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Algorithmic Construction of Efficient Fractional Factorial Designs With Large Run Sizes
, 2007
"... Fractional factorial designs are widely used in practice and typically chosen according to the minimum aberration criterion. A sequential algorithm is developed for constructing efficient fractional factorial designs. A construction procedure is proposed that only allows a design to be constructed f ..."
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Fractional factorial designs are widely used in practice and typically chosen according to the minimum aberration criterion. A sequential algorithm is developed for constructing efficient fractional factorial designs. A construction procedure is proposed that only allows a design to be constructed from its minimum aberration projection in the sequential buildup process. To efficiently identify nonisomorphic designs, designs are divided into different categories according to their moment projection patterns. A fast isomorphism check procedure is developed by matching the factors using their deleteonefactor projections. A method is proposed for constructing minimum aberration designs using only a partial catalog of some good designs. Minimum aberration designs are constructed for 128 runs up to 64 factors, 256 runs up to 28 factors, and 512, 1024, 2048, and 4096 runs up to 23 or 24 factors. Furthermore, this algorithm is used to completely enumerate all 128run designs of resolution 4 up to 30 factors, all 256run designs of resolution 4 up to 17 factors, all 512run designs of resolution 5, all 1024run designs of resolution 6, and all 2048 and 4096run designs of resolution 7.
Evolutionary model type selection for global surrogate modeling
 2054 SURROGATE MODELING AND ADAPTIVE SAMPLING TOOLBOX FOR COMPUTER BASED DESIGN
"... Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualizati ..."
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Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type.
Computationalfluiddynamicsbased kriging optimization tool for aeronautical combustion chambers
 AIAA Journal
"... Current stateoftheart in Computational Fluid Dynamics (CFD) provides reasonable reacting flow predictions and is already used in industry to evaluate new concepts of gas turbine engines. In parallel, optimization techniques have reached maturity and several industrial activities benefit from enh ..."
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Current stateoftheart in Computational Fluid Dynamics (CFD) provides reasonable reacting flow predictions and is already used in industry to evaluate new concepts of gas turbine engines. In parallel, optimization techniques have reached maturity and several industrial activities benefit from enhanced search algorithms. However, coupling a physical model with an optimization algorithm to yield a decision making tool, needs to be undertaken with care to take advantage of the current computing power while satisfying the gas turbine industrial constraints. Among the many delicate issues for such tools to contribute efficiently to the gas turbine industry, combustion is probably the most challenging and optimization algorithms are not easily applicable to such problems. In our study, a fully encapsulated algorithm addresses the issue by making use of a new multiobjective optimization strategy based on an iteratively enhanced metamodel (Kriging) coupled to a Design of Experiments (DoE) method and a fully parallel three dimensional (3D) CFD solver to model turbulent reacting flows. With this approach, the computer cost needed for thousands of CFD computations is greatly reduced while ensuring an automatic error reduction of the approximated response function. Preliminary assessments of the search algorithm against simple analytical test functions prove the strategy to be efficient and robust. Application to a 3D industrial aeronautical combustion chamber demonstrates the approach to be feasible with currently available computing power. One result of the optimization is that possible design changes can improve performance and durability of the studied engine. With the advent of
ROBUST SIMULATIONOPTIMIZATION USING METAMODELS
"... Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi’s view of the uncertain world, but replaces his statistical techniques by eithe ..."
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Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi’s view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the wellknown Economic Order Quantity (EOQ) model. 1
Contents lists available at ScienceDirect
"... journal homepage: www.elsevier.com/locate/neucom Design of experiments on neural network’s training for nonlinear time series forecasting ..."
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journal homepage: www.elsevier.com/locate/neucom Design of experiments on neural network’s training for nonlinear time series forecasting
Jury:
, 2009
"... Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen door ..."
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Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen door
www.samsi.info Simulation in Industrial Statistics
, 2005
"... DMS0112069. Any opinions, findings, and conclusions or recommendations expressed in this material are ..."
