Optimal Set Of Design Parameters Research Articles

Analysis of Lithium Niobate Electrooptic Long-Period Waveguide Gratings

We present an approximate step-index waveguide model for the study of lithium-niobate (LiNbO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) electrooptic (EO) long-period waveguide grating filters. The model takes into account the non-uniform spatial distributions of the EO coefficient and the applied electric field. Using this model, we analyze in detail the effects of the grating and waveguide parameters on the performance of the filter with the objective of identifying a set of optimal design parameters for the realization of an efficient filter. Guided by the results from the analysis, we fabricated a 12-mm-long EO grating, which produced a 24-dB rejection band at a driving voltage of 49 V.

Optical design and multi-objective optimization for U-type 2X zoom projection optics

In this research, a newly developed U-type 2X zoom optical lens design for Digital Light Processing (DLP) projector was proposed. With two prisms assembled in a non-coaxial optical system, the design could take advantage of compacting overall volume size. But in this design, the value of Relative Illuminance (RI) at wide angle position seems lower than that of coaxial optical system. If we corrected the design to improve the value of RI, Modulation Transfer Function (MTF) value would become lower relatively. In this study, we integrated the Taguchi method and principal component analysis (PCA) to optimize the multiple quality characteristics (RI and MTF) of this U-type projector lens. With the combination of the methods, a set of optimum design parameters was well selected to balance the values of MTF and RI and improve the MTF 16.42% and RI 1.54%. It was concluded that the integration of the Taguchi method and PCA succeeded in optimizing the MTF and RI values, and the U-type projector lens could be well designed without sacrificing system performance.

Optimal Design of a Parallel Micromanipulator for Precision Machining

A parallel micromanipulator (PmM) which can be applied into the precision machining is optimized in the paper. This paper adopts a methodology to determine a set of optimal design parameters of PmM whose workspace is as close as possible of being equal to a prescribed cuboid dexterous workspace (PCDW). The kinematic problem is analyzed in brief to determine the design parameters and their relation. Then, an optimal design procedure which adopts the generalized pattern search algorithm in the genetic algorithm and direct search toolbox of Matlab is proposed to solve these problems. As an applying example, the results of four cases PCDW to PmM are presented. And the design result is compared with a concept of the distance between the best state of the PmM and the requirement of the operation task. The method and result of this paper are very useful for the design of micromanipulator for the machining field which requires the high precision and accuracy.

A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

Multi-objective design and extended optimization for developing a miniature light emitting diode pocket-sized projection display

This research proposes a new extended optimization method for a miniature light emitting diode (LED) pocket-sized projection display, introducing integration of the Taguchi method and principal component analysis in order to optimize the multiple quality characteristics of an LED pocket-sized projection display. With the aid of interactive optimization, control factors with three different levels are carefully selected in the complicated preliminary experiments. A set of optimal design parameters is well selected for best results on the combined effects of the total luminous flux, illumination uniformity, and the packing size of the system. The selected control factors are inclusive of major lens and system specifications, such as lens overall length, X-CUB semi-aperture, length of light integrator, width of integrator, total internal reflection (TIR) prism entering semi-diameter for the TIR prism, air-gap of the TIR prism, and digital micromirror device (DMD) position; an L18 orthogonal array is applied and implemented in the experiments. According to experimental results, the optimal design parameters for the projection display can be determined as A1 (lens specifications: type I), B3 (lens length: overall length), C1 (X-CUB semi-aperture: 8 mm), D3 (integrator length: 36.6 mm), E2 (integrator width: 3.5 mm), F2 (TIR prism entering semi-diameter: 11 mm), G1 (TIR prism air-gap: 1.0024 mm), and H1 (DMD location: −0:5 mm). In addition, analysis of variance (ANOVA) is also employed to identify the factor A (lens specifications), factor D (integrator length), factor F (TIR prism entering semi-diameter), and factor G (TIR prism air-gap) as key parameters, which account for 71.82% of the total variance. The other factors when compared are found to have relatively weaker impacts on the process design. Furthermore, a confirmation experiment of the optimal design parameters shows that the aforesaid multiple performance characteristics are optimized to achieve the best levels. It is concluded that Taguchi method and principal component analysis (PCA) combine to optimize and then minimize the LED pocket-sized projection display system, which not only yields a sufficient understanding of the effects of control factors, but also produces an optimized design to ensure that the LED pocket-sized projection display system exhibits the best multiple performance characteristics.

