Optimal Latin hypercube sampling-based surrogate model in NAPLs contaminated groundwater remediation optimization process

Abstract A surrogate model based groundwater optimization model was developed to solve the non-aqueous phase liquids (NAPLs) contaminated groundwater remediation optimization problem. To illustrate the impact of sampling method improvement to the surrogate model performance improvement, aiming at a nitrobenzene contaminated groundwater remediation problem, optimal Latin hypercube sampling (OLHS) method was introduced to sample data in the input variables feasible region, and a radial basis function artificial neural network was used to construct a surrogate model. Considering the surrogate model's uncertainty, a chance-constrained programming (CCP) model was constructed, and it was solved by genetic algorithm. The results showed the following, for the problem considered in this study. (1) Compared with the Latin hypercube sampling (LHS) method, the OLHS method improves the space-filling degree of sample points considerably. (2) The effects of the two sampling methods on surrogate model performance were analyzed through comparison of goodness of fit, residual and uncertainty. The results indicated that the OLHS-based surrogate model performed better than the LHS-based surrogate model. (3) The optimal remediation strategies at 99%, 95%, 90%, 85%, 80% and 50% confidence levels were obtained, which showed that the remediation cost increased with the confidence level. This work would be helpful for increasing surrogate model performance and lowering the risk of a groundwater remediation strategy.

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Aerodynamic Efficiency Optimization of the 1st Stage of Transonic High Pressure Turbine through Lean and Sweep Angles

International Journal of Turbo and Jet Engines ◽

10.1515/tjj-2016-0080 ◽

2019 ◽

Vol 36 (3) ◽

pp. 245-256

Author(s):

Yoonki Kim ◽

Sanga Lee ◽

Kwanjung Yee ◽

Young-Seok Kang

Keyword(s):

High Pressure ◽

Surrogate Model ◽

Design Space ◽

Latin Hypercube Sampling ◽

Expected Improvement ◽

Total Efficiency ◽

High Pressure Turbine ◽

Sweep Angle ◽

Design Variables ◽

Optimal Latin Hypercube Sampling

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.

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Structure and System Nonprobabilistic Reliability Solution Method Based on Enhanced Optimal Latin Hypercube Sampling

International Journal of Structural Stability and Dynamics ◽

10.1142/s0219455414500345 ◽

2015 ◽

Vol 15 (01) ◽

pp. 1450034 ◽

Cited By ~ 2

Author(s):

Xin-Dang He ◽

Wen-Xuan Gou ◽

Yong-Shou Liu ◽

Zong-Zhan Gao

Keyword(s):

Solution Method ◽

Probability Distributions ◽

Latin Hypercube Sampling ◽

Computational Effort ◽

Interval Uncertainty ◽

Limited Information ◽

Latin Hypercube ◽

Convex Model ◽

Reliability Method ◽

Optimal Latin Hypercube Sampling

Using the convex model approach, the bounds of uncertain variables are only required rather than the precise probability distributions, based on which it can be made possible to conduct the reliability analysis for many complex engineering problems with limited information. This paper aims to develop a novel nonprobabilistic reliability solution method for structures with interval uncertainty variables. In order to explore the entire domain represented by interval variables, an enhanced optimal Latin hypercube sampling (EOLHS) is used to reduce the computational effort considerably. Through the proposed method, the safety degree of a structure with convex modal uncertainty can be quantitatively evaluated. More importantly, this method can be used to deal with any general problems with nonlinear and black-box performance functions. By introducing the suggested reliability method, a convex-model-based system reliability method is also formulated. Three numerical examples are investigated to demonstrate the efficiency and accuracy of the method.

