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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 11)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 62)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 39)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 11)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 4)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Bernoulli     Full-text available via subscription   (Followers: 9)
Biometrical Journal     Hybrid Journal   (Followers: 11)
Biometrics     Hybrid Journal   (Followers: 52)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Building Simulation     Hybrid Journal   (Followers: 2)
Bulletin of Statistics     Full-text available via subscription   (Followers: 4)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 11)
Demographic Research     Open Access   (Followers: 15)
Electronic Journal of Statistics     Open Access   (Followers: 8)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Full-text available via subscription   (Followers: 5)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 3)
International Statistical Review     Hybrid Journal   (Followers: 13)
International Trade by Commodity Statistics - Statistiques du commerce international par produit     Full-text available via subscription  
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 4)
Journal of Applied Statistics     Hybrid Journal   (Followers: 21)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 21)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 20)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 6)
Journal of Forecasting     Hybrid Journal   (Followers: 17)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Interactive Marketing     Hybrid Journal   (Followers: 10)
Journal of Mathematics and Statistics     Open Access   (Followers: 8)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 5)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 21, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 27)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 30)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Modelling of Mechanical Systems     Full-text available via subscription   (Followers: 1)
Monte Carlo Methods and Applications     Hybrid Journal   (Followers: 6)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 2)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 5)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 8)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 34)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Probability Surveys     Open Access   (Followers: 4)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 128)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 15)
Sankhya A     Hybrid Journal   (Followers: 2)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 38)
SourceOCDE Comptes nationaux et Statistiques retrospectives     Full-text available via subscription  
SourceOCDE Statistiques : Sources et methodes     Full-text available via subscription  
SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques     Full-text available via subscription   (Followers: 1)
SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance     Full-text available via subscription   (Followers: 2)
SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques     Full-text available via subscription   (Followers: 1)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
SourceOECD Monthly Statistics of International Trade     Full-text available via subscription   (Followers: 1)
SourceOECD National Accounts & Historical Statistics     Full-text available via subscription  
SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE     Full-text available via subscription   (Followers: 2)
SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie     Full-text available via subscription  
SourceOECD Statistics Sources & Methods     Full-text available via subscription   (Followers: 1)
SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Applications in Genetics and Molecular Biology     Hybrid Journal   (Followers: 5)
Statistical Communications in Infectious Diseases     Hybrid Journal  
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methodology     Hybrid Journal   (Followers: 7)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistical Science     Full-text available via subscription   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics & Risk Modeling     Hybrid Journal   (Followers: 3)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Statistics and Economics     Open Access   (Followers: 1)
Statistics in Medicine     Hybrid Journal   (Followers: 196)
Statistics, Politics and Policy     Hybrid Journal   (Followers: 6)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 14)
Stochastic Models     Hybrid Journal   (Followers: 3)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 7)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 23)
The Annals of Applied Probability     Full-text available via subscription   (Followers: 8)
The Annals of Probability     Full-text available via subscription   (Followers: 10)
The Annals of Statistics     Full-text available via subscription   (Followers: 34)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 11)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Structural and Multidisciplinary Optimization
Journal Prestige (SJR): 1.458
Citation Impact (citeScore): 3
Number of Followers: 12  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1615-1488 - ISSN (Online) 1615-147X
Published by Springer-Verlag Homepage  [2652 journals]
  • Reliability-based multi-objective optimization in tunneling alignment
           under uncertainty
    • Abstract: This paper develops a framework of reliability-based multi-objective optimization (RBMO) in tunnel alignment. This study considers the two targets, the limit support pressure (LSP) and maximum ground surface deformation (MGSD), during the new tunnel’s excavation for safety and cost-saving purposes. The hybrid particle swarm optimization-neural network (PSO-NN) is used to construct the meta-model of the LSP and MGSD, based on the 100 groups of finite element numerical results of two tunnel’s excavation. The uncertainty from the soil material property and the meta-model has been considered in the RBMO as well. Through the Monte-Carlo simulation, the probability constraints in the RBMO are determined. Finally, this study entails an illustrative case to examine the superiority of the RBMO in comparison with the deterministic multi-objective optimization (DMO) and reliability-based single-objective optimization (RBSO). Through selecting the best solution of all the Pareto optimal solutions based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach, the optimized relative location of the newly built tunnel based on the RBMO is safer than that based on the RBSO under the tighter constraint for the LSP. In comparison with the RBSO, the RBMO generates a smaller LSP value with comparable MGSD value.
