Emerald | Engineering Computations | Table of Contents http://www.emeraldinsight.com/0264-4401.htm Table of contents from the most recently published issue of Engineering Computations Journal en-gb Tue, 29 Jul 2014 00:00:00 +0100 2014 Emerald Group Publishing Limited editorial@emeraldinsight.com support@emeraldinsight.com 60 Emerald | Engineering Computations | Table of Contents http://www.emeraldinsight.com/common_assets/img/covers_journal/eccover.gif http://www.emeraldinsight.com/0264-4401.htm 120 157 Cogeneration design problem: computational complexity analysis and solution through an expert system http://www.emeraldinsight.com/journals.htm?issn=0264-4401&volume=31&issue=6&articleid=17114673&show=abstract <strong>Abstract</strong><br /><br /><B>Purpose</B> - The objective of this work is twofold: i) to analyse the computational complexity of the cogeneration design problem; ii) to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.<B>Design/methodology/approach</B> - The complexity of the cogeneration problem is analysed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.<B>Findings</B> - The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.<B>Originality/value</B> - The expert system approach is not a "blind" method, i.e., it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions. Article literatinetwork@emeraldinsight.com (José Alexandre Matelli, Jonny Carlos Silva, Edson Bazzo) Tue, 29 Jul 2014 00:00:00 +0100 Robust design of Mars entry micro-probe with evidence theory and multi-fidelity strategies http://www.emeraldinsight.com/journals.htm?issn=0264-4401&volume=31&issue=6&articleid=17114650&show=abstract <strong>Abstract</strong><br /><br /><B>Purpose</B> - A multi-disciplinary robust design optimization method for micro Mars entry probe (no more than 0.8 meter in diameter) is proposed. The main purpose is to design a Mars entry probe, not only the geometric configuration, but the trajectory and Thermal Protection System (TPS). In the design optimization, the uncertainties of atmospheric and aerodynamic parameters are taken into account. The probability distribution information of the uncertainties are supposed to be unknown in the design. To ensure accuracy levels, time-consuming numerical models are coupled in the optimization. Multi-fidelity approach is designed for model management to balance the computational cost and accuracy. <B>Design/methodology/approach</B> - Uncertainties which can not defined by usual Gaussian probability distribution are modeled with degree of belief, and optimized through with multiple-objective optimization method. The optimization objectives are set to be the thermal performance of the probe TPS and the corresponding belief values. Robust Pareto Front is obtained by an improved Multi-objective Density Estimator Algorithm. Multi-fidelity management is performed with an Artificial Neural Network surrogate model. Analytical model is used first, and then with the improvement of accuracy, rather complex numerical models are activated. ANN updates the database during the optimization, and makes the solutions finally converge to a high-level accuracy.<B>Findings</B> - The optimization method provides a way for conducting complex design optimization involving multi-discipline and multi- fidelity models. Uncertainty effects are analyzed and optimized through multi-disciplinary robust design. Because of the micro size, and uncertain impacts of aerodynamic and atmospheric parameters, simulation results show the performance trade-off by the uncertainties. Therefore an effective robust design is necessary for micro entry probe, particularly when details of model parameter are not available.<B>Originality/value</B> - The optimization is performed through a new developed MOEDA. Affinity Propagation algorithm partitions adaptively the samples by passing and analyzing messages between data points. Local Principle Component techniques are employed to resample and reproduce new individuals in each cluster. A strategy similar to NSGA-II selects data with better performance, and converges to the Pareto front. Models with different fidelity levels are incorporated in the multi-disciplinary design via ANN surrogate model. Database of aerodynamic coefficients is updated in an online manner. The computational time is greatly reduced while keeping nearly the same accuracy level of high fidelity model. Article literatinetwork@emeraldinsight.com (Hou Liqiang, Cai Yuanli, Zhang Rongzhi, Li Hengnian, Li Jisheng) Tue, 29 Jul 2014 00:00:00 +0100 Gains tuning of a PI-Fuzzy controller by genetic algorithms http://www.emeraldinsight.com/journals.htm?issn=0264-4401&volume=31&issue=6&articleid=17114661&show=abstract <strong>Abstract</strong><br /><br /><B>Purpose</B> - Nowadays, in order to improve current applications, Industry incorporates to their solution approaches artificial intelligence techniques and methodologies like Fuzzy Logic, neural networks and/or genetic algorithms. Artificial intelligence techniques complement classical methodologies and include concepts that simulate the way humans solve problems or how processes work in nature. In this work, the Fuzzy Logic system cancels the effects of load perturbances in an energy plant, by implementing a secondary controller which complements the main controller. We use genetic algorithms to tune this new secondary controller. We particularize our proposal for three specific applications: control the angular speed and position of a Direct Current motor and control the output voltage of a DC/DC Buck converter.