Method Of Moving Asymptotes Research Articles

Enhancing the cooling performance of thermocouples: a power-constrained topology optimization procedure

Heat pumping through thermoelectric devices has many advantages over traditional cooling. However, their current efficiency is a limiting factor in their implementation. In this paper, we approach the non-convex topology optimization of thermoelectrical elements for cooling applications through the method of moving asymptotes (MMA) to improve their cooling capabilities per watt usage. The optimization problem is defined for a given power budget, aiming for the minimum temperature with a known heat pumping need. The introduction of power as a constraint justifies the introduction of the voltage gradient across the thermocouple as a design variable to maintain the thermoelectrical device in its optimum power-to-heat extraction ratio. To better understand the convergence of this non-convex problem, we present a two-variable analytical thermoelectric optimization model. This example provides information on how to select the penalty parameters used to scale the three material coefficients involved in the problem to obtain lower objective values and better convergence using MMA. The analytical model shows the non-convexity of the problem and provides the recommendation to use penalization coefficients of the form pk=pσ>pα=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_k=p_{\\sigma }>p_{\\alpha }=1$$\\end{document} for the thermal conductivity, electrical conductivity, and Seebeck coefficients. We tested these penalization coefficients through optimizations of a model based on the 1MC10-031 commercial thermoelectric-cooler (TEC) using the finite element method (FEM). These penalization coefficients provided local minima without the need for volume constraints. With this procedure, we found designs that provided temperatures close to 10 degrees lower using 60% less semiconductor material volume compared to the initial design.

Optimization of acoustic porous material absorbers modeled as rigid multiple microducts networks: Metamaterial design using additive manufacturing

This research presents a strategy to enhancing sound absorption in porous acoustic metamaterials through the optimization of micro-duct networks. The study employs a combination of analytical and numerical methods, systematically adjusting micro-duct diameters within a 2D grid to optimize absorption coefficients at specific frequency bands. The Finite Element Transfer Method (FETM) is utilized for modeling, supported by a multi-objective function influenced by the surface impedance of the network. This approach ensures practical applicability and manufacturability of the designed metamaterial. A significant aspect of the study is the development of an analytical mobility matrix for each microduct, integrated through the Transfer Matrix Method using the visco-thermal dissipation theory. This integration results in a physically coherent model, for which a semi-analytical sensitivity analysis related to the microduct diameters can be directly performed. The optimization employs the Method of Moving Asymptotes (MMA), effectively managing the complexity associated with a large number of design variables. Subsequently, the optimized structure is adapted into a 3D grid, facilitating prototype creation using Additive Manufacturing Technology (AMT) with a specific polymer material. The research methodology is validated through four distinct test cases, each demonstrating the efficacy and adaptability of the optimization tools and techniques. Experimental validation, conducted using an impedance tube, indicates a significant shift in the first absorption maximum from 2500 Hz to 1000 Hz, achieved without altering the material thickness. Additional validation through 3D Finite Element Method (FEM) modeling, which includes visco-thermal effects, further confirms the acoustic efficiency of the final designs. While the potential for achieving lower sub-wavelength conditions is recognized, the study opts for simpler structures, considering the current limitations of 3D printing technology. This study contributes to both theoretical understanding and practical application in acoustic material design, emphasizing the potential for customizing materials to specific frequency ranges. The integration of FETM modeling with MMA provides a systematic and effective approach for optimizing acoustic materials, particularly in enhancing absorption at lower frequencies.