      PubDate: 2021-03-08
  • Topology optimization of multi-scale structures: a review
    • Abstract: Multi-scale structures, as found in nature (e.g., bone and bamboo), hold the promise of achieving superior performance while being intrinsically lightweight, robust, and multi-functional. Recent years have seen a rapid development in topology optimization approaches for designing multi-scale structures, but the field actually dates back to the seminal paper by Bendsøe and Kikuchi from 1988 (Computer Methods in Applied Mechanics and Engineering 71(2): pp. 197–224). In this review, we intend to categorize existing approaches, explain the principles of each category, analyze their strengths and applicabilities, and discuss open research questions. The review and associated analyses will hopefully form a basis for future research and development in this exciting field.
      PubDate: 2021-03-08
  • Optimization-based model calibration of marginal and joint output
           distributions utilizing analytical gradients
    • Abstract: Optimization-based model calibration (OBMC) determines statistical parameters of unknown input variables by optimizing statistical similarity between simulations and observations. Gradient-based optimization algorithms have been utilized for OBMC since they require less computational cost compared with non-gradient optimization algorithms. However, OBMC using numerical gradients such as finite differences has disadvantages in the aspect of computational cost and numerical instability. For these reasons, a previous study derived analytical gradients under the assumption that performance functions are a linear single response and unknown inputs follow a normal distribution. However, to apply the model calibration to general engineering problems, analytical gradients need to be derived without any assumption. Therefore, it is proposed in this study to derive analytical gradients of marginal and joint likelihood functions to calibrate both linear and nonlinear responses. In addition, the analytical gradients are derived for five distribution types of unknown input random variables to be estimated: normal, lognormal, Gumbel, extreme, and uniform distributions. For derivation of the analytical gradients, the chain rule is utilized to combine the derivatives of the inverse function of cumulative distribution function (CDF), performance function, and joint likelihood function. Numerical examples and one engineering example using finite element analysis (FEA) are employed to verify the accuracy and efficiency of the proposed analytical gradient, and it is demonstrated that accurately calibrated output responses can be obtained by the proposed method by finding a better optimum in OBMC.
      PubDate: 2021-03-08
  • Fully adaptive isogeometric topology optimization using MMC based on
           truncated hierarchical B-splines
    • Abstract: In the present work, we introduce a fully adaptive isogeometric topology optimization using moving morphable components through truncated hierarchical B-splines (THITO-MMC), where the hierarchical computational mesh of adaptive isogeometric analysis method is locally refined and coarsened simultaneously during topology optimization. The complete adaptivity of the hierarchical mesh and the associated hierarchical function space is achieved by a fully adaptive mark strategy. We successfully apply THITO-MMC method to two-dimensional and three-dimensional topology optimization problems of maximizing structural stiffness, with or without taking different values of reactivating parameter into consideration. Numerical results show that THITO-MMC delivers an effective MMC-based topology optimization method for what concerns not only the convergence rate of topology optimization but also the computational efficiency without deteriorating the optimized structural stiffness. The comparison with adaptive isogeometric topology optimization with only hierarchical local refinement considered demonstrates that our fully adaptive scheme returns identical optimized results while strongly improving convergence rate and decreasing computational burden involved in obtaining displacement field.
      PubDate: 2021-03-02
  • Robust design optimization under dependent random variables by a
           generalized polynomial chaos expansion
    • Abstract: New computational methods are proposed for robust design optimization (RDO) of complex engineering systems subject to input random variables with arbitrary, dependent probability distributions. The methods are built on a generalized polynomial chaos expansion (GPCE) for determining the second-moment statistics of a general output function of dependent input random variables, an innovative coupling between GPCE and score functions for calculating the second-moment sensitivities with respect to the design variables, and a standard gradient-based optimization algorithm, establishing direct GPCE, single-step GPCE, and multi-point single-step GPCE design processes. New analytical formulae are unveiled for design sensitivity analysis that is synchronously performed with statistical moment analysis. Numerical results confirm that the proposed methods yield not only accurate but also computationally efficient optimal solutions of several mathematical and simple RDO problems. Finally, the success of conducting stochastic shape optimization of a steering knuckle demonstrates the power of the multi-point single-step GPCE method in solving industrial-scale engineering problems.
