Search results for: optimal damper placement

Authors: Sarabjeet Singh Sidhu, Ajay Batish, Sanjeev Kumar

Abstract:

This paper reports the optimal process conditions for machining of three different types of MMC’s 65vol%SiC/A356.2; 10vol%SiC-5vol%quartz/Al and 30vol%SiC/A359 using PMEDM process. MRR, TWR, SR and surface integrity were evaluated after each trial and contributing process parameters were identified. The four responses were then collectively optimized using TOPSIS and optimal process conditions were identified for each type of MMC. The density of reinforced particles shields the matrix material from spark energy hence the high MRR and SR was observed with lowest reinforced particle. TWR was highest with Cu-Gr electrode due to disintegration of the weakly bonded particles in the composite electrode. Each workpiece was examined for surface integrity and ranked as per severity of surface defects observed and their rankings were used for arriving at the most optimal process settings for each workpiece. 

Keywords: Metal matrix composites (MMCs), Metal removal rate (MRR), Surface roughness (SR), Surface integrity (SI), Tool wear rate (TWR), Technique for order preference by similarity to ideal solution (TOPSIS).

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Authors: Zoran Nikoloski, Narsingh Deo, Ludek Kucera

Abstract:

Despite the recent surge of research in control of worm propagation, currently, there is no effective defense system against such cyber attacks. We first design a distributed detection architecture called Detection via Distributed Blackholes (DDBH). Our novel detection mechanism could be implemented via virtual honeypots or honeynets. Simulation results show that a worm can be detected with virtual honeypots on only 3% of the nodes. Moreover, the worm is detected when less than 1.5% of the nodes are infected. We then develop two control strategies: (1) optimal dynamic trafficblocking, for which we determine the condition that guarantees minimum number of removed nodes when the worm is contained and (2) predictive dynamic traffic-blocking–a realistic deployment of the optimal strategy on scale-free graphs. The predictive dynamic traffic-blocking, coupled with the DDBH, ensures that more than 40% of the network is unaffected by the propagation at the time when the worm is contained.

Keywords: Network worms, distributed detection, optimaltraffic-blocking, individual-based simulation.

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Authors: R. Mokhtari Malekabadi, S. Ghaed Rahmati, S. Aram

Abstract:

Optimal cultural site selection is one of the ways that can lead to the promotion of citizenship culture in addition to ensuring the health and leisure of city residents. This study examines the social and cultural needs of the community and optimal cultural site allocation and after identifying the problems and shortcomings, provides a suitable model for finding the best location for these centers where there is the greatest impact on the promotion of citizenship culture. On the other hand, non-scientific methods cause irreversible impacts to the urban environment and citizens. But modern efficient methods can reduce these impacts. One of these methods is using geographical information systems (GIS). In this study, Analytical Hierarchy Process (AHP) method was used to locate the optimal cultural site. In AHP, three principles (decomposition), (comparative analysis), and (combining preferences) are used. The objectives of this research include providing optimal contexts for passing time and performing cultural activities by Shiraz residents and also proposing construction of some cultural sites in different areas of the city. The results of this study show the correct positioning of cultural sites based on social needs of citizens. Thus, considering the population parameters and radii access, GIS and AHP model for locating cultural centers can meet social needs of citizens.

Keywords: Analytical Hierarchy Process (AHP), geographical information systems (GIS), Cultural site, locating, Shiraz.

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Authors: Madhukar Nagare, Pankaj Dutta, Amey Kambli

Abstract:

Perishable goods constitute a large portion of retailer inventory and lose value with time due to deterioration and/or obsolescence. Retailers dealing with such goods required considering the factors of short shelf life and the dependency of sales on inventory displayed in determining optimal procurement policy. Many retailers follow the practice of using two bins - primary bin sales fresh items at a list price and secondary bin sales unsold items at a discount price transferred from primary bin on attaining certain age. In this paper, mathematical models are developed for primary bin and for secondary bin that maximizes profit with decision variables of order quantities, optimal review period and optimal selling price at secondary bin. The demand rates in two bins are assumed to be deterministic and dependent on displayed inventory level, price and age but independent of each other. The validity of the model is shown by solving an example and the sensitivity analysis of the model is also reported.