Concurrent topology optimization of sandwich structures with multi-configuration and variable-diameter lattice infill

The superior stiffness-to-weight and strength-to-weight mechanical advantages of sandwich structures can be fully exploited through concurrent design of entire topology, infill configuration and density, where the high-performance yet complicated structure can be fabricated through additive manufacturing. However, the emerging design challenges are concurrent design updating related to sandwich topology, infill configuration and density, which is a design problem with continuous and discrete variables mathematically. In this paper, a concurrent topology optimization is proposed for sandwich structures with multi-configuration and variable-diameter lattice infill. Three design variable fields are employed to describe the fundamental topology considering sandwich structural topology, infill configuration and density simultaneously. Corresponding material interpolation model is developed by combining DSP-based shell-infill description and multi-response latent-variable surrogate model based effective material property calculation. Two-stage design model is formulated as a rough design model considering all design variables followed by a refined design model with only infill density variables, which is developed to strictly satisfy the material allowance constraint due to the mapping between discrete infill configuration variables and continuous latent configuration variables. Corresponding sensitivities of compliance and constraint with respect to the structural topology, infill configuration and density variables are derived, and the method of moving asymptotes (MMA) is employed to solve the design model efficiently. Several numerical examples are presented to systematically demonstrate the effectiveness of the proposed approach.

Topology optimization of adaptive sandwich plates with magnetorheological core layer for improved vibration attenuation.

In this study the optimum topology distribution of the magnetorheological elastomer (MRE) layer in an adaptive sandwich plate is investigated. The adaptive sandwich plate consists of an MR elastomer layer embedded between two thin elastic plates. A finite element model has been first formulated to derive the governing equations of motion. A design optimization methodology incorporating the developed finite element model has been subsequently developed to identify the optimum topology treatment of the MR layer to enhance the vibration control in wide-band frequency range. For this purpose, the dynamic compliance and density of each element are defined as the objective function and design variables in the optimization problem, respectively. The method of the solid isotropic material with penalization (SIMP), is extended for material properties interpolation leading to a new MRE-based penalization (MREP) model. Method of moving asymptotes (MMA) has been subsequently utilized to solve the optimization problem. The developed finite element model and design optimization method are first validated using benchmark problems. The proposed design optimization methodology is then effectively utilized to investigate the optimal topologies of the magnetorheological elastomer (MRE) core layer in MRE-based sandwich plates under various boundary and loading conditions. Results show the effectiveness of the proposed design optimization methodology for topology optimization of MRE-based sandwich panels to mitigate the vibration in wide range of frequencies.

Stochastic optimization of levitation control system for maglev vehicles subjected to random guideway irregularity

The dynamic performance of maglev vehicle-bridge coupled systems subjected to random guideway irregularity, especially the levitation stability of the running vehicle subsystem, is a critical issue affecting the safe operation and the comfort riding of maglev railways. This study is devoted to stochastic optimization of the levitation control system considering the coupled vibration of maglev vehicle and bridge under the random guideway irregularity. To alleviate the computational burden in the stochastic optimization process, the explicit expressions of the critical responses of the maglev vehicle-bridge coupled systems and the sensitivities of critical responses with respect to the feedback gains of the proportional-integral-derivative (PID) levitation controller are respectively constructed in terms of the guideway irregularity by virtue of the dimension-reduced formulation capability of the explicit time-domain method (ETDM), enabling stochastic dynamic analysis and stochastic sensitivity analysis to be executed at high efficiency. The stochastic optimization problem is formulated as the minimization of the standard deviation of the air gap variation with the constraint on the standard deviation of the current variation, and the optimal values of the PID gains are searched by the gradient-based method of moving asymptotes (MMA), in which the statistical moments and moment sensitivities of the critical responses required in the optimization process are obtained by ETDM. A numerical example with a 2-degree-of-freedom maglev vehicle moving on a multi-span simply-supported bridge is used to demonstrate the efficacy of ETDM for the stochastic analysis of maglev vehicle-bridge coupled systems under guideway irregularity. An engineering example involving a 3-car maglev train traversing a multi-span simply-supported bridge is further presented to show the effectiveness of the ETDM-based approach for the stochastic optimization of large-scale engineering problems. The levitation stability of the maglev train under the action of random guideway irregularity is considerably improved with the use of optimal feedback gains.