      PubDate: 2021-03-01
  • An adaptive PCE-HDMR metamodeling approach for high-dimensional problems
    • Abstract: Metamodel-based high-dimensional model representation (HDMR) has recently been developed as a promising tool for approximating high-dimensional and computationally expensive problems in engineering design and optimization. However, current stand-alone Cut-HDMRs usually come across the problem of prediction uncertainty while combining an ensemble of metamodels with Cut-HDMR results in an implicit and inefficient process in response approximation. To this end, a novel stand-alone Cut-HDMR is proposed in this article by taking advantage of the explicit polynomial chaos expansion (PCE) and hierarchical Cut-HDMR (named PCE-HDMR). An intelligent dividing rectangles (DIRECT) sampling method is adopted to adaptively refine the model. The novelty of the PCE-HDMR is that the proposed multi-hierarchical algorithm structure by integrating PCE with Cut-HDMR can efficiently and robustly provide simple and explicit approximations for a wide class of high-dimensional problems. An analytical function is first used to illustrate the modeling principles and procedures of the algorithm, and a comprehensive comparison between the proposed PCE-HDMR and other well-established Cut-HDMRs is then made on fourteen representative mathematical functions and five engineering examples with a wide scope of dimensionalities. The results show that the proposed PCE-HDMR has much superior accuracy and robustness in terms of both global and local error metrics while requiring fewer number of samples, and its superiority becomes more significant for polynomial-like functions, higher-dimensional problems, and relatively larger PCE degrees.
      PubDate: 2021-02-27
  • An MDO-based methodology for static aeroelastic scaling of wings under
           non-similar flow
    • Abstract: The classical aeroelastic scaling theory used to design scaled models is based on the assumption that complete flow similarity exists between the full aircraft and the scaled model. When this condition is satisfied, the scaling problem of the model can be treated as a structural design problem only, where the scaled aerodynamic shape is preserved. If, on the other hand, this hypothesis no longer holds—if the scaled model is constrained to fly at low speed and low altitude, for example—and both the aerodynamic shape and the flexibility of the structure are exactly scaled, then the static response exhibits significant discrepancies in the aerodynamic loads and structural displacement. To design a flying demonstrator with scaled static response when flow similarity cannot be fulfilled, we present a multidisciplinary optimization-based method that allows some freedom in the design of the wing shape (while keeping the scaled wingspan) to update the wing geometry and structural properties to ensure equivalent scaled loads and overall wing displacement. To illustrate this method, we apply it to a 1:5 version of the uCRM wing at subsonic flight condition. While the errors in air loads using the classical theory are around 16%, the presented method achieves errors lower than 1%, with a good agreement for the wingtip displacement.
      PubDate: 2021-02-27
  • Multi-objective optimization of multi-directional functionally graded
           beams using an effective deep feedforward neural network-SMPSO algorithm
    • Abstract: This paper proposes an intelligent multi-objective optimization approach using the deep feedforward neural network (DNN) integrated with the speed-constrained multi-objective particle swarm optimization (SMPSO) to give the so-called DNN-SMPSO algorithm for solving multi-objective optimization problems of two-dimensional functionally graded (2D-FG) beams under a static load and free vibration. In the proposed approach, a high accurate DNN integrated with an intelligent sampling technique is used as a surrogate model to replace time-consuming numerical models in predicting objectives and constraints during the optimization process. Meanwhile, the SMPSO algorithm is utilized to search a set of Pareto-optimal solutions which show the best trade-off solutions of the required objectives. The ceramic volume fraction values at control points defined by the isogeometric analysis (IGA) framework are taken into account as continuous design variables and input parameters of the DNN model while the objectives and constraints are considered as output signals. In order to avoid the overfitting phenomena and speed up the training process of the DNN model, the state-of-the-art dropout and mini-batch techniques are applied. Additionally, various activation functions, optimizers, and hyper-parameters such as number of hidden layers and hidden units of the DNN model are surveyed. The accuracy, efficiency, and applicability of the proposed method are illustrated through two different multi-objective optimization examples of the 2D-FG beams with various boundary conditions. Optimal results obtained by the DNN-SMPSO method are compared with those of other methods to investigate the reliability of the proposed method. The optimal material distribution of the 2D-FG beams is described by two-dimensional Non-Uniform Rational B-spline (2D-NURBS) basis functions. Through the obtained numerical results, the DNN-SMPSO shows its accuracy, effectiveness, and capability in solving multi-objective optimization problems of engineering structures, especially in aspect of saving the computational cost. In addition, the attained optimal material distribution is useful for the 2D-FG beam fabrication.
