Structural Optimization Method Research Articles

Nonlinear electromechanical topology optimization method for stretchable electronic interconnect structures

Nonlinear electromechanical topology optimization method for stretchable electronic interconnect structures

Structural Optimization Study on a Three-Degree-of-Freedom Piezoelectric Ultrasonic Transducer

A three-degree-of-freedom (3-DOF) piezoelectric ultrasonic transducer is a critical component in elliptical and longitudinal ultrasonic vibration-assisted cutting processes, with its geometric structure directly influencing its performance. This paper proposes a structural optimization method based on a convolutional neural network (CNN) and non-dominated sorting genetic algorithm II (NSGA2). This method establishes a transducer lumped model to obtain the electromechanical coupling coefficients (X-ke and Z-ke) and thermal power (X-P) indicators, evaluating the bending and longitudinal vibration performance of the transducer. By creating a finite element model of the transducer with mechanical losses, a dataset of different transducer performance parameters, including the tail mass, piezoelectric stack, and dimensions of the horn, is obtained. Training a CNN model with this dataset yields objective functions for the relationship between different transducer geometric structures and performance parameters. The NSGA2 algorithm solves the X-ke and Z-ke objective functions, obtaining the Pareto set of the transducer geometric dimensions and determining the optimal transducer geometry in conjunction with X-P. This method achieves simultaneous improvements in X-ke and Z-ke of the transducer by 22.33% and 25.89% post-optimization and reduces X-P to 18.97 W. Furthermore, the finite element simulation experiments of the transducer validate the effectiveness of this method.

Sound transmission loss optimization of clamped double-panels

The panel cavity structure is one of the key components of the aircraft (vehicle) body and is among the main noise transmission pathways. Based on the modal superposition and Galerkin method, this paper realizes the theoretical model of sound insulation of the clamped, double-panel structure. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to realize the sound insulation of the clamped double-panel structure. Through optimization, the fitting function and law of structural surface density and the optimized normal weighted sound insulation Pareto fronts were obtained. The results show that among the optimization, for the Pareto front cases, their double-panel thickness ratio h1/ h2 is relatively far away from 1, and the corresponding cavity thickness H is relatively large. The influence of boundary conditions and size effects of lightweight sound insulation optimization are also discussed. The research on the influence of boundary and size indicates that the difference in the optimal weighted sound insulation Pareto fronts corresponding to the same surface density is mostly within the 1 dB range. Both the boundary and thickness of the panel will affect the frequency STL, while the boundary conditions or structure size changed, even the total thickness of panels needs to be the same, and the structure can also have similar weighted sound transmission loss ( Rw) when the thickness ratio of the double-panel structure is chosen properly. The difference of material effects is also discussed. This research provides a method for the sound insulation optimization of clamped double-panel structures concerning the boundary and size effect.

A Systematic Optimization Method for Permanent Magnet Synchronous Motors Based on SMS-EMOA.

The efficient design of Permanent Magnet Synchronous Motors (PMSMs) is crucial for their operational performance. A key design parameter, cogging torque, is significantly influenced by various structural parameters of the motor, complicating the optimization of motor structures. This paper proposes an optimization method for PMSM structures based on heuristic optimization algorithms, named the Permanent Magnet Synchronous Motor Self-Optimization Lift Algorithm (PMSM-SLA). Initially, a dataset capturing the efficiency of motors under various structural parameter scenarios is created using finite element simulation methods. Building on this dataset, a batch optimization solution aimed at PMSM structure optimization was introduced to identify the set of structural parameters that maximize motor efficiency. The approach presented in this study enhances the efficiency of optimizing PMSM structures, overcoming the limitations of traditional trial-and-error methods and supporting the industrial application of PMSM structural design.

Controlling the morphologies of femtosecond laser-induced periodic surface structure on silicon by combining deep learning with energy deposition model

As an important patterning method, femtosecond laser-induced periodic surface structure (LIPSS) has attracted widespread attention in recent years. Due to the complex physical processes involved in the femtosecond laser scanning process, it is difficult to predict the required LIPSS morphologies, which hinders rapid customization of the large-area LIPSS patterns. In this study, a structural optimization method combining data-driven deep learning with energy deposition model was proposed to control LIPSS morphologies of silicon. After femtosecond laser processing under dynamic irradiation condition, four LIPSS morphological types were well defined based on experimental results. Deep learning model was constructed to extract data features by labeling and encoding sample data. The accuracy of self-correlation validation reached 98.0% and the accuracy of cross-correlation validation reached 91.9%. Our results show that the distribution of LIPSS morphological types exhibits the dependence of energy deposition, which is the joint effect of the effective pulse number and accumulative fluence. Moreover, the period of groove (super-wavelength LIPSS) increases with the increase of energy deposition, while the period of LSFL (low spatial frequency LIPSS) slightly decreases with the increase of energy deposition. Through this structural optimization method, customization and online monitoring of large-area LIPSS patterns in the future industrial applications.

