applications.     

Exploring the Threshold Voltage Characteristics and Short Channel Behavior of Gate Engineered Front Gate Stack MOSFET with Graded Channel

Silicon - The present work focuses on formulating a detailed two dimensional analytical model of the proposed Triple Metal Stacked Front Gate Oxide Double Gate MOSFET with step graded channel...     

A wrapper-filter feature selection technique based on ant colony optimization

Neural Computing and Applications - Ant colony optimization (ACO) is a well-explored meta-heuristic algorithm, among whose many applications feature selection (FS) is an important one. Most...     

Optimization of symbolic feature extraction for pattern classification

The concept of symbolic dynamics has been used in recent literature for feature extraction from time series data for pattern classification. The two primary steps of this technique are partitioning of time series to optimally generate symbol sequences and subsequently modeling of state machines from such symbol sequences. The latter step has been widely investigated and reported in the literature. However, for optimal feature extraction, the first step needs to be further explored. The paper addresses this issue and proposes a data partitioning procedure to extract low-dimensional features from time series while optimizing the class separability. The proposed procedure has been validated on two examples: (i) parameter identification in a Duffing system and (ii) classification of fatigue damage in mechanical structures, made of polycrystalline alloys. In each case, the classification performance of the proposed data partitioning method is compared with those of two other classical data partitioning methods, namely uniform partitioning (UP) and maximum entropy partitioning (MEP).     

Electrochemical evaluation of functionalized graphene oxide filled PVA-chitosan biohybrid for supercapacitor applications

The evolution of eco-friendly and cost efficient energy storage devices is eminent because of depletion of ozone layer and environmental issues. Biopolymer nanocomposites are the most auspicious aspirant to intercept hazardous situations. Chemical functionalization of graphene oxide (GO) is an essential component to avoid agglomeration of nanoparticles and it also helps in end-use applications. Poly(vinyl alcohol) and chitosan biocomposite is prepared by magnetic stirring method followed by dispersion of nanofiller by ultrasonication process. The aim is to emphasize on usage of metal-free material that is bionanocomposite, to exploit and enhance electrical properties of resultant product. The major drawback of chitosan is its low mechanical strength which can be overcome by employing GO and poly(vinyl alcohol). Scanning electron microscopy and transmission electron microscopy studies examined the uniform distribution of GO nanoparticles and functionalization improved the dispersion by creating nitrogen and hydroxyl moieties, this increases the intermolecular interactions between nanofiller and biopolymer matrix. The investigation of thermal analysis revealed the improved thermal stability and lower weight reduction on incorporation of GO and chemically functionalized GO. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48610.     

Pythagorean fuzzy TOPSIS for multicriteria group decision-making with unknown weight information through entropy measure

In this study, a new technique for order preference by similarity to ideal solution (TOPSIS)-based methodology is proposed to solve multicriteria group decision-making problems within Pythagorean fuzzy environment, where the information about weights of both the decision makers (DMs) and criteria are completely unknown. Initially, generalized distance measure for Pythagorean fuzzy sets (PFSs) is defined and used to initiate a new Pythagorean fuzzy entropy measure for computing weights of the criteria. In the decision-making process, at first, weights of DMs are computed using TOPSIS through the geometric distance model. Then, weights of the criteria are determined using the entropy weight model through the newly defined entropy measure for PFSs. Based on the evaluated criteria weights, TOPSIS is further applied to obtain the score value of alternatives corresponding to each decision matrix. Finally, the score values of the alternatives are aggregated with the calculated DMs’ weights to obtain the final ranking of the alternatives to avoid the loss of information, unlike other existing methods. Several numerical examples are considered, solved, and compared with the existing methods.