      PubDate: 2021-02-27
  • Tabu efficient global optimization with applications in additive
    • Abstract: Methods based on Gaussian stochastic process (GSP) models and expected improvement (EI) functions have been promising for box-constrained expensive optimization problems. These include robust design problems with environmental variables having set-type constraints. However, the methods that combine GSP and EI sub-optimizations suffer from the following problem, which limits their computational performance. Efficient global optimization (EGO) methods often repeat the same or nearly the same experimental points. We present a novel EGO-type constraint-handling method that maintains a so-called tabu list to avoid past points. Our method includes two types of penalties for the key “infill” optimization, which selects the next test runs. We benchmark our tabu EGO algorithm with five alternative approaches, including DIRECT methods using nine test problems and two engineering examples. The engineering examples are based on additive manufacturing process parameter optimization informed using point-based thermal simulations and robust-type quality constraints. Our test problems span unconstrained, simply constrained, and robust constrained problems. The comparative results imply that tabu EGO offers very promising computational performance for all types of black-box optimization in terms of convergence speed and the quality of the final solution.
      PubDate: 2021-02-26
  • Support vector machine-based importance sampling for rare event estimation
    • Abstract: Structural reliability analysis aims at computing failure probability with respect to prescribed performance function. To efficiently estimate the structural failure probability, a novel two-stage meta-model importance sampling based on the support vector machine (SVM) is proposed. Firstly, a quasi-optimal importance sampling density function is approximated by SVM. To construct the SVM model, a multi-point enrichment algorithm allowing adding several training points in each iteration is employed. Then, the augmented failure probability and quasi-optimal importance sampling samples can be obtained by the trained SVM model. Secondly, the current SVM model is further polished by selecting informative training points from the quasi-optimal importance sampling samples until it can accurately recognize the states of samples, and the correction factor is estimated by the well-trained SVM model. Finally, the failure probability is obtained by the product of augmented failure probability and correction factor. The proposed method provides an algorithm to efficiently deal with multiple failure regions and rare events. Several examples are performed to illustrate the feasibility of the proposed method.
      PubDate: 2021-02-25
  • Identification of marginal and joint CDFs using bivariate type I interval
           multiply censored data for RBDO of a pick-up device of a pilot mining
    • Abstract: In this paper, joint probability distribution for the size and mass of deep-sea manganese nodules is investigated and reliability-based design optimization (RBDO) of a deep-sea pilot mining robot is performed. As the size and mass of the manganese nodules are strongly correlated and their data are given as bivariate type I interval multiply censored data, a new statistical modeling method should be developed to deal with these issues. However, this is significantly difficult as the conventional methods cannot resolve these issues and there is no prior knowledge of the two physical properties. The proposed method, which employs the multinomial distribution to define the likelihood function and the Akaike information criterion to select the fittest marginal distribution and copula, provides a systematic approach to find the joint probability distribution using the type I interval multiply censored data. To demonstrate the accuracy and effectiveness of the proposed method, two numerical examples are tested. Then, the RBDO of the pilot mining robot is performed using the joint probability distribution resulted from the proposed method.
      PubDate: 2021-02-25
  • Value-driven design for product families: a new approach for estimating
           value and a novel industry case study
    • Abstract: Advanced product platform and product family design methods are needed to define and optimize the value they bring to a company. Maximizing platform commonality and individual product performance often fails to realize the most valuable product family during optimization; however, few examples exist in the literature to explore these trade-offs. This paper introduces a novel industry case study to explore the differences between “traditional” multidisciplinary design optimization (MDO) and value-driven design (VDD) approaches to product family design. The case study involves a family of five commercially-available washing machines and integrates multidisciplinary analyses, simulations, mathematical models, and response surface models to obtain ratings for individual product attributes. These attributes are then aggregated into a value function for the product family using a novel approach to estimate sales volume and a demand sensitivity curve derived from publicly available data. We then formulate and solve a “traditional” MDO product family design problem using a multi-objective genetic algorithm to minimize performance deviation and a product family penalty function. A novel VDD formulation is then introduced and solved to maximize the net present value (NPV) for the firm producing the family of products. Visualization and comparison of the results illustrate that the “traditional” MDO formulation can find several promising solutions for the product family, but it fails to find solutions that maximize the value to the firm. The results also provide a benchmark for researchers to explore alternative value function formulations and solution approaches for product family design using the novel industry case study.