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DMS0112069. Any opinions, findings, and conclusions or recommendations expressed in this material are
AN ANALYTICAL APPROACH TO LOW OBSERVABLE MAINTENANCE PRACTICES USING SIMULATION AND MARGINAL ANALYSIS
"... The F22 Raptor is a unique aircraft with many technological advantages and superior capabilities. The aircraft’s stealth capability is a function of many design aspects, including coatings that cover the outside of the aircraft and help mitigate radar detection. Maintaining these Low Observable coa ..."
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The F22 Raptor is a unique aircraft with many technological advantages and superior capabilities. The aircraft’s stealth capability is a function of many design aspects, including coatings that cover the outside of the aircraft and help mitigate radar detection. Maintaining these Low Observable coatings has its own set of challenges to include an inexperienced work force, time consuming procedures, and demanding maneuvers of a fifth generation fighter aircraft. Another challenge facing the F22 fleet is low aircraft availability, where the aircraft is down for numerous reasons. Using a simulation built in ARENA, process improvements to Low Observable maintenance can be quantified with a goal of improving aircraft availability. One example of process improvements, the use of extra stock panels is tested in the simulation to see the potential marginal improvement to Aircraft Availability. 1 THE AIRCRAFT: F22 RAPTOR Designed as a fifth generation fighter aircraft and a member of the Global Strike Task Force, the F22 is capable of air to air and air to ground missions. The aircraft’s stealth capability, known as Low Observable (LO) is essential to maintaining its superiority. Proper maintenance of the stealth capability insures the Raptor will avoid radar detection and can perform all its varied capabilities (Hill 2010). The Raptor is stationed at a variety of locations which adds to the complexity of aircraft maintenance. 1.1 Aircraft Availability Aircraft Availability is a metric that assesses the readiness of a wing or fleet to meet its stated mission. In its simplest terms, aircraft availability can be defined as the ratio of time available to do a mission to total time a unit possesses the aircraft. Equation 1 shows the mathematical formula for calculating availability.
A MODIFICATION OF CHENG’S METHOD: AN ALTERNATIVE FACTOR SCREENING METHOD FOR STOCHASTIC SIMULATION MODELS
"... Factor Screening experiments identify those factors with significant effect on a selected output. We propose a modification of Cheng’s method as a new factor screening alternative for simulation models whose output has homogeneous variance and can be described by a secondorder polynomial function. ..."
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Factor Screening experiments identify those factors with significant effect on a selected output. We propose a modification of Cheng’s method as a new factor screening alternative for simulation models whose output has homogeneous variance and can be described by a secondorder polynomial function. The performance of the proposed model is compared with several other factor screening alternatives through an empirical evaluation. The results show that the proposed method sustains its efficiency and accuracy as the number of factors or the homogeneous variance increases. However, its accuracy degrades as variance heterogeneity increases. 1
SMART ELECTROMAGNETIC SIMULATIONS: GUIDE LINES FOR DESIGN OF EXPERIMENTS TECHNIQUE
"... Abstract—Electromagnetic design problems usually involve a large number of varying parameters. A designer can use different kinds of models in order to achieve optimum design. Some models, e.g., finiteelement model, can be very precise: however, it requires large computational costs (i.e., CPU time ..."
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Abstract—Electromagnetic design problems usually involve a large number of varying parameters. A designer can use different kinds of models in order to achieve optimum design. Some models, e.g., finiteelement model, can be very precise: however, it requires large computational costs (i.e., CPU time). Therefore, the designer should use a screening process to reduce the number of parameters in order to reduce the required computational time. In this paper, using the Design of Experiments (DOE) approach to reduce the number of parameters is explored. The benefits of this technique are tremendous. For example, once researchers realize how much insight and information can be obtained in a relatively short amount of time from a welldesigned experiment, DOE would become a regular part of the way they approach their simulation projects. The main objective of this paper is to apply the DOE technique to electromagnetic simulations of different systems and to explore its effectiveness on a new field, namely the magnetic refrigeration systems. The methodology of the DOE is presented to assess the effects of the different variables and their interaction involved in electromagnetic simulations design and optimization processes. 1.