Latin Hypercube Sampling Technique Research Articles

Influence of morphological variations on cervical spine segmental responses from inertial loading

ABSTRACTObjectives: The objective of this study was to investigate the influence of morphological variations in osteoligamentous lower cervical spinal segment responses under postero-anterior inertial loading.Methods: A parametric finite element model of the C5–C6 spinal segment was used to generate models. Variations in the vertebral body and facet depth (anteroposterior), posterior process length, intervertebral disc height, facet articular process height and slope, segment orientation ranging from lordotic to straight, and segment size were parameterized. These variations included male–female differences. A Latin hypercube sampling method was used to select parameter values for model generation. Forces and moments associated with the inertial loading were applied to the generated model segments. The 7 parameters were grouped as local or global depending on the number of spinal components involved in the shape variation. Four output responses representing overall segmental and soft tissue responses were analyzed for each model variation: response angle of the segment, anterior longitudinal ligament stretch, anterior capsular ligament stretch, and facet joint compression in the posterior region. Pearson's correlation coefficient was used to compute the correlations of these output responses with morphological variations.Results: Fifty models were generated from the parameterized model using a Latin hypercube sampling technique. Variation in response angle among the models was 4° and was most influenced by change in the combined dimension of vertebral body and facet depth, followed by size of the segment. The maximum anterior longitudinal ligament stretch varied between 0.1 and 0.3 and was strongly influenced by the change in the segment orientation. The anterior facet joint region sustained tension, whereas the posterior region sustained compression. For the anterior capsular ligament stretch, the most influential global variation was segment orientation, whereas the most influential local variations were the facet height and facet angle parameters. In the case of posterior facet joint compression, segment orientation was again most influential, whereas among the local variations, the facet angle had the most influence.Conclusion: Shape variations in the intervertebral disc influenced segmental rotation and ligament responses; however, the influence of shape variations in the facet joint was confined to capsular ligament responses. Response angle was most influenced by the vertebral body depth variations, explaining greater segmental rotations in female spines. Straighter spine segments sustained greater posterior facet joint compression, which may offer an explanation for the higher incidence of whiplash-associated disorders among females, who exhibit a straighter cervical spine. The anterior longitudinal ligament stretch was also greater in straighter segments. These findings indicate that the morphological features specific to the anatomy of the female cervical spine may predispose it to injury under inertial loading.

High efficiency design of an impulse turbine used in oscillating water column to harvest wave energy

Abstract Wave energy harvesting systems mostly have low power production capability because of unoptimized design of the system components. A bidirectional flow impulse-turbine used in such a system has efficiency less than 40%, and it is required to design the turbine for a higher efficiency. Present work finds an optimal design and shows design-variable sensitivity to the turbine efficiency. The problem is solved using numerical analysis technique. The flow through the turbine was analyzed by solving the Reynolds-averaged Navier-Stokes equations (RANSE). The design variables; namely number of rotor blades and number of guide vane, guide vane angle and guide vane profile were modified to maximize the turbine efficiency. Using the Latin hypercube sampling technique, sample points were selected from a design space defined by lower and upper limits of the variables. Then, several surrogates were constructed using the RANSE calculated results, and the turbine performance was optimized. The results show that guide vane angle is the most sensitive parameter, while the guide vane profile has negligible effect on efficiency. A hybrid genetic algorithm searched the optimal design point. The relative mean efficiency enhancement over a wide range of flow coefficient was approximately 24%, while it was 28% at maximum efficiency point.

