The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform
O.k. Lim - One of the best experts on this subject based on the ideXlab platform.
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Reliability-based design optimization of Volume Fraction distribution in functionally graded composites
Computational Materials Science, 2013Co-Authors: Yoojeong Noh, Y.j. Kang, S.j. Youn, J.r. Cho, O.k. LimAbstract:Abstract Functionally graded material (FGM) has a continuous and functional distribution of Volume Fractions of constituent particles, which leads to superior thermo-mechanical performance to classical laminated composite materials. Since the thermo-mechanical characteristics of an FGM depend on the Volume Fraction distribution, it is important to tailor appropriate Volume Fraction distribution that satisfies the desired performance requirements under given loading and boundary conditions. Even though numerical optimization technique may serve as an excellent material tailoring tool, the capacity of current manufacturing techniques of FGM may not yield the Target Volume Fraction. To deal with uncertainty in the manufacturing process, a reliability-based design optimization (RBDO) for FGM composite is proposed. In RBDO, a finite number of Volume Fractions of homogenized FGM layers and material properties are considered as random variables, with statistical information such as mean, standard deviation, and statistical distributions. Design of experiments and response surface models are used to obtain explicit forms of thermal stresses for RBDO formulation. It is observed through the numerical experiment that the RBDO finds the optimized Volume Fraction distribution with high reliability, such that the graded layers do not fail in the presence of manufacturing uncertainty.
Yoojeong Noh - One of the best experts on this subject based on the ideXlab platform.
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Reliability-based design optimization of Volume Fraction distribution in functionally graded composites
Computational Materials Science, 2013Co-Authors: Yoojeong Noh, Y.j. Kang, S.j. Youn, J.r. Cho, O.k. LimAbstract:Abstract Functionally graded material (FGM) has a continuous and functional distribution of Volume Fractions of constituent particles, which leads to superior thermo-mechanical performance to classical laminated composite materials. Since the thermo-mechanical characteristics of an FGM depend on the Volume Fraction distribution, it is important to tailor appropriate Volume Fraction distribution that satisfies the desired performance requirements under given loading and boundary conditions. Even though numerical optimization technique may serve as an excellent material tailoring tool, the capacity of current manufacturing techniques of FGM may not yield the Target Volume Fraction. To deal with uncertainty in the manufacturing process, a reliability-based design optimization (RBDO) for FGM composite is proposed. In RBDO, a finite number of Volume Fractions of homogenized FGM layers and material properties are considered as random variables, with statistical information such as mean, standard deviation, and statistical distributions. Design of experiments and response surface models are used to obtain explicit forms of thermal stresses for RBDO formulation. It is observed through the numerical experiment that the RBDO finds the optimized Volume Fraction distribution with high reliability, such that the graded layers do not fail in the presence of manufacturing uncertainty.
Zoltan K. Nagy - One of the best experts on this subject based on the ideXlab platform.
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Parametric, Optimization-Based Study on the Feasibility of a Multisegment Antisolvent Crystallizer for in Situ Fines Removal and Matching of Target Size Distribution
Industrial & Engineering Chemistry Research, 2016Co-Authors: Bradley J. Ridder, Aniruddha Majumder, Zoltan K. NagyAbstract:We have computationally investigated the use of the multisegment, multiaddition, plug-flow crystallizer (MSMA-PFC) for use in producing pharmaceutical crystals. A population balance framework was used to model the crystallization process. The dissolution of crystals can be modeled when solubility is below saturation. The evolved Volume Fraction distributions were optimized in a least-squares sense by manipulating a vector of decision variables in order to hit a Target Volume Fraction distribution. The genetic algorithm was used for optimization. A reduced orthogonal array experimental design was used to examine the effect of several kinetic parameters and total crystallizer length. The results indicate that the parameters which govern nucleation are the most sensitive, followed by those for growth. Dissolution does not appreciably occur in any of the optimizations. The reason the optimization does not add any pure solvent is likely due to the addition of pure solvent causing a simultaneous decrease in con...
S.j. Youn - One of the best experts on this subject based on the ideXlab platform.
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Reliability-based design optimization of Volume Fraction distribution in functionally graded composites
Computational Materials Science, 2013Co-Authors: Yoojeong Noh, Y.j. Kang, S.j. Youn, J.r. Cho, O.k. LimAbstract:Abstract Functionally graded material (FGM) has a continuous and functional distribution of Volume Fractions of constituent particles, which leads to superior thermo-mechanical performance to classical laminated composite materials. Since the thermo-mechanical characteristics of an FGM depend on the Volume Fraction distribution, it is important to tailor appropriate Volume Fraction distribution that satisfies the desired performance requirements under given loading and boundary conditions. Even though numerical optimization technique may serve as an excellent material tailoring tool, the capacity of current manufacturing techniques of FGM may not yield the Target Volume Fraction. To deal with uncertainty in the manufacturing process, a reliability-based design optimization (RBDO) for FGM composite is proposed. In RBDO, a finite number of Volume Fractions of homogenized FGM layers and material properties are considered as random variables, with statistical information such as mean, standard deviation, and statistical distributions. Design of experiments and response surface models are used to obtain explicit forms of thermal stresses for RBDO formulation. It is observed through the numerical experiment that the RBDO finds the optimized Volume Fraction distribution with high reliability, such that the graded layers do not fail in the presence of manufacturing uncertainty.
J.r. Cho - One of the best experts on this subject based on the ideXlab platform.
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Reliability-based design optimization of Volume Fraction distribution in functionally graded composites
Computational Materials Science, 2013Co-Authors: Yoojeong Noh, Y.j. Kang, S.j. Youn, J.r. Cho, O.k. LimAbstract:Abstract Functionally graded material (FGM) has a continuous and functional distribution of Volume Fractions of constituent particles, which leads to superior thermo-mechanical performance to classical laminated composite materials. Since the thermo-mechanical characteristics of an FGM depend on the Volume Fraction distribution, it is important to tailor appropriate Volume Fraction distribution that satisfies the desired performance requirements under given loading and boundary conditions. Even though numerical optimization technique may serve as an excellent material tailoring tool, the capacity of current manufacturing techniques of FGM may not yield the Target Volume Fraction. To deal with uncertainty in the manufacturing process, a reliability-based design optimization (RBDO) for FGM composite is proposed. In RBDO, a finite number of Volume Fractions of homogenized FGM layers and material properties are considered as random variables, with statistical information such as mean, standard deviation, and statistical distributions. Design of experiments and response surface models are used to obtain explicit forms of thermal stresses for RBDO formulation. It is observed through the numerical experiment that the RBDO finds the optimized Volume Fraction distribution with high reliability, such that the graded layers do not fail in the presence of manufacturing uncertainty.