Keywords: Retail Inventory, Perishable Products, Two Bin, Profitable Sales.

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Authors: R. Sharma, M. Gopal

Abstract:

This paper presents a new problem solving approach that is able to generate optimal policy solution for finite-state stochastic sequential decision-making problems with high data efficiency. The proposed algorithm iteratively builds and improves an approximate Markov Decision Process (MDP) model along with cost-to-go value approximates by generating finite length trajectories through the state-space. The approach creates a synergy between an approximate evolving model and approximate cost-to-go values to produce a sequence of improving policies finally converging to the optimal policy through an intelligent and structured search of the policy space. The approach modifies the policy update step of the policy iteration so as to result in a speedy and stable convergence to the optimal policy. We apply the algorithm to a non-holonomic mobile robot control problem and compare its performance with other Reinforcement Learning (RL) approaches, e.g., a) Q-learning, b) Watkins Q(λ), c) SARSA(λ).

Keywords: Markov Decision Process (MDP), Mobile robot, Policy iteration, Simulation.

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Authors: Songtao Chang, Yongji Wang, Lei Liu, Dangjun Zhao

Abstract:

Reentry trajectory optimization is a multi-constraints optimal control problem which is hard to solve. To tackle it, we proposed a new algorithm named CDEN(Constrained Differential Evolution Newton-Raphson Algorithm) based on Differential Evolution( DE) and Newton-Raphson.We transform the infinite dimensional optimal control problem to parameter optimization which is finite dimensional by discretize control parameter. In order to simplify the problem, we figure out the control parameter-s scope by process constraints. To handle constraints, we proposed a parameterless constraints handle process. Through comprehensive analyze the problem, we use a new algorithm integrated by DE and Newton-Raphson to solve it. It is validated by a reentry vehicle X-33, simulation results indicated that the algorithm is effective and robust.

Keywords: reentry vehicle, trajectory optimization, constraint optimal, differential evolution.

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Authors: E. Menga, S. Hernandez

Abstract:

Finite Element Models (FEMs) are widely used in order to study and predict the dynamic properties of structures and usually, the prediction can be obtained with much more accuracy in the case of a single component than in the case of assemblies. Especially for structural dynamics studies, in the low and middle frequency range, most complex FEMs can be seen as assemblies made by linear components joined together at interfaces. From a modelling and computational point of view, these types of joints can be seen as localized sources of stiffness and damping and can be modelled as lumped spring/damper elements, most of time, characterized by nonlinear constitutive laws. On the other side, most of FE programs are able to run nonlinear analysis in time-domain. They treat the whole structure as nonlinear, even if there is one nonlinear degree of freedom (DOF) out of thousands of linear ones, making the analysis unnecessarily expensive from a computational point of view. In this work, a methodology in order to obtain the nonlinear frequency response of structures, whose nonlinearities can be considered as localized sources, is presented. The work extends the well-known Structural Dynamic Modification Method (SDMM) to a nonlinear set of modifications, and allows getting the Nonlinear Frequency Response Functions (NLFRFs), through an ‘updating’ process of the Linear Frequency Response Functions (LFRFs). A brief summary of the analytical concepts is given, starting from the linear formulation and understanding what the implications of the nonlinear one, are. The response of the system is formulated in both: time and frequency domain. First the Modal Database is extracted and the linear response is calculated. Secondly the nonlinear response is obtained thru the NL SDMM, by updating the underlying linear behavior of the system. The methodology, implemented in MATLAB, has been successfully applied to estimate the nonlinear frequency response of two systems. The first one is a two DOFs spring-mass-damper system, and the second example takes into account a full aircraft FE Model. In spite of the different levels of complexity, both examples show the reliability and effectiveness of the method. The results highlight a feasible and robust procedure, which allows a quick estimation of the effect of localized nonlinearities on the dynamic behavior. The method is particularly powerful when most of the FE Model can be considered as acting linearly and the nonlinear behavior is restricted to few degrees of freedom. The procedure is very attractive from a computational point of view because the FEM needs to be run just once, which allows faster nonlinear sensitivity analysis and easier implementation of optimization procedures for the calibration of nonlinear models.