Application of an intelligent model developed from experimental data to building design for fire safety

Computational fluid dynamics (CFD) techniques are widely adopted to simulate the behavior of fire. However, CFD suffers from the shortcoming of requiring extensive computer storage and a lengthy computational time. In practical applications, although comprehensive field information on velocities, temperatures, pressure, and the fractions of different constitutes can be obtained from CFD simulations, the user may only be interested in few important parameters that index the performance of a compartment design in the event of a fire. The height of the thermal interface (HTI) is one such key index, and refers to the average height above floor level inside a fire compartment at which the temperature gradient is highest. In practice, a fire compartment is considered untenable when the HTI drops below the respiratory level of the occupants, and in optimizing the design of a fire system, another set of design parameters (e.g., the width of the door opening) must be examined if the HTI of a fire compartment design is evaluated by CFD as being too low. This trial and error exercise then continues until a close to optimum set of design parameters is achieved. This approach is theoretically feasible, but requires lengthy computational time. This paper proposes the application of an Artificial Neural Network (ANN) approach as a fast alternative to CFD models to simulate the behavior of a compartment fire. A novel ANN model named GRNNFA has been specially developed for fire studies. It is a hybrid ANN model that combines the General Regression Neural Network (GRNN) and Fuzzy ART (FA). The GRNNFA model features a network structure that grows incrementally, stable learning, and the absence of the noise embedded in experimental fire data. It has been employed to establish a system response surface based on the training samples collected from a full-scale experiment on compartment fire. However, as the available training samples may not be sufficient to describe the behavior of all systems, and especially those involving fire data, this paper proposes that extra knowledge be acquired from human experts. Human expert network training has thus been developed to remedy established system response surface problems. After transforming the remedied system response surface to the problem domain, a Genetic Algorithm (GA) is applied to evaluate the close to optimum set of design parameters.

AquoMin: A software tool for Mass-Exchange Networks targeting and design

This study addresses the work developed at Process Integration Group of CPQ to build a software tool in the area of Pollution Prevention, regarding the Mass-Exchange Networks (MEN) targeting and design. Given a set of mass transfer operations and their limiting data, AquoMin can analyse different options to reduce fresh water consumption and minimise wastewater production. The available strategies to optimise these targets are (i) process without waste water reutilisation, (ii) process with waste reutilisation, and (iii) process with waste regeneration reuse. These options are analysed in two stages: first the targeting and then the design. The former stage uses algorithms based on pinch analysis to obtain the minimum consumption of external water and also the minimum wastewater production. These targets are then used in the second stage, the MEN design. The regeneration reuse strategy needs an extra step in the MEN design stage, which uses pinch analysis concepts to generate a mass balance model to obtain a set of optimal design parameters, the Split Operation Parameters (SOP). Furthermore, work on regeneration with recycle and reuse has been also developed. It was created a general algorithm to obtain the targets for this strategy: the external water source flowrate and the regeneration and recycle flowrate. A small AquoMin tutorial is presented using an Example Problem. The regeneration reuse strategy is focused and the methodology employed in both targeting and design stages were improved. Different scenarios evaluating the MEN structure's complexity and equipment cost were also developed and compared in the regeneration reuse strategy. In the regeneration recycle and reuse strategy, the Example Problem was used to emphasise a new option for the regeneration concentration value and the results of all the strategies are compared. Finally, a five operations’ Case Study was solved to obtain the targets and designs to accomplish the three Process Integration Strategies with a final reduction of 60% in the fresh water consumption.

Optimization of spatial tracks using a morphing approach

AbstractThis paper describes an approach for optimizing multibody systems involving spatial tracks, addressing the problem of finding an optimal set of design parameters when no good initial guess is available. In this setting, it is common that, due to nonlinearities of the objective function and singularities in the kinematic model, the optimization fails to converge or to find feasible points at all. The present paper presents a three‐stage procedure for sequentially guiding a standard optimization routine from an arbitrary initial guess to the optimal configuration. The approach has been tested when optimizing real roller coaster tracks with respect to passenger acceleration and compared with a genetic algorithm implementation, showing that objective function morphing renders faster for the regarded type of systems. (© 2006 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

Optimization of the volumetric accuracy of high-speed computer numerical control milling with dynamic quality characteristics

This paper used the Taguchi robust method to develop an optimized high-speed computer numerical control (CNC) milling technique that can be used for multi-geometrical shapes. The designed shapes studied included the three basic geometries of rectangle, circle and triangle as the distinct characteristics of the workpiece. To increase the robustness of the manufacturing process, the experiment was designed to consider also the effect of manufacturing noises and to compound them as two extreme conditions for assessing the quality characteristics of the workpiece. The experimental results revealed that, under the ideal function model of volume versus volume, the optimum set of design parameters for the high-speed CNC milling process can reduce the manufacturing variation by 81.97 per cent. In other words, the manufacturing accuracy was improved 5.55 times, a substantial improvement to the robustness of the process. Further, by analysing the variance, it was shown that experimental errors accounted for only 0.0204 per cent. The experiment showed that a good selection and combination of control and noise factors could result in high robustness of the experimental results.