Probabilistic Seismic Collapse Analysis of Jacket Offshore Platforms

This paper analyses and evaluates the variability of seismic demand and capacity of a case study jacket offshore platform considering different sources of uncertainty. The aleatoric uncertainty due to variability of near-fault ground motions, as well as uncertain properties of the damping ratio, material elastic modulus, material yield strength, mass, and gravity load have been investigated. The main aim of this study is to pursue which sources of the uncertainty considerably affect the seismic response of the platform. To this end, the sensitivity analysis was conducted not only in the particular earthquake level, but also in all other ranges of intensity using incremental dynamic analysis (IDA) with a special focus on the seismic collapse fragility. In order to reduce the number of simulations, the Latin hypercube sampling (LHS) scheme has been utilized as an efficient sampling procedure to combine the effects of modeling random variables. The collapse fragility curves are derived for each of the model realizations created with LHS technique. Thereafter, the summarized random fragility curve is compared with the deterministic mean parameter model fragility curve. It is found that the uncertainty in the mass and gravity load on the platform are the most influential variables, which can notably alter the IDA and collapse fragility curves. Furthermore, the random combination of the considered sources of uncertainty shifts the median of collapse fragility away from the mean parameter model collapse fragility. Overall, the effects of the uncertainties do not lead to notable changes in the summarized collapse fragility curve.

A comparison of latin hypercube sampling techniques for a supply chain network design problem

Currently, supply chain network design becomes more complex. In designing a supply chain network to withstand changing events, it is necessary to consider the uncertainties and risks that cause network disruptions from unexpected events. The current research related to the designing problem considers network disruptions using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS) techniques. Both have a disadvantage that sample points or disruption locations are not scattered entirely sample space leading to high variation in objective function values. The purpose of this study is to apply a modified LHS or Improved Distributed Hypercube Sampling (IHS) techniques to reduce the variation. The results show that IHS techniques provide smaller standard deviation than that of the LHS technique. In addition, IHS can reduce not only the number of sample size but also and the computational time.

Reliability analysis of blade fatigue life based on fuzzy intelligent multiple extremum response surface method

In order to more reasonable analyze the dynamic reliability of aero-engine blade with coupling failure mode. A fuzzy intelligent multiple extremum response surface method (FIMERSM) was proposed. Considering the coupling effect of temperature load and centrifugal load, the maximum stress point, the maximum strain point and the minimum life point on blade were found by deterministic analysis. Then, the density of blade, rotor speed, elastic modulus, blade-tip temperature, blade-root temperature, fatigue strength coefficient, fatigue strength exponent, fatigue ductility coefficient, fatigue ductility exponent, blade width, blade thickness, blade torsion angle, and blade height as input random variables. By using Latin hypercube sampling technique, the sample values of the input random variables were acquired and finite element basic equation was calculated for each samples which obtained the corresponding dynamic output response of their stress, strain, and low cycle fatigue life within the analysis time domain. By taking the entire maximum values of the dynamic output response in the analysis time domain as new output response, the fuzzy intelligent multiple extremum response surface function (FIMERSF) was established. Finally, the dynamic reliability of the blade structure were obtained by using the Monte Carlo method (MCM) large amount linkage sampling of the input random variables and take it into the FIMERSF to calculate the output response. The results imply that the comprehensive reliability of blade is 99.46%. Through the comparison of MCM, Multiple extremum response surface method (MERSM) and FIMERSM, the computational results show that the FIMERSM has high computational precision and computational efficiency.

Processor Design Space Exploration via Statistical Sampling and Semi-Supervised Ensemble Learning

The design space exploration (DSE) has become a major challenge in microprocessors design due to the increasing complexity of microprocessor architecture and the extremely time-consuming software simulation technology. To more effectively and efficiently perform DSE, recently machine learning techniques are widely explored to build predictive models with a small set of simulations. However, most previous models are supervised models and the training samples are randomly selected. Thus they still suffered from high simulation cost or low prediction accuracy. In order to minimize the simulation overhead for DSE, this paper proposes an efficient DSE method which combines Latin hypercube sampling and semi-supervised ensemble learning technique. Latin hypercube sampling is first employed to select a small set of representative design points for simulation. Then a semi-supervised learning based AdaBoost model (SemiBoost) is proposed to predict the responses of the configurations that have not been simulated. We conduct extensive evaluations on the benchmarks of SPEC CPU2006 suite, and the experimental results demonstrate that the proposed SemiBoost model is superior to existing state-of-the-art models in terms of both efficiency and effectiveness.

