scholarly journals Inverse homogenization using the topological derivative

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Àlex Ferrer ◽  
Sebastián Miguel Giusti
Keyword(s):  
Optimization Algorithms ◽  
Material Design ◽  
Topological Derivative ◽  
Gradient Algorithm ◽  
Optimal Topology ◽  
Inverse Homogenization ◽  
Homogenization Problem ◽  
Content Type ◽  
Classical Optimization ◽  
Derivative Concept

PurposeThe purpose of this study is to solve the inverse homogenization problem, or so-called material design problem, using the topological derivative concept.Design/methodology/approachThe optimal topology is obtained through a relaxed formulation of the problem by replacing the characteristic function with a continuous design variable, so-called density variable. The constitutive tensor is then parametrized with the density variable through an analytical interpolation scheme that is based on the topological derivative concept. The intermediate values that may appear in the optimal topologies are removed by penalizing the perimeter functional.FindingsThe optimization process benefits from the intermediate values that provide the proposed method reaching to solutions that the topological derivative had not been able to find before. In addition, the presented theory opens the path to propose a new framework of research where the topological derivative uses classical optimization algorithms.Originality/valueThe proposed methodology allows us to use the topological derivative concept for solving the inverse homogenization problem and to fulfil the optimality conditions of the problem with the use of classical optimization algorithms. The authors solved several material design examples through a projected gradient algorithm to show the advantages of the proposed method.

Improvement of a topological level-set approach to find optimal topology by considering body forces

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meisam Takalloozadeh ◽  
Gil Ho Yoon
Keyword(s):  
Topology Optimization ◽  
Level Set ◽  
Thermal Loading ◽  
Topological Derivative ◽  
Optimization Process ◽  
Optimal Topology ◽  
Content Type ◽  
Body Forces ◽  
Applied Forces ◽  
Level Set Approach

Purpose Body forces are always applied to structures in the form of the weight of materials. In some cases, they can be neglected in comparison with other applied forces. Nevertheless, there is a wide range of structures in civil and mechanical engineering in which weight or other types of body forces are the main portions of the applied loads. The optimal topology of these structures is investigated in this study. Design/methodology/approach Topology optimization plays an increasingly important role in structural design. In this study, the topological derivative under body forces is used in a level-set-based topology optimization method. Instability during the optimization process is addressed, and a heuristic solution is proposed to overcome the challenge. Moreover, body forces in combination with thermal loading are investigated in this study. Findings Body forces are design-dependent loads that usually add complexity to the optimization process. Some problems have already been addressed in density-based topology optimization methods. In the present study, the body forces in a topological level-set approach are investigated. This paper finds that the used topological derivative is a flat field that causes some instabilities in the optimization process. The main novelty of this study is a technique used to overcome this challenge by using a weighted combination. Originality/value There is a lack of studies on level-set approaches that account for design-dependent body forces and the proposed method helps to understand the challenges posed in such methods. A powerful level-set-based approach is used for this purpose. Several examples are provided to illustrate the efficiency of this method. Moreover, the results show the effect of body forces and thermal loading on the optimal layout of the structures.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

scholarly journals Evaluation of vehicle lightweighting to reduce greenhouse gas emissions with focus on magnesium substitution

2018 ◽  
Vol 16 (6) ◽  
pp. 869-888 ◽  
Author(s):  
Siddharth Kulkarni ◽  
David John Edwards ◽  
Erika Anneli Parn ◽  
Craig Chapman ◽  
Clinton Ohis Aigbavboa ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Fuel Economy ◽  
Internal Combustion Engines ◽  
Mechanical Performance ◽  
Material Design ◽  
Torsional Stiffness ◽  
Gas Emissions ◽  
Content Type

