New Methodology For Optimization Research Articles

A new methodology for optimization of overcurrent protection relays in active distribution networks regarding thermal stress curves

In this paper, a novel method for optimizing and coordinating directional overcurrent relays in active distribution networks considering thermal equivalent short-circuit current is proposed. A modified genetic algorithm is used as a tool in order to simultaneously minimize the operating times of the primary relays and maximize the time delay between the operation of backup relays and their respective protected elements’ thermal stress curves. In other words, the objective function to be minimized is defined as the sum of the operating times of all primary relays and the reciprocal sum of the thermal violation check. The latter part is necessary because a protection engineer needs to be sure that the operation of a backup relay is as far as possible from the thermal stress curve of the protected element. Needless to say, for every solution, the algorithm ensures the proper coordination between primary and backup relay pairs throughout the network. The simulation is carried out for a closed loop medium voltage distribution network and the results are presented to demonstrate the effectiveness of the proposed approach. The main benefit of this optimization process is that it indicates whether the existing protection system can operate safely below the thermal stress curves of the protected elements’ or whether additional measures are required.

Analysis of Different Uniflow Scavenging Options for a Medium-Duty 2-Stroke Engine for a U.S. Light-Truck Application

Abstract The work presented here seeks to compare different means of providing uniflow scavenging for a 2-stroke engine suitable to power a U.S. light-duty truck. Through the “end-to-end” nature of the uniflow scavenging process, it can in theory provide improved gas-exchange characteristics for such an engine operating cycle; furthermore, because the exhaust leaves at one end and the fresh charge enters at the other, the full circumference of the cylinder can be used for the ports for each flow, and therefore, for a given gas exchange angle-area demand, expansion can theoretically be maximized over more traditional loop-scavenging approaches. This gives a further thermodynamic advantage. The three different configurations studied, which could all utilize uniflow scavenging, were the opposed piston, the poppet-valve with piston-controlled intake ports, and the sleeve valve. These are described and all are compared in terms of indicated fuel consumption for the same cylinder swept volume, compression ratio, and exhaust pressure, for the same target indicated mean effective pressure (IMEP) and indicated specific power. A new methodology for optimization was developed using a one-dimensional engine simulation package which also took into account charging system work. The charging system was assumed to be a combination of supercharger and turbocharger to permit some waste energy recovery. As a result of this work, it was found that the opposed-piston configuration provides the best attributes since it allows maximum expansion and minimum heat transfer. Its advantage over the other two (whose results were very close) was of the order of 8.3% in terms of net specific fuel consumption (NSFC) (defined as indicted specific fuel consumption (ISFC) net of supercharger power). Part of its advantage also stems from its requirement for minimum air supply system work, included in this NSFC value. Interestingly, it was found that existing experiential guidelines for port angle-area specification for loop-scavenged, piston-ported engines using crankcase compression could also be applied to all of the other scavenging types. This has not been demonstrated before. The optimization process that was subsequently developed allowed port design to be tailored to specific targets, in this case lowest NSFC. The paper therefore presents a fundamental comparison of scavenging systems using a new approach, providing new insights and information which have not been shown before.

FieldOpt: A powerful and effective programming framework tailored for field development optimization

Petroleum field development involves critical decisions such as well location, well completion design and optimal control setting that have a significant impact on revenues and costs. These decisions are associated with a large degree of engineering effort that commonly involves time-consuming reservoir simulations to compute the performance of different field development scenarios. Increasingly within the petroleum industry, software tools and optimization methodology are developed and implemented to support and augment the various decision-making processes. The overall aim of these tools is to increase productivity and improve decision quality. Within this context, this work introduces an open-source, extensible, tailor-made programming framework: FieldOpt. FieldOpt’s primary purpose is rapid prototyping and testing of optimization procedures to solve critical field development problems. The framework is implemented in C++ and provides an efficient integration of mathematical optimization procedures with reservoir simulation.FieldOpt’s modular architecture and use of object-oriented programming allow the users to adapt and extend the code with ease. The architecture has proven successful in facilitating new optimization algorithms, new use cases, and new methodology for optimization with minimal effort and change to internal data structures and data flow.Three use cases are presented to demonstrate the optimization capabilities of FieldOpt as well as the flexibility and ease-of-use of the software regarding configuring various optimization procedures to solve a range of field development problems. The three use cases presented optimize on well control, well completion design and well placement parameters, respectively. For each case, both the configuration of the algorithms and problems within FieldOpt as well as the final solution and performance of the different optimization procedures are discussed. In all three cases, FieldOpt was able to find significant improvements or crucial information to decision making.

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

A new methodology for optimization and prediction of rate of penetration during drilling operations

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.