Probabilistic approach for estimating heat fluid exit temperature correlation in a linear parabolic trough solar collector

This paper demonstrates a probabilistic modelling approach for establishing the correlation of Heat transfer fluid (HTF) temperature, obtained at the exit of a linear Parabolic trough collector (PTC). For the purpose, an analytical heat transfer (Physical) model was developed and validated by comparing its result with the published values. The correlation was then fitted on a dataset obtained by simulating this physical model over the sampled values acquired by applying the Latin hypercube sampling (LHS) technique over a realistic distribution of factors. The preliminary correlation was based on twenty-six natural, design and operational factors, including normal solar radiation, HTF flow rate, ambient conditions and different attributes associated with geometry and material of the concentrating reflector, receiver, sleeve and selective coating. The coefficient of determination (R2) for correlation was 98.4 %. After analyzing the significance of each factor, a simplified correlation was proposed with only nine factors. The applications associated with this work include the design and simulation of direct and indirect steam generation systems, solar-assisted power generation, space heating, cooling, refrigeration and desalination.

Uncertainty analysis of the strength capacity and failure path for a transmission tower under a wind load

Many tower structures have collapsed during strong wind events; therefore, the objective of the present study is to estimate the strength capacity of transmission towers accurately and to identify all potential failure modes. An uncertainty analysis method for tower structures subjected to a wind load is presented. Subsequently, random samples of material properties and section dimensions are generated based on the Latin Hypercube Sampling technique and then used to establish uncertain finite element models for transmission towers. A static non-linear buckling analysis for structures subjected to a wind load is conducted using ANSYS software. Based on tower models incorporating uncertainty, our analysis reveals that there are six possible initial failure tower members but only one for the deterministic model, indicating that the uncertainties regarding material properties and section dimensions should be taken into account. Furthermore, a sensitivity analysis is conducted, and the results reveal that the uncertainty of material properties has a stronger influence than the uncertainty of section dimensions. Finally, the influence of wind attack angle is discussed, and the initial failure positions and corresponding probabilities for various wind attack angles are obtained. The results show that the most unfavorable wind attack angle is 0° and that the most probable failure position of the tower of interest is the middle of the tower body.

Support vector machine based reliability analysis of concrete dams

This paper presents possible combination of structural responses of concrete dams with machine learning techniques. Support vector machine (SVM) method is adopted and two broad applications are presented: one for a simplified flood reliability assessment of gravity dams and the other for detailed nonlinear seismic finite element method (FEM) based analysis. Up to seventeen random variables are considered in the former example and the results of SVM contrasted with classical reliability analyses techniques (i.e., first- and second-order reliability methods, Monte Carlo simulation, Latin Hypercube and importance sampling techniques). For the latter example, a FEM-SVM based hybrid methodology is proposed for reduction of number of nonlinear analyses. A discussion is provided on the relation between the optimal earthquake intensity measures, the damage states and the accuracy of prediction. It is found that the family of SVM (i.e. standard, least squares, multi-class and regression) is an useful and effective tool for classification, response prediction and reliability analysis of the concrete dams with reasonable accuracy.

Strategic Planning for Managing Municipal Solid Wastes with Consideration of Multiple Stakeholders

Management of municipal solid waste (MSW) involves multiple stakeholders such as government agencies, suppliers, consumers, providers of treatment/recycle services, and transporters. An optimal management strategy should be based on creating synergistic opportunities that benefit the multiple participants. This paper presents a multi-objective optimization approach for the strategic planning of a municipal solid waste management system. The formulation considers the involved tasks such as recycle, reuse, transportation, separation, and distribution. The proposed approach also accounts for the multiple stakeholders with the objective of maximizing the benefit to all the participating stakeholders. The Latin Hypercube sampling technique is adopted to systematically generate weights for the different stakeholders. A case study from Mexico is analyzed where three scenarios are considered. The first one considers that the separation cost is absorbed by each recycling company. The second one assumes that the go...

