Thank you for reading numerical heat transfer and fluid flow. Maybe you have knowledge that, people have search numerous times for their favorite books like this numerical heat transfer and fluid flow, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their computer. numerical heat transfer and fluid flow is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the numerical heat transfer and fluid flow is universally compatible with any devices to read.

Numerical Heat Transfer And Fluid Flow

Proceedings of world academy of science, engineering and technology, vol 8

Casson fluid flow over an unsteady stretching surface

In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged In order to tackle this problem, clustering methods are employed to optimize energy consumption, gather data and also enhance the effective lifetime of the network In spite of the clustering methods advantages, there are still some important challenges such as choosing a sensor as a cluster head (CH), which has a significant effect in energy efficiency In clustering phase, nodes are divided into some clusters and then some nodes, named CH, are selected to be the head of each cluster In typical clustered WSNs, nodes sense the field and send the sensed data to the CH, then, after gathering and aggregating data, CH transmits them to the Base Station Node clustering in WSNs has many advantages, such as scalability, energy efficiency, and reducing routing delay In this paper, several clustering methods are studied to demonstrate advantages and disadvantages of them Among them, some methods deal with homogenous network, whereas some deals with heterogeneous In this paper, homogenous and heterogeneous methods of clustering are specifically investigated and compared to each other

Survey on clustering in heterogeneous and homogeneous wireless sensor networks

http://publications.lib.chalmers.se/records/fulltext/180053/local_180053.pdf

Quantitative numerical analysis of flow past a circular cylinder at Reynolds number between 50 and 200

Numerical simulation of laminar flow past a circular cylinder

Cloud computing is the driving power behind the current technological era. Virtualization is rightly referred to as the backbone of cloud computing. Impacts of virtualization employed in high performance computing (HPC) has been much reviewed by researchers. The overhead in the virtualization layer was one of the reasons which hindered its application in the HPC environment. Recent developments in virtualization, especially the OS container based virtualization provides a solution that employs a lightweight virtualization layer and promises lesser overhead. Containers are advantageous over virtual machines in terms of performance overhead which is a major concern in the case of both data intensive applications and compute intensive applications. Currently, several industries have adopted container technologies such as Docker. While Docker is widely used, it has certain pitfalls such as security issues. The recently introduced CoreOS Rkt container technology overcomes these shortcomings of Docker. There has not been much research on how the Rkt environment is suited for high performance applications. The differences in the stack of the Rkt containers suggest better support for high performance applications. High performance applications consist of CPU-intensive and data-intensive applications. The High Performance Linpack Library and the Graph500 are the commonly used computation intensive and data-intensive benchmark applications respectively. In this work, we explore the feasibility of this inter-operable Rkt container in high performance applications by running the HPL and Graph500 applications and compare its performance with the commonly used container technologies such as LXC and Docker containers.

/pdf/exploring-the-support-for-high-performance-applications-in-2s9hv95knf.pdf

Exploring the support for high performance applications in the container runtime environment

Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multiobjective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm suffers from a high order of complexity, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in reducing the runtime complexity, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which reduces the run-time complexity using the simple principle of space-time trade-off. The improved algorithm is applied to the problem of classifying types of leukemia based on microarray data. Results of comparative tests are presented showing that the improved algorithm performs well on large populations.

Improved NSGA-II Based on a Novel Ranking Scheme

Transitional flow past a circular cylinder in the lower subcritical regime (Re = 3900) has been analysed using Large Eddy Simulation (LES) coupled to Smagorinsky and dynamic sub grid scale models. These simulations have been carried out using a parallel multiblock structured finite volume code which is based on SIMPLE algorithm. The predictions are validated against detailed measurement data for mean as well as turbulence quantities. The present LES prediction in general agree reasonably well with the measurement data in the near wake region but deviates from the measurement data in the far wake region which may be due to the coarse resolution of the grid in this region. The influence of the SGS model on mean flow quantities as well as on the flow structures are also discussed.

LES of Flow past Circular Cylinder at Re = 3900

The fog computing paradigm has emanated as a widespread computing technology to support the execution of the internet of things applications. The paradigm introduces a distributed, hierarchical layer of nodes collaboratively working together as the Fog layer. User devices connected to Fog nodes are often non-stationary. The location-aware attribute of Fog computing, deems it necessary to provide uninterrupted services to the users, irrespective of their locations. Migration of user application modules among the Fog nodes is an efficient solution to tackle this issue. In this paper, an autonomic framework MAMF, is proposed to perform migrations of containers running user modules, while satisfying the Quality of Service requirements. The hybrid framework employing MAPE loop concepts and Genetic Algorithm, addresses the migration of containers in the Fog environment, while ensuring application delivery deadlines. The approach uses the pre-determined value of user location for the next time instant, to initiate the migration process. The framework was modelled and evaluated in iFogSim toolkit. The re-allocation problem was also mathematically modelled as an Integer Linear Programming problem. Experimental results indicate that the approach offers an improvement in terms of network usage, execution cost and request execution delay, over the existing approaches.

A. Kandasamy

Papers

Numerical simulation of laminar flow past a circular cylinder

Exploring the support for high performance applications in the container runtime environment

Improved NSGA-II Based on a Novel Ranking Scheme

LES of Flow past Circular Cylinder at Re = 3900

Mobility aware autonomic approach for the migration of application modules in fog computing environment