Modeling and optimization of EBG structure using response surface methodology for antenna applications

Electromagnetic Bandgap (EBG) structures are widely used with microstrip antennas for improving antenna efficiency due to their ability to suppress surface waves. To have proper control of surface wave band-gap, it is necessary to have efficient models of the dispersion characteristics of EBG in the irreducible Brillouin zone. A new methodology for optimization of a modified mushroom EBG is presented, based on Response Surface Modeling (RSM) along with Design of Experiments (DoE). Four geometrical parameters which control the surface wave band gap namely the patch width (w), gap between the patches (2g), via radius(r) and the slot width(s) are considered as independent factors and DoE is carried out using Finite Element Analysis (FEA) simulation. The lower (F1) and upper (F2) frequencies of the band gap are extracted from the dispersion characteristics. Mathematical models for F1 and F2 are developed using non-linear regression analysis. Analysis of Variance (ANOVA) is used to study the effect of EBG parameters on the stop band frequencies. The optimum EBG parameters for a stop band in 5–8 GHz frequency range are determined and are found to be: patch width, 6.48 mm, gap, 0.49 mm, via radius, 0.25 mm and slot width, 0.48 mm. The statistical model is validated by experimental results.

Simplex‐based adaptive parametric model order reduction for applications in optimization

AbstractA new methodology for optimization using parametric reduced order models is introduced. An adaptive scheme to place the expansion points in the reduction step is combined with a smart parametric space sampling in the optimization step to produce very reliable reduced order models. The methodology is illustrated in a demonstrative electromagnetic problem with appreciable gain in computational time. This technique is of general application and can be used to speed up optimization processes in many areas of engineering.

An intelligent deployment method of geo-sensor networks in 3D environment

ABSTRACTWireless networks have gained considerable popularity during recent years. Optimum deployment of sensors in wireless networks has turned into one of the most significant topics of this area. Extensive research has been conducted on deployment of sensors in networks for achieving maximum coverage. However, it seems that most of the researches have treated it simplistically, that is, environmental conditions are ignored. This research attempts to propose a new methodology for optimization of sensor network coverage by combining geospatial information system concepts and techniques and Artificial Bee Colony (ABC) algorithm. The environmental conditions of the area are taken into consideration in this methodology and the deployment of a sensor network takes place through smart searching in the real environment. The efficiency of the proposed methodology is compared with that of Voronoi-based algorithm, for the network coverage optimization. The Voronoi-based algorithm traps after some iterations and does not improve the coverage. However, ABC algorithm searches the space thoroughly and detects the holes in a random manner. As a result, the network coverages yielded by ABC algorithm for non-urban and urban areas were 7.05% and 8.43% more than that those yielded by Voronoi-based algorithm, respectively.

Design of Experiment – An Integration of Fisher, Taguchi and Shainin DOE Methodology

Design of experiment (DOE) is the focal collective of quality technique and is widely used in Research and Development (R&D) organization to optimize product design parameters. There are three main DOE approaches that commonly adopted by R&D companies, which are the classical Fisher’s DOE, Taguchi DOE and Shainin DOE. This paper explores how these three main DOE approaches could be integrated to optimize product design parameters in a Research and Development company. The paper starts with enlightenment of the common DOE steps, follows by discussion of issues faced by the Research and Development company during the implementation of the three DOE approaches in product design. The paper subsequently illustrates how the three DOE approaches could be integrated as a new methodology for optimization of product design parameters. The integrated DOE methodology presented in this paper offers an alternative and effective way to conduct DOE in a dynamic product design and development environment.

Contour finish turning operations with coated grooved tools: Optimization of machining performance

This paper presents a new methodology for optimization of machining performance in contour finish turning operations. Two machining performance measures, chip breakability and surface roughness, are considered as optimization criteria due to their importance in finishing operations. Chip breakability covers two major factors: chip shape and size. Comprehensive case studies are presented to demonstrate the determination and application of optimal cutting conditions through experimental validation.

New Methodology for Optimization of Freshwater Inflows to Estuaries

The objective of this work is to develop a general methodology for determination of the optimal freshwater inflows into bays and estuaries to balance freshwater demands with the harvest of various types of estuarine resources. The methodology is based upon solving a large-scale nonlinear programming problem formulated in an optimal-control framework. Constraints of the optimization problem include regression equations for harvest of the various species that express fishery harvest as a function of the quantity of freshwater inflow. The stochastic element of the problem (i.e. the uncertainty associated with the regression equations for harvest) is considered by expressing constraints in a chance-constrained formulation. A nonlinear programming optimizer is interfaced with a hydrodynamic transport model to implicitly solve the hydrodynamic-salinity constraint equations for salinity levels. An augmented Lagrangian method is introduced to incorporate the salinity constraints into the objective so that the problem size for the optimizer is significantly reduced. A computer model OPTFLOW has been developed by interfacing a simulator for the hydrodynamic-salinity (HYD-SAL) with a nonlinear optimizer (GRG2) to apply the methodology by the present writers in an accompanying companion paper. An efficient approximation scheme is developed for evaluation of the objective function and its reduced gradient to reduce the computational effort dramatically. The new methodology can provide a very useful tool for decision makers to quantitatively analyze various water-management strategies.