      PubDate: 2021-02-25
  • Multidisciplinary design optimization of underwater glider for improving
    • Abstract: Underwater glider (UG) is widely applied for long-term ocean observation, the gliding range of which is mainly influenced by its design. In this paper, the design parameters that have obvious influence on the gliding range, including the buoyancy factor, compressibility of the pressure hull, hydrodynamic coefficients, and motion parameters, are selected based on the gliding range model of UG. Due to their complicate coupling relationship in the design of the UG, the multidisciplinary optimization (MDO) design framework integrating the collaborative optimization (CO) method and approximate model technology is adopted to optimize the key parameters by taking the gliding range as the optimization target. The results show that the optimization leads to an increase of the gliding range of Petrel-L as much as 83.3% when the hotel load is 0.5 W, which is verified by the sea trial. The optimization is applicable to other types of underwater gliders.
      PubDate: 2021-02-25
  • Multi-fidelity modeling with different input domain definitions using deep
           Gaussian processes
    • Abstract: Multi-fidelity approaches combine different models built on a scarce but accurate dataset (high-fidelity dataset), and a large but approximate one (low-fidelity dataset) in order to improve the prediction accuracy. Gaussian processes (GPs) are one of the popular approaches to exhibit the correlations between these different fidelity levels. Deep Gaussian processes (DGPs) that are functional compositions of GPs have also been adapted to multi-fidelity using the multi-fidelity deep Gaussian process (MF-DGP) model. This model increases the expressive power compared to GPs by considering non-linear correlations between fidelities within a Bayesian framework. However, these multi-fidelity methods consider only the case where the inputs of the different fidelity models are defined over the same domain of definition (e.g., same variables, same dimensions). However, due to simplification in the modeling of the low fidelity, some variables may be omitted or a different parametrization may be used compared to the high-fidelity model. In this paper, deep Gaussian processes for multi-fidelity (MF-DGP) are extended to the case where a different parametrization is used for each fidelity. The performance of the proposed multi-fidelity modeling technique is assessed on analytical test cases and on structural and aerodynamic real physical problems.
      PubDate: 2021-02-23
  • Multi-fidelity surrogate model-assisted fatigue analysis of welded joints
    • Abstract: In this study, Kriging based multi-fidelity (MF) surrogate models are constructed to accelerate the fatigue analysis of welded joints. The influence of leg length, leg height, the width of the specimen, and load in the fatigue test are taken into consideration. In the construction of the MF surrogate model, the finite element model that is calibrated with the experiment is chosen as the high-fidelity (HF) model, while the finite element model that is not calibrated with the experiment is considered as the low-fidelity (LF) model, aiming to capture the trend of the HF model. The Leave-one-out (LOO) verification method is used to evaluate the benefits of the three types of Kriging-based MF surrogate models comparing to the single-fidelity one. The results show that the accuracy improvement of MF surrogate models compared with the HF Kriging surrogate model is between 49.17 and 79.92%, while it is between 53.4 and 87.99% compared with the LF Kriging surrogate model. To determine the most suitable MF surrogate models for different responses of the welded single lap joint, three different MF uncertainty quantification (UQ) metrics are used to evaluate the prediction errors of the MF surrogate models. Based on the results of the UQ, a comprehensive ranking for the MF surrogate models is provided by introducing the entropy weighting-based technique for order preference by similarity to ideal solution (EW-TOPSIS). The developed methods can also be generalized to the selection of the MF surrogate model for other engineering applications.
      PubDate: 2021-02-23
  • Topology optimization of shape memory polymer structures with programmable
    • Abstract: We present a novel optimization framework for optimal design of structures exhibiting memory characteristics by incorporating shape memory polymers (SMPs). SMPs are a class of memory materials capable of undergoing and recovering applied deformations. A finite-element analysis incorporating the additive decomposition of small strain is implemented to analyze and predict temperature-dependent memory characteristics of SMPs. The finite element method consists of a viscoelastic material modelling combined with a temperature-dependent strain storage mechanism, giving SMPs their characteristic property. The thermo-mechanical characteristics of SMPs are exploited to actuate structural deflection to enable morphing toward a target shape. A time-dependent adjoint sensitivity formulation implemented through a recursive algorithm is used to calculate the gradients required for the topology optimization algorithm. Multimaterial topology optimization combined with the thermo-mechanical programming cycle is used to optimally distribute the active and passive SMP materials within the design domain. This allows us to tailor the response of the structures to design them with specific target displacements, by exploiting the difference in the glass-transition temperatures of the two SMP materials. Forward analysis and sensitivity calculations are combined in a PETSc-based optimization framework to enable efficient multi-functional, multimaterial structural design with controlled deformations.