Potential performance of parametric communications

From a designer viewpoint, a parametric array should not appear different from any other type of acoustic transducer and should be described by a limited set of design equations together with their range of validity. In this paper, these design equations are stated and discussed. They are used to optimize the acoustic parameters of an underwater communication system using parametric transduction and to evaluate its performance in terms of signal-to-noise ratio and data-rate limits as a function of transmission range. It turns out that, for a maximum data transmission rate at a given range, there is a set of optimum design parameters which is a function of the array size only. This means that, once given an operational range, the primary frequency, the electrical power, the maximum acoustic source level, and the directionality of the transducer can be deduced directly from the array diameter.

Numerical simulation of smoke movement in clean room environments

A computational model predicting extent of smoke clouds from possible clean room fires has been developed using CO 2 concentration as a marker for smoke concentration, which is believed to be more rigorous than the conventional approach relying on temperature profiles. The computational results are compared with results of reduced scale clean room fire tests and are found to be favorable enough to be used in field applications. The numerical model is employed for simulations of clean room fires with a typical full-scale geometry. The impact of air circulation rate, fire size, and fire location on the extent of CO 2 clouds, which could be converted to that of smoke clouds, has been analyzed. Sprinkler actuation and smoke detector locations for fast response with respect to different operating conditions are also discussed. Overall, it has been illustrated that numerical simulations can be adopted as a very useful tool in investigating and customizing optimal set of design parameters at a new facility or estimating a scale of contamination at an existing facility against potential clean room fires.

The Application of Neural Networks and Statistical Methods to Process Design in Metal Forming Processes

This study shows the correlation between the design methodology of artificial neural networks (ANNs) and the statistical design of experiments (DOE) approach which are demonstrated for process design in various metal forming processes. After investigating the effect of each parameter upon the characteristics, an orthogonal array (OA) table and the characteristics are applied to an ANN as training data and then an optimal and robust set of design parameters is selected. Using the rigid plastic FEM method, simulations are performed and the results of the ANN are confirmed. This technique uses smaller runs than the conventional method to find the optimal set of design parameters for the design requirements. This new technique can be used in a wide range of metal forming process designs.

A study on the static and stability characteristics of the oil-lubricated herringbone groove journal bearing

An oil lubricated Herringbone aroove jounal bearing(HGJB) with eight-circular-profile grooves on the non-rotating bearing surface is analyzed numerically and experimentally. The load carrying capacity, attitude angle, stiffness and damping coefficients are obtained numerically for the various bearing configurations. The onset speed of instability is also examined for the various eccentricity ratios. The configuration parameters of HGJB, such as groove depth ratio, groove width ratio, and groove angle, are dependent on each other because the grooves are generated by using eight small balls rolling over the inner surface of the sleeve with press fit. Therefore, it is not allowed to suggest a set of optimal design parameters such as the one for the rectangular profile HGJB. The overall results from numerical and experimental analysis prove that the circular profile HGJB has an excellent stability characteristics and the higher load carrying capacity than the plain journal bearing.

Reliability based structural analysis and optimization in X window environment

A graphic-based application in X window environment using Open Software Foundation (OSF)'s Motif toolkit is developed for comprehensive reliability based analysis and design of structures. The application is interfaced with an efficient safety index calculation code for conducting probabilistic analysis of structures for different performance functions using statistical data of random variables. It is also integrated with a reliability based optimization code for obtaining an optimum set of design parameters by determining analytical sensitivities and constructing approximations for the reliability constraint. The graphic user-interface includes interface with external graphic packages along with Motif window-widget layout to enhance visual perceiving capabilities and interpretational facilities. Probability analysis and design of turbine blades and frame structures with multidisciplinary requirements were addressed during the development of the analysis and design codes.

