• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.3
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• pp.74-81
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• 2019

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4166-4178
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• 2021

The Control Rod Drive Mechanism (CRDM) is an essential part of the reactor, which realizes the start-stop and power adjustment of the reactor by lifting and lowering the control rod assembly. As a moving part in CRDM, the latch directly contacts with the control rod assembly, and the life of latch is closely related to the service life of the reactor. In this paper, the relationship between the life of the latch and the step stress, friction stress, and impact stress in the process of movement is analyzed, and the optimization methodology and process of latch life based on the approximate model are proposed. The design variables that affect the life of the latch are studied through the experimental design, and the optimization objective of design variables based on the latch life is established. Based on this, an approximate model of the life of the latch is built, and the multi-objective optimization of the life of the latch is optimized through the NSGA-II algorithm.

• Korean Journal of Computational Design and Engineering
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• v.17 no.4
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• pp.282-293
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• 2012

This paper presents an error-bounded B-spline fitting technique to approximate unorganized data within a prescribed error tolerance. The proposed approach includes two main steps: leastsquares minimization and error-bounded approximation. A B-spline hypervolume is first described as a data representation model, which includes its mathematical definition and the data structure for implementation. Then we present the least-squares minimization technique for the generation of an approximate B-spline model from the given data set, which provides a unique solution to the problem: overdetermined, underdetermined, or ill-conditioned problem. We also explain an algorithm for the error-bounded approximation which recursively refines the initial base model obtained from the least-squares minimization until the Euclidean distance between the model and the given data is within the given error tolerance. The proposed approach is demonstrated with some examples to show its usefulness and a good possibility for various applications.

• Bulletin of the Korean Chemical Society
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• v.18 no.9
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• pp.965-972
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• 1997

An approximate molecular theory of classical fluids based on the nonrandom lattice statistical-mechanical theory is presented. To obtain configurational Helmholtz free energy and equation of state (EOS), the lattice-hole theory of the Guggenheim combinatorics is approximated by introducing the nonrandom two-fluid theory. The approximate nature in the derivation makes the model possible to unify the classical lattice-hole theory and to describe correctly the configurational properties of real fluids including macromolecules. The theory requires only two molecular parameters for a pure fluid. Results obtained to date have demonstrated that the model correlates quantitatively the first- and second-order thermodynamic properties of real fluids. The basic simplicity of the model can readily be generalized to multicomponent systems. The model is especially relevant to (multi) phase equilibria of systems containing molecularly complex species.

• Journal of Radiation Protection and Research
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• v.20 no.4
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• pp.227-236
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• 1995

Using beam data and accurate 3D dose model, a study of the spatial dose distribution for various arcs was carried out. The dose dirstibution generated by the accurate dose model could be represented by a simple approximate analytic form which is convenient and very efficient for calculating dose distribution iteratively in the optimization procedure. We developed an empirical cylindrical dose model to compute dose for one full rotational arc or partial rotational arc. After a tedious search for fits to a collection of 200 points of accurate dose data, we found simple formular with 7 parameters search. As a consequence, the programs required approximately less than 1 second to compute dose for one single arc on a 20 by 20 matrix (400 points) using fast approximate dose model. In conclusion the fast approximate dose model give dose distributions similar to the accurate dose model, which makes this fast dose model an attractive alternative to the accurate 3D dose model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.85-91
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• 2015

The press-fit shaft is an important part used in automobiles, vessels, and trains. This study proposes an optimized design method to reduce damage that may occur in the press-fitted shaft by modifying the shape of the boss step of the press-fitted shaft. To reduce the time and cost of running the optimized design method, an approximate design optimization is applied and an optimized algorithm is generated using a genetic algorithm that is widely used in engineering fields and an approximate model using a response surface method. The planned experiments for the data that are needed to generate the approximate model use a central composite design (CCD) and Latin hypercube sampling (LHS), and the results of the approximate optimization using the above two design of experiments are to be compared.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.755-767
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• 2020

A significant development has been made on a new fatigue damage model applicable to Gaussian wide band stress response spectra using numerical approximation methods such as data processing, time simulation, and regression analysis. So far, most of the alternative approximate models provide slightly underestimated or overestimated damage results compared with the rain-flow counting distribution. A more reliable approximate model that can minimize the damage differences between exact and approximate solutions is required for the practical design of ships and offshore structures. The present paper provides a detailed description of the development process of a new fatigue damage model. Based on the principle of the Gaussian wide band model, this study aims to develop the best approximate fatigue damage model. To obtain highly accurate damage distributions, this study deals with some prominent research findings, i.e., the moment of rain-flow range distribution MRR(n), the special bandwidth parameter μk, the empirical closed form model consisting of four probability density functions, and the correction factor QC. Sequential prerequisite data processes, such as creation of various stress spectra, extraction of stress time history, and the rain-flow counting stress process, are conducted so that these research findings provide much better results. Through comparison studies, the proposed model shows more reliable and accurate damage distributions, very close to those of the rain-flow counting solution. Several significant achievements and findings obtained from this study are suggested. Further work is needed to apply the new developed model to crack growth prediction under a random stress process in view of the engineering critical assessment of offshore structures. The present developed formulation and procedure also need to be extended to non-Gaussian wide band processes.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.257-265
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• 2021

Many studies have been performed to predict a reliable and accurate stress-range distribution and fatigue damage regarding the Gaussian wide-band stress response due to multi-peak waves and multiple dynamic loads. So far, most of the approximation models provide slightly inaccurate results in comparison with the rain-flow counting method as an exact solution. A step-by-step study was carried out to develop new approximate spectral moments that are close to the rain-flow counting moment, which can be used for the development of a fatigue damage model. Using the special parameters and bandwidth parameters, four kinds of parameter-based combinations were constructed and estimated using the R-squared values from regression analysis. Based on the results, four candidate empirical formulas were determined and compared with the rain-flow counting moment, probability density function, and root mean square (RMS) value for relative distance. The new approximate spectral moments were finally decided through comparison studies of eight response spectra. The new spectral moments presented in this study could play an important role in improving the accuracy of fatigue damage model development. The present study shows that the new approximate moment is a very important variable for the enhancement of Gaussian wide-band fatigue damage assessment.

• Journal of Korean Society for Quality Management
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• v.10 no.1
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• pp.7-12
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• 1982

Arvesen (1969) has shown a procedure which produces an approximate confidence interval for a variance component in unbalanced one-way classification model. In this paper, his work is extended to the case when the model of interest is unbalanced two-way classification. Following the procedure described in this paper, approximate confidence intervals are computed by a Monte Carlo simulation.