      PubDate: 2021-02-21
  • A new model updating strategy with physics-based and data-driven models
    • Abstract: For engineering simulation models, insufficient experimental data and imperfect understanding of underlying physical principles often make predictive models inaccurate. It is difficult to reduce the model bias effectively with limited information. To improve the predictive performances of the models, this paper proposes a new model updating strategy utilizing a data-driven model to integrate with a physics-based model. One of the main strengths of the proposed method is that it maximizes the utilization of existing limited information by combining physics-based and data-driven models built based on different principles. First, the physics-based model is updated via selecting a suitable updating method and updating formulation. A data-driven model is then constructed using the Gaussian process (GP) regression. Finally, a weight combination is employed to obtain the updated predictive model where the weights of experimental sites and non-experimental sites are determined by the minimum discrepancy of probability distributions of the posterior error and another data-driven model, respectively. The Sandia thermal challenge problem is used to demonstrate the effectiveness of the proposed method.
      PubDate: 2021-02-20
  • Stiffener layout optimization of shell structures with B-spline
           parameterization method
    • Abstract: Thin-walled shell structures are widely used in aeronautical and aerospace engineering. This paper develops an effective B-spline parameterization method for stiffener layout optimization of shell structures. Height variables are defined by B-spline control parameters to characterize the stiffener layout reinforcing the shell structure. A continuous height field is subsequently generated via B-spline and basis functions. In view of possible curvatures of shell structures, the height field is projected from parametric space onto the shell structure by means of the parametric mapping. In this work, the finite element method is adopted with the solid-shell coupling method used for structural analysis. Pseudo-densities associated with solid elements are determined based on the B-spline parameterization and Heaviside function. Several numerical examples are dealt with to demonstrate the proposed method. Compared with the standard density-based method, the proposed method produces checkerboard-free design results with a clear layout and naturally avoids overhang stiffeners.
      PubDate: 2021-02-19
  • Phononic band gap optimization in truss-like cellular structures using
           smooth P-norm approximations
    • Abstract: The emergence of additive manufacturing and the advances in structural optimization have boosted the development of tailored cellular materials. These modern materials with complex architectures show higher structural efficiency when compared to traditional materials. In particular, truss-like cellular structures show great potential to be applied in lightweight applications due to their large strength/stiffness to mass ratio. Besides lightweight, these materials may exhibit incredible vibration isolation properties known as phononic band gaps. The present investigation addresses the topology optimization of two-dimensional (2D) truss-like cellular structures. The formulation aims to find the optimal geometrical and mechanical properties of each truss element to create a material exhibiting outstanding vibration (elastic wave) isolation at a certain frequency range (band gap). A new method to handle the non-differentiation of repeated eigenvalues, as well as mode switching, is proposed, where P-norms are used to create continuous approximation for extreme frequency values for all wave vectors of the band diagram. Results show that the proposed formulation is effective and avoids convergence problems associated to mode switching and to repeated eigenvalues.
      PubDate: 2021-02-19
  • An efficient estimation of failure probability in the presence of random
           and interval hybrid uncertainty
    • Abstract: In the presence of random and interval hybrid uncertainty (RI-HU), the safety degree of the structure system can be quantified by the upper and lower bounds of failure probability. However, there is a lack of efficient methods for estimating failure probability under RI-HU in present. Therefore, a novel method is proposed in this paper. In the proposed method, the interval variables are extended to the random variables by assigning a priori probability density function, in which the conditional density estimation (CDE)–based method and conditional probability estimation (CPE)–based method are proposed, and the failure probability varying with the interval variables can be obtained by only one group Monte Carlo simulation (MCS). Since the computational complexity of CPE is much lower than that of CDE, the CPE-based method is mainly concerned. In the CPE-based method, the conditional failure probability on a realization of the extended interval vector is approximated by that on a differential region adjacent to the corresponding realization; then, the density function estimation required in the CDE can be avoided. In order to ensure the accuracy of the CPE, a strategy is proposed to adaptively select the differential region, in which the MCS can be combined with the CPE (CPE + MCS) and the adaptive Kriging can be nested into the CPE + MCS for improving the efficiency. To improve the efficiency further, the meta-model importance sampling nested Kriging is combined with the CPE-based method. The presented examples illustrate the superiority of the proposed method over the existing methods.
      PubDate: 2021-02-19
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