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.     

Fetal electrocardiogram modeling using hybrid evolutionary firefly algorithm and extreme learning machine

Multidimensional Systems and Signal Processing - Extraction of fetal electrocardiogram (FECG) from the abdominal region of the mother’s skin is challenge task due to the high overlapping of...     

Novel Fault Management Framework Using Markov Chain in Wireless Sensor Networks: FMMC

Wireless Personal Communications - Due to using wireless sensor nodes (WSNs) in inaccessible areas and applying limitations in making nodes to reduce costs, these networks are prone to faults. The...     

Frequency selective millimeter‐wave channel estimation based on subspace enhancement algorithms

In millimeter wave (mmW) communication systems, hybrid architecture, including the analog‐digital precoder and combiner matrices, is employed to take advantage of the multistream transceiver. In practice, mmW channel is assumed to be frequency‐selective, since the signal bandwidth is larger than the coherence bandwidth. Hence, orthogonal frequency‐division multiplexing signaling can be remedial. So far, most of the previous works on the frequency‐selective channel estimation have focused on the single measurement vector (SMV) form, whereas finding and exploiting the proper multimeasurement vector (MMV) model can improve upon the estimation procedure based on compressive sensing (CS) concepts. In fact, the estimation procedure based on the MMV model has a faster convergence speed than the SMV method specially, when the training frames are small. In this paper, we first extract the MMV model of the channel. In this model, the rank‐deficiency occurs as the number of training frames is less or equal to the sparsity level. Thus, the conventional estimation methods fail to provide the desirable performance. To overcome this issue, we propose two rank‐aware algorithms based on the enhancement of the observed signal subspace. The first algorithm assumes to know the sparsity level, while the second faces to the lack of knowledge about the sparsity level. The simulation results corroborate the fact that the proposed methods outperform the conventional CS algorithms such as Simultaneous Orthogonal Matching Pursuit.     

ANNM: A New Method for Adding Noise Nodes Which are Used Recently in Anonymization Methods in Social Networks

Wireless Personal Communications - One of the main concerns at the time of production or share of information on social networking sites for scientific research and business analysis is privacy....     

Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains

Data envelopment analysis (DEA) is a method for measuring performance of decision making units (DMUs). Conventional DEA models view DMUs as black boxes. Network DEA (NDEA) models have been developed to overcome this shortfall. This paper develops a new NDEA model based on modified enhanced Russell measure model. This paper measures performance of humanitarian supply chains (HSCs) by an NDEA model. Capabilities of the proposed model are addressed by theorems. However, in the real world, there might be stochastic data. This paper presents a stochastic version of the proposed NDEA model to measure the performance of HSCs. We analyse main properties of our model. We present a case study to demonstrate the applicability of the proposed model.     

Exergetic and environmental performance improvement in cement production process by driving force distribution

This paper presents an investigation of the effects of temperature gradient distribution by the aid of a secondary burner on exergetic and environmental functions of the cement production process. For this reason, the burning system of the cement production (kiln &; preheater) process was simulated in four thermal areas. Three lines of cement production with 2,000, 2,300 and 2,600 ton/day were investigated. Fuel injection ratio into the secondary burner, from 10 to 40 percent was studied for each line. The obtained results show that, for cyclone preheaters, fuel injection into the secondary burner up to a proportion resulting in the minimum temperature required for alite formation (2,200 °C) in the kiln burning zone is suitable. For shaft preheaters, however, according to percent calcinations, there exists an optimum proportion for 15 to 20 percent injection fuel into secondary burner. Finally, it was shown that the secondary burner application can reduce the exergy losses about 25 percent, which leads to a reduction of the green house gases of about 35000 cubic meters per year for each ton per day of clinker production.     

Degradation of methotrexate by UV/peroxymonosulfate: Kinetics,effect of operational parameters and mechanism

Methotrexate (MTX) is one of the most consumed anti-cancer drugs in the pharmaceutical market around the world. The widespread occurrence of MTX in aquatic environment through hospital effluent has attracted increasing concern due to its potential to induce water pollution. In the present study, the degradation of MTX in aqueous medium was investigated by UV-activated peroxymonosulfate (PMS). A significant improvement in degradation rate by increasing UV intensity and PMS concentration while the decrease in degradation efficiency with the increase of solution pH and initial concentration of MTX was observed. The proposed UV/PMS process could achieve more than 90% MTX degradation in 30 min with a good mineralization degree (65%). A pseudofirst order kinetic model was employed and successfully predicted the degradation of MTX. The effect of other operational parameters such as the initial concentration of the targeted compound, dosage of oxidant (PMS), solution pH and UV intensity on the degradation rate were investigated. At the last, the main transform intermediates were identified using LC-MS and possible degradation pathways were proposed. The results show that UV/ PMS can be used as an efficient technology to treat pharmaceuticals such as methotrexate containing water and wastewater.     

Determination of residual organic matter in extracted oil sands using a low temperature ashing method

The application of a low temperature ashing method for estimating total residual organic matter (toluene insolubles) in oil sands is described. A linear correlation exists between organic carbon content and loss on ignition at 400 ± 10 °C of solvent extracted oil sands. The ratio between total organic carbon and the weight loss on ignition (CT/LOl) owing to the removal of residual organic matter is much lower than that obtained for toluene soluble bitumen fractions, indicating very different chemical composition for the residual organic matter. The measured carbon content of the residual organic matter in oil sands suggests that this material could be a mixture of various fractions contained in resins, asphaltenes, asphaltic acids and humic acids.