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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 148)
Statistics in Medicine     Hybrid Journal   (Followers: 134)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 50)
Sociological Methods & Research     Hybrid Journal   (Followers: 43)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 19)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Optimization Methods and Software     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Engineering With Computers     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Stochastic Models     Hybrid Journal   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

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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  [2469 journals]
  • Enhanced growth method for topology and geometry optimization of truss
           structures

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      Abstract: Abstract In this paper, we present an enhanced growth method based on virtual displacements and strains fields for generating optimal design in terms of topology and geometry of plane trusses without the need of a generation of so-called ground structure. The method has been applied to the single load case problem with stress and size constraints in plastic design. In order to demonstrate the reliability and accuracy of the proposed method, three examples are carried out: Hemp cantilever, Chan cantilever and McConnel structure.
      PubDate: 2022-08-05
       
  • A new chance reliability-based design optimization approach considering
           aleatory and epistemic uncertainties

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      Abstract: Abstract Aleatory and epistemic uncertainties, which coexist widely in the preliminary design phase of engineering structures, should be appropriately controlled for safety purposes. A methodology of hybrid reliability analysis and optimization based on chance theory is proposed in this paper. Random variables are adopted to describe aleatory uncertainty with sufficient statistical data. On the other hand, uncertain variables are used to quantify epistemic uncertainty with objective limited information or subjective expert opinions. More specifically, a metric termed chance measure is introduced to formulate a chance reliability indicator (CRI) for modeling structural reliability in the presence of hybrid uncertainty. Then, two CRI estimation methods denoted as crisp equivalent model and uncertain random simulation (URS) methods, are developed for the mixed reliability assessment. Furthermore, an efficient CRI-based design optimization (CRBDO) model is established under prescribed chance reliability constraints. Two solving strategies, including crisp mathematical programming and URS combined with genetic algorithm strategies, are presented to solve the CRBDO model and obtain optimal results. Finally, the performance of the constructed analysis model, as well as the feasibility of the corresponding solution technique, is verified by four engineering applications.
      PubDate: 2022-08-05
       
  • A Kriging-based adaptive parallel sampling approach with threshold value

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      Abstract: Abstract Most of Kriging-based adaptive sampling approaches were focused only on the sequence architectures for producing limited (i.e., one or two) updating points, but few attention was given to the parallel sampling strategy to obtain multiple updating points in one iteration. In this study, a novel Kriging-based adaptive parallel sampling approach (KAPS-MEIGF) is proposed. This parallel sampling approach selects the first most informative update point by maximizing the expected improvement of global fit (EIGF) criterion that considers both the bias and variance information. Then the parallel sampling criterion with a threshold value is used to generate multiple potential sample points. Particularly, the cross-validation strategy is used to dynamically balance the global exploitation and local exploitation. The results of 18 test cases show that the proposed KAPS-MEIGF outperforms the EIGF sampling approach but it is worse than the maximum mean square error (MMSE) sampling approach and the combined expectation (CE) sampling approach for most of the 2-dimension test cases. However, for high-dimensional complex problems, KAPS-MEIGF exhibits significantly competing performance compared with the MMSE sampling approach and the CE sampling approach, which indicates the robustness of stability of KAPS-MEIGF. In addition, the running speed of KAPS-MEIGF is 2.8–30.9 times that of the MMSE sampling approach. Therefore, it is a very promising sampling approach to build Kriging models for the problems with diverse characteristics, especially for simulation-based high-dimensional problems. Finally, a multi-objective optimization of rotating impeller module with static cascade (RIM-SC) for rotary separated range hood illustrates the engineering application value of KAPS-MEIGF method. The result shows that the searching efficiency of KAPS-MEIGF method is about 4.4 times higher when compared to other sequence sampling strategy. For the optimized RIM-SC, both separation efficiency and exhaust airflow rate at design condition are improved by 20.3% and 63.6%, respectively, and the impeller input power is decreases by 2.8%.
      PubDate: 2022-08-05
       
  • Shape and material optimization of problems with dynamically evolving
           interfaces applied to solid rocket motors

