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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Biometrics     Hybrid Journal   (Followers: 51)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 17)
Building Simulation     Hybrid Journal   (Followers: 2)
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: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
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: 2)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 38, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 82)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Journal of Forecasting     Hybrid Journal   (Followers: 19)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
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: 34)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Journal of Statistical Software     Open Access   (Followers: 16, 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  
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
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: 23)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
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)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 6)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 16)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Review of Economics and Statistics     Hybrid Journal   (Followers: 138)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 17)
Sankhya A     Hybrid Journal   (Followers: 3)
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: 40)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 8)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 122)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Stochastic Models     Hybrid Journal   (Followers: 2)
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: 10)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Journal Cover
Structural and Multidisciplinary Optimization
Journal Prestige (SJR): 1.458
Citation Impact (citeScore): 3
Number of Followers: 10  
 
  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]
  • Reliability-based bottom-up manufacturing cost optimisation for composite
           aircraft structures

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      Abstract: A novel methodology is presented for the reliability-based manufacturing cost optimisation of composite aircraft structures. A comprehensive bottom-up costing approach is employed, enabling precise manufacturing cost estimation in terms of material, machine, labour, tooling, and indirect costs. This approach splits the manufacturing process into many individual activities, which can be combined in many different ways, allowing the proposed optimisation methodology to be applied to a wide range of composite aircraft structures. A genetic algorithm (GA) is coupled with a deep neural network (DNN) to efficiently determine the optimal composite ply stacking sequence for every part of an assembled structure. A numerical example featuring a composite-stiffened aircraft fuselage panel is investigated. The reliability of the panel is measured in terms of its buckling resistance, and its manufacturing cost is estimated based on the individual costs of over 20 activities. The labour times for each activity were estimated based on data collected from an aerospace company specialising in the manufacture of advanced composite aircraft structures. Results indicate that material, machine, labour, and tool costs can vary significantly depending on the level of structural reliability required, demonstrating the importance of accounting for non-material costs when designing composite aircraft structures.
      PubDate: 2022-05-12
       
  • An efficient and easy-to-extend Matlab code of the Moving Morphable
           Component (MMC) method for three-dimensional topology optimization

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      Abstract: Explicit topology optimization methods have received ever-increasing interest in recent years. In particular, a 188-line Matlab code of the two-dimensional (2D) Moving Morphable Component (MMC)-based topology optimization method was released by Zhang et al. (Struct Multidiscip Optim 53(6):1243-1260, 2016). The present work aims to propose an efficient and easy-to-extend 256-line Matlab code of the MMC method for three-dimensional (3D) topology optimization implementing some new numerical techniques. To be specific, by virtue of the function aggregation technique, accurate sensitivity analysis, which is also easy-to-extend to other problems, is achieved. Besides, based on an efficient identification algorithm for load transmission path, the degrees of freedoms (DOFs) not belonging to the load transmission path are removed in finite element analysis (FEA), which significantly accelerates the optimization process. As a result, compared to the corresponding 188-line 2D code, the performance of the optimization results, the computational efficiency of FEA, and the convergence rate and the robustness of optimization process are greatly improved. For the sake of completeness, a refined 218-line Matlab code implementing the 2D-MMC method is also provided.
      PubDate: 2022-05-09
       
  • A two-stage point selection strategy for probability density evolution
           method-based reliability analysis

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      Abstract: To alleviate the computational cost of probability density evolution method (PDEM) for structural reliability analysis, the relative contributions of different representative points to the failure probability are probed, thereby a two-stage point selection (TPS) strategy is proposed for the PDEM. In the first stage, a candidate pool of representative points is generated in the whole probability space. In the second stage, an adaptive point-selection function called the PDEM-oriented Information Entropy (PIE) is proposed based on the Kriging predictor to sequentially select those influential representative points from the first-stage-generated candidate pool. In this regard, the failure probability can be assessed by solving the generalized probability density evolution equations (GDEEs) associated with the second-stage-selected representative points. This method is called the TPS-based PDEM. Three examples are studied to validate the efficacy of the TPS-based PDEM. Results demonstrate that the proposed approach enables to fully identify all the important representative points under the desirable settings of two key PIE-related parameters. As a result, it gains a consistent accuracy to the standard PDEM, while achieving a substantial savings in terms of both the number of runs of structural response analyses and the computational time, especially for the complex practical engineering problems.
      PubDate: 2022-05-07
       