Optimization of a Micromixer with Two‐Layer Serpentine Crossing Channels at Multiple Reynolds Numbers

AbstractMultiobjective optimizations of a micromixer with two‐layer serpentine crossing channels were performed at two different Reynolds numbers by using a multiobjective genetic algorithm and surrogate modeling based on a Navier‐Stokes analysis. The optimizations included three dimensionless design variables, namely, ratios of the diagonal channel width to the pitch length, the main channel width to the pitch length, and the channel depth to the pitch length. The design space was confirmed by a parametric study, and the Latin hypercube sampling technique was used to select design points within the design space. The Kriging meta‐model was applied for surrogate modeling of the objective functions. Concave Pareto‐optimal fronts representing the trade‐offs between the objective functions were obtained through the optimizations.

Capacity, damage and fragility models for steel buildings: a probabilistic approach

Recently proposed capacity-based damage indices and parametric models for capacity curves are applied to frame steel buildings located in soft soils of the Mexico City. To do that, the seismic performance of 2D models of low-, mid- and high-rise buildings is assessed. Deterministic and probabilistic nonlinear static and incremental dynamic analyses are implemented. Monte Carlo simulations and the Latin Hypercube sampling technique are used. Seismic actions are selected among accelerograms recorded in the study area. Spectral matching techniques are applied, so that the acceleration time histories have a predefined mean response spectrum and controlled error. The design spectrum of the Mexican seismic code for the zone is used as target spectrum. The well-known Park and Ang damage index allows calibrating the capacity-based damage index. Both damage indices take into account the contribution to damage of the stiffness degradation and of the energy dissipation. Damage states and fragility curves are also obtained and discussed in detail. The results reveal the versatility, robustness and reliability of the parametric model for capacity curves, which allow modelling the nonlinear part of the capacity curves by the cumulative integral of a cumulative lognormal function. However, these new capacity-based damage index and capacity models have been tested for and applied to 2D frame buildings only; they have not been applied to 3D building models yet. The Park and Ang and the capacity-based damage indices show that for the analysed buildings, the contribution to damage of the stiffness degradation is in the range 66–77% and that of energy loss is in the range 29–34%. The lowest contribution of energy dissipation (29%) is found for the low-rise, more rigid, building. The energy contribution would raise with the ductility of the building and with the duration of the strong ground motion. High-rise frame buildings in soft soils of Mexico City show the worst performance so that the use of adequate braced frames to control the displacements could be recommended.

Assessment of uncertainty and sensitivity analyses for ORYZA model under different ranges of parameter variation

Abstract We explore the effects of different ranges of parameter variation (RPV) on sensitivity and uncertainty analyses for ORYZA_V3 model. In this study, a latin hypercube sampling (LHS) technique is used to generate parameter sample sets, and a regression-based method is employed for the sensitivity analysis on 16 crop parameters. Then, a top-down concordance coefficient (TDCC) is calculated to assess the stability of parameter sensitivity rankings across diverse RPV. Furthermore, coefficients of variation (CV) and 90% confidence intervals (90CI) of daily model outputs are analyzed by considering uncertainty in observations. We find that the increasing RPV multiplies the CV of daily model outputs, whereas the RPV has no effect on the CV’s change rule over time. The 90CI of model outputs include most of the observations when the RPV is more than ±30% perturbation. The standardized regression coefficient (SRC) of some parameters are obviously minified when the RPV is ±5% or ±50% perturbation. The results highlights the importance of RPV selection in the sensitivity and uncertainty analysis of crop model, and ±30% perturbation was suggested when the RPV cannot be specifically obtained.