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      Abstract: Abstract This paper studies design problems where the performance is dominated by the dynamic evolution of interfaces due to chemical processes. Considering the representative example of a solid rocket motor, the shape of the interface between the solid fuel and the gas inside the combustion chamber at the beginning of the burn process and the reference burn rate of a functionally graded propellant are optimized to achieve a desired thrust over time. The initial fuel–gas interface is described by a level set function parameterized by geometric primitives and B-splines. The reference burn rate distribution is discretized by multi-variate B-splines. The thrust is predicted by a semi-analytical approach that requires modeling the recession of the fuel–gas interface. To this end, a stabilized finite element formulation of the Hamilton–Jacobi equation is used to describe the evolution of the level set function during the burn process. The optimization problem is solved by a nonlinear programming method, and the design sensitivities are evaluated by the adjoint method. The proposed optimization approach is studied with numerical examples in 2D and 3D, involving configurations with more than \(6 \times 10^{4}\) optimization variables and \(12 \times 10^{6}\) state variables. The optimization results show that this approach provides a promising design tool for problems with dynamically evolving interfaces due to surface reactions. However, the results also reveal that the simplicity of the recession and thrust models requires limiting the design freedom through a carefully chosen design parameterization. Furthermore, additional constraints need to be imposed to prevent unphysical designs.
      PubDate: 2022-08-05
       
  • Toward digital twin development for additively manufactured turbine blades
           with experimental and analytical methods

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      Abstract: Abstract The development of a digital twin is supported by a validated digital replica. To create a digital replica, accurate knowledge of the part being digitally re-created is required. However, that does not automatically equate to an accurate representation of the component. This research applied Additive Manufacturing (AM) techniques to create ten turbine blades. By characterizing the material properties of the AM build plate and the geometric variations of each printed specimen through laboratory measurements, a digital replica Finite Element Model (FEM) was created for each specimen. The digital replica development applied state-of-the-practice methods to understand how AM variations impacted FEM predictions. The work focused on the ability to accurately predict stress values for the purpose of improving fatigue life predictions. The accuracy of the digital replicas were assessed by comparing the FEM predictions of mass, volume, natural frequencies, and location dependent strain values against the printed specimen test measurements. Comparisons between the digital replica models and the “as-designed” baseline model quantified the influences of material properties and small geometric variations in model predictions.
      PubDate: 2022-08-05
       
  • A machine learning accelerated inverse design of underwater acoustic
           polyurethane coatings

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      Abstract: Abstract Here we propose a detailed protocol to enable an accelerated inverse design of acoustic coatings for underwater sound attenuation application by coupling Machine Learning and an optimization algorithm with Finite Element Models (FEM). The FEMs were developed to obtain the realistic performance of the polyurethane (PU) acoustic coatings with embedded cylindrical voids. The frequency dependent viscoelasticity of PU matrix is considered in FEM models to substantiate the impact on absorption peak associated with the embedded cylinders at low frequencies. This has been often ignored in previous studies of underwater acoustic coatings, where usually a constant frequency-independent complex modulus was used for the polymer matrix. The key highlight of the proposed optimization framework for the inverse design lies in its potential to tackle the computational hurdles of the FEM when calculating the true objective function. This is done by replacing the FEM with an efficiently computable surrogate model developed through a Deep Neural Network. This enhances the speed of predicting the absorption coefficient by a factor of \(4.5 \times 10^3\) compared to FEM model and is capable of rapidly providing a well-performing, sub-optimal solution in an efficient way. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids. This is accomplished by accommodating attenuation mechanisms, including Fabry–P \(\acute{e}\) rot resonance and Bragg scattering of the layers of voids. Furthermore, the proposed protocol enables the inverse and targeted design of underwater acoustic coatings through accelerating the exploration of the vast design space compared to time-consuming and resource-intensive conventional trial-and-error forward approaches.
      PubDate: 2022-08-05
       
  • An enhanced finite step length method for structural reliability analysis
           and reliability-based design optimization