  • Discontinuity layout optimization using unstructured meshes and material
           layering in 2D

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      Abstract: The discontinuity layout optimization (DLO) is a method that numerically approximates the critical failure surface in soils, concrete, and materials alike. The most common form of the method approximates the critical failure surface using straight discontinuity segments in a piecewise manner. These segments are selected through an optimization problem from a highly redundant network of pre-generated discontinuities. The method’s lack of popularity (compared to traditional methods; e.g., methods of slices and FEM-based property reduction) is partly due to the difficulty in generating a sufficiently rich solution space, i.e., a redundant discontinuity network. This problem is augmented when various material layers compose the analysis domain; a typical setting in geotechnical engineering. This work proposes a novel discontinuity generation method that allows for unstructured and irregular domains, including different material layers in the said domain. While a regular grid of points can be tweaked to match the domain and material boundaries with good results, the proposed generation methodology readily adapts the needed domain discretization to these boundaries. In addition, this work proposes extensions to the standard DLO formulation allowing to: (1) consider the groundwater effect (seepage analysis), and (2) calculate the critical safety factor of a problem.
      PubDate: 2022-05-07
       
  • Vector-angle geometric mapping-based directional importance sampling
           method for reliability analysis

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      Abstract: In reliability analysis, the probability density function (PDF) of the directional importance sampling method is based on a multi-dimensional vector (i.e., multivariate), thus it is inefficient to obtain the important directional vectors (IDVs) by sampling each dimensional component randomly. In this paper, an efficient solution approach of vector-angle geometric mapping is proposed. Firstly, the angles between IDVs and the design point position vector are set as the important direction angles (IDAs) in the standard Gaussian space. By exploring the geometric relationship between IDVs and IDAs, the PDF of multi-dimensional IDV can be converted into the PDF of one-dimensional IDA, following which, the cumulative distribution function of IDA is derived by integration. Further, the cumulative distribution is sampled uniformly using the Latin hypercube technique, and then the uniform IDAs are generated by inversion. Finally, the IDVs are shown by geometric mapping of the IDAs. The research results show that the PDF of IDA is jointly determined by the two parameters, dimensionality and reliability index. Therefore, the distribution characteristics of IDA can be explored and diagrammatically represented, and the obtained IDVs can be used repeatedly for other reliability analysis with the same mentioned parameters to improve the computational efficiency. The applicability, accuracy, and robustness of the proposed approach are proved on illustrative examples, battery pack and truss structure engineering applications.
      PubDate: 2022-05-06
       
  • A passive load alleviation aircraft wing: topology optimization for
           maximizing nonlinear bending–torsion coupling

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      Abstract: Aircraft wings with passive load alleviation morph their shape to a configuration where the aerodynamic forces are reduced without the use of an actuator. In our research, we exploit geometric nonlinearities of the inner wing structure to maximize load alleviation. In order to find designs with the desired properties, we propose a topology optimization approach. Passive load alleviation is achieved through bending–torsion coupling. The wing twist will reduce the angle of attack, thus lowering the aerodynamic forces. Consequently, the objective function is to maximize the torsion angle. Since shape morphing should only affect loads that exceed normal maneuvering loads, a displacement constraint is enforced, preventing torsion at lower force levels. Maximizing the displacement will lead to topologies for which the finite element solver cannot find a solution. To circumvent this, we propose adding a compliance value to the objective function. This term has a weighting function, which controls how much influence the compliance value has: after a set number of iterations, the initially high level of influence will drop. We used a geometric nonlinear finite element formulation with a linear elastic material model. The addition of an energy interpolation scheme reduces mesh distortion. We successfully applied the proposed methodology to two different test cases resembling an aircraft wing box section. These test cases illustrate the methodology’s potential for designing new geometries with the desired nonlinear behavior. We discuss what design features can be deduced and how they achieve the nonlinear structural response.
      PubDate: 2022-05-06
       
  • A sequential multi-fidelity surrogate-based optimization methodology based
           on expected improvement reduction