Keywords: Frequency response, nonlinear dynamics, structural dynamic modification, softening effect, rubber.

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Authors: Miguel A. Orellana, Sonia E. Ruiz, Juan Bojórquez

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Optimal load factors (dead, live and seismic) used for the design of buildings may be different, depending of the seismic ground motion characteristics to which they are subjected, which are closely related to the type of soil conditions where the structures are located. The influence of the type of soil on those load factors, is analyzed in the present study. A methodology that is useful for establishing optimal load factors that minimize the cost over the life cycle of the structure is employed; and as a restriction, it is established that the probability of structural failure must be less than or equal to a prescribed value. The life-cycle cost model used here includes different types of costs. The optimization methodology is applied to two groups of reinforced concrete buildings. One set (consisting on 4-, 7-, and 10-story buildings) is located on firm ground (with a dominant period Ts=0.5 s) and the other (consisting on 6-, 12-, and 16-story buildings) on soft soil (Ts=1.5 s) of Mexico City. Each group of buildings is designed using different combinations of load factors. The statistics of the maximums inter-story drifts (associated with the structural capacity) are found by means of incremental dynamic analyses. The buildings located on firm zone are analyzed under the action of 10 strong seismic records, and those on soft zone, under 13 strong ground motions. All the motions correspond to seismic subduction events with magnitudes M=6.9. Then, the structural damage and the expected total costs, corresponding to each group of buildings, are estimated. It is concluded that the optimal load factors combination is different for the design of buildings located on firm ground than that for buildings located on soft soil.

Keywords: Life-cycle cost, optimal load factors, reinforced concrete buildings, total costs, type of soil.

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Authors: Joseph C. Chen, Venkata Karthik Jakka

Abstract:

The main objective of this research is to optimize tensile strength and dimensional accuracy in injection molding processes using Taguchi Parameter Design. An L16 orthogonal array (OA) is used in Taguchi experimental design with five control factors at four levels each and with non-controllable factor vibration. A total of 32 experiments were designed to obtain the optimal parameter setting for the process. The optimal parameters identified for the shrinkage are shot volume, 1.7 cubic inch (A4); mold term temperature, 130 ºF (B1); hold pressure, 3200 Psi (C4); injection speed, 0.61 inch3/sec (D2); and hold time of 14 seconds (E2). The optimal parameters identified for the tensile strength are shot volume, 1.7 cubic inch (A4); mold temperature, 160 ºF (B4); hold pressure, 3100 Psi (C3); injection speed, 0.69 inch3/sec (D4); and hold time of 14 seconds (E2). The Taguchi-based optimization framework was systematically and successfully implemented to obtain an adjusted optimal setting in this research. The mean shrinkage of the confirmation runs is 0.0031%, and the tensile strength value was found to be 3148.1 psi. Both outcomes are far better results from the baseline, and defects have been further reduced in injection molding processes.

Keywords: Injection molding processes, Taguchi Parameter Design, tensile strength, shrinkage test, high-density polyethylene, HDPE.

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Authors: Daniela Elena Marinescu, Ioana Manafi, Dumitru Marin

Abstract:

We consider a Principal-Agent model with the Principal being a seller who does not know perfectly how much the buyer (the Agent) is willing to pay for the good. The buyer-s preferences are hence his private information. The model corresponds to the nonlinear pricing problem of Maskin and Riley. We assume there are three types of Agents. The model is solved using “informational rents" as variables. In the last section we present the main characteristics of the optimal contracts in asymmetric information and some possible extensions of the model.