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      Abstract: Abstract The finite step length (FSL) method is extensively used for structural reliability analysis due to its robustness and efficiency compared with traditional Hasofer–Lind and Rackwitz–Fiessler (HL-RF) method. However, it may generate a large computational effort when it faces some complex nonlinear limit state functions. This study explains the basic reason of inefficiency of the FSL method and proposes an enhanced finite step length (EFSL) method to improve the ability for solving complex nonlinear problems, and then apply it to reliability-based design optimization (RBDO). The tactic is to present an iterative control criterion to compensate for the deficiency of the FSL method in the oscillation amplitude criterion, which solves the problem of large computational effort caused by unchanged step length during the iterative process. Then, a comprehensive step length adjustment formula is presented, which can adaptively adjust the step length to achieve fast convergence for limit state functions with different degrees of nonlinearity. Following that, the proposed method is combined with the double loop method (DLM) to improve the efficiency and robustness for solving complex RBDO problems. The robustness and efficiency of the proposed method compared to other commonly used first-order reliability analysis methods are demonstrated by five numerical examples. In addition, four design problems are used to validate the proposed EFSL-based DLM which is effective for solving complex nonlinear RBDO problems.
      PubDate: 2022-08-05
       
  • An integrated two-step strategy for an optimal design of liquid-cooled
           channel layout based on the MMC–density approach

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      Abstract: Abstract This paper proposes an integrated two-step strategy for an optimal design of liquid-cooled channel layout based on the moving morphable component (MMC)-density approach. The proposed strategy intends to take the advantage of both the MMC approach for its high flexibility in searching a physically reasonable layout and the density approach for its better capacity of topology description. On the basis of the above-mentioned strategy, an intermediate layout is obtained through MMC approach and further optimized as initial solution of density approach step. Through density approach step, the final layout shows smoother boundary while retaining reasonable feature size. The original contributions of this paper are as follows: (i) An assembled quadratic Bézier curves component is proposed to describe the largely curved channel with limited numbers of optimization variables and computation order. (ii) Benefited from explicit geometric description, adaptive mesh refinement (AMR) is applied in MMC approach step for the first time. The application of AMR, from the numerical point of view, has two key ingredients to be highlighted: (i) the accuracy of solution in fluid–solid boundary region can be ensured with relatively limited computational cost. (ii) The contradiction that the difference step of MMC updating needs to be both as small as possible and integer multiple of the mesh size can be avoided. The performance of our methodology is demonstrated by numerical examples aiming for maximal heat exchange with power dissipation constraint. The main finding reveals that the proposed strategy can offer reasonable channel layout with better thermal performance, compared with conventional density approach. The whole numerical implementation relies on OpenFOAM and PETSc open-source software packages.
      PubDate: 2022-08-05
       
  • Stochastic analysis of a crash box under impact loading by an adaptive
           POD-PCE model

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      Abstract: Abstract Propagating uncertainty through a crash problem is very difficult due to non-linear and non-smooth behavior. The required number of model evaluations is often high, and therefore the computational cost is prohibitive. To deal with such problems, an adaptive meta-model is developed using a polynomial chaos expansion (PCE) and a proper orthogonal decomposition (POD). The adaptive meta-model is used for uncertainty quantification and for global sensitivity analysis of a crash box under impact loading. The time-dependent uncertain response quantities are expressed with the reduced POD modes. The predicted stochastic contact force and impactor velocity by the adaptive meta-model are quite close to the actual simulations. The time-dependent mean and standard deviation for all responses are predicted quite well with low number of model evaluations. Furthermore, it is found that the material property and the crash box thickness are the most influential parameters for the contact force, and the impactor mass is the most influential parameter for the total dissipated energy.
      PubDate: 2022-08-05
       
  • Stress-based topology optimization with the parameterized level-set method
           based on radial basis functions

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      Abstract: Abstract Based on the parameterized level-set method using radial basis functions, a topology optimization method is proposed that can account for stress minimization and stress-constraint problems. First, the mathematical models of stress minimization and stress-constraint problems are separately established. In the mathematical model, the p-norm function is used as a stress aggregation function for both of the problems, and for the stress-constraint problem, the adaptive scaling constraint method to measure maximum stress in the structure is used. The shape derivative is then used to obtain the normal velocities in the parameterized level-set method, and an improved strategy based on weighted least square method is proposed to smooth the normal velocities at every nodal point in the design area in order to match the velocity in parameterized level-set method. Subsequently, an augmented Lagrange multiplier is given to make the transitions of both optimization problems stable during the convergence process. Finally, the effectiveness and efficiency of the proposed optimization method in solving stress minimization and stress-constraint problems are demonstrated through several classical numerical examples.
      PubDate: 2022-08-05
       