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      Abstract: This paper presents a novel computation-aware multi-fidelity surrogate-based optimization (MFSBO) methodology and a new sequential and adaptive sampling strategy based on expected improvement reduction (EIR). Given a fixed computational budget in each iteration, the EIR-based infill determines the data source and samples of infill by hypothetically interrogating the effect of samples and simulation fidelity on reducing the expected improvement, and enables low-fidelity batch infills within a dynamically varying trust-region to improve exploration as needed to accelerate the MFSBO process. The co-Kriging method is utilized to combine the data from different data sources with varying fidelities and computational costs. The EIR-based infill is then compared with other infill strategies in terms of convergence rate and design accuracy. Results indicate that the proposed method achieves a faster convergence rate and more accurate optimal design during MFSBO for all case studies.
      PubDate: 2022-05-05
       
  • Topography optimisation of fluid flow between parallel plates of
           spatially-varying spacing: revisiting the origin of fluid flow topology
           optimisation

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      Abstract: Abstract This paper presents a topography optimisation model for fluid flow with varying channel height. First, the origin of topology optimisation for fluid flow problems is revisited. Here, a Poiseuille-based frictional resistance term was first introduced by Borrvall and Petersson (Int J Numer Methods Fluids 41(1):77–107, 2003) to parametrise regions of solid and fluid. However, the traditional model only works for true topology optimisation, where it is used to approximate solid regions as areas with very small channel height and, thus, very high frictional resistance. Herein, it is shown that if the channel height is allowed to vary continuously, the minimum channel height is relatively large and/or meaning is attributed to intermediate design field values, then the predictions of the traditional model are wrong. To remedy this problem, this work introduces an augmentation of the mass conservation equation to allow for continuously varying channel heights. The proposed planar model describes fully developed flow between two plates of varying channel height. It allows for a significant reduction in the number of degrees of freedom, while generally ensuring a high accuracy for low-to-moderate Reynolds numbers in the laminar regime. The accuracy and limitations of both the traditional and proposed models are explored using in-depth parametric studies. The proposed model is used to optimise the height of the fluid channel between two parallel plates and, thus, the topography of the plate surfaces for a flow distribution problem. Lastly, it is shown that when introducing penalisation into the proposed height-based design parametrisation, the proposed model can produce designs of similar performance as the traditional resistance term interpolation. Thus, the proposed model bridges a gap between topography and topology optimisation of fluid flow, since it is able to perform both seamlessly with good accuracy.
      PubDate: 2022-05-03
       
  • Topology optimization with automated derivative computation for
           multidisciplinary design problems

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      Abstract: Abstract Topology optimization has drawn increasing attention as a method to aid the design of engineering systems. Gradient-based optimization is typically used to solve these problems because of its efficiency in dealing with a large number of design variables. However, there is a large amount of implementation effort required for both the forward model formulation and the derivative computation of the model, especially for multiphysics problems. This paper addresses these challenges by presenting a general-purpose topology optimization platform called ATOmiCS, built in a modular framework to facilitate the formulation of complex, multiphysics problems with fully automated derivative computation. ATOmiCS automates the derivative computation by coupling FEniCS—a multiphysics partial differential equation solver with accessible symbolic partial derivatives—with OpenMDAO, a modular framework for multidisciplinary design optimization that can automatically solve the adjoint equation for the total derivatives. ATOmiCS is implemented as an open-source toolbox with online documentation and has been used in a graduate-level class. The features of ATOmiCS are demonstrated using three case studies: compliance minimization of cantilever beams with linear and nonlinear elasticity models, compliance minimization of a battery pack with the thermoelastic equation and an unstructured mesh, and optimization of liquid crystal elastomer using a modified elastic equation for shape matching. The results demonstrate the characteristics that, as a whole, make ATOmiCS a unique topology optimization toolbox: modularity and flexibility with respect to operations such as filtering and penalization; ease of implementation of governing equations, type of elements, and solvers for systems of equations; and fully automated derivative computation for gradient-based optimization.
      PubDate: 2022-04-28
       
  • An interval-oriented dynamic robust topology optimization (DRTO) approach
           for continuum structures based on the parametric Level-Set method (PLSM)
           and the equivalent static loads method (ESLM)

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      Abstract: Abstract In this paper, an efficient dynamic robust topology optimization method is proposed which aims to optimize overall dynamic response of the structure at full time. To solve the problem that the process of obtaining the dynamic response is too complex through multiple iterations and sensitivity analysis of nonlinear equations is difficult, the equivalent static load method is adopted. The dynamic equilibrium equation is transformed to multiple equivalent static equilibrium equations. The parametric Level-Set method based on the multi-quadric spline is used to balance the stability of topology optimization and efficiency. Uncertainty characterization methods based on interval model are used to account for the impact of uncertainties. The uncertainties are effectively quantified in topology optimization. On the basis of deducing the sensitivity information of the optimization model, the proposed method is applied to three examples, in which an important structure in aeronautical engineering, high accuracy rocket sled, is included. Three examples illustrate the effectiveness, necessity and influence of important parameters of the dynamic robust topology optimization method from different aspects.
      PubDate: 2022-04-25
       
