LMFBR system analysis: Impact of heat transport system on core thermal-hydraulics

In discussing LMFBR thermal-hydraulic analysis, this paper focuses on the heat transport system and its impact on the predicted core behavior, particularly during off-normal or protected accident transients. Following a brief background of related work in the area of system simulation for both loop and pool-type LMFBR designs, modeling considerations for individual components such as reactor core, piping, pumps, heat exchangers and check valves, together with the overall integrated approach to system simulation, are discussed. The need for, and current approaches to, modeling pool stratification are also examined. The role of buoyancy forces in the system is clarified, with particular emphasis on its increasing influence during flow decay. Sample results are presented to illustrate the influence of system modeling details, and selection of component parameters and operational mode, on predicted core thermal-hydraulic response during protected loss-of-flow transients. From a systematic study of the effect of pump inertia for a flow coastdown to natural circulation event in a loop-type design, it is found that certain combinations of primary and secondary pump inertias can lead to core flow reversal for a sustained period, and eventual boiling in the hot fuel channel. This effect, based on its impact on core flow, is even more pronounced in pool-type designs.     

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.

     

Game‐Based Simulation for Cross‐Cultural Decision Making Training

Increasing globalization has had a major impact on manufacturing and service industries as well as on coalition operations conducted by the military. What is common to both the commercial and military sectors is the recent surge in interest in cross‐cultural decision making (CCDM) training. Existing CCDM training approaches tend to employ either some form of multi‐agent simulation or some variant of classical game theory. Despite their manifest benefits, these approaches have specific limitations that need to be overcome to create an effective cross‐cultural training system. Multi‐agent simulations typically lack theoretical underpinnings while classical game theory‐based approaches take a limited view of strategic decision making. Specifically, by adopting a Western view of rationality, game‐theoretic approaches fail to accommodate considerations such as fairness, altruism and reciprocity. Empirical research in strategic economic games has shown that humans respond to more than merely monetary incentives. In particular, research has shown that cultural norms play a central role in human decision making behavior, especially in non‐Western cultures. This paper presents an innovative approach to game‐based simulation that combines findings from behavioral game theory with classical game theory and multi‐agent simulation to exploit the strengths of each approach while making learning enjoyable, memorable, and fun. An illustrative game‐based simulation for CCDM training is also presented. The simulation framework is equally applicable to teaching other soft skills as well as skills that are too hazardous or too expensive to teach in the realworld through live exercises. © 2012 Wiley Periodicals, Inc.     

Models for Cost-Benefit Analysis of RFID Implementations in Retail Stores

In this paper, we focus on the models for analyzing radio frequency identification (RFID) implementations in terms of their costs and benefits. We consider the supply chain comprised of the manufacturer, distributor, retailer, and the consumer. We classify the transactions generated by different RFID tag reads and discuss implementations of the data networks connecting the tag readers at the retail store. Our models consider the cost of implementations including the cost of tag readers, the communication network cost and other infrastructure costs. The analytical models we propose consider the benefits of these implementations including automatic checkout at retail stores and reduced inventory costs due to efficient shelf replenishment.     

High-dynamic-range airborne tracking and fire control radarsubsystem

Two high-dynamic-range receiver subsystems for use in airborne radar fire control and tracking applications are described. The X -band dual-channel monopulse tracking receiver operates at 9.36±0.290 GHz with a 6-dB noise figure and a linear instantaneous dynamic range of 42 dB. A total of 80 dB of RF and IF gain control is programmable with less than ±15° phase and ±1 dB amplitude tracking errors. The Doppler radar receiver operates at 9.3±0.15 GHz and has a 4.6-dB noise figure with ⩾80 dB of instantaneous dynamic range. An 18-dB sensitivity time control (STC) circuit and a 60-dB dump attenuator allow close-in target reception     

The Impact of Nonthermal Technologies on the Microbiological Quality of Juices: A Review

Fruit and vegetable juices are rich sources of nutrients that support microbiological growth and ultimately undergo rapid deterioration of safety and quality. The loss of nutritional quality of juices due to intensive thermal processing is a major problem encountered during the treatment of commercially preserved liquid foods. Conventional thermal processing technologies inactivate microorganisms and enzymes and extend the shelf life of foods but exert negative effects on nutritional and organoleptic properties of juices, for example, a loss of vitamins, of a desirable flavor, and of bioactive compounds and development of different sensory profiles as a result of heating. Nonthermal technologies including ultrasonication, a pulsed electric field, high‐pressure processing, irradiation, and their combinations are suitable alternatives for achieving the same preservation effect without the adverse effects of heat on the quality of juices and meet consumer demand for clean‐label, safe, and wholesome products without compromising their nutritional properties.     

A Differential Capacitive Torque Sensor With Optimal Kinematic Linearity

This paper presents a new design for a torque sensor based on a differential capacitive technology. An arc-radial mechanism is adopted to achieve high rotary/radial motion linearity. Kinematic model of this assembly is derived via vector loop equations. A nonlinearity index is formulated and practical kinematic constraints are imposed. Results show that very high kinematic linearity could be achieved by this new device     

Wireless Industrial Monitoring and Control Using a Smart Sensor Platform

A wireless smart sensor platform (based on patent pending technologies, Ramamurthy ) targeted for instrumentation and predictive maintenance systems is presented. The generic smart sensor platform with "plug-and-play" capability supports hardware interface, payload and communications needs of multiple inertial and position sensors, and actuators, using a RF link (Wi-Fi, Bluetooth, or RFID) for communications, in a point-to-point topology. The design also provides means to update operating and monitoring parameters as well as sensor/RF link specific firmware modules "over-the-air." Sample implementations for industrial applications and system performance are discussed     

Prediction of decay heat removal capabilities for LMFBRs and comparison with experiments

The decay heat removal capability of LMFBR plants is inverstigated in two steps: the transition phase and the established phase of natural circulation. Major parameters affecting the transition phase are briefly reviewed. This paper, then, considers two analytical approaches to study plant behavior for the established phase, i.e., the long-term heat dissipation capability of the plant. Advanced thermohydraulic system simulation codes (SSC-L and SSC-P) are used to study the response of the CRBRP, FFTF and PHENIX plants. Simplified analyses were also performed for these plants, as well as to interpret the actual whole plant test data from PFR, PHENIX and EBR-II. All of these studies have shown that the mixed mean reactor core temperature rise is proportional to the 0.58 exponent of the shutdown steady power level.     

A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low- and high-level features from MRI volumes. Attention and Squeeze-Excitation (SE) modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive fields. The proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867, 0.813, and 0.787, as well as a sensitivity of 0.93, 0.88, and 0.83 for Whole Tumor, Tumor Core, and Enhancing Tumor, on test dataset respectively. Experimental results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art models. Overall, the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.