Trust region based moving asymptotes method: A stabilized optimizer for stress-constrained topology optimization

Inconsistent topologies and oscillations during iterations are commonly observed when solving stress-constrained problems, indicating inadequate stability in topology optimization. The trust region based moving asymptotes (TRMA) method is proposed in this paper. To comprehensively investigate the stability of the TRMA method, the Trinity Evaluation Framework is proposed in this paper. It focuses on three types of dependencies in topology optimization, i.e. mesh dependence, parameter dependence, and initial topology dependence. The corresponding evaluation vector is proposed to measure the stability of the TRMA method. Numerical examples are given, indicating strong stability of the TRMA method in topology optimization of stress-constrained problems.

Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation

In the concurrent optimization of topology and fiber orientation, the design of smooth fiber helps to maintain the stability of numerical calculation and the compatibility of the manufacturing process. However, the improvement of fiber continuity is often accompanied by a significant decrease in the overall structural stiffness. Aiming at this problem, this paper proposes a topology optimization method for anisotropic materials with smooth fiber orientation. This method improves the smoothness of fiber orientation and reduces stiffness loss by introducing a fiber angle constraint strategy and adaptive filtering technology. The fiber angle constraint strategy integrates the created angle constraint function into the Method of Moving Asymptotes (MMA) to complete the strong constraint of the angle. This strategy quantifies the continuity of the fiber and effectively improves the continuity of the fiber. At the same time, the application of adaptive filtering technology can adjust a reasonable fiber angle distribution on the basis of smoothing fibers, thereby enhancing the stiffness of the overall structure. In addition, this paper shows the complete optimization process and MATLAB code implementation and verifies the effectiveness of the method through a series of numerical examples, that is, on the basis of improving fiber continuity, the stiffness of the whole structure is guaranteed, and then the effective balance between the two is realized.

Non-probabilistic reliability-based multi-scale topology optimization of thermo-mechanical continuum structures with stress constraints

A reliability-based non-probabilistic multiscale topology optimization (NRBMTO) method with stress constraints is proposed for thermo-mechanical continuous structures with uncontrollable stresses. The physical parameters, external loads and temperature values at the macro scale, are regarded as non-probabilistic uncertain parameters in the optimization of structural topologies with complex physical fields at multi-scale. The homogenization-based finite element method is employed to quantify thermo-mechanical structures with multi-scale uncertain parameters in the established multi-scale topology model. The ellipsoid model is applied to describe the uncertainty of non-probabilistic random variables, and the non-probabilistic reliability index is obtained by estimating the failure probability based on the first-order reliability method (FORM). The unit stresses are aggregated to the global maximum stresses with the normalized p-norm function, taking into account the mechanical and thermal stresses. The sensitivity information of the compliance and stress constraint to the macro- and micro-design variables and uncertain variables are derived simultaneously. The macro- and micro- design variables are solved by the method of moving asymptotes (MMA), respectively. Several numerical examples are given to verify the effectiveness and feasibility of the proposed NRBMTO method. The results demonstrate that the optimized structure based on the NRBMTO method provides better security with reliability index β=3 and minimum compliance (244.39) while stress is controlled below 235 MPa compared to the classical deterministic multiscale topology optimization (DMTO) method.

Stabilized time‐series moving morphable components method for topology optimization

AbstractThe moving morphable components (MMC) method has been widely used for topology optimization due to its ability to provide an explicit description of topology. However, the MMC method may encounter the instability issue during iteration. Specifically, the iteration history is highly sensitive to parameters of the optimizer, that is, the move limits in the method of moving asymptotes (MMA). Additionally, the final topology obtained from the MMC method usually depends on the initial values. To address these issues and improve the stability of the MMC method in practical applications, this article introduces two strategies. The first strategy is based on the time‐series MMC (TSMMC) method, which proposes a unified description of curved components. However, the use of control‐points‐based design variables may introduce instability into the iteration process due to the strong locality associated with these variables. To mitigate this, global design variables have been incorporated into the formulation. Numerical examples demonstrate that this mixed formulation, combining global and local design variables, can enhance stability significantly. To further enhance stability, the second strategy involves using the trust region‐based moving asymptotes (TRMA) method as the optimizer instead of MMA. The TRMA method incorporates an accuracy control mechanism, resulting in stable and fast convergence behavior, as demonstrated in the numerical examples.