Geometry-force interactive design and optimization method of cable dome structures

Cable dome is a typical flexible structural system. Its initial geometry determines the prestress distribution and affects the structural performance. Using geometric parameters to establish the mechanical equation of initial state can effectively realize the geometric-force interactive design and optimization of cable dome structure. Consequently, a standardized equation is established in this paper to uniformly express the geometric-force relationship, utilizing a feasible prestress distribution function expressed by geometric parameters. Subsequently, an interactive design method for initial geometry and prestress is presented. The proposed method is theoretically expounded in terms of geometric design, prestress design and shape optimization. Two typical cable dome cases, Geiger and Levy, are successfully applied to accomplish the geometric-force design and optimization of the initial state. The results show that the efficient design of initial state can be completed by establishing the interaction between geometry and force without repeated algebraic calculation. The optimized initial geometry can significantly improve the Structural stiffness.

Modal Analysis of Automobile Wheels based on Lightweight Technology

Compared with traditional wheels, magnesium alloy wheels have the advantages of higher strength, toughness, lighter weight, beautiful appearance and so on. This paper takes magnesium alloy wheel as the research object, and uses Pro/e and ANSYS to help complete a lot of work such as 3D model construction and finite element analysis and calculation, to ensure that the magnesium alloy wheel can meet its strength requirements while being lightweight. The strength analysis and modal analysis of the designed wheel model are carried out under various working conditions. According to the analysis results, the first design model is further simplified, and then the calculation and analysis are repeated many times. It is found that the new model meets the mechanical properties requirements under multi-working conditions, ensures that the requirements of lightweight design are met, and provides relevant theories and methods for wheel design and structural optimization of other materials.

Optimization Method for Key Structures of Absorbers at Specific Frequencies Based on Fitting Functions

Optimization Method for Key Structures of Absorbers at Specific Frequencies Based on Fitting Functions

Assessing landscape fragmentation and ecological connectivity to support regional spatial planning: A case study of Jiangsu province, China

Urbanization in China has led to land-use changes resulting in landscape fragmentation, natural habitats loss and ecological connectivity decline. Aiming to support sustainable landscape planning, we proposed an integrated method for ecological structure assessment and optimization including morphological spatial pattern analysis (MSPA), minimal cumulative resistance (MCR) model and circuit theory to assess landscape fragmentation and ecological connectivity change at regional scale. A modified connectivity index is also proposed by employing the index of relative quality of ecological patches and corridors. We applied the combined methods to Jiangsu province in eastern China for multi-temporal landscape analysis and optimization. The results showed that the combined analysis revealed both fragmentation trend of ecological patches and ecological connectivity reduction with regard to all the species as a whole in the study area from 2000 to 2018. While, the trend has been slowed down to a certain extent lately, which is related to a series of ecological protection policies implemented in the study area, including ecological redlines policy, national park construction and returning marginal farmland into forest or grassland. Furthermore, suggestions for improving the ecological network in the study area were proposed. We proposed to improve the ecological network by fostering twenty-five potential ecological sources, maintaining twenty-five pinch points, restoring eleven dredging corridors and planning fifty-three stepping stones. Overall, we argue that the combined method can reveal both structural connectivity and functional connectivity, and the time series analysis can provide dynamics situation for better understanding and planning landscape process. It can also provide planners with a valuable strategic tool that advances the practical utility of landscape ecology techniques for spatial planning, and the combined application of the method helps to reduce some of the limitations of each individual method.

A smart structural optimization method of magnetorheological damper for ultra-precision machine tool

To address the problem of multi-source vibration in ultra-precision machine tools, a vibration reduction stand was designed by replacing passive damping components with magnetorheological dampers (MRDs). In this work, the structural parameters of MRDs were optimized using an improved pelican optimization algorithm (IPOA) to realize the maximum capability in reducing vibration. Firstly, the working principle of MRDs was explained, and the mathematical models of MRDs were established. Then, an IPOA based on singer chaotic mapping, nonlinear inertia weight factor, and Cauchy mutation strategy was proposed to enhance the global search capability and convergence efficiency of the algorithm. Subsequently, the IPOA was applied to optimize key structural parameters of MRDs, including output damping force, controllable damping range, response time, and power consumption. Finally, COMSOL Multiphysics software was used to verify the effectiveness of the proposed algorithm by comparing the magnetic induction intensity distribution of MRDs before and after optimization, as well as the variation of the four performance indexes under the different applied currents. After being optimized using the proposed IPOA, the MRDs can deliver a larger maximum damping force and a wider damping controllable range, with less power consumption and quick response, which could meet the requirement for vibration suppression of ultra-precision machine tools.