Purpose Vehicle weight reduction represents a viable means of meeting tougher regulatory requirements designed to reduce fuel consumption and control greenhouse gas emissions. This paper aims to present an empirical and comparative analysis of lightweight magnesium materials used to replace conventional steel in passenger vehicles with internal combustion engines. The very low density of magnesium makes it a viable material for lightweighting given that it is lighter than aluminium by one-third and steel by three-fourth. Design/methodology/approach A structural evaluation case study of the “open access” Wikispeed car was undertaken. This included an assessment of material design characteristics such as bending stiffness, torsional stiffness and crashworthiness to evaluate whether magnesium provides a better alternative to the current usage of aluminium in the automotive industry. Findings The Wikispeed car had an issue with the rocker beam width/thickness (b/t) ratio, indicating failure in yield instead of buckling. By changing the specified material, Aluminium Alloy 6061-T651 to Magnesium EN-MB10020, it was revealed that vehicle mass could be reduced by an estimated 110 kg, in turn improving the fuel economy by 10 per cent. This, however, would require mechanical performance compromise unless the current design is modified. Originality/value This is the first time that a comparative analysis of material substitution has been made on the Wikispeed car. The results of such work will assist in the lowering of harmful greenhouse gas emissions and simultaneously augment fuel economy.

Impact of material concentration and distribution on composite parts manufactured via multi-material jetting

2018 ◽  
Vol 24 (5) ◽  
pp. 872-879 ◽  
Author(s):  
Nicholas Alexander Meisel ◽  
David A. Dillard ◽  
Christopher B. Williams
Keyword(s):  
Material Properties ◽  
Viscoelastic Properties ◽  
Design Space ◽  
Base Material ◽  
Material Design ◽  
Mechanical Analysis ◽  
Material Composition ◽  
Significant Shift ◽  
Content Type ◽  
Property Changes

Purpose Material jetting approximates composite material properties through deposition of base materials in a dithered pattern. This microscale, voxel-based patterning leads to macroscale property changes, which must be understood to appropriately design for this additive manufacturing (AM) process. This paper aims to identify impacts on these composites’ viscoelastic properties due to changes in base material composition and distribution caused by incomplete dithering in small features. Design/methodology/approach Dynamic mechanical analysis (DMA) is used to measure viscoelastic properties of two base PolyJet materials and seven “digital materials”. This establishes the material design space enabled by voxel-by-voxel control. Specimens of decreasing width are tested to explore effects of feature width on dithering’s ability to approximate macroscale material properties; observed changes are correlated to multi-material distribution via an analysis of ingoing layers. Findings DMA shows storage and loss moduli of preset composites trending toward the iso-strain boundary as composition changes. An added iso-stress boundary defines the property space achievable with voxel-by-voxel control. Digital materials exhibit statistically significant changes in material properties when specimen width is under 2 mm. A quantified change in same-material droplet groupings in each composite’s voxel pattern shows that dithering requires a certain geometric size to accurately approximate macroscale properties. Originality/value This paper offers the first quantification of viscoelastic properties for digital materials with respect to material composition and identification of the composite design space enabled through voxel-by-voxel control. Additionally, it identifies a significant shift in material properties with respect to feature width due to dithering pattern changes. This establishes critical design for AM guidelines for engineers designing with digital materials.

A topology optimization algorithm based on topological derivative and level-set function for designing phononic crystals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rolando Yera ◽  
Luisina Forzani ◽  
Carlos Gustavo Méndez ◽  
Alfredo E. Huespe
Keyword(s):  
Topology Optimization ◽  
Level Set ◽  
Optimization Algorithm ◽  
Phononic Crystal ◽  
Phononic Crystals ◽  
Lagrangian Function ◽  
Topological Derivative ◽  
Content Type ◽  
Level Set Function ◽  
Set Function

PurposeThis work presents a topology optimization methodology for designing microarchitectures of phononic crystals. The objective is to get microstructures having, as a consequence of wave propagation phenomena in these media, bandgaps between two specified bands. An additional target is to enlarge the range of frequencies of these bandgaps.Design/methodology/approachThe resulting optimization problem is solved employing an augmented Lagrangian technique based on the proximal point methods. The main primal variable of the Lagrangian function is the characteristic function determining the spatial geometrical arrangement of different phases within the unit cell of the phononic crystal. This characteristic function is defined in terms of a level-set function. Descent directions of the Lagrangian function are evaluated by using the topological derivatives of the eigenvalues obtained through the dispersion relation of the phononic crystal.FindingsThe description of the optimization algorithm is emphasized, and its intrinsic properties to attain adequate phononic crystal topologies are discussed. Particular attention is addressed to validate the analytical expressions of the topological derivative. Application examples for several cases are presented, and the numerical performance of the optimization algorithm for attaining the corresponding solutions is discussed.Originality/valueThe original contribution results in the description and numerical assessment of a topology optimization algorithm using the joint concepts of the level-set function and topological derivative to design phononic crystals.

