Multiobjective Optimization of 3D-Printed Injection Molds via Hybrid Latin Hypercube Sampling-Delaunay Triangulation Approach

2021 ◽  
pp. 15-19
Author(s):  
Baris Burak Kanbur ◽  
Suping Shen ◽  
Volkan Kumtepeli ◽  
Yi Zhou ◽  
Fei Duan
Keyword(s):  
Multiobjective Optimization ◽  
Delaunay Triangulation ◽  
Latin Hypercube Sampling ◽  
Latin Hypercube ◽  
3D Printed

scholarly journals Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 512
Author(s):  
Younhee Choi ◽  
Doosam Song ◽  
Sungmin Yoon ◽  
Junemo Koo
Keyword(s):  
Latin Hypercube Sampling ◽  
Building Design ◽  
Gain Coefficient ◽  
Design Parameters ◽  
Latin Hypercube ◽  
Heating And Cooling ◽  
Factor Design ◽  
Heating Loads ◽  
Cooling Loads ◽  
Interest In Research

Interest in research analyzing and predicting energy loads and consumption in the early stages of building design using meta-models has constantly increased in recent years. Generally, it requires many simulated or measured results to build meta-models, which significantly affects their accuracy. In this study, Latin Hypercube Sampling (LHS) is proposed as an alternative to Fractional Factor Design (FFD), since it can improve the accuracy while including the nonlinear effect of design parameters with a smaller size of data. Building energy loads of an office floor with ten design parameters were selected as the meta-models’ objectives, and were developed using the two sampling methods. The accuracy of predicting the heating/cooling loads of the meta-models for alternative floor designs was compared. For the considered ranges of design parameters, window insulation (WDI) and Solar Heat Gain Coefficient (SHGC) were found to have nonlinear characteristics on cooling and heating loads. LHS showed better prediction accuracy compared to FFD, since LHS considers the nonlinear impacts for a given number of treatments. It is always a good idea to use LHS over FFD for a given number of treatments, since the existence of nonlinearity in the relation is not pre-existing information.

Latin Hypercube Sampling

2019 ◽  
pp. 29-44
Author(s):  
Guojun Gan ◽  
Emiliano A. Valdez
Keyword(s):  
Latin Hypercube Sampling ◽  
Latin Hypercube

Uncertainty Quantification of Turbulence Model Coefficients via Latin Hypercube Sampling Method

2010 ◽  
Author(s):  
Matthew C. Dunn ◽  
Babak Shotorban ◽  
Abdelkader Frendi
Keyword(s):  
Uncertainty Quantification ◽  
Turbulent Flows ◽  
Turbulence Model ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Recirculation Region ◽  
Free Shear Layer ◽  
Latin Hypercube ◽  
Reattachment Point ◽  
Turbulent Dissipation

This paper is concerned with the propagation of uncertainties in the values of turbulence model coefficients and parameters in turbulent flows. These coefficients and parameters are determined from experiments performed on elementary flows and they are subject to uncertainty. The widely used k–ε turbulence model is considered. It consists of model transport equations for the turbulence kinetic energy and rate of turbulent dissipation. Both equations involve various model coefficients about which adequate knowledge is assumed known in the form of probability density functions. The study is carried out for the flow over a 2D backward-facing step configuration. The Latin Hypercube Sampling method is employed for the uncertainty quantification purposes as it requires a smaller number of samples compared to the conventional Monte-Carlo method. The mean values are reported for the flow output parameters of interest along with their associated uncertainties. The results show that model coefficient variability has significant effects on the streamwise velocity component in the recirculation region near the reattachment point and turbulence intensity along the free shear layer. The reattachment point location, pressure, and wall shear are also significantly affected.

Development of Statistical Core Thermal Design Methodology Using a Modified Latin Hypercube Sampling Method

1991 ◽  
Vol 94 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Seung-Hyuk Lee ◽  
Hyun-Koon Kim ◽  
Sang-Ryeol Park ◽  
Soon-Heung Chang
Keyword(s):  
Design Methodology ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Thermal Design ◽  
Latin Hypercube

Soil sampling design optimization by using conditioned Latin Hypercube sampling    

2021 ◽  
Author(s):  
Fatemeh Hateffard ◽  
Tibor József Novák
Keyword(s):  
Principal Component ◽  
Latin Hypercube Sampling ◽  
Feature Space ◽  
Simple Random Sampling ◽  
Soil Sampling ◽  
Latin Hypercube ◽  
Landsat Images ◽  
Original Distribution ◽  
Spatial Coverage ◽  
Elevation Model

<p>One of the most critical steps in digital soil mapping is finding a sampling approach to cover a good spatial coverage of the area regarding the soil spatial variation. In this matter, environmental variables can aid in taking samples in more innovative and more precise locations while reducing the soil sampling efforts such as time and costs. Conditioned Latin hypercube sampling (cLHS) is a stratified random design strategy that perfectly represents the variability of auxiliary variables in feature space. This study applied this method and compared it to simple random sampling to optimize sampling designs for mapping in the agricultural study site in Hungary. The covariates were indices extracted by the digital elevation model and Landsat images. The principal component analysis (PCA) was applied to reduce the data overlap and select the most important variables as the model's inputs. By computing the statistical criteria (mean, variance, standard deviation, etc.) for covariates and comparing these results between the sampling populations and the entire one, we may conclude that both designs gave almost similar predictions. However, for most covariates, statistical means of cLHS provide the closest approximation compared to the random approach sampling method, but the statistical variances and SDs retrieved similar results. Furthermore, the histogram distribution of most variables in the cLHS was following more closely to the original distribution of the environmental covariates. Overall, considering the type of the study site and the chosen variables, it seems that cLHS is a more applicable method.</p> <p> </p>

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