Investigation into the Computational Costs of Using Genetic Algorithm and Simulated Annealing for the Optimization of Explicit Friction Factor Models

Research reports show that the accuracies of many explicit friction factor models, having different levels of accuracies and complexities, have been improved using genetic algorithm (GA), a global optimization approach. However, the computational cost associated with the use of GA has yet to be discussed. In this study, the parameters of sixteen explicit models for the estimation of friction factor in the turbulent flow regime were optimized using two popular global search methods namely genetic algorithm (GA) and simulated annealing (SA). Based on 1000 interval values of Reynolds number (Re) in the range of and 100 interval values of relative roughness () in the range of , corresponding friction factor (f) data were obtained by solving Colebrook-White equation using Microsoft Excel spreadsheet. These data were then used to modify the parameters of the selected explicit models. Although both GA and SA led to either moderate or significant improvements in the accuracies of the existing friction factor models, SA outperforms the GA. Moreover, the SA requires far less computational time than the GA to complete the corresponding optimization process. It can therefore be concluded that SA is a better global optimizer than GA in the process of finding an improved explicit friction factor model as an alternative to the implicit Colebrook-White equation in the turbulent flow regime.

MULTI-MONTE-CARLO METHOD FOR GENERAL DYNAMIC EQUATION CONSIDERING PARTICLE COAGULATION

Monte-Carlo （MC） method is widely adopted to take into account general dynamic equation （GDE） for particle coagulation, however popular MC method has high computation cost and statistical fatigue. A new Multi-Monte-Carlo （MMC） method, which has characteristics of time-driven MC method, constant number method and constant volume method, was promoted to solve GDE for coagulation. Firstly MMC method was described in details, including the introduction of weighted fictitious particle, the scheme of MMC method, the setting of time step, the judgment of the occurrence of coagulation event, the choice of coagulation partner and the consequential treatment of coagulation event. Secondly MMC method was validated by five special coagulation cases in which analytical solutions exist. The good agreement between the simulation results of MMC method and analytical solutions shows MMC method conserves high computation precision and has low computation cost. Lastly the different influence of different kinds of coagulation kernel on the process of coagulation was analyzed： constant coagulation kernel and Brownian coagulation kernel in continuum regime affect small particles much more than linear and quadratic coagulation kernel,whereas affect big particles much less than linear and quadratic coagulation kernel.

Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locations.To ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in WSNs.Moreover,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of WSNs.Here,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and authentication.By examining state information,SLA projects a trust model to enhance efficacy of data delivery.Indeed,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational costs.Specifically,this model acts as a verification strategy to accelerate?attackers and to handle isolation.This strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic routing.Simulation is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other approaches.The anticipated model shows better trade off in comparison to the prevailing ones.

Feasibility study of symmetric solution of molecular argon flow inside microscale nozzles

The computational cost of numerical methods in microscopic-scales such as molecular dynamics(MD) is a deterrent factor that limits simulations with a large number of particles. Hence, it is desirable to decrease the computational cost and run time of simulations, especially for problems with a symmetrical domain. However, in microscopic-scales, implementation of symmetric boundary conditions is not straightforward. Previously, the present authors have successfully used a symmetry boundary condition to solve molecular flows in constant-area channels. The results obtained with this approach agree well with the benchmark cases. Therefore, it has provided us with a sound ground to further explore feasibility of applying symmetric solutions of micro-fluid flows in other geometries such as variable-area ducts. Molecular flows are solved for the whole domain with and without the symmetric boundary condition. Good agreement has been reached between the results of the symmetric solution and the whole domain solution. To investigate robustness of the proposed method, simulations are conducted for different values of affecting parameters including an external force, a flow density, and a domain length. The results indicate that the symmetric solution is also applicable to variable-area ducts such as micro-nozzles.

Block Iterative STMV Algorithm and Its Application in Multi-Targets Detection

STMV beamforming algorithm needs inversion operation of matrix, and its engineering application is limited due to its huge computational cost. This paper proposed block iterative STMV algorithm based on one-phase regressive filter, matrix inversion lemma and inversion of block matrix. The computational cost is reduced approximately as 1/4 M times as original algorithm when array number is M. The simulation results show that this algorithm maintains high azimuth resolution and good performance of detecting multi-targets. Within 1 - 2 dB directional index and higher azimuth discrimination of block iterative STMV algorithm are achieved than STMV algorithm for sea trial data processing. And its good robustness lays the foundation of its engineering application.