Keywords: Adverse selection, asymmetric information, informational rent, nonlinear pricing, optimal contract

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Authors: B. Bahmani Firouzi, T. Niknam, M. Nayeripour

Abstract:

With Power system movement toward restructuring along with factors such as life environment pollution, problems of transmission expansion and with advancement in construction technology of small generation units, it is expected that small units like wind turbines, fuel cells, photovoltaic, ... that most of the time connect to the distribution networks play a very essential role in electric power industry. With increase in developing usage of small generation units, management of distribution networks should be reviewed. The target of this paper is to present a new method for optimal management of active and reactive power in distribution networks with regard to costs pertaining to various types of dispersed generations, capacitors and cost of electric energy achieved from network. In other words, in this method it-s endeavored to select optimal sources of active and reactive power generation and controlling equipments such as dispersed generations, capacitors, under load tapchanger transformers and substations in a way that firstly costs in relation to them are minimized and secondly technical and physical constraints are regarded. Because the optimal management of distribution networks is an optimization problem with continuous and discrete variables, the new evolutionary method based on Ant Colony Algorithm has been applied. The simulation results of the method tested on two cases containing 23 and 34 buses exist and will be shown at later sections.

Keywords: Distributed Generation, Optimal Operation Management of distribution networks, Ant Colony Optimization(ACO).

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Authors: Z. Nemec, R. Matousek

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The paper describes the evaluation of quality of control for cases of controlled non-minimal phase plants. Control circuits containing non-minimal phase plants have different properties, they manifest reversed reaction at the beginning of unit step response. For these types of plants are developed special criterion of quality of control, which considers the difference and can be helpful for synthesis of optimal controller tuning. All results are clearly presented using Matlab/Simulink models.

Keywords: control design, non-minimal phase system, optimalcriteria, power plant, heating plant, water turbine, Matlab, Simulink.

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Authors: R. Parameshwaran, R. Karunakaran, S. Iniyan, Anand A. Samuel

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The objective of this study is to present the test results of variable air volume (VAV) air conditioning system optimized by two objective genetic algorithm (GA). The objective functions are energy savings and thermal comfort. The optimal set points for fuzzy logic controller (FLC) are the supply air temperature (Ts), the supply duct static pressure (Ps), the chilled water temperature (Tw), and zone temperature (Tz) that is taken as the problem variables. Supply airflow rate and chilled water flow rate are considered to be the constraints. The optimal set point values are obtained from GA process and assigned into fuzzy logic controller (FLC) in order to conserve energy and maintain thermal comfort in real time VAV air conditioning system. A VAV air conditioning system with FLC installed in a software laboratory has been taken for the purpose of energy analysis. The total energy saving obtained in VAV GA optimization system with FLC compared with constant air volume (CAV) system is expected to achieve 31.5%. The optimal duct static pressure obtained through Genetic fuzzy methodology attributes to better air distribution by delivering the optimal quantity of supply air to the conditioned space. This combination enhanced the advantages of uniform air distribution, thermal comfort and improved energy savings potential.

Keywords: Energy savings, fuzzy logic, Genetic algorithm, Thermal Comfort

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Authors: K. Balamurugan, R. Muralisachithanandam, V. Dharmalingam, R. Srikanth

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This paper deals with the optimal choice and allocation of multi FACTS devices in Deregulated power system using Evolutionary Programming method. The objective is to achieve the power system economic generation allocation and dispatch in deregulated electricity market. Using the proposed method, the locations of the FACTS devices, their types and ratings are optimized simultaneously. Different kinds of FACTS devices are simulated in this study such as UPFC, TCSC, TCPST, and SVC. Simulation results validate the capability of this new approach in minimizing the overall system cost function, which includes the investment costs of the FACTS devices and the bid offers of the market participants. The proposed algorithm is an effective and practical method for the choice and allocation of FACTS devices in deregulated electricity market environment. The standard data of IEEE 14 Bus systems has been taken into account and simulated with aid of MAT-lab software and results were obtained.

Keywords: FACTS devices, Optimal allocation, Deregulated electricity market, Evolutionary programming, Mat Lab.

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Authors: Yiqiong Yuan, Jun Sun, Dongmei Zhou, Jianan Sun

Abstract:

In this paper, we presented a Multi-Objective Random Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based on RDPSO and crowding distance sorting to improve the convergence and distribution with less computation cost. MORDPSO-CD makes the most of RDPSO to approach the true Pareto optimal solutions fast. We adopt the crowding distance sorting technique to update and maintain the archived optimal solutions. Introducing the crowding distance technique into MORDPSO can make the leader particles find the true Pareto solution ultimately. The simulation results reveal that the proposed algorithm has better convergence and distribution.