Electromagnetic and thermal parameter identification method for best prediction of temperature distribution on transformer tank covers

2015 ◽  
Vol 34 (2) ◽  
pp. 485-495 ◽  
Author(s):  
Patricia Penabad Durán ◽  
Paolo Di Barba ◽  
Xose Lopez-Fernandez ◽  
Janusz Turowski
Keyword(s):  
Temperature Distribution ◽  
Measurement Error ◽  
Parameter Identification ◽  
Objective Function ◽  
Measurement Errors ◽  
Optimization Algorithms ◽  
Identification Problem ◽  
Content Type ◽  
Identification Method ◽  
Wide Range

Purpose – The purpose of this paper is to describe a parameter identification method based on multiobjective (MO) deterministic and non-deterministic optimization algorithms to compute the temperature distribution on transformer tank covers. Design/methodology/approach – The strategy for implementing the parameter identification process consists of three main steps. The first step is to define the most appropriate objective function and the identification problem is solved for the chosen parameters using single-objective (SO) optimization algorithms. Then sensitivity to measurement error of the computational model is assessed and finally it is included as an additional objective function, making the identification problem a MO one. Findings – Computations with identified/optimal parameters yield accurate results for a wide range of current values and different conductor arrangements. From the numerical solution of the temperature field, decisions on dimensions and materials can be taken to avoid overheating on transformer covers. Research limitations/implications – The accuracy of the model depends on its parameters, such as heat exchange coefficients and material properties, which are difficult to determine from formulae or from the literature. Thus the goal of the presented technique is to achieve the best possible agreement between measured and numerically calculated temperature values. Originality/value – Differing from previous works found in the literature, sensitivity to measurement error is considered in the parameter identification technique as an additional objective function. Thus, solutions less sensitive to measurement errors at the expenses of a degradation in accuracy are identified by means of MO optimization algorithms.

Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazin A. M. Al Janabi
Keyword(s):  
Risk Management ◽  
Portfolio Optimization ◽  
Real World ◽  
Optimization Algorithms ◽  
Liquidity Risk ◽  
Portfolio Risk ◽  
Market Conditions ◽  
Content Type ◽  
Illiquid Market ◽  
Investment Portfolios

Purpose This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios. Design/methodology/approach This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart. Findings To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach. Practical implications The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk. Originality/value The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

Linear and nonlinear buckling analysis for the material design optimization of wind turbine blades

2019 ◽  
Vol 10 (6) ◽  
pp. 749-765
Author(s):  
Efstathios E. Theotokoglou ◽  
Georgios Balokas ◽  
Evgenia K. Savvaki
Keyword(s):  
Wind Turbine ◽  
Design Optimization ◽  
Turbine Blades ◽  
Material Design ◽  
Fiber Reinforced Polymers ◽  
Fiber Reinforced ◽  
Reinforced Polymers ◽  
Content Type ◽  
Buckling Behavior ◽  
Load Carrying

Purpose The purpose of this paper is to investigate the buckling behavior of the load-carrying support structure of a wind turbine blade. Design/methodology/approach Experimental experience has shown that local buckling is a major failure mode that dominantly influences the total collapse of the blade. Findings The results from parametric analyses offer a clear perspective about the buckling capacity but also about the post-buckling behavior and strength of the models. Research limitations/implications This makes possible to compare the response of the different fiber-reinforced polymers used in the computational model. Originality/value Furthermore, this investigation leads to useful conclusions for the material design optimization of the load-carrying box girder, as significant advantages derive not only from the combination of different fiber-reinforced polymers in hybrid material structures, but also from Kevlar-fiber blades.

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