Simultaneous model building and validation with uniform designs of experiments

Narayanan, Ajith; Торопов, В. В.; Wood, Alastair S.; Campean, Felician

doi:10.1080/03052150701399978

Cited by 31 publications

(26 citation statements)

References 19 publications

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“…where U is a pseudo-potential energy of DOE points, L ij is the distance between points i and j where i ≠ j, F is the objective function to be minimized, W is weighting factors, and, b, v, m denote model building, model validation and merged DOEs respectively [33].…”

Section: Optimisation Strategymentioning

confidence: 99%

Energy thermal management in commercial bread-baking using a multi-objective optimisation framework

Khatir

Taherkhani

Paton

et al. 2015

Applied Thermal Engineering

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In response to increasing energy costs and legislative requirements energy efficient high-speed air impingement jet baking systems are now being developed. In this paper, a multi-objective optimisation framework for oven designs is presented which uses experimentally verified heat transfer correlations and high fidelity Computational Fluid Dynamics (CFD) analyses to identify optimal combinations of design features which maximise desirable characteristics such as temperature uniformity in the oven and overall energy efficiency of baking. A surrogate-assisted multi-objective optimisation framework is proposed and used to explore a range of practical oven designs, providing information on overall temperature uniformity within the oven together with ensuing energy usage and potential savings.

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Section: Optimisation Strategymentioning

confidence: 99%

Energy thermal management in commercial bread-baking using a multi-objective optimisation framework

Khatir

Taherkhani

Paton

et al. 2015

Applied Thermal Engineering

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“…This data is similarly coupled to the large scale via a pressure gradient -mass flow rate relationship. The small scale data is represented by a Moving Least Squares (MLS) approximation, a metamodel describing this relationship is built and validated using k-fold Cross Validation (CV) in a method similar to that used by Loweth et al [35] and Narayanan et al [36]. This method employs an Optimum Latin Hypercube (OLHC) to populate the DoE used for small scale simulations, in order to span the entire design space as effectively as possible with the fewest number of designs [37,38].…”

Section: Introductionmentioning

confidence: 99%

Two-scale EHL: Three-dimensional topography in tilted-pad bearings

Boer

Hewson

Thompson

et al. 2014

Tribology International

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Derived from the Heterogeneous Multiscale Methods (HMM), a two-scale method is developed for the analysis of Elastohydrodynamic Lubrication (EHL) and micro-EHL in tilted-pad bearings with threedimensional topography. A relationship linking the pressure gradient to mass flow rate is derived and represented in the bearing domain through homogenisation of near-periodic simulations describing the Fluid Structure Interaction (FSI) of topographical features. For the parameters investigated the influence of compressibility and piezoviscosity was found to be more significant than that of non-Newtonian (shear-thinning) behaviour on textured bearing performance. As the size of topography increased twoscale solutions demonstrated that at constant load the coefficient of friction increased and the minimum film thickness decreased over a range of pad lengths and tilt angles.

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“…The bottom level optimization aims at preserving the given values of the out-ofplane lamination parameters while shuffling the given number of plies to satisfy the layup rules and blending requirements.A permutation GA (Michalewicz 1992;Bates et al 2004;Narayanan et al 2007) is an ideal tool for such a composite laminate optimization problem. Each string in the coding represents a unique stacking sequence.…”

Section: Bottom Level Optimization Using a Permutation Gamentioning

confidence: 99%

Weight and mechanical performance optimization of blended composite wing panels using lamination parameters

Liu

Торопов

Barton

et al. 2015

Struct Multidisc Optim

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In this paper, a lamination parameter-based approach to weight optimization of composite aircraft wing structures is addressed. It is a bi-level procedure where at the top level lamination parameters and numbers of plies of the pre-defined angles (0, 90, 45 and −45°) are used as design variables, the material volume is treated as an objective function to be minimized subject to the buckling, strength and ply percentage constraints. At the bottom level the optimum stacking sequence is obtained subject to the requirements on blending and preservation of mechanical properties. To ensure composite blending, a multi-stage optimization is performed by a permutation genetic algorithm aiming at matching the lamination parameters passed from the top level optimization as well as satisfying the layup rules. Two new additional criteria, the 90°ply angle jump index and the stack homogeneity index, are introduced to control the uniformity of the three ply angles (0, 90, 45 and −45) spread throughout the stack as well as improve the stack quality and mechanical performance by encouraging 45°angle change between neighbouring groups of plies. The results of the application of this approach are Page 2 of 28 compared to published results to demonstrate the potential of the developed technique. Keywords:Laminated composite, optimization, stacking sequence, blending, lamination parameters IntroductionStacking sequence optimization of laminated composite structures to satisfy ply continuity (blending) requirements has recently attracted considerable attention by Gürdal et al. (1999), Kristinsdottir et al. (2001), Liu and Haftka (2001), Seresta et al. (2007), Liu and Krog (2008), Liu et al. (2011). Liu et al. (2000 presented a bi-level (global and bottom) strategy for optimization of a composite wing box structure. At the global level, continuous optimization of thicknesses of 0, 90, 45 and 45°plies was performed to minimize the weight of a wing box subject to strain and buckling constraints. For a given number of plies of each orientation and in-plane loads, a permutation genetic algorithm (GA) was used at the bottom level to optimize the stacking sequence in order to maximize the buckling load. The optimum buckling load, which was treated as a function of the loading and the numbers of plies of 0, 90, 45 and 45°orientation, was evaluated by a cubic polynomial response surface approximation. This bi-level approach was also used for maximization of buckling load of composite panels by Liu et al. (2004), layup optimization of anisotropic laminated composite panels by Bloomfield et al. (2009) and stacking sequence optimization of blended composite structures by Liu et al. (2011). The use of lamination parameters to represent the in-plane and flexural stiffness in the optimization of laminated composites was first used by Tsai et al. (1968) and later applied to the buckling optimization of orthotropic laminated plates by Fukunaga and Hirano (1982). Miki (1982), Fukunaga and Chou (1988), and Fukunaga and Sekine (1993) also u...

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Simultaneous model building and validation with uniform designs of experiments

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Cited by 31 publications

References 19 publications

Energy thermal management in commercial bread-baking using a multi-objective optimisation framework

Energy thermal management in commercial bread-baking using a multi-objective optimisation framework

Two-scale EHL: Three-dimensional topography in tilted-pad bearings

Weight and mechanical performance optimization of blended composite wing panels using lamination parameters

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