Efficient strategy for topology optimization of stochastic viscoelastic damping structures

In this study, the dynamic topology optimization (TO) of stochastic viscoelastic damping structures (VDSs) is performed for the first time. However, the expensive computation cost seriously hinders the TO process. To address this problem, an efficient strategy is proposed. On the one hand, in the stochastic structural response analysis, a fully adaptive method based on direct probability integral method is presented to determine the number and locations of samples. Meanwhile, to improve the computational efficiency of structural response for each sample, a piecewise model-order reduction method based on Krylov subspace is adopted to generate the orthonormal basis and project the original large-scale system onto a low-order system. On the other hand, to overcome the optimization challenge arising from large number of design variables in the density-based topology method, a material-field series-expansion method is employed to describe the topology layout and significantly reduce the number of design variables. Moreover, the sensitivity of the optimization model is derived by the adjoint method and the method of moving asymptotes (MMA) is used to efficiently update the design variables. Some numerical examples comprehensively demonstrate the effectiveness and efficiency of the proposed strategy. The results indicate that the uncertainty of structural parameters greatly affect both the topology layout of VDS and vibration damping capacity.

Topology optimization of broadband acoustic transition section: a comparison between deterministic and stochastic approaches

This paper focuses on the topology optimization of a broadband acoustic transition section that connects two cylindrical waveguides with different radii. The primary objective is to design a transition section that maximizes the transmission of a planar acoustic wave while ensuring that the transmitted wave exhibits a planar shape. Helmholtz equation is used to model linear wave propagation in the device. We utilize the finite element method to solve the state equation on a structured mesh of square elements. Subsequently, a material distribution topology optimization problem is formulated to optimize the distribution of sound-hard material in the transition section. We employ two different gradient-based approaches to solve the optimization problem: namely, a deterministic approach using the method of moving asymptotes (MMA), and a stochastic approach utilizing both stochastic gradient (SG) and continuous stochastic gradient (CSG) methods. A comparative analysis is provided among these methodologies concerning the design feasibility and the transmission performance of the optimized designs, and the computational efficiency. The outcomes highlight the effectiveness of stochastic techniques in achieving enhanced broadband acoustic performance with reduced computational demands and improved design practicality. The insights from this investigation demonstrate the potential of stochastic approaches in acoustic applications, especially when broadband acoustic performance is desired.

Efficient and Accurate Separable Models for Discretized Material Optimization: A Continuous Perspective Based on Topological Derivatives

Multi-material design optimization problems can, after discretization, be solved by the iterative solution of simpler sub-problems which approximate the original problem at an expansion point to first order. In particular, models constructed from convex separable first order approximations have a long and successful tradition in the design optimization community and have led to powerful optimization tools like the prominently used method of moving asymptotes (MMA). In this paper, we introduce several new separable approximations to a model problem and examine them in terms of accuracy and fast evaluation. The models can, in general, be nonconvex and are based on the Sherman–Morrison–Woodbury matrix identity on the one hand, and on the mathematical concept of topological derivatives on the other hand. We show a surprising relation between two models originating from these two—at a first sight—very different concepts. Numerical experiments show a high level of accuracy for two of our proposed models while also their evaluation can be performed efficiently once enough data has been precomputed in an offline stage. Additionally it is demonstrated that suboptimal decisions can be avoided using our most accurate models.

An intelligent body frame structure modeling and optimization system based on conceptual design

The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.