  • Topology optimization incorporating a passageway for powder removal in
           designs for additive manufacturing

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      Abstract: Abstract In powder-based additive manufacturing, the unused powder must be removed after printing. Topology optimization has been applied to designs for additive manufacturing, which may lead to designs with enclosed voids, where the powder will be trapped inside during printing. A topology optimization method incorporating a powder removal passageway is developed to avoid the powder being trapped inside the structure. The passageway is generated by connecting the entrance, all voids, and the exit sequentially. Each void is limited to have only one pair of inlet and outlet to guarantee a single-path flow to facilitate powder removal after the additive manufacturing. The path of the passageway is optimized to minimize its influence on structural stiffness. The proposed optimization method was applied to two practical case studies where the powder removal passageways were generated successfully.
      PubDate: 2022-08-05
       
  • An outer approximation bi-level framework for mixed categorical structural
           optimization problems

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      Abstract: Abstract In this paper, mixed categorical structural optimization problems are investigated. The aim is to minimize the weight of a truss structure with respect to cross-section areas, materials, and cross-section type. The proposed methodology consists of using a bi-level decomposition involving two problems: master and slave. The master problem is formulated as a mixed-integer linear problem where the linear constraints are incrementally augmented using outer approximations of the slave problem solution. The slave problem addresses the continuous variables of the optimization problem. The proposed methodology is tested on three different structural optimization test cases with increasing complexity. The comparison to state-of-the-art algorithms emphasizes the efficiency of the proposed methodology in terms of the optimum quality, computation cost, as well as its scalability with respect to the problem dimension. A challenging 120-bar dome truss optimization problem with 90 categorical choices per bar is also tested. The obtained results showed that our method is able to solve efficiently large-scale mixed categorical structural optimization problems.
      PubDate: 2022-08-05
       
  • Topology optimization using difference-based equivalent static loads

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      Abstract: Abstract Topology optimization of crash-related problems usually involves a huge number of design variables as well as nonlinearities in geometry, material, and contact. The Equivalent Static Load (ESL) method provides an approach to solve such problems. This method has recently been extended under the name Difference-based Equivalent Static Load (DiESL) method to employ a set of Finite Element models, each describing the deformed geometry at an individual time step. Only sizing optimization problems were considered so far. In this paper, the DiESL method is extended to topology optimization utilizing a Solid Isotropic Material with Penalization approach (SIMP). The method is tested using an example of a rigid pole colliding with a simple beam, where the intrusion of the pole is minimized. The initial velocity of the pole is varied in order to examine the influence of inertia effects on the optimized structures. It is shown that the results differ significantly depending on the chosen initial velocity and, consequently, that they exhibit inertia effects. This cannot be seen in the results derived by the standard ESL method. Consequently, the results of the DiESL method’s show a considerable improvement compared to those of the standard ESL method.
      PubDate: 2022-08-05
       
  • Density-based topology optimization of a surface cooler in turbulent flow
           using a continuous adjoint turbulence model

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      Abstract: Abstract The present work focuses on the application of density-based topology optimization to the design of a surface cooler. This kind of device is used to cool down the oil circuit in aircraft engines thanks to the cold air in the bypass stream, and is subject to severe heat duty and pressure drop requirements. The optimization is carried out with an in-house implementation of the density method in OpenFOAM. A continuous adjoint strategy is employed to compute the sensitivities with respect to the design variables. Avoiding the so-called “frozen turbulence” assumption, the variations of the turbulent viscosity are taken into account in the computation of the sensitivity. The proposed model also considers the influence of the design variables on the wall distance function occurring in the formulation of the Spalart–Allmaras turbulence model. A simplified two-dimensional model is first employed to tune the optimization and the density model parameters. Then, the methodology is applied to a large-scale three-dimensional case, and the results are compared to a reference straight-fin geometry. The performance is finally evaluated with a reference solver, showing that the density model overestimates both the heat exchange and the total pressure loss, but that the methodology is still able to provide efficient designs in turbulent flow, starting from a very remote initialization.
      PubDate: 2022-08-05
       
  • Structural stochastic identification considering modeling uncertainty
           through sparse grid and similar system analysis

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      Abstract: Abstract In this paper, a structural identification problem considering modeling uncertainty is investigated, which not only needs to identify the unknown parameters of mechanical structure, but also accurately quantifies the influence of stochastic modeling uncertainty on the unknown structural parameters. As a typical inverse problem, the solving of structural stochastic identification faces the double nesting of uncertainty propagation analysis and inverse calculations. In order to overcome this difficult, a novel uncertain inverse method based on sparse grid and similar system analysis is proposed. Firstly, this structural stochastic identification problem is decomposed into several deterministic inverse problems by sparse grid technique. Since the small variations of uncertain variables at any two adjacent sparse grid nodes, the corresponding two systems at sparse grid nodes are similar. Thus, by adequately utilizing this similarity, the similar system analysis strategy is innovatively developed, which reduces these time-consuming deterministic inverse problems into only one deterministic optimization inverse and a few forward problem calculations. Therefore, the inverse efficiency is significantly improved. Subsequently, the statistical moments and the probability density functions of the unknown structural parameters will be identified by utilizing the deterministic inverse results and their concentrated probabilities at the sparse grid nodes. Finally, the practicability of uncertain inverse method will be illustrated by four examples.
      PubDate: 2022-08-05
       
  • An improved system for efficient shape optimization of vehicle
           aerodynamics with “noisy” computations

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      Abstract: Abstract To efficiently achieve tangible improvements in the aerodynamic objectives of a vehicle based on a computational fluid dynamics (CFD) simulation that produces unavoidable noise, an improved system is proposed. This system, called regression kriging with re-interpolation (RKri)-based efficient global optimization (EGO) with a pseudo expected improvement (PEI) criterion (RKri-EGO-PEI), is used to directly filter out the noise produced by the CFD simulation, maintain a smooth trend of the surrogate model, and conduct point infills in a parallel manner. To guarantee optimization processes for tuning the hyper-parameters of RKri and searching for a solution of appropriate quality to the PEI function, the performance advantages of the optimizers on a parallel EGO algorithm called EGO-PEI are comprehensively investigated. Then, the best is chosen as the optimizer for the RKri-EGO-PEI system. To confirm the performance of the proposed system, RKri-EGO-PEI competes with ordinary kriging-based EGO-PEI (OK-EGO-PEI) and RKri-based EGO (RKri-EGO) systems on a real-world optimization problem of vehicle aerodynamics. The results of the investigation show that the performance of the optimizer with a higher central goal of exploration–exploitation can not only promote a higher-level convergence of the EGO-PEI algorithm within an appropriate number of point infills, but also ensure the same convergence level of the EGO-PEI algorithm as that using other optimizers, with fewer iterations. In addition, RKri-EGO-PEI searches for a lower drag coefficient (Cd) of the vehicle model with a faster speed and smaller wall-clock time cost than those of OK-EGO-PEI and RKri-EGO under an optimization problem with “noisy” computations.
      PubDate: 2022-08-05
       
  • Topology optimization for lift–drag problems incorporated with
           distributed unstructured mesh adaptation

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      Abstract: Abstract This note introduces the distributed unstructured mesh adaptation into the fluid-related topology optimization which is a first step in that direction. We incorporate three different remeshing techniques (isotropic, anisotropic, or body-fitted adaptive mesh refinement) into the reaction–diffusion equation-based fluid topology optimization method. It requires a fully distributed framework (including scalable domain decomposition, matrix assembly, parallel interpolation, linear solver) that very few general purpose libraries offer. In addition, this note is the first attempt to conduct a comparative study by showcasing two different flow modeling strategies with their advantages and disadvantages. More specifically, the “separate” modeling, relying on the surface-capturing technique, i.e., body-fitted mesh, allows the disjoint reunion of a global mesh that contains several (fluid/solid) subdomains. The no-slip boundary conditions can be applied on the moving fluid–solid interface. The “hybrid” modeling, on the other hand, relying on the monolithic formulation, can be incorporated with iso-/anisotropic meshes. For comparison and for accessing the constructed framework, a lift–drag optimization problem and a classical minimal power dissipation problem are formulated. Various two- and three-dimensional numerical examples are presented to validate the computational efficiency of this framework.
      PubDate: 2022-08-05
       
  • Topology optimization of multi-story buildings under fully non-stationary
           stochastic seismic ground motion

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      Abstract: Abstract Topology optimization has been mainly addressed for structures under static loads using a deterministic setting. Nonetheless, many structural systems are subjected to uncertain dynamic loads, and thus efficient approaches are required to evaluate the optimal topology in such kind of applications. Within this framework, the present paper deals with the topology optimization of multi-story buildings subjected to seismic ground motion. Because of the inherent randomness of the earthquakes, the uncertain system response is determined through a random vibration-based approach in which the seismic ground motion is described as filtered white Gaussian noise with time-varying amplitude and frequency content (i.e., fully non-stationary seismic ground motion). The paper is especially concerned with the assessment of the dynamic response sensitivity for the gradient-based numerical solution of the optimization problem. To this end, an approximated construction of the gradient is proposed in which explicit, exact derivatives with respect to the design variables are computed analytically through direct differentiation for a sub-assembly of elements (up to a single element) resulting from the discretization of the optimizable domain. The proposed strategy is first validated for the simpler case of stationary base excitation by comparing the results with those obtained using an exact approach based on the adjoint method, and its correctness is ultimately verified for the more general case of non-stationary seismic ground motion. Overall, this validation demonstrates that the proposed approach leads to accurate results at low computational effort. Further numerical investigations are finally presented to highlight to what extent the features of the non-stationary seismic ground motion influence the optimal topology.
      PubDate: 2022-08-05
       
  • Sensitivity analysis of discrete variable topology optimization

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      Abstract: Abstract This paper studies sensitivity analysis for discrete variable topology optimization. Minimum compliance of plane stress structures is considered. The element thickness is the design variable and is named as element density, whose value is 0 or 1. According to the concerned element density and its surrounding density distribution, all the design elements are classified into three types: white interface elements, black elements, and white isolated elements. Their sensitivities are studied by shape sensitivity analysis, topological and configuration derivative, respectively. The white interface element sensitivity obtained by shape sensitivity justifies the sensitivity filter. Based on theoretical deduction and inspired by the analytical, topological derivative formula, the black element sensitivity for inserting the square hole that is consistent with the background finite element mesh is a linear combination of three quadratic forms of stress components. The combination coefficients are dependent on material constants and irrelevant to the stress and strain state, which can be determined by parameter fitting through special load conditions. The white isolated element sensitivity can also be determined by parameter fitting inspired by the configuration derivative. The obtained formula resolves the paradox of the white isolated element sensitivity. The present can further solidify the theoretical foundation for the discrete variable topology optimization methods via Sequential Approximate Integer Programming (SAIP).
      PubDate: 2022-08-05
       
  • A hybrid MCDM-based optimization method for cutting-type energy-absorbing
           structures of subway vehicles

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      Abstract: Abstract The structural optimal design, as an effective way to improve the crashing performance of energy-absorbing structures (EASs), still faces some challenges, e.g., multiple conflicting objectives and the non-uniqueness of Pareto solutions. To address these problems, this study proposes a hybrid optimization approach that combines the theories of multi-objective optimization and multiple-criteria decision-making to select the most preferred solution from the Pareto set with the consideration of engineering practitioners’ preferences. Specifically, this approach integrates the modified non-dominated sorting genetic algorithm (g-NSGA-II), fuzzy DEMATEL method, and GRA to handle the structural optimization problem in the field of passive safety protection for trains. First of all, a coupled thermal–structural finite element model of a cutting-type EAS is established and verified by experimental results. Next, a sensitivity analysis is conducted to explore the effects of knife count on the crashing performance of the structure and select the best alternative. After that, multi-objective optimization of this structure is performed using the proposed hybrid optimization method. Analysis and discussion are conducted to verify the effectiveness and practicability of this proposed approach. The optimization results show that the proposed conjoint method can be effectively employed to solve the multi-objective optimization problem of EASs.
      PubDate: 2022-08-05
       
 
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