Scale adaptive simulation of vortex structures past a square cylinder

The scale adaptive simulation（SAS） turbulence model is evaluated on a turbulent flow past a square cylinder using the open-source CFD package OpenF OAM 2.3.0. Two and three-dimensional simulations are performed to determine global quantities like drag and lift coefficients and Strouhal number in addition to mean and fluctuating velocity profiles in the recirculation and wake regions. SAS model is evaluated against the Shear Stress Transport k-ω（SST） model and also compared with previously reported results based on DES, LES and DNS turbulence approaches. Results show that global quantities along with mean velocity profiles are well-captured by 2-D SAS model. The 3-D SAS model also succeeded in providing comparable results with recently published DES study on Reynolds shear stress and velocity fluctuation components using about 12 times lower computational cost. It is shown that large values of the SAS model constant result in too dissipative behavior, so that proper calibration of the SAS model constant for different turbulent flows is vital.

Efficient identity-based signature scheme with batch authentication for delay tolerant mobile sensor network

Identity-based cryptography （IBC） has drawn a lot of attentions in delay tolerant environment. However, the high computational cost of IBC becomes the most critical issue in delay tolerant mobile sensor network （DTMSN） because of the limited processing power. In this paper, an efficient identify-based signature scheme with batch authentication （ISBA） is proposed for DTMSN. ISBA designs an online/offline signature with batch authentication to reduce the computational cost, and improves data delivery mechanism to increase the number of messages for each batch authentication. Simulation results show that ISBA not only realizes a lower computational cost than existed schemes, but also does not induce negative impact on the delivery performance.

A fuzzy-logic-based approach to accurate modeling of a double gate MOSFET for nanoelectronic circuit design

The double gate （DG） silicon MOSFET with an extremely short-channel length has the appropriate fea- tures to constitute the devices for nanoscale circuit design. To develop a physical model for extremely scaled DG MOSFETs, the drain current in the channel must be accurately determined under the application of drain and gate voltages. However, modeling the transport mechanism for the nanoscale structures requires the use of overkill meth- ods and models in terms of their complexity and computation time （self-consistent, quantum computations ）. Therefore, new methods and techniques are required to overcome these constraints. In this paper, a new approach based on the fuzzy logic computation is proposed to investigate nanoscale DG MOSFETs. The proposed approach has been implemented in a device simulator to show the impact of the proposed approach on the nanoelectronic cir- cuit design. The approach is general and thus is suitable for any type ofnanoscale structure investigation problems in the nanotechnology industry.

An Efficient Mapped WENO Scheme Using Approximate Constant Mapping

We present a novel mapping approach for WENO schemes through the use of an approximate constant mapping function which is constructed by employing an approximation of the classic signum function.The new approximate constant mapping function is designed to meet the overall criteria for a proper mapping function required in the design of the WENO-PM6 scheme.The WENO-PM6 scheme was proposed to overcome the potential loss of accuracy of the WENO-M scheme which was developed to recover the optimal convergence order of the WENO-JS scheme at critical points.Our new mapped WENO scheme,denoted as WENOACM,maintains almost all advantages of the WENO-PM6 scheme,including low dissipation and high resolution,while decreases the number of mathematical operations remarkably in every mapping process leading to a significant improvement of efficiency.The convergence rates of the WENO-ACM scheme have been shown through one-dimensional linear advection equation with various initial conditions.Numerical results of one-dimensional Euler equations for the Riemann problems,the Mach 3 shock-density wave interaction and the Woodward-Colella interacting blastwaves are improved in comparison with the results obtained by the WENO-JS,WENO-M and WENO-PM6 schemes.Numerical experiments with two-dimensional problems as the 2D Riemann problem,the shock-vortex interaction,the 2D explosion problem,the double Mach reflection and the forward-facing step problem modeled via the two dimensional Euler equations have been conducted to demonstrate the high resolution and the effectiveness of the WENO-ACM scheme.The WENOACM scheme provides significantly better resolution than the WENO-M scheme and slightly better resolution than the WENO-PM6 scheme,and compared to the WENOM and WENO-PM6 schemes,the extra computational cost is reduced by more than 83%and 93%,respectively.

A Combinatorial Auction-Based Collaborative Cloud Services Platform

In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction and improve the scalability of the cloud, which is divided into three layers： The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider（CSP）, and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users.In fact, the aim of the coalition formation is to find suitable partners for a particular CSP. However, identifying a suitable combination of partners to form the coalition is an NP-hard problem. Hence, we propose approximation algorithms for the coalition formation. The Breadth Traversal Algorithm（BTA） and Revised Ant Colony Algorithm（RACA） are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price. In addition, the performance of the market model was compared to the existing CA-based model in terms of economic efficiency.