Comparison of the performance enhancement of vacuum ejector by means of structure optimization and bypass methods

The ejector can create a vacuum environment without electricity and produce no pollutants, making it widely used in various fields. However, during the working process, a large amount of high-pressure air needs to be continuously supplied to maintain the vacuum level. Given the current energy shortages, it is essential to enhance the entrainment capacity and reduce the energy requirements of the vacuum ejector. To achieve this goal, a two-stage vacuum ejector (TSVE) and structure optimization are adopted. While achieving the same vacuum level (80 kPa or 90 kPa), the air consumption (m˙p) and entrainment ratio (Er) of vacuum ejectors with various shapes are compared. Through structural optimization, five high-performance vacuum ejectors (Pp < 0.6 MPa, Er > 0.65) are selected. Computational Fluid Dynamics (CFD) is applied to observe the mixing processes in the vacuum ejector. In addition, the optimized vacuum ejector and TSVE are compared to the original one. An experimental system is designed for this purpose. The findings indicate that both methods can enhance the performance of the vacuum ejector. However, the benefits of TSVE are more pronounced at lower vacuum levels, while the advantages of the optimized vacuum ejector become more obvious as the vacuum level increases.

Multiscale topology optimization of cellular structures with high thermal conductivity and large convective surface area

Cellular structures have excellent thermal performance due to their high specific surface areas and porosities. This paper proposes a multiscale topology optimization (MTO) method of cellular structures with high thermal conductivity and large convective surface area, which enables them to have rapid cooling capability. In the MTO of a cellular structure, the triply periodic minimal surface (TPMS) lattices are adopted and Kriging metamodels are constructed to predict their effective thermal conductivity tensors and surface areas. The thermal compliance of the cellular structure is firstly minimized under the given volume constraint by topology optimization. Then, maximizing the convective surface area of the cellular structure is considered as the objective to when imposing the optimized thermal compliance as the constraint. After conducting topology optimization, the cellular structure with high thermal conductivity and large convective surface area is achieved. Additionally, in the above MTO, isogeometric analysis (IGA) is employed to improve the accuracy of numerical analysis. A numerical example is provided to verify the effectiveness of the proposed method. By both simulation and experiment, the high thermal conductivity, large convective surface area and rapid cooling capability of the optimized cellular structure are verified.

Catalytically Active Carbon for Oxygen Reduction Reaction in Energy Conversion: Recent Advances and Future Perspectives.

The shortage and unevenness of fossil energy sources are affecting the development and progress of human civilization. The technology of efficiently converting material resources into energy for utilization and storage is attracting the attention of researchers. Environmentally friendly biomass materials are a treasure to drive the development of new-generation energy sources. Electrochemical theory is used to efficiently convert the chemical energy of chemical substances into electrical energy. In recent years, significant progress has been made in the development of green and economical electrocatalysts for oxygen reduction reaction (ORR). Although many reviews have been reported around the application of biomass-derived catalytically active carbon (CAC) catalysts in ORR, these reviews have only selected a single/partial topic (including synthesis and preparation of catalysts from different sources, structural optimization, or performance enhancement methods based on CAC catalysts, and application of biomass-derived CACs) for discussion. There is no review that systematically addresses the latest progress in the synthesis, performance enhancement, and applications related to biomass-derived CAC-based oxygen reduction electrocatalysts synchronously. This review fills the gap by providing a timely and comprehensive review and summary from the following sections: the exposition of the basic catalytic principles of ORR, the summary of the chemical composition and structural properties of various types of biomass, the analysis of traditional and the latest popular biomass-derived CAC synthesis methods and optimization strategies, and the summary of the practical applications of biomass-derived CAC-based oxidative reduction electrocatalysts. This review provides a comprehensive summary of the latest advances to provide research directions and design ideas for the development of catalyst synthesis/optimization and contributes to the industrialization of biomass-derived CAC electrocatalysis and electric energy storage.