  • A deep reinforcement learning framework for life-cycle maintenance
           planning of regional deteriorating bridges using inspection data

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      Abstract: Abstract Determination of regional deteriorating bridges’ maintenance strategies for minimizing life-cycle risks and costs constructs a complex optimization problem. Improper maintenance strategies lead to budget waste and ineffective maintenances. To optimize the life-cycle maintenance strategies of regional bridges, this study develops a deep reinforcement learning (DRL)-based framework to enable the agent to learn better maintenance actions in an interactive environment by trial and error. It is able to generate several optimal maintenance strategies to match with different budget constraints under the premise of increasing the maintenance cost-effectiveness to the greatest extent possible. The framework contains the whole optimization process from data collection to regional functions establishment to reinforcement learning training. The regional structural deterioration features and the effect of maintenance actions are determined by years of regional inspection reports. The regional probabilistic models are incorporated to simulate the stochastic process. The regional life-cycle maintenance strategies are optimized with the deep Q-networks model. The proposed framework is substantiated by developing 100-year maintenance strategies for part of highway bridges. The results show that the trained optimal maintenance strategies match well with the given budget constraints and maximize the life-cycle cost-effectiveness of maintenance actions. By employing the trained maintenance strategies, the conditions of regional bridges are controlled at a good level. Besides, the trained optimal DRL-based strategies have better performance than traditional condition-fixed strategies.
      PubDate: 2022-04-24
       
  • Reliability-based multi-objective optimization incorporating
           process–property–performance relationship of double-pulse MIG welding
           using hybrid optimization strategy

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      Abstract: Abstract As an effective lightweight technique, reliability-based multi-objective optimization (RBMO) for welding process parameters of aluminum alloy sheets demonstrates the unprecedented potential and stability in the automobile manufacturing. In order to ensure load-bearing capacity and assembly feasibility of welded joints in the body structure, the process–property–performance (3P) relationship should be fully considered in optimizing double-pulse MIG (DP-MIG) welding process parameters. This study proposes an RBMO design of welding process parameters that employed a hybrid optimization strategy (HOS), which includes screening significant parameters, building process–property meta-models, and searching optimal solution under performance requirements. Combining entropy weight and technique for order preference by similarity to an ideal solution for Plackett–Burman design is used to screen significant parameters. Then, the response surface methodology based on central composite design is used to construct the regression models between significant factors and responses. Also, the reliability of each response is analysed through the Monte Carlo simulation and Design for Six Sigma design. The non-dominated sorting genetic algorithm and multi-objective decision criteria based on performance requirements are employed to find the optimal solution of RBMO. The effectiveness and applicability of the proposed HOS method are demonstrated by optimization of DP-MIG welding process parameters, which could yield improvements of load-bearing, geometry performance of welded joints and their robustness. The combination of 3P relationships and optimization design reveals the internal connection between design and manufacturing, and provides a guideline for DP-MIG welding parameters design. RBMO can be generally applied to types of jointing technology in automotive manufacturing and the proposed HOS method can be used in the optimization design of multiple influencing factors and multiple responses.
      PubDate: 2022-04-21
       
  • Shape preserving design with topology optimization for structures under
           harmonic resonance responses

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      Abstract: Abstract For a structural system composed of functional components in a vibration environment, it is of great importance to suppress the dynamic warping deformation of these local regions to ensure the performance and functionality of the system. Especially for a vibrating structure under a resonance response with critical deformations, such dynamic shape preserving design (SPD) problem is addressed to maintain local performances using topology optimization in this paper. The structure is assumed to be linear and elastic, with Rayleigh damping, and subjected to a time-harmonic external excitation with a resonant frequency. The elastic work describing the maximum strain energy in a vibration period is defined to quantitatively measure the extreme warping deformation of local functional components. A normalized constraint on local elastic work is further introduced into a dynamical topology optimization model while maximizing the first-order eigenfrequency. Moreover, to preserve the outlines of void regions (e.g., openings), a dynamic artificial weak element (AWEdyn) technique is proposed to help measure and suppress the local deformation of voids. Numerical tests show that the dynamic elastic work could accurately describe the deformations of resonance structures. The effects of shape preservation are successfully achieved through topology optimization by suppressing warping deformations in subdomains.
      PubDate: 2022-04-20
       
