Hongsheng Su, Shilin Song^*, Xingsheng Wang

Energy Engineering, Vol.121, No.11, pp. 3289-3303, 2024, DOI:10.32604/ee.2024.054643 - 21 October 2024

Abstract This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling More >

Nazia Afrin^a , Yuwen Zhang^b,*

Frontiers in Heat and Mass Transfer, Vol.12, pp. 1-6, 2019, DOI:10.5098/hmt.12.27

Abstract The surrogate-based analysis and optimization of thermal damage in living biological tissue by laser irradiation are discussed in this paper. Latin Hypercube Sampling (LHS) and Response Surface Model (RSM) are applied to study the surrogate-based optimization of thermal damage in tissue using a generalized dual-phase lag model. Response value of high temperature as a function of input variables and the relationship of maximum temperature and thermal damage as a function of input variables are investigated. Comparisons of SBO model and simulation results for different sample sizes are examined. The results show that every input variable More >

Heekun Ju, Hyung-Jo Jung^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.1, pp. 7-7, 2019, DOI:10.32604/icces.2019.05846

Abstract Latin hypercube sampling (LHS) is widely applied to estimate a probabilistic floor response spectrum (FRS) of nonlinear nuclear power plant (NPP) structure. ASCE 4-16 Standards recommend that the minimum number of simulations should be larger than 30 when using LHS. Although this recommendation is commonly used for the minimum number of the simulation, there is no theoretical background. The variability of the estimations may exist according to the number of the simulation. Stated differently, the minimum number of the simulation may be varied depending on the characteristics of the problem (i.e., problem-dependent). In this context,… More >

Shuangsheng Zhang^1,5, Hanhu Liu¹, Jing Qiang^2,*, Hongze Gao^3,*, Diego Galar⁴, Jing Lin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 373-394, 2019, DOI:10.32604/cmes.2019.03825

Abstract Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X₀ , Y₀) and release time (T₀), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in… More >

Jieping Liu¹, Lingxin Zhang^1,*, Haohao Zhang², Tao Liu¹

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 123-142, 2019, DOI:10.32604/sdhm.2019.04486

Abstract As there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed… More >