Design and implementation of a relational database of macroscopic cross-sections for steady-state and transient analysis in a medium sized PWR

This paper deals with the design and implementation of a relational database of macroscopic cross-sections as well as validation and its usage as a component in steady-state and transient analysis. These types of analysis are definitely designated as data-driven processes in which their behavior is changed based on data. The data in this manner can be the variation of fuel composition density as well as the fuel temperature during the reactor operation or in some accidents. In order to gain the accurate results in transient analysis, applying these variations is inevitable. Therefore, for each changing in fuel behavior, the new set of macroscopic cross-sections must be reproduced by performing the lattice calculations at run-time. During recent years some approaches have been done in both academic and commercial areas to take advantage of already calculated cross-sections instead of recalculate them by lattice codes during the analysis. Using this approach leverages the computation performance by reducing the floating point operations which can decrease the CPU load as well as the calculation time. Although this approach delivers a good performance in terms of time and computation, when a large number of fuel or reactor core circumstances are subjected to be analyzed the accuracy is became an important point. In this situation the cross-sections for all expected conditions must be already calculated. This cause the large number of cross-section data which must be stored and managed somehow. The system applied in this study to store and manage these cross-section data sets is a relational database management system (RDBMS) which is based on Microsoft SQL Server 2008 Management System. The node wise macroscopic cross-sections for each nominal and off-nominal of a typical medium sized PWR reactor core conditions which are calculated by WIMS-D5 and CITATION codes are tabulated in this system and can be retrieved by structured query language extremely rapid on demand.     

The release rate and antimicrobial activity of calcium-alginate films containing self-microemulsifying Thymus vulgaris essential oil against Escherichia coli and Staphylococcus aureus

The aim of this study was to fabricate antimicrobial calcium-alginate-based films containing the self-microemulsifying thyme essential oil (TEO) formulations using Tween 80 as the surfactant, and acetic (AA) or propionic (PA) acids as the cosurfactants. A Ca-alginate film containing nano-emulsified TEO as well as a neat Ca-alginate film were considered as the controls. The scanning electron microscopy micrographs showed a highly porous texture for SME films, which resulted in an increase in water vapor permeability and water absorption capacity of these films. The SME films released the TEO completely within 155 min and inhibited the growth of S. aureus and E. coli in in vitro antimicrobial tests. The population of S. aureus and E. coli reduced significantly in ground beef covered with SME films. The results of this study showed that self-microemulsifying TEO films could effectively increase the shelf life of ground beef by controlling its microbial population.     

Using the red-near infrared spectral to estimate ground cover based on vegetative indices

Rapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.     

Application of fuzzy c-means algorithm as a novel method to predict density of mixtures of Athabasca bitumen and heavy n-alkane

The significant number of oil reservoir are bitumen and heavy oil. One of the approaches to enhance oil recovery of these types of reservoir is dilution of reservoir oil by injection of a solvent such as tetradecane into the reservoirs to modify viscosity and density of reservoir fluids. In this investigation, an effective and robust estimating algorithm based on fuzzy c-means (FCM) algorithm was developed to predict density of mixtures of Athabasca bitumen and heavy n-alkane as function of temperature, pressure and weight percent of the solvent. The model outputs were compared to experimental data from literature in different conditions. The coefficients of determination for training and testing datasets are 0.9989 and 0.9988. The comparisons showed that the proposed model can be an applicable tool for predicting density of mixtures of bitumen and heavy n-alkane.     

Truck Loading and Fatigue Damage Analysis for Girder Bridges Based on Weigh-in-Motion Data

Based on data collected by weigh-in-motion (WIM) measurements, truck traffic is synthesized by type and loading condition. Three-dimensional nonlinear models for the trucks with significant counts are developed from the measured data. Six simply supported multigirder steel bridges with spans ranging from 10.67 m (35 ft) to 42.67 m (140 ft) are analyzed using the proposed method. Road surface roughness is generated as transversely correlated random processes using the autoregressive and moving average model. The dynamic impact factor is taken as the average of 20 simulations of good road roughness. Live-load spectra are obtained by combining static responses with the calculated impact factors. A case study of the normal traffic from a specific site on the interstate highway I-75 is illustrated. Static loading of the heaviest in each truck type is compared with that of the American Association of State Highway and Transportation Officials standard design truck HS20-44. Several important trucks causing fatigue damage are found.     

A multi-product multi-period inventory control problem under inflation and discount: a parameter-tuned particle swarm optimization algorithm

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a particle swarm optimization (PSO) algorithm is proposed to find a near-optimal solution. Since there is no bench mark available in the literature to justify and validate the results, a genetic algorithm is presented as well. In order to compare the performances of the two algorithms in terms of the fitness function and the required CPU time, they are first tuned using the Taguchi approach, in which a metric called “smaller is better” is used to model the response variable. Then, some numerical examples are provided to demonstrate the application and to validate the results obtained. The results show that, while both algorithms have statistically similar performances, PSO tends to be the better algorithm in almost all problems.     

Physical modification of polymeric support layer for thin film composite forward osmosis membranes by metal–organic framework-based porous matrix membrane strategy

Physical modification of support layers (SLs) for thin-film composite (TFC) forward osmosis (FO) membranes is the main goal of this study. Accordingly, the strategy of metal–organic framework (MOF)-based porous matrix membrane (PMM) was used for the fabrication of controllable SLs. Fourteen different TFC FO membranes were successfully fabricated by interfacial polymerization (IP) technique over the fourteen different SLs made of polyetherimide (PEI), polyethersulfone (PES), and twelve MOF-based PMM. The controllable MOF particles, fabricated SLs, and TFC membranes were characterized by Fourier-transform infrared spectroscopy (FTIR), powder X-ray diffraction (PXRD), dynamic light scattering (DLS), field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), contact angle (CA), inductively coupled plasma (ICP), and developed SHN1 method. The results showed that the PMM strategy can lead to an increase in the degree of crosslinking of polyamide (PA) as a result of physical modification of the original SLs. Also, the PMM strategy reduced the structural parameters and hence the internal concentration polarization (ICP) was controlled. However, according to the characteristic curve, physical modification of the structure of PES and PEI by MOF-based PMM strategy caused a small and dramatic effect (respectively) on the performance of the TFC FO membranes. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 137, 48672.