  • Reduced-order modeling of conductive polymer pressure sensors using finite
           element simulations and deep neural networks

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      Abstract: Abstract Polymer sensors with in-plane electrodes have promising applications in electronic skin development. In-plane electrodes are commonly configured as interdigitated to have low resistance. However, it is desirable to optimize the electrode design to maximize sensitivity while minimizing power consumption. In this paper, a method for the efficient analysis of pressure sensors made of conductive polymer composites is presented. This finite element method is used to simulate a sensor, and a data-driven model for predicting the resistance response for different electrode shapes is constructed. Multi-fidelity models are used to reduce the simulation time for generating the training data. Convolutional neural networks trained for different 10 × 10 input electrode shapes predict the initial and change in resistance with mean errors of 0.80% and 0.26%, respectively. As an outcome of the model, we optimized electrode shape for highest sensitivity and lowest power consumption and achieved 36% higher sensitivity than common interdigitated electrodes. The constructed reduced-order model represents the main features of conductive polymer sensors with different electrodes. Therefore, the model can be applied to any polymer sensor with limited numerical or experimental data through transfer learning.
      PubDate: 2022-04-20
       
  • Improving mechanical ice protection systems with topology optimization

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      Abstract: Abstract In the context of more electrical aircraft, electromechanical de-icing systems provide a low-energy solution to protect aircraft’s surfaces from ice buildup. Such systems produce deformation of the protected surface leading to a stress production within the ice and, ultimately, to ice shedding thanks to fracture. However, these systems may show limitations when it comes to completely protect a given surface. Ice delamination is often restricted to a part of the surface and the remaining ice either requires more energy to be removed or is just impossible to remove. In this paper, topology optimization of the substrate covered by ice is thus investigated to increase fracture propagation and ice shedding. For that purpose, an optimization problem, involving the energy release rate but also the mass and the substrate stress, is formulated. The numerical results show how the delamination efficiency of mechanical based ice protection systems can be improved through the topology modification of the substrate.
      PubDate: 2022-04-20
       
  • Three-scale concurrent topology optimization for the design of the
           hierarchical cellular structure

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      Abstract: Abstract Natural structures and some researches about artificial three-level structures have demonstrated that these structures have good performance in many aspects. For obtaining such structures, the traditional two-scale design method needs very finer meshes, which lead to expensive computational costs. Therefore, our purpose is to propose an efficient topology optimization method for designing the three-level structure with excellent performance. The proposed method that is also called the three-scale design method in this paper divides the design domains into three scales, which are connected by the homogenization method. At each scale, the optimal material layout can be found by using the SIMP (Solid Isotropic Material with Penalization) method. Then, the proposed three-scale method is integrated into the topology optimization, two optimization strategies are provided to design the three-level structure. The first design strategy considers structural compliance as an optimization objective, which is usually common in multi-scale design. The decoupled sensitivity analysis method is used to improve the computational efficiency of this algorithm. Another effective strategy is to take buckling performance as the optimization objective, it can build a direct link between the good structural performance of the multi-level structure and optimization formula. Several numerical examples are provided to verify the effectiveness of the two design strategies. Meanwhile, the results of performance analysis show that adding a third scale does improve the performance of the structure in some aspects, such as buckling performance, robustness and ultra-light.
      PubDate: 2022-04-18
       
  • Gradient-based size, shape, and topology optimization of single-layer
           reticulated shells subject to distributed loads

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      Abstract: Abstract Structural optimization is an increasingly popular tool for identifying efficient designs for unstrained grid shells. In many recent studies on this topic, distributed loads are approximated or omitted, despite being the predominant load for shells. Moreover, optimization problems in which the coordinates of loadbearing nodes serve as design variables have barely been considered in literature. Consequently, some associated problems are left unresolved, e.g., loadbearing nodes being relocated toward the supports. This paper aims to address both issues by implementing a gradient-based algorithm to optimize the size, shape, and topology of a single-layer reticulated shell structure subjected to a distributed load. Scaling factors for the cross-sectional dimensions of each bar serve as size variables, and the nodal coordinates serve as shape design variables, while topology changes are achieved by filtering out redundant members. Limiting the deflection of the cladding material is proposed as a physically motivated technique to prevent loadbearing nodes from moving toward the supports and yielding unusable results. The proposed method is demonstrated for six test case structures in six different problem scenarios. For each case, the mass of a glass covered steel roof structure is minimized under displacement, stability, strength, deflection, shape, and compliance constraints. The results were on average 30 \(\%\) lighter compared to a conventional size optimization, and the gains were less significant if a higher second load case was imposed, and more significant if a larger deviation from the target surface was allowed.
      PubDate: 2022-04-18
       