<B>Design/methodology/approach</B> - We use Genetic Algorithms for tuning a Proportional-Integral Fuzzy Controller. Our proposal defines a new objective function in comparison with literature approaches. The main key in our new objective function is combining the best features of Integral Square Error (ISE) function and taking out the overshoot response.<B>Findings</B> - In order to demonstrate our proposed methodology based on genetic algorithms tuning a Proportional-Integral Fuzzy controller, we apply the literature benchmark to our solution. The results are compared with the following techniques: Robust control, continuous PID control, discrete PID control, Optimal Control, Fuzzy Control and Artificial Neural Network based control. Comparisons are presented in terms of setting time and overshot.<B>Originality/value</B> - Results demonstrate that integral square error or integral of absolute value of error function do not provide the desired response. Achieved results demonstrate the usefulness of our proposal to eliminate the overshoot of the traditional behaviour without lost any of the main features of the literature methodologies. Article literatinetwork@emeraldinsight.com (Carlos Betancor, Javier Sosa, Juan A. Montiel-Nelson, Aurelio Vega) Tue, 29 Jul 2014 00:00:00 +0100 An efficient PMA-based reliability analysis technique using radial basis function http://www.emeraldinsight.com/journals.htm?issn=0264-4401&volume=31&issue=6&articleid=17114633&show=abstract <strong>Abstract</strong><br /><br /><B>Purpose</B> - The performance measure approach (PMA) is widely adopted for reliability analysis and reliability-based design optimization because of its robustness and efficiency compared to reliability index approach (RIA). However, it has been reported that PMA involves repeat evaluations of probabilistic constraints therefore it is prohibitively expensive for many large-scale applications. In order to overcome these disadvantages, this study proposes an efficient PMA-based reliability analysis technique using radial basis function (RBF).<B>Design/methodology/approach</B> - The RBF is adopted to approximate the implicit limit state functions in combination with Latin Hypercube Sampling (LHS) strategy. The advanced mean value (AMV) method is applied to obtain the most probable point (MPP) with the prescribed target reliability and corresponding probabilistic performance measure to improve analysis accuracy. A sequential framework is proposed to relocate the sampling center to the obtained MPP and reconstruct RBF until a criteria is satisfied.<B>Findings</B> - Our method is shown to be better in the computation time to the PMA based on the actual model. The analysis results of probabilistic performance measure are accurately close to the reference solution. Five numerical examples are presented to demonstrate the effectiveness of the proposed method.<B>Originality/value</B> - The main contribution of this paper is to propose a new reliability analysis technique using reconstructed RBF approximate model. The originalities of this paper may lie in: (1) investigating the PMA using metamodel techniques, (2) using RBF instead of the other types of metamodels to deal with the low efficiency problem. Article literatinetwork@emeraldinsight.com (Minh Quang Chau, Xu Han, Chao Jiang, Ying Chun Bai, Trong Nhan Tran, Van Huy Truong) Tue, 29 Jul 2014 00:00:00 +0100 Three level hierarchical decision making model with GA http://www.emeraldinsight.com/journals.htm?issn=0264-4401&volume=31&issue=6&articleid=17114695&show=abstract <strong>Abstract</strong><br /><br /><B>Purpose</B> - In this paper we propose a numerical algorithm able to describe the Stackelberg strategy for a multi level hierarchical 3-person game via genetic algorithm evolution process. There is only one player for each hierarchical level: there is an upper level leader (player L0), an intermediate level leader (player L1) who acts as a follower for L0 and as a leader for the lower level player (player F) that is the sole actual follower of this situation.<B>Design/methodology/approach</B> - We present a computational result via genetic algorithm approach. The idea of the Stackelberg-GA is to bring together genetic algorithms and Stackelberg strategy in order to process a genetic algorithm to build the Stackelberg strategy. Any player acting as a follower makes his decision at each step of the evolutionary process, playing a simple optimization problem whose solution is supposed to be unique. <B>Findings</B> - A GA procedure to compute the Stackelberg equilibrium of the 3-level hierarchical problem is given. An application to a Authority -Provider- User (APU) model in the context of wireless networks is discussed. The algorithm convergence is illustrated by means of some test cases.<B>Research limitations/implications</B> - The solution to each level of hierarchy is supposed to be unique.<B>Originality/value</B> - The paper demonstrates the possibility of using computational procedures based on GAs in hierarchical three level decision problems extending previous results obtained in the classical two level case. Article literatinetwork@emeraldinsight.com (Egidio D'Amato, Elia Daniele, Lina Mallozzi, Giovanni Petrone) Tue, 29 Jul 2014 00:00:00 +0100