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Surrogating for High Dimensional Computationally Expensive Multi-Modal Functions with Elliptical Basis Function Models

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.733.880 ◽

2015 ◽

Vol 733 ◽

pp. 880-884 ◽

Cited By ~ 1

Author(s):

Wei Zeng ◽

Xian Chao Wang ◽

Ying Sheng Wang

Keyword(s):

Design Process ◽

Surrogate Model ◽

Latin Hypercube Sampling ◽

Maximum Error ◽

Test Sample ◽

Approximation Technique ◽

Design Strategies ◽

Computational Fluid Dynamics Analysis ◽

Optimal Latin Hypercube Sampling ◽

Function Models

In the engineering design process, approximation Technique could guarantee the fitting precision, speed up the design process and reduce design costs. To a certain extent, surrogate models could replace time-consuming and highly accurate computational fluid dynamics analysis gradually. In this paper, we take Optimal Latin Hypercube Sampling experimental design strategies to determine the sample space and error analysis test sample, adopt the principle of infilling criteria based on the maximum error to improve the accuracy of the surrogate model, test the unimodal and multimodal expensive functions of 10 dimension, 20 dimensions and 30 dimensions, study the performance and scope of EBF-NN surrogate model based on infilling criteria by comparing the RBF-NN surrogate model.

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A stochastic optimization model based on adaptive feedback correction process and surrogate model uncertainty for DNAPL-contaminated groundwater remediation design

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-018-1559-4 ◽

2018 ◽

Vol 32 (11) ◽

pp. 3195-3206 ◽

Cited By ~ 5

Author(s):

Xue Jiang ◽

Wenxi Lu ◽

Jin Na ◽

Zeyu Hou ◽

Yanxin Wang ◽

...

Keyword(s):

Stochastic Optimization ◽

Model Uncertainty ◽

Optimization Model ◽

Surrogate Model ◽

Groundwater Remediation ◽

Contaminated Groundwater ◽

Adaptive Feedback ◽

Correction Process ◽

Remediation Design ◽

Feedback Correction

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An adaptive dynamic surrogate model using a constrained trust region algorithm: application to DNAPL-contaminated-groundwater-remediation design

Hydrogeology Journal ◽

10.1007/s10040-020-02130-0 ◽

2020 ◽

Vol 28 (4) ◽

pp. 1285-1298

Author(s):

Jiannan Luo ◽

Wenxi Lu ◽

Qingchun Yang ◽

Yefei Ji ◽

Xin Xin

Keyword(s):

Surrogate Model ◽

Groundwater Remediation ◽

Trust Region ◽

Contaminated Groundwater ◽

Trust Region Algorithm ◽

Adaptive Dynamic ◽

Remediation Design

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Application of Monte Carlo sampling and Latin Hypercube sampling methods in pumping schedule design during establishing surrogate model

2011 International Symposium on Water Resource and Environmental Protection ◽

10.1109/iswrep.2011.5892983 ◽

2011 ◽

Cited By ~ 1

Author(s):

Yin Jinhang ◽

Lu Wenxi ◽

Xin Xin ◽

Zhang Lei

Keyword(s):

Monte Carlo ◽

Surrogate Model ◽

Sampling Methods ◽

Latin Hypercube Sampling ◽

Monte Carlo Sampling ◽

Latin Hypercube ◽

Schedule Design

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Combining Latin Hypercube Sampling with Other Variance Reduction Techniques

SSRN Electronic Journal ◽

10.2139/ssrn.2179680 ◽

2012 ◽

Author(s):

Natalie Packham

Keyword(s):

Variance Reduction ◽

Latin Hypercube Sampling ◽

Latin Hypercube ◽

Variance Reduction Techniques ◽

Reduction Techniques

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

Energies ◽

10.3390/en14020512 ◽

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.

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The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

Geosciences ◽

10.3390/geosciences11080322 ◽

2021 ◽

Vol 11 (8) ◽

pp. 322

Author(s):

Evelina Volpe ◽

Luca Ciabatta ◽

Diana Salciarini ◽

Stefania Camici ◽

Elisabetta Cattoni ◽

...

Keyword(s):

Probability Density ◽

Central Italy ◽

Model Performance ◽

Probability Density Functions ◽

Slope Instability ◽

Complex Nature ◽

Density Functions ◽

Umbria Region ◽

Input Variables ◽

The Impact

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate.

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