COMPARISON OF HYBRID METHODS WITH DIFFERENT META MODEL USED IN BRIDGE MODEL-UPDATING

Two hybrid model updating methods by integration of Gaussian mutation particle swarm optimization method, Latin Hypercube Sampling technique and meta models of Kriging and Back-Propagation Neural Network respectively were proposed, and the methods make the convergence speed of the model updating process faster and the Finite Element Model more adequate. Through the application of the hybrid methods to model updating process of a self-anchored suspension bridge in-service with extra-width, which showed great necessity considering the ambient vibration test results, the comparison of the two proposed methods was made. The results indicate that frequency differences between test and modified model were narrowed compared to results between test and original model after model updating using both methods as all the values are less than 6%, which is 25%−40% initially. Furthermore, the Model Assurance Criteria increase a little illustrating that more agreeable mode shapes are obtained as all of the Model Assurance Criteria are over 0.86. The particular advancements indicate that a relatively more adequate Finite Element Model is yielded with high efficiency without losing accuracy by both methods. However, the comparison among the two hybrid methods shows that the one with Back-Propagation Neural Network meta model is better than the one with Kriging meta model as the frequency differences of the former are mostly under 5%, but the latter ones are not. Furthermore, the former has higher efficiency than the other as the convergence speed of the former is faster. Thus, the hybrid method, within Gaussian mutation particle swarm optimization method and Back-Propagation Neural Network meta model, is more suitable for model updating of engineering applications with large-scale, multi-dimensional parameter structures involving implicit performance functions.

Multi-objective reliability design optimization of a novel side door negative Poisson’s ratio impact beam

By possessing a good capacity for energy absorption and a lightweight structure, the negative Poisson’s ratio (NPR) structure has very fine prospects for application in vehicle engineering. By combining the traditional side door impact beam and a NPR structure, a novel side door NPR impact beam is first proposed in this work to improve the side impact crashworthiness in automobiles. The performance of the side door NPR impact beam is first studied in detail by comparison with a traditional side door impact beam and aluminum-foam-filled impact beam. To make further improvement on the performance of the side door NPR impact beam, the multi-objective design optimization while considering reliability is also investigated in this work. The parametric model of the NPR structure is established to improve modeling efficiency when the shape and topology are changed. A Latin hypercube sampling technique, orthogonal design, and a response surface model are then combined to construct the surrogate models. A radial-based importance sampling technique (RBIS) and multi-objective particle swarm optimization algorithm (MOPSO) are applied in the inner and outer loop respectively to find the optimal multi-objective reliability solutions. The results indicate that the side impact crashworthiness is improved remarkably by the side door NPR impact beam and the structure is further improved by the multi-objective reliability optimization. The studies in this work also serve as a good example for other improvements in automobile performance.

Integrating multi-dynamic virtual cellular manufacturing systems into multi-market allocation and production planning

This article presents a two-stage approach to solve a multi-dynamic virtual cellular manufacturing system in multi-market allocation and production planning. The first stage identifies the plant locations and grouping parts–machines–workers’ assignment to virtual cells, while the second stage determines the production volume for each plant, the allocated amounts to each market and the operations’ allocation to the virtual cells. The goal of the proposed mathematical model is to minimize total expected cost consisting of holding, outsourcing, inter-cell material handling, external transportation, fixed cost for producing each part in each plant as well as machine and labour salaries. It is assumed that the parts’ processing times on each machine with workers as well as the products’ demands are stochastic and described by discrete scenario enrichment using the sample average approximation and Latin hypercube sampling techniques. To solve such a stochastic model, an efficient method based on the genetic alg...

Multi-objective robust design optimization of a novel NPR energy absorption structure for vehicles front ends to enhance pedestrian lower leg protection

The unique structure of negative Poisson’s ratio (NPR) materials provides a good energy absorption capacity that has found wide application in automotive and aeronautics industries. The present work proposes a novel NPR energy absorption structure installed between the bumper and anti-collision beam of automobiles for improving pedestrian lower leg protection. The performance of the proposed NPR structure based on tibia acceleration, knee bending angle, and knee shear displacement is evaluated by comparison with the performance of a conventional energy absorption structure. The performance of the proposed structure is further improved by conducting multi-objective design optimization with consideration for the perturbation induced by parameter uncertainties. For optimization, a parametric model of the NPR structure is first established based on the relationships between parameters. To promote computational efficiency, an optimal Latin hypercube sampling technique and the dual response surface method are combined to build surrogate models between responses and inputs. A multi-objective particle swarm optimization algorithm and six sigma criteria are then applied to obtain the optimal design parameters of the structure. The optimization results are validated by comparisons with the results of multi-objective deterministic optimization. The comparison results show that the proposed NPR energy absorption structure improves pedestrian lower leg protection significantly, and its performance is further improved by the proposed multi-objective robust design optimization. This study serves as a good example of the safety promotion provided by applying NPR structures with enhanced energy absorption capacity in vehicles.

Shape optimization of a bidirectional impulse turbine via surrogate models

ABSTRACTIn this work, a bidirectional impulse turbine used in a wave energy device is simulated using a CFD technique and a shape optimization performed with multiple surrogates-assisted multi-objective evolutionary algorithm. The surrogates have been generated using the validated CFD technique. The objectives for the optimization are to maximize the shaft power and to minimize the pressure drop across the turbine. The hub and the tip thickness of the rotor blade profiles are modified and considered as the design variables. The Latin hypercube sampling technique generated design points are evaluated in the CFD solver, the objective responses are used to construct multiple surrogates, and a Pareto optimal front is generated through a multi-objective optimization approach. The turbine efficiency, which is the function of pressure drop and shaft power, is relatively increased by 10.4%.Abbreviations: BPS: best PRESS; GV: Guide vane; KRG: kriging; LE: leading edge; MOO: multi-objective optimization; OWC: oscillating water column; PRESS: predicted error sum of squares; PS: pressure side or pressure surface; RB: rotor blade; RBNN: radial basis neural network; Ref: reference; RMS: root mean square; RSA: response surface approximation; SS: suction side or suction surface; TE: trailing edge; WAS: weighted average surrogates

Multi-objective robust design optimization of a novel negative Poisson’s ratio bumper system

Negative Poisson’s ratio (NPR) structure has outstanding performances in lightweight and energy absorption, and it can be widely applied in automotive industries. By combining the front anti-collision beam, crash box and NPR structure, a novel NPR bumper system for improving the crashworthiness is first proposed in the work. The performances of the NPR bumper system are detailed studied by comparing to traditional bumper system and aluminum foam filled bumper system. To achieve the rapid design while considering perturbation induced by parameter uncertainties, a multi-objective robust design optimization method of the NPR bumper system is also proposed. The parametric model of the bumper system is constructed by combining the full parametric model of the traditional bumper system and the parametric model of the NPR structure. Optimal Latin hypercube sampling technique and dual response surface method are combined to construct the surrogate models. The multi-objective robust optimization results of the NPR bumper system are then obtained by applying the multi-objective particle swarm optimization algorithm and six sigma criteria. The results yielded from the optimizations indicate that the energy absorption capacity is improved significantly by the NPR bumper system and its performances are further optimized efficiently by the multi-objective robust design optimization method.

Spatial variability and sensitivity analysis on the compressive strength of hollow concrete block masonry wallettes

This probabilistic study investigates the effect of material uncertainties on the numerical analysis of masonry wallette axial compressive strength. Detailed micro-modeling techniques are adopted to model hollow concrete block masonry by separately describing the specific equations for material constituents (blocks and mortar) and block-mortar interfaces. A Latin hypercube sampling technique is used to generate random input variables from the empirical distribution functions of the material parameters. A quasi-static nonlinear analysis is then carried out using a Monte Carlo simulation to evaluate the effect of the spatial variability of material parameters on the compressive behavior of masonry wallettes. The results show that ignoring the spatial variability of material properties may cause a model to overestimate the probability of compression failure. Additionally, the independent and cooperative effects of the material parameters on masonry compressive strength are investigated using first order, second order, and total sensitivity indices. The numerical results indicate that block tensile strength influences masonry compressive strength the most. Several parameters for masonry compressive strength are also ranked in order of importance.