Optimal design of neural networks using the Taguchi method

In the last five years, many new learning algorithms have been designed and developed to train neural networks for solving complex problems in a wide variety of domains. One of the principal deficiencies with current neural network research is associated with the design of the neural networks. The design of a neural network involves the selection of an optimal set of design parameters to achieve fast convergence speed during training and the required accuracy during recall. These design parameters include both the micro-structural and macro-structural aspects of a neural network. This paper describes an innovative application of the Taguchi method for the determination of these parameters to meet the training speed and accuracy requirements. Using the Taguchi method, both the micro-structural and macro-structural aspects of the neural network design parameters can be considered concurrently. The feasibility of using this approach is demonstrated in this paper by optimizing the design parameters of a back-propagation neural network for determining operational policies for a manufacturing system. Results drawn from this research show that the Taguchi method provides an effective means to enhance the performance of the neural network in terms of the speed for learning and the accuracy for recall.

Design Window Search Based on AI Technique.

The design process of a new reactor consists of broad technical areas. The work of each area may be carried out independently to achieve design target or to satisfy design criteria. The decisions made in one area will affect the design basis in most other area. The design process is thus iteratively performed. To satisfy all design criteria, many parametric survey calculations should be carried out in general. In any case it would be difficult to find an optimal set of design parameters. Such a situation in design problems would be fairly different from that in anomaly diagnosis problems with respect to an application of the AI technique. To reduce the number of iterations at the stage of the design study, it is useful to draw a design window in which the design is feasible. Any parameter set inside a design window area can satisfy design criteria or goals. Design windows obtained for various combinations of design parameters in each area help designers with the decision of design parameters. Even in this case, a considerable amount of calculations must be performed by varying parameter values. At the present work, we intended to search automatically a design window with use of AI based techniques in order to save cost and time. One of the authors has proposed the design window search algorithm for a reactor structural problem and shown its validity(1). Since there are many design parameters, a design window is generally a sub-space of multi-dimensional one. As the first step, we investigated a procedure to search a window in two-dimensional space, that is, for two parameters. The search procedure and an example applied to the thermal hydraulic design are described below.

Impact of thermal constraints on the optimal design of high-level waste repositories in geologic media

An approximate, semi-analytical heat conduction model for predicting the time-dependent temperature distribution in the region of a high-level waste repository has been developed. The model provides the basis for a systematic, inexpensive examination of the impact of several independent thermal design constraints on key repository design parameters and for determining the optimal set of design parameters which satisfy these constraints. Illustrative calculations have been carried out for conceptual repository designs for spent pressurized water reactor (PWR) fuel and reprocessed PWR high-level waste in salt and granite media.

Optimized design of GaAs FET's for low-noise microwave amplifiers

In order to optimize the performance of GaAs Schottky-barrier gate FET's for small-signal amplification at a given microwave frequency, a design procedure has been established on the basis of simple analytical theories of the junction-gate FET. The design goal is to simultaneously satisfy the specifications of quantities for which there are trade-off's. These are the power gain, noise figure, and input reflection coefficient in 50-ohm lines, which represent major figure-of-merits for amplifying devices. An equivalent circuit model is constructed to include intrinsic small-signal elements calculated from the theory of Lehovec and Zuleeg, and noise current sources from van der Ziel's theory. Parasitic elements are also taken into account to reflect the state-of-the-art processing and packaging techniques. A simple, systematic method for analysing a complicated equivalent network including noise sources is devised and applied in computations. Three major design parameters of the Schottky-barrier FET, i.e. gate length, channel thickness and channel doping density are taken as independent variables in order to obtain the optimized design. An experiment is carried out to prove the validity of this theory. It is found that the theory predicts the optimized device performance with sufficient accuracy, and gives a set of optimum design parameters to a good approximation. The optimized unit, with a gate length of 1·5 μm, shows a unilateral gain as high as 21·5 dB, an F min as low as 2·6 dB, and an input reflection coefficient around 0·85 at 4 GHz.

A technique which combines modified pattern search methods with composite designs and polynomial constraints to solve constrained optimization problems

This paper presents a method of selecting design parameters which optimizes a specific measure (aircraft design example: minimum weight, maximum mission effectiveness) and guarantees designated levels of response in specified areas (such as combal ceiling, acceleration time). The method employs direct search optimization applied to a nonlinear functional constrained by nonlinear surfaces. The composite design technique is combined with regression methods to determine adequate surface representations with a minimum of required data points. A sensitivity analysis is conducted at the optimum set of design parameters to test for uniqueness.