Keywords: Multi-objective optimization, random drift particle swarm optimization, crowding distance, Pareto optimal solution.

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Authors: Hong Son Hoang, Remy Baraille

Abstract:

The (sub)-optimal soolution of linear filtering problem with correlated noises is considered. The special recursive form of the class of filters and criteria for selecting the best estimator are the essential elements of the design method. The properties of the proposed filter are studied. In particular, for Markovian observation noise, the approximate filter becomes an optimal Gevers-Kailath filter subject to a special choice of the parameter in the class of given linear recursive filters.

Keywords: Linear dynamical system, filtering, minimum meansquare filter, correlated noise

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Authors: Shield B. Lin, Sameer Abdali

Abstract:

Computer simulations were performed using MATLAB/Simulink for a vibration isolation system for astronaut’s exercise platform. Simulation parameters initially were based on an on-going experiment in a laboratory at NASA Johnson Space Center. The authors expanded later simulations to include other parameters. A discrete proportional-integral-derivative controller with a low-pass filter commanding a linear actuator served as the active control unit to push and pull a counterweight in balancing the disturbance forces. A spring-damper device is used as an optional passive control unit. Simulation results indicated such design could achieve near complete vibration isolation with small displacements of the exercise platform.

Keywords: Control, counterweight, isolation, vibration.

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Authors: Mohammad Reza Pirestani, Hamid Reza Vosoghifar, Pegah Jazayeri

Abstract:

In designing river intakes and diversion structures, it is paramount that the sediments entering the intake are minimized or, if possible, completely separated. Due to high water velocity, sediments can significantly damage hydraulic structures especially when mechanical equipment like pumps and turbines are used. This subsequently results in wasting water, electricity and further costs. Therefore, it is prudent to investigate and analyze the performance of lateral intakes affected by sediment control structures. Laboratory experiments, despite their vast potential and benefits, can face certain limitations and challenges. Some of these include: limitations in equipment and facilities, space constraints, equipment errors including lack of adequate precision or mal-operation, and finally, human error. Research has shown that in order to achieve the ultimate goal of intake structure design – which is to design longlasting and proficient structures – the best combination of sediment control structures (such as sill and submerged vanes) along with parameters that increase their performance (such as diversion angle and location) should be determined. Cost, difficulty of execution and environmental impacts should also be included in evaluating the optimal design. This solution can then be applied to similar problems in the future. Subsequently, the model used to arrive at the optimal design requires high level of accuracy and precision in order to avoid improper design and execution of projects. Process of creating and executing the design should be as comprehensive and applicable as possible. Therefore, it is important that influential parameters and vital criteria is fully understood and applied at all stages of choosing the optimal design. In this article, influential parameters on optimal performance of the intake, advantages and disadvantages, and efficiency of a given design are studied. Then, a multi-criterion decision matrix is utilized to choose the optimal model that can be used to determine the proper parameters in constructing the intake.

Keywords: Diversion structures lateral intake, multi criteria decision making, optimal design, sediment control.

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Authors: M. Palanivel, R. Uthayakumar

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In this paper, Economic Order Quantity (EOQ) based model for non-instantaneous Weibull distribution deteriorating items with power demand pattern is presented. In this model, the holding cost per unit of the item per unit time is assumed to be an increasing linear function of time spent in storage. Here the retailer is allowed a trade-credit offer by the supplier to buy more items. Also in this model, shortages are allowed and partially backlogged. The backlogging rate is dependent on the waiting time for the next replenishment. This model aids in minimizing the total inventory cost by finding the optimal time interval and finding the optimal order quantity. The optimal solution of the model is illustrated with the help of numerical examples. Finally sensitivity analysis and graphical representations are given to demonstrate the model.

Keywords: Power demand pattern, Partial backlogging, Time dependent holding cost, Trade credit, Weibull deterioration.