Multi-scale concurrent topology optimization of cellular structures with multiple microstructures for minimizing dynamic response in the time domain

This paper proposes a concurrent topology optimization method for optimizing the structures that are periodically filled with multiple microstructures excited by dynamic response in the time domain. At the macroscale, different microstructures are considered as different materials. To generate the various distribution of different microstructures, a multi-material interpolation approach based on the Solid Isotropic Material with Penalization (SIMP) is integrated. At the microscale, the energy-based homogenization method (EBHM) is employed to determine the macroscopic effective properties of the microstructures. All macroscale elements with identical material are represented by a distinct microstructure. For the proportional damping model, the HHT-α is implemented as a time integration technique to obtain the dynamic response of multi-scale and assumed multi-material structures. The objective function of the topology optimization problem is to minimize the mean dynamic compliance. Combined differentiate-then-discretize method with the adjoint variable method, the sensitivity analysis applies the gradient-based Zhang-Paulino-Ramos Jr. (ZPR) algorithm or Method of Moving Asymptotes (MMA) to update the design variables under multiple constraints in the time and space-discretized system. The proposed approach is numerically performed through 2D and 3D examples to demonstrate its effectiveness.

Multi-objective topology optimization for thermo-elastic systems based on periodic constraints

Thermo-elastic systems have a wide range of applications in various engineering fields. Traditional single-objective topology optimization (TO) design of thermos-elastic structures is difficult to achieve the combined optimum of multiple properties, and the non-periodic TO design of results are complex and difficult to fabricate. This paper introduces a multi-objective TO formulation based on density for designing thermo-elastic structures with periodic constraints, aiming to overcome the aforementioned issues. This paper assesses two competing weighted objective functions, the first function corresponds to structural and thermal compliance, while the second pertains to regional temperature and global stress. To solve the optimization issue, we utilize the p-norm function with a modified coefficient for evaluating the maximum temperature and stress values. Additionally, the adjoint variable method is employed to evaluate the sensitivity of various objectives, while the method of moving asymptotes (MMA) is used to update the design variables. Then the influence of different weight coefficients and subregion numbers regarding thermos-elastic coupled analysis is demonstrated through numerical examples. The results show that the complexity of the subregion features decreases as the number of subregions increases, and that changing both the number of subregions and the weighting coefficients results in changes in the overall structural performance. Finally, the geometric model is reconstructed using the TO results, and the structural performance is validated through the simulation of the reconstructed model. The results indicate that the TO results obtained by the method of this paper have predefined performance.

Concurrent topology optimization of multiscale piezoelectric actuators

As an important component of smart structural devices, piezoelectric composites have strong macroscopic electromechanical coupling properties which often depend on their microstructural geometries and material parameters. Therefore, optimizing the microstructural design is crucial for achieving the desired macroscale effective properties. To this end, this paper conducts a concurrent multiscale topology optimization (TO) to optimize the material distribution of piezoelectric actuators, aiming to maximize the electrical and mechanical energy transmission to meet specific engineering requirements. Firstly, an energy method is used to homogenize the microstructures and obtain the effective parameters of the piezoelectric material. Then, the piezoelectric material with penalization and polarization (PEMAP-P) approach in conjunction with the adjoint method is employed to calculate the sensitivity of objective and constraint functions. The sensitivities at both the macroscale and microscale are obtained by considering the interscale coupling effects. Finally, the concurrent bi-level iteration based on the optimality criteria (OC) or the method of moving asymptotes (MMA) is conducted. Through this research, we have illuminated the relations between the optimized performance of the actuator and the material volume fractions at each scale. We have also compared in detail the differences between two-scale and single-scale designs, as well as the differences between the simplified half-symmetric geometric model and the full geometric model. We find that a larger volume fraction of material either at the macroscale or microscale generates a larger transmitted displacement magnitude under the same applied electric field, and for a given total material, the transmitted maximum increases with increasing microstructural volume fraction.

Loudspeaker cabinet design by topology optimization

Using material distribution-based topology optimization, we optimize the bandpass design of a loudspeaker cabinet targeting low frequencies. The objective is to maximize the loudspeaker’s output power for a single frequency as well as a range of frequencies. To model the loudspeaker’s performance, we combine a linear electromechanical transducer model with a computationally efficient hybrid 2D–3D model for sound propagation. The adjoint variable approach computes the gradients of the objective function with respect to the design variables, and the Method of Moving Asymptotes (MMA) solves the topology optimization problem. To manage intermediate values of the material indicator function, a quadratic penalty is added to the objective function, and a non-linear filter is used to obtain a mesh independent design. By carefully selecting the target frequency range, we can guide the optimization algorithm to successfully generate a loudspeaker design with the required bandpass character. To the best of our knowledge, this study constitutes the first successful attempt to design the interior structure of a loudspeaker cabinet using topology optimization.

