Through comparative studies on sampling property and metamodel accuracy, the new approach is shown to outperform other sequential sampling methods for global metamodelling and is comparable to the one-stage sampling method while providing more flexibility in a sequential metamodelling procedure. In this study, Latin hypercube sampling was applied to determine the effects of parameter uncertainties of input-parameters on the LCA of biodiesel from three kinds of feedstock, namely jatropha oil, fish oil (FO), and waste cooking oil (WCO), based on the conditions in Vietnam. In the field of design of computer experiments (DoCE), Latin hypercube designs are frequently used for the approximation and optimization of black-boxes. The sequential sampling is formulated as an optimization problem, with the objective being the Maximin Distance, a space-filling criterion, and the constraints based on a set of pre-specified minimum one-dimensional distances to achieve the approximate one-dimensional projective property. Abstract: A Latin Hypercube Design (LHD) is a statistical design of experiments, which was first defined in 1979. This paper aims at providing a short overview of the research in Latin hypercube design of experiments with some hypotheses to explain its extensive use. 1979) for computer experiments (Partial Differntial Equations, Numerical Methods and Simulations), by which experimentations are performed in computers using physical models and finite-element-based methods (Santner et al. The goal in this article is to develop an efficient and effective sequential Quasi-LHD (Latin Hypercube design) sampling method to maintain and balance the two aforementioned properties. Latin hypercube design was first introduced by McKay (McKay et al. A Moving Least Squares (MLS) metamodel was built on a fifty-point Optimal Latin Hypercube (OLH) Design of Experiments (DoE), where each point represented a. Through comparative studies on sampling property and metamodel accuracy, the new approach is shown to outperform other sequential sampling methods for global metamodelling and is comparable to the one-stage sampling method while providing more flexibility in a sequential metamodelling procedure.ĪB - Space-filling and projective properties are desired features in the design of computer experiments to create global metamodels to replace expensive computer simulations in engineering design. Latin hypercube sampling (LHS) is a method of dividing each of the dimensions in the experimental design into regions with equal levels and extracting one. The sequential sampling is formulated as an optimization problem, with the objective being the Maximin Distance, a space-filling criterion, and the constraints based on a set of pre-specified minimum one-dimensional distances to achieve the approximate one-dimensional projective property. This approach conducts an initial experiment with a computer code using a Latin hypercube design and then runs a follow-up experiment with additional runs. The goal in this article is to develop an efficient and effective sequential Quasi-LHD (Latin Hypercube design) sampling method to maintain and balance the two aforementioned properties. The first step for a successful surrogate modeling and statistical analysis is the planning of the input configuration that is used to exercise the simulation code. N2 - Space-filling and projective properties are desired features in the design of computer experiments to create global metamodels to replace expensive computer simulations in engineering design. The design space for each factor is divided. hypercube designs for selecting experimental designs. The views expressed are those of the authors and do not necessarily reflect the views of the sponsors. The Latin Hypercube technique is a class of experimental designs that efficiently sample large design spaces. The grant support from National Science Foundation (CMMI – 0522662) and the China Scholarship Council are greatly acknowledged. Here the values (A, B and C) correspond to the three diffusion recipes and the parameter (p1. = (r), 0 is optimal.T1 - Optimizing latin hypercube design for sequential sampling of computer experiments Table 3.3 shows a Latin Hypercube design with three parameters.
0 Comments
Leave a Reply. |