Shape optimization of flow split ducting elements using an improved Box complex method
TL;DR: In this article, an improved version of the Box complex method is proposed specifically for computational fluid dynamics-based optimization of fluid flow ducting elements, which substantially accelerate the convergence with approximately 50% reduction in computational effort for the T-junction and manifold problems.
Abstract: Iterative search methods, such as the Box complex method, can be used for inverse shape design problems. In the present article, an improved version of the Box complex method is proposed specifically for computational fluid dynamics-based optimization of fluid flow ducting elements. The original Box complex method is improved by (1) assigning non-uniform weights for the estimation of the centroid, (2) using a reduced reflection factor for accelerated convergence, and (3) introducing measures to prevent premature breakdown of the iterative process. The success of the improved Box complex method over the original Box complex method is demonstrated on two benchmark functions and by applying it to two fluid flow problems of engineering. The improved method is shown to substantially accelerate the convergence with approximately 50% reduction in computational effort for the T-junction and manifold problems.
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TL;DR: In this paper, a derivative-free level-set-based topology optimization method (DFLS-TO) was proposed and used for channel structure design, where quantum-behaved particle swarm optimization (QPSO) was used to optimize the level set values at the knot points.
Abstract: A derivative-free level-set-based topology optimization method (DFLS-TO) was proposed and was used for channel structure design. Unlike in a conventional level-set method, quantum-behaved particle swarm optimization (QPSO) was used in DFLS-TO to optimize the level-set values at the knot points. The values at the non-knot points were interpolated by solving the Laplace equation. QPSO was combined with the Tchebycheff decomposition method to search for optimal level-set values in multi-objective problems. The channel boundary was represented by an iso-contour of the level-set function, and the B-spline method was used to convert the zigzag-like boundary into a smooth boundary. The lattice Boltzmann method (LBM) was integrated with the immersed boundary method to simulate the fluid flow. The Spalart–Allmaras model was incorporated with the LBM during the simulation of turbulent flows. To demonstrate the applicability of DFLS-TO, optimization of a pipe bend and a fluid distributor were performed.
4 citations
25 Sep 2022
TL;DR: In this paper , a new compact diffuser design that can be incorporated in a heat exchanger for flow distribution is presented, which employs several baffles arranged as frustum of cones with an aerofoil cross-section along the flow direction.
Abstract: Diffuser is a flow device which is used to reduce the velocity of flow. The performance of a diffuser is evaluated using metrics like flow uniformity and pressure recovery. The present work is part of development of a new compact diffuser design that can be incorporated in a heat exchanger for flow distribution. The design concept employs several baffles arranged as frustum of cones with an aerofoil cross-section along the flow direction. The performance enhancement is obtained by optimal positioning of these baffles, and this problem is solved as a numerical optimisation problem. The performance of the diffuser is evaluated using a CFD model, and the same is used for computing the design sensitivity and to perform optimisation.
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TL;DR: A new method for finding the maximum of a general non-linear function of several variables within a constrained region is described, and shown to be efficient compared with existing methods when the required optimum lies on one or more constraints.
Abstract: A new method for finding the maximum of a general non-linear function of several variables within a constrained region is described, and shown to be efficient compared with existing methods when the required optimum lies on one or more constraints. The efficacy of using effective constraints to eliminate variables is demonstrated, and a program to achieve this easily and automatically is described. Finally, the performance of the new method (the "Complex" method) with unconstrained problems, is compared with those of the Simplex method, from which it was evolved, and Rosenbrock's method.
1,285 citations
"Shape optimization of flow split du..." refers methods in this paper
...Simplex methods, such as Box’s complex method (Box 1965), which is applicable for constrained optimization problems, appear to be more effective in certain cases (Haque 1985, 1986; Tran and Kang 2013)....
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Book•
29 Feb 2004
1,194 citations
"Shape optimization of flow split du..." refers methods in this paper
...The Box complex method works efficiently with a convex feasible region (Deb 2004)....
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...Algorithm for the original Box complex method (Deb 2004)....
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Book•
29 Jan 1999
TL;DR: Improving the algorithm foundations advanced operators writing a genetic algorithm applications of genetic algorithms and showing the benefits of incorporating reinforcement learning into genetic algorithms.
Abstract: An introduction to genetic algorithms for scientists and engineers , An introduction to genetic algorithms for scientists and engineers , کتابخانه الکترونیک و دیجیتال - آذرسا
1,021 citations
"Shape optimization of flow split du..." refers methods in this paper
...Hence, a unimodal, convex, multi-dimensional test function, namely, the first function of De Jong or the sphere function (Coley 1999), represented by Equation (1), is selected for analysis:...
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...Hence, a unimodal, convex, multi-dimensional test function, namely, the first function of De Jong or the sphere function (Coley 1999), represented by Equation (1), is selected for analysis: f (x) = i=n∑ i=1 x2i (1) A plot of the test function is given in Figure 1....
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...A tougher test function for optimization is the second function of De Jong, which is also known as Rosenbrock’s valley function or the banana function (Coley 1999)....
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01 Jan 2002
291 citations
"Shape optimization of flow split du..." refers methods in this paper
...The objective is to ensure that desired flow distribution is achieved at the branch outlet by contouring the junctions using two Bézier curves (Sederberg 2012) which together define the junction (Figure 5)....
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TL;DR: In this paper, a multi-objective optimization study of a gas cyclone separator has been performed using the response surface methodology (RSM) and CFD data, and a new set of geometrical ratios (design) has been obtained to achieve the best performance.
Abstract: The low-mass loading gas cyclone separator has two performance parameters, the pressure drop and the collection efficiency (cut-off diameter) In this paper, a multi-objective optimization study of a gas cyclone separator has been performed using the response surface methodology (RSM) and CFD data The effects of the inlet height, the inlet width, the vortex finder diameter and the cyclone total height on the cyclone performance have been investigated The analysis of design of experiment shows a strong interaction between the inlet dimensions and the vortex finder diameter No interaction between the cyclone height and the other three factors was observed The desirability function approach has been used for the multi-objective optimization A new set of geometrical ratios (design) has been obtained to achieve the best performance A numerical comparison between the new design and the Stairmand design confirms the superior performance of the new design As an alternative approach for applying RSM as a meta-model, two radial basis function neural networks (RBFNNs) have been used Furthermore, the genetic algorithms technique has been used instead of the desirability function approach A multi-objective optimization study using NSGA-II technique has been performed to obtain the Pareto front for the best performance cyclone separator
129 citations
"Shape optimization of flow split du..." refers methods in this paper
...…features with genetic algorithms), firefly algorithms and surfacemethods (Avvari and Jayanti 2013; Chen et al. 2015; Diez, Campana, and Stern 2015; Elsayed and Lacor 2013; Eyi and Yumuşak 2015; Kazemzadeh-Parsi et al. 2015; Raghavan et al. 2013; Safikhani, Hajiloo, and Ranjbar 2011; Said et al.…...
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