A new efficient parametric level set method based on radial basis function-finite difference for structural topology optimization

To overcome a significant challenge in traditional parameterized level set methods based on globally supported radial basis functions, we propose employing a local differentiation construction of radial basis functions using finite difference, a technique previously applied to solving partial differential equations but novel in the context of topology optimization. We present a novel parameterized level set method for structural topology optimization of compliance minimization and compliant mechanism, with the main aim of reducing computational costs associated with fully dense matrices when approximating systems with a large number of collocation points. The new scheme implemented with rectangular mesh elements and polygonal mesh generation accommodates both rectangular and complex design domains. Numerical results are provided to demonstrate the algorithm's effectiveness.

Topology optimization structure design of shape memory alloy with multiple constraints

As an emerging functional material, shape memory alloy (SMA) exhibits remarkable mechanical properties and finds diverse applications across industries. This paper presents a topology optimization framework based on the bi-directional evolutionary structural optimization (BESO) method for designing SMA structures, which maximizes structural stiffness under multiple constraints of specified volume fraction, displacement, and fundamental frequency. A phenomenological constitutive model is utilized to simulate the mechanical behavior of SMA accurately. The unit virtual load method is employed to determine sensitivities. Several optimized SMA beam structures and simply-supported cube structures are designed under different thermal-mechanical loads, and their displacement, mean compliance, and fundamental frequency are evaluated throughout the optimization process. The results demonstrate that the proposed framework successfully customizes the SMA topology structure with adjustable displacement and fundamental frequency, and the optimized schemes exhibit more considerable deformation and more uniform mechanical properties than their initial counterparts. The proposed framework has higher computational efficiency than the traditional SIMP-based SMA topology optimization design method.

A crashworthiness optimization method of subway underframe structures based on the differential evolution of the weighted graph representation

A crashworthiness optimization method of subway underframe structures based on the differential evolution of the weighted graph representation

An Eigenfrequency-Constrained Topology Optimization Method with Design Variable Reduction

The dynamic response of structures heavily relies on eigenfrequency, so the optimization of eigenfrequency is valuable in various working conditions. The bi-directional evolutionary structural optimization (BESO) method has been widely applied due to its ability to eliminate grayscale elements. Based upon BESO, this paper introduces a topology optimization method that incorporates eigenfrequency constraints and reduces the number of design variables. In this method, the optimization objective was to minimize compliance. The Lagrange multiplier was used to introduce eigenfrequency constraints, allowing for coordinated control of compliance and eigenfrequency. To prevent oscillation during the optimization process, the sensitivity was normalized. Additionally, to achieve faster convergence, the variables were reduced after meeting volume constraints. The numerical examples demonstrate the effectiveness of this method in increasing the eigenfrequency of the structure and avoiding resonance.

An adaptive phase-field method for structural topology optimization

In this work, we develop an adaptive algorithm for the efficient numerical solution of the minimum compliance problem in topology optimization. The algorithm employs the phase field approximation and continuous density field. The adaptive procedure is driven by two residual type a posteriori error estimators, one for the state variable and the other for the first-order optimality condition of the objective functional. The adaptive algorithm is provably convergent in the sense that the sequence of numerical approximations generated by the adaptive algorithm contains a subsequence convergent to a solution of the continuous first-order optimality system. We provide several numerical simulations to show the distinct features of the algorithm.

Designing piezoelectric stretcher through multiphysics topology for enhanced photodynamic therapy in cancer treatment

This study presents an advancement in Photodynamic Therapy (PDT) by introducing piezoelectric stretchers designed to enhance tissue surface characteristics. With a focus on mitigating the impact of irregular tissue surfaces on light distribution, we propose a structural topology optimization method to design a stretcher that utilizes the piezoelectric effect for precise tissue stretching. The aim is to achieve uniform laser irradiance across treated tissue, thereby maximizing therapy efficiency. Our approach involves the formulation of finite element models that consider the piezoelectric effect and the elastic response in a multiphysics framework. Mesh convergence tests were conducted to strike a balance between accuracy and computational efficiency in evaluating the designed piezoelectric stretcher. The investigation into the application of piezoelectric stretchers for enhancing laser irradiance on non-uniform tissue reveals significant improvements in surface uniformity with increasing electric potential. Notably, the disparity between regions of high laser focusing and defocusing decreases as the stretcher expands, achieved through the application of higher voltages. The observed enhancement from no expansion to full expansion is quantified at 31 %. The implementation of electrically driven piezoelectric tissue expansion results in a more uniform tissue surface, leading to an augmentation of laser irradiance. In the absence of expansion, distinct areas of laser focus and near-zero irradiance are noted. Conversely, an increased potential yields a predominantly even surface, with 100 % extension achieving commendable laser irradiance uniformity.

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation.

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms.