Weighted Latin Hypercube Sampling to Estimate Clearance-to-stop for Probabilistic Design of Seismically Isolated Structures in Nuclear Power Plants

Han, Minsoo,Hong, Kee-Jeung,Cho, Sung-Gook 한국지진공학회 2018 한국지진공학회논문집 Vol.22 No.2

This paper proposes extension of Latin Hypercube Sampling (LHS) to avoid the necessity of using intervals with the same probability area where intervals with different probability areas are used. This method is called Weighted Latin Hypercube Sampling (WLHS). This paper describes equations and detail procedure necessary to apply weight function to WLHS. WLHS is verified through numerical examples by comparing the estimated distribution parameters with those from other methods such as Random Sampling and Latin Hypercube Sampling. WLHS provides more flexible way on selecting samples than LHS. Accuracy of WLHS estimation on distribution parameters is depending on the selection of weight function. The proposed WLHS is applied to seismically isolated structures in nuclear power plants. In this application, clearance-to-stops (CSs) calculated using LHS proposed by Huang et al. [1] and WLHS proposed in this paper, respectively, are compared to investigate the effect of choosing different sampling techniques.

Asymptotic Comparison of Latin Hypercube Sampling and Its Stratified Version

Lee, Jooho The Korean Statistical Society 1999 Journal of the Korean Statistical Society Vol.28 No.2

Latin hypercube sampling(LHS) introduced by McKay et al. (1979) is a widely used method for Monte Carlo integration. Stratified Latin hypercube sampling(SLHS) proposed by Choi and Lee(1993) improves LHS by combining it with stratified sampling. In this article it is shown that SLHS yields an asymptotically more accurate than both stratified sampling and LHS.

Improving Sampling Using Fuzzy LHS in Healthcare Supply Chain

Saman Siadati,Mohammad Jafar Tarokh,Rassoul Noorossana 대한산업공학회 2018 Industrial Engineeering & Management Systems Vol.17 No.2

Considering the effects of risk on supply chain in healthcare industry, we must provide a mathematical model based on the risk to re-design the supply chain network, which is a part of the optimization module, random sampling meth-ods use. One of the objectives for applying sampling methods is to determine the best method (by reducing the vari-ance and computational time) for different sizes. The large number of random parameters of the objective function value led to very high variance that required using methods for reducing the variance. In this research, our approach to handle risk analysis problems in mean approximation is using traditional sampling method namely Latin hypercube sampling. However, to reduce error in correlations between variables, it is proposed to perform a fuzzy method on the intervals to eliminate uncertainty in statistical values. Limitations in hypercube sampling will be discussed and numerical results involving a FLHS are presented and compared with Monte Carlo, simple LHS and other types of LHS. We show that the proposed method can affect the precision of mean and variance values.

크리깅 메타모델과 유전자 알고리즘을 이용한 초고압 가스차단기의 형상 최적 설계

곽창섭(Chang-Seob Kwak),김홍규(Hong-Kyu Kim),차정원(Jeong-Won Cha) 대한전기학회 2013 전기학회논문지 Vol.62 No.2

We describe a new method for selecting design variables for shape optimization of high-voltage gas circuit breaker using a Kriging meta-model and a genetic algorithm. Firstly we sample balance design variables using the Latin Hypercube Sampling. Secondly, we build meta-model using the Kriging. Thirdly, we search the optimal design variables using a genetic algorithm. To obtain the more exact design variable, we adopt the boundary shifting method. With the proposed optimization frame, we can get the improved interruption design and reduce the design time by 80%. We applied the proposed method to the optimization of multivariate optimization problems as well as shape optimization of a high - voltage gas circuit breaker.

대리 모델을 이용한 의료폐기물 멸균분쇄용 파쇄기의 민감도 해석

김도훈,무하마드 무자밀 아자드,살만 칼리드,김흥수 대한기계학회 2023 大韓機械學會論文集A Vol.47 No.1

Medical waste has been excessively generated in various medical facilities due to COVID-19, and its treatment has become an important concern. Previously, an optimized medical waste sterilization and shredding system was developed for hospital scale but due to increased demand, it is necessary to scale such a system for different facilities. Therefore, in this paper, a sensitivity analysis for the design variables of the shredding system has been conducted and a surrogate model is developed for stress estimation. The surrogate model was generated using LHS (Latin hypercube sampling), which can represent the overall information of the design domain with a limited number of samples. The surrogate model was then used to increase the number of samples for sensitivity analysis which helped in reducing the computational time for finite element analysis. The sensitive variables for the shredder system were then estimated using sensitivity analysis. Consequently, an efficient design framework for various capacities of medical waste shredder was suggested using sensitivity analysis and a data-driven surrogate model. COVID-19로 인하여 각종 의료시설에서 많은 의료폐기물이 발생했고 의료폐기물의 처리방법이 중요해졌다. 기존 병원 규모에 최적화된 의료폐기물 멸균 및 파쇄 시스템을 개발하였지만, 다양한 스케일의 시스템 설계가 필요하다. 본 논문에서는 파쇄기의 설계 변수에 대한 민감도 분석을 수행하고 응력 추정을 위한 대리 모델을 개발했다. 대리 모델은 설계 영역에 대한 전반적인 정보를 적은 수의 표본으로 나타낼 수 있는 라틴 하이퍼큐브 샘플링을 이용하여 생성했다. 반응 표면 대리 모델을 사용하여 민감도 분석에 필요한 표본의 개수를 증가시켰다. 대리 모델을 도입함으로써 유한요소 해석에 소요되는 시간을 줄였다. 민감한 변수는 민감도 분석을 통해 선별했다. 결과적으로 민감도 분석과 데이터 기반 대리 모델을 통하여 다양한 용량을 가진 의료폐기물 파쇄기의 효율적인 설계 방안을 제안했다.

Probabilistic Analysis To Analyze Uncertainty Incorporating Copula Theory

Li Bin,Shahzad Muhammad,Munir Hafiz Mudassir,Nawaz Asif,Fahal Nabeel Abdelhadi Mohamed,Khan Muhammad Yousaf Ali,Ahmed Sheeraz 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.1

The emerging trend of distribution generation with existing power system network leads uncertainty factor. To handle this uncertainty, it is a provocation for the power system control, planning, and operation engineers. Although there are numerous techniques to model and evaluate these uncertainties, but in this paper the integration of Copula theory with Improved Latin-hypercube Sampling (ILHS) are incorporated for Probabilistic load Flow (PLF) evaluation. In probabilistic research approaches, the dominant interest is to achieve appropriate modelling of input random variables and reduce the computational burden. To address the said problem, Copula theory is applied to execute the modelling and interaction among input random variables of the active power system network. Considering the real discrete data, the ILHS is adopted. The load fl ow accessibility of the power system is carefully modeled by considering the dependence and uncertainty factors. Modifi ed IEEE 14-bus system is employed to analyze the effi ciency and performance of the proposed model using active power system network. Output power of two wind energy farms situated in New Jersey are obtained for accuracy comparison. The proposed technique shows the superiority in PLF evaluation.

집속효율 향상을 위한 외장유동노즐 가속 구간의 최적설계 연구

이진우,김윤제,진정민 한국군사과학기술학회 2019 한국군사과학기술학회지 Vol.22 No.6

There is a need to use sheath flow nozzle to detect bioaerosol such as virus and bacteria due to their characteristics. In order to enhance the detection performance depending on nozzle parameters, numerical analysis was carried out using a commercial code, ANSYS CFX. Eulerian-lagrangian approach method is used in this simulation. Multiphase flow characteristics between primary fluid and solid were considered. The detection performance was evaluated based on the results of flow field in nozzle chamber such as focusing efficiency and swirl strength. In addition, Latin hypercube sampling(LHS) of design of experiment(DOE) was used for generating a near-random sampling. Then, the acceleration section is optimized using response surface method(RSM). Results show that the optimized model achieved a 6.13 % in a focusing efficiency and 11.47 % increase in swirl strength over the reference model.

Robust optimization of a hybrid control system for wind-exposed tall buildings with uncertain mass distribution

Ilaria Venanzi,Annibale Luigi Materazzi 국제구조공학회 2013 Smart Structures and Systems, An International Jou Vol.12 No.6

In this paper is studied the influence of the uncertain mass distribution over the floors on the choice of the optimal parameters of a hybrid control system for tall buildings subjected to wind load. In particular, an optimization procedure is developed for the robust design of a hybrid control system that is based on an enhanced Monte Carlo simulation technique and the genetic algorithm. The large computational effort inherent in the use of a MC-based procedure is reduced by the employment of the Latin Hypercube Sampling. With reference to a tall building modeled as a multi degrees of freedom system, several numerical analyses are carried out varying the parameters influencing the floors’ masses, like the coefficient of variation of the distribution and the correlation between the floors’ masses. The procedure allows to obtain optimal designs of the control system that are robust with respect to the uncertainties on the distribution of the dead and live loads.

Robust optimization of a hybrid control system for wind-exposed tall buildings with uncertain mass distribution

Venanzi, Ilaria,Materazzi, Annibale Luigi Techno-Press 2013 Smart Structures and Systems, An International Jou Vol.12 No.6

In this paper is studied the influence of the uncertain mass distribution over the floors on the choice of the optimal parameters of a hybrid control system for tall buildings subjected to wind load. In particular, an optimization procedure is developed for the robust design of a hybrid control system that is based on an enhanced Monte Carlo simulation technique and the genetic algorithm. The large computational effort inherent in the use of a MC-based procedure is reduced by the employment of the Latin Hypercube Sampling. With reference to a tall building modeled as a multi degrees of freedom system, several numerical analyses are carried out varying the parameters influencing the floors' masses, like the coefficient of variation of the distribution and the correlation between the floors' masses. The procedure allows to obtain optimal designs of the control system that are robust with respect to the uncertainties on the distribution of the dead and live loads.

심층 신경망 기법을 이용한 유도탄 공력 하중 예측 모델 개발

유한필,이민술,김규홍,김형진 한국전산유체공학회 2022 한국전산유체공학회지 Vol.27 No.3

In the present study, prediction models for aerodynamic loads of missile configurations were developed using multi-layered perceptron. Aerodynamic coefficients were extracted automatically from Missile DATCOM. Sample points for parametric missile shapes were determined using Latin Hypercube Sampling. A multi-layered perceptron was constructed using Tensorflow. A hyperparameter set with minimum Mean Squared Error(MSE) was determined by genetic algorithm. The trained neural network model was also tested for a verification configuration by comparing the true and predicted values. Trained neural network models give accurate results with MSE for test data set between and , and relative error below 5%.