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Authors: V. Tereshchenko, D. Taran

Abstract:

In this paper we propose a new approach to constructing the Delaunay Triangulation and the optimum algorithm for the case of multidimensional spaces (d ≥ 2). Analysing the modern state, it is possible to draw a conclusion, that the ideas for the existing effective algorithms developed for the case of d ≥ 2 are not simple to generalize on a multidimensional case, without the loss of efficiency. We offer for the solving this problem an effective algorithm that satisfies all the given requirements. But theoretical complexity of the problem it is impossible to improve as the Worst - Case Optimality for algorithms of solving such a problem is proved.

Keywords: Delaunay triangulation, multidimensional space, Voronoi Diagram, optimal algorithm.

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Authors: Xianbiao Jia, Hong Li, Yang Liu, Zhichao Fang

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In this paper, an H1-Galerkin mixed finite element method is discussed for the coupled Burgers equations. The optimal error estimates of the semi-discrete and fully discrete schemes of the coupled Burgers equation are derived.

Keywords: The coupled Burgers equation, H1-Galerkin mixed finite element method, Backward Euler's method, Optimal error estimates.

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

Abstract:

Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on timecontrolled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSO algorithm is a versatile management model for the operation of realworld water distribution system.

Keywords: JPSO, operation, optimization, water distribution system.

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Authors: Akbar H. Borzabadi, Omid S. Fard

Abstract:

In this paper we introduce an approach via optimization methods to find approximate solutions for nonlinear Fredholm integral equations of the first kind. To this purpose, we consider two stages of approximation. First we convert the integral equation to a moment problem and then we modify the new problem to two classes of optimization problems, non-constraint optimization problems and optimal control problems. Finally numerical examples is proposed.

Keywords: Fredholm integral equation, Optimization method, Optimal control, Nonlinear and linear programming

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Authors: Leila Jafari, Viliam Makis, Akram Khaleghei G.B.

Abstract:

In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1 which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed, illustrated by a numerical example.

Keywords: Condition-Based Maintenance, Semi-Markov Decision Process, Multivariate Bayesian Control Chart, Partially Observable System, Two-unit System.

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Authors: Ming-Hui Lee, Shou-Jen Hsu

Abstract:

This study presents an active vibration control technique to reduce the earthquake responses of a retained structural system. The proposed technique is a synthesis of the adaptive input estimation method (AIEM) and linear quadratic Gaussian (LQG) controller. The AIEM can estimate an unknown system input online. The LQG controller offers optimal control forces to suppress wall-structural system vibration. The numerical results show robust performance in the active vibration control technique.

Keywords: Active vibration control, AIEM, LQG, Optimal control

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Authors: Yohei Saika, Hiroki Sakaematsu, Shota Akiyama

Abstract:

We investigated statistical performance of Bayesian inference using maximum entropy and MAP estimation for several models which approximated wave-fronts in remote sensing using SAR interferometry. Using Monte Carlo simulation for a set of wave-fronts generated by assumed true prior, we found that the method of maximum entropy realized the optimal performance around the Bayes-optimal conditions by using model of the true prior and the likelihood representing optical measurement due to the interferometer. Also, we found that the MAP estimation regarded as a deterministic limit of maximum entropy almost achieved the same performance as the Bayes-optimal solution for the set of wave-fronts. Then, we clarified that the MAP estimation perfectly carried out phase unwrapping without using prior information, and also that the MAP estimation realized accurate phase unwrapping using conjugate gradient (CG) method, if we assumed the model of the true prior appropriately.

Keywords: Bayesian inference using maximum entropy, MAP estimation using conjugate gradient method, SAR interferometry.