  • Topology optimisation for rotor-stator fluid flow devices

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      Abstract: Abstract Multi-component devices such as flow machines, heat exchangers, and electric motors present parts with different physical properties and operating in different states. Optimisation algorithms may improve the performance of these devices, and the simultaneous optimisation of a set of parts may harness the interaction of these parts to generate improved designs. Particularly, rotating flow devices such as pumps and turbines present rotating and stationary components. If a description of the fluid flow between the rotating and stationary parts is desired, it is necessary to model solid at different velocities. However, the standard topology optimisation formulation for fluid flow problems considers only a single stationary solid or a single rotating solid in a rotating reference frame. Thus, this work proposes a topology optimisation formulation capable of solving fluid flow problems with different solid velocities. The idea is to add mutually exclusive Darcy terms to the linear momentum equation. Each Darcy term models a different rotation and only one term may be active at each element. The method uses two discrete design variable fields. The moving limits of the optimisation algorithm are adjusted to handle the two discrete design variable fields, and extra constraints are added to ensure proper phase transitions. The algorithm is applied to two design problems: a Tesla pump and a labyrinth seal. The governing equations are solved by the Finite Element Method, and the optimisation is solved by an approach based on the Topology Optimisation of Binary Structures (TOBS) algorithm, with each linearized subproblem being solved through integer linear programming with a branch-and-bound algorithm.
      PubDate: 2022-04-15
       
  • Integrated topology and packaging optimization for multi-phase
           multi-component problems

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      Abstract: Abstract Methods for integrated topology and packaging optimization are used for addressing coupled material and component distribution problems in lightweight systems design. Traditionally, these numerical tools incorporate component position design variables within standard topology optimization to efficiently embed objects within structural loadpaths. More recently, position-based variables have been replaced by component pseudo-densities, offering a new class of solution process referred to as component-existence models. This work presents an extended component-existence model with developments to the core theory, numerical methods, and best practices from initial implementations. Notably, the extended method developed here introduces multi-phase component modeling (solid-only and solid-void components) and generalizes the associated problem statement and numerical framework to support multiple geometries and packaging constraints. This includes aspects such as packaging symmetry and packaging sub-domains in general multi-component problems. Further enabling these capabilities are enhancements to the overall implementation, including incorporation of a commercial finite element analysis engine, new routines to smooth component pseudo-density discretization, and updates to manage component-to-component overlap avoidance. The resulting component-existence model demonstrates capabilities beyond previous implementations in various 3D single-phase, multi-phase, and multi-component problems and is discussed with respect to the key practical and numerical challenges within the field.
      PubDate: 2022-04-15
       
  • Lightweight design of variable-angle filament-wound cylinders combining
           Kriging-based metamodels with particle swarm optimization

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      Abstract: Abstract Variable-angle filament-wound (VAFW) cylinders are herein optimized for minimum mass under manufacturing constraints, and for various design loads. A design parameterization based on a second-order polynomial variation of the tow winding angle along the axial direction of the cylinders is utilized to explore the nonlinear steering-thickness dependency in VAFW structures, whereby the thickness becomes a function of the filament steering angle. Particle swarm optimization coupled with three Kriging-based metamodels is used to find the optimum designs. A single-curvature Bogner–Fox–Schmit–Castro finite element is formulated to accurately and efficiently represent the variable stiffness properties of the shells, and verifications are performed using a general purpose plate element. Alongside the main optimization studies, a vast analysis of the design space is performed using the metamodels, showing a gap in the design space for the buckling strength that is confirmed by genetic algorithm optimizations. Extreme lightweight while buckling-resistant designs are reached, along with non-conventional optimum layouts thanks to the high degree of thickness build-up tailoring.
      PubDate: 2022-04-14
       
 
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