Multi–materials topology optimization using deep neural network for coupled thermo–mechanical problems

In this paper, a density-based topology optimization method using neural networks is used to design domains composed of multi-materials under combined thermo–mechanical loading. The neural network produces the topology density field by minimizing a loss function defined for the problem. Length scale control is conducted using a Fourier space projection. JAX, a high-performance automatic differentiation (AD) Python library is used to build an end-to-end differentiable neural network. The model can handle the sensitivity analysis automatically using the backpropagation process (automatic differentiation of loss function) and the need for solving adjoint equations manual sensitivity analysis is removed. The optimization problem is solved by minimizing the loss function of the network and other optimization algorithms such as the Method of Moving Asymptotes (MMA) are not required. The performance of the method is demonstrated through several examples and compared with those obtained from the popular Solid Isotropic Material with Penalization (SIMP) method. The network is able to handle high-resolution re-sampling, resulting in a more refined and smooth variation of optimal topologies.

EMsFEM based concurrent topology optimization method for hierarchical structure with multiple substructures

An efficient concurrent topology optimization method based on the Extended Multiscale Finite Element Method (EMsFEM) is proposed to design the hierarchical structure with multiple substructures in this paper. Firstly, the mathematical description model for the topology of hierarchical structures and the interpolation model for material are established at both the macro and substructural levels. Then the concurrent topology optimization formulation for hierarchical structures is proposed based on EMsFEM. After that, the sensitivity analysis scheme of the objective function and constraint functions is given, based on which the Method of Moving Asymptotes (MMA) is employed to update design variables. The assumption of separation of length scales is not required in the proposed method, which avoids the disconnection between adjacent substructures. Besides, without limiting the topology and spatial distribution of substructures in advance, the concurrent topology optimization for hierarchical structures with multiple substructures can be realized using this method. The provided numerical examples verify the correctness and effectiveness of the proposed sensitivity analysis method as well as the feasibility, effectiveness and scalability of the proposed concurrent topology optimization method for hierarchical structures. Finally, the influence of the initial substructure configurations and the number of substructure types on the final configuration and mechanical performance of hierarchical structures are explored with a series of numerical examples.

Reliability-based topology optimization of fractionally-damped structures under nonstationary random excitation

This contribution proposes an effective reliability-based topology optimization (RBTO) framework for the layout of viscoelastic dampers of energy-dissipating structures under nonstationary seismic excitation, in which the constitutive behavior of viscoelastic damper is described by the novel fractional derivative models. To formulate the RBTO problem, the installation number of fractional viscoelastic dampers is minimized under the constraints of dynamic reliability, and the design variables are chosen as the damper parameters and the existence variables of the potential fractional viscoelastic dampers. The explicit responses are first established for the fractionally-damped structure, and the explicit response sensitivities with respect to the design variables are further derived through the adjoint variable method (AVM). On this basis, an explicit time-domain method (ETDM) is developed for explicit formulation of the statistical moments of responses and the moment sensitivities of the fractionally-damped structure, which are further employed to analytically derive the first-passage dynamic reliabilities and the reliability sensitivities of the structure based on the classical level-crossing process theory. Finally, the RBTO problem for the layout of fractional viscoelastic dampers is solved with the gradient-based method of moving asymptotes (MMA), in which the existence variables of fractional viscoelastic dampers can be driven to binary solutions by the solid isotropic material with penalization (SIMP) technique. An engineering application involving a building frame structure installed with fractional viscoelastic dampers is presented to validate the feasibility of the proposed ETDM-based RBTO framework.