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Authors: Seksan Chunothaisawat, Pongchanun Luangpaiboon

Abstract:

There are two common types of operational research techniques, optimisation and metaheuristic methods. The latter may be defined as a sequential process that intelligently performs the exploration and exploitation adopted by natural intelligence and strong inspiration to form several iterative searches. An aim is to effectively determine near optimal solutions in a solution space. In this work, a type of metaheuristics called Ant Colonies Optimisation, ACO, inspired by a foraging behaviour of ants was adapted to find optimal solutions of eight non-linear continuous mathematical models. Under a consideration of a solution space in a specified region on each model, sub-solutions may contain global or multiple local optimum. Moreover, the algorithm has several common parameters; number of ants, moves, and iterations, which act as the algorithm-s driver. A series of computational experiments for initialising parameters were conducted through methods of Rigid Simplex, RS, and Modified Simplex, MSM. Experimental results were analysed in terms of the best so far solutions, mean and standard deviation. Finally, they stated a recommendation of proper level settings of ACO parameters for all eight functions. These parameter settings can be applied as a guideline for future uses of ACO. This is to promote an ease of use of ACO in real industrial processes. It was found that the results obtained from MSM were pretty similar to those gained from RS. However, if these results with noise standard deviations of 1 and 3 are compared, MSM will reach optimal solutions more efficiently than RS, in terms of speed of convergence.

Keywords: Ant colony optimisation, metaheuristics, modified simplex, non-linear, rigid simplex.

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Authors: Ashok Kumar, Narottam Chand, Vinod Kumar

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Due to the limited energy resources, energy efficient operation of sensor node is a key issue in wireless sensor networks. Clustering is an effective method to prolong the lifetime of energy constrained wireless sensor network. However, clustering in wireless sensor network faces several challenges such as selection of an optimal group of sensor nodes as cluster, optimum selection of cluster head, energy balanced optimal strategy for rotating the role of cluster head in a cluster, maintaining intra and inter cluster connectivity and optimal data routing in the network. In this paper, we propose a protocol supporting an energy efficient clustering, cluster head selection/rotation and data routing method to prolong the lifetime of sensor network. Simulation results demonstrate that the proposed protocol prolongs network lifetime due to the use of efficient clustering, cluster head selection/rotation and data routing.

Keywords: Wireless sensor networks, clustering, energy efficient, localization.

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Authors: Mahdi Fakoor, Seyed Mohammad Navid Ghoreishi

Abstract:

Composite patching is a common way for reinforcing the cracked pipes and cylinders. The effects of composite patch reinforcement on fracture parameters of a cracked pipe depend on a variety of parameters such as number of layers, angle, thickness, and material of each layer. Therefore, stacking sequence optimization of composite patch becomes crucial for the applications of cracked pipes. In this study, in order to obtain the optimal stacking sequence for a composite patch that has minimum weight and maximum resistance in propagation of cracks, a coupled Multi-Objective Genetic Algorithm (MOGA) and Finite Element Method (FEM) process is proposed. This optimization process has done for longitudinal and transverse semi-elliptical cracks and optimal stacking sequences and Pareto’s front for each kind of cracks are presented. The proposed algorithm is validated against collected results from the existing literature.

Keywords: Multi objective optimization, Pareto front, composite patch, cracked pipe.

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Authors: David Calderón Villegas, Thomas Kalitzky

Abstract:

Run-of-river (RoR) hydropower projects represent a viable, clean, and cost-effective alternative to dam-based plants and provide decentralized power production. However, RoR schemes’ cost-effectiveness depends on the proper selection of site and design flow, which is a challenging task because it requires multivariate analysis. In this respect, this study presents the development of an investment decision support tool for assessing the optimal size of an RoR scheme considering the technical, environmental, and cost constraints. The net present value (NPV) from a project perspective is used as an objective function for supporting the investment decision. The tool has been tested by applying it to an actual RoR project recently proposed in Colombia. The obtained results show that the optimum point in financial terms does not match the flow that maximizes energy generation from exploiting the river's available flow. For the case study, the flow that maximizes energy corresponds to a value of 5.1 m3/s. In comparison, an amount of 2.1 m3/s maximizes the investors NPV. Finally, a sensitivity analysis is performed to determine the NPV as a function of the debt rate changes and the electricity prices and the CapEx. Even for the worst-case scenario, the optimal size represents a positive business case with an NPV of 2.2 USD million and an internal rate of return (IRR) 1.5 times higher than the discount rate. 

Keywords: small hydropower, renewable energy, RoR schemes, optimal sizing, financial analysis

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