Structural Optimization Method Research Articles

Structure Design And Optimization Approach For Low RCS Screws

With the rapid development of radar stealth and detection technology, large numbers of screws and rivets have become a major factor that affecting the aircraft’s invisibility. This is because these components have a significant impact on the overall radar cross section of the aircraft, when the electromagnetic scattering characteristic of strong scattering sources is greatly reduced. By analyzing the scattering contribution of the head and body to the screw, it has been obtained that the geometric structure of the head plays a primary role in influencing the overall radar cross section of the screw. According to the scattering mechanism and stealth principle, a shape design method for the low RCS screw is proposed, and then we investigated the scattering characteristic of the octagonal, hexadecagonal, and circular head screws respectively. Furthermore, a structure optimization approach for low RCS screws is introduced which takes the angle of the incident wave into account. This has resulted in the development of a corresponding triangular screw with low detection capabilities. The simulation results show that within the operating frequency band of radar, the triangular head screw has a maximum RCS reduction of approximately 30.1dB compared to a traditional round head screw in [-90°,90°]. On average, there is a reduction of 12.6dB. The simulated results demonstrate the effectiveness and feasibility of the proposed design and optimization method for low RCS screw structures

Physics‐informed neural operator solver and super‐resolution for solid mechanics

AbstractPhysics‐Informed Neural Networks (PINNs) have solved numerous mechanics problems by training to minimize the loss functions of governing partial differential equations (PDEs). Despite successful development of PINNs in various systems, computational efficiency and fidelity prediction have remained profound challenges. To fill such gaps, this study proposed a Physics‐Informed Neural Operator Solver (PINOS) to achieve accurate and fast simulations without any required data set. The training of PINOS adopts a weak form based on the principle of least work for static simulations and a strong form for dynamic systems in solid mechanics. Results from numerical examples indicated that PINOS is capable of approximating solutions notably faster than the benchmarks of PINNs in both static an dynamic systems. The comparisons also showed that PINOS reached a convergence speed of over 20 times faster than finite element software in two‐dimensional and three‐dimensional static problems. Furthermore, this study examined the zero‐shot super‐resolution capability by developing Super‐Resolution PINOS (SR‐PINOS) that was trained on a coarse mesh and validated on fine mesh. The numerical results demonstrate the great performance of the model to obtain accurate solutions with a speed up, suggesting effectiveness in increasing sampling points and scaling a simulation. This study also discusses the differentiation methods of PINOS and SR‐PINOS and suggests potential implementations related to forward applications for promising machine learning methods for structural designs and optimization.

Numerical simulation and thermodynamic test analysis of plate heat exchanger based on topology optimization

Efficient heat transfer in PHEs relies on optimizing flow distribution to minimize thermal dead zones. Existing studies have predominantly focused on single-phase fluids as working media and employed density-based methods for structural optimization of microchannel heat exchangers at small scales. In this study, the Brinkman term from the porous media flow model is introduced in an innovative manner, along with the utilization of a material density interpolation model, which builds upon the density-based approach. A novel two-phase fluid topology optimization method is proposed, specifically tailored for large-scale PHE operating conditions, resulting in the successful design of a new PHE topology. To compare the performance of the two heat exchangers, finite element simulation, visualization experiments, and thermodynamic performance testing are conducted. The simulation results reveal that the heat transfer capacity of the topological PHE is 810 W with pressure drops of 18.8 kPa, while the dimple PHE exhibits heat transfer capacity of 652 W with pressure drops of 25.9 kPa. Compared to the dimple PHE, the topological PHE demonstrates 24.2 % improvement in heat transfer performance and 27.8 % reduction in pressure drop. The dimensionless comprehensive heat transfer coefficient (j/f) indicates that the overall performance of the topological PHE significantly surpasses that of the traditional PHE. Furthermore, the visualization experiment results demonstrate that the complex texture structure of the topological PHE effectively induces fluid motion, compelling the fluid to flow along the texture direction. This significantly mitigates the issue of uneven flow within the PHE, thus enhancing heat transfer efficiency. Specifically, the topological PHE exhibits inlet–outlet temperature difference of 10.3 °C and pressure drops of 11.2 kPa, while the dimple PHE shows inlet–outlet temperature difference of 6 °C and pressure drops of 12.9 kPa. The findings of this study provide guidance for future research endeavors aimed at evaluating potential solutions to advance heat transfer in PHEs and facilitate the implementation of thermal management strategies.

The path-engulfment method for topology optimization of structures

To address the challenge of establishing and solving mathematical models for engineering structural optimization, a new topological optimization method that integrates load-transfer path theory with the engulfment algorithm is presented in this paper. The presented method applies the load-transfer path theory to identify the main load-bearing areas of the structure and utilizes the principle of concentrating more materials in relatively high-stress regions and fewer materials in relatively low-stress regions. An engulfment algorithm is introduced to optimize the material distribution. A comparative analysis between the presented and variable-density methods revealed that the path-engulfment method enhances the structural stiffness and strength while reducing its mass, confirming its precision and efficacy in structural optimization. The path-engulfment method was implemented on a truck crane frame, resulting in an optimized structure with increased stiffness and strength and reduced mass compared to the original design. Furthermore, this method eliminates the need for establishing and solving complex mathematical models while addressing issues related to checkerboards and gray-scale elements. A smooth boundary approach was introduced by leveraging the engulfment algorithm, enabling the direct application of the optimized structure for manufacturing purposes, particularly in engineering applications.

Development of a new environmentally friendly and efficient centrifugal variable diameter metering device.

The design of the maize metering device involves centrifugal variable diameter pneumatic and cleaning mechanisms, aiming to enhance the performance and power efficiency of pneumatic maize metering devices. Leveraging the impact of changes in centrifugal diameter and the guidance and positioning of airflow, we optimize the hole insert, seeding plate, seed limit board, and integrated front shell. This optimization facilitates the adjustment of both the quantity and posture of seed filling. As a result, seeds can form a uniform flow within the annular cavity, reducing the wind pressure necessary for regular operation and decreasing power consumption. A quadratic regression orthogonal rotation combination experiment is conducted using a self-made experiment bench, considering ground speed, wind pressure, and seeding rate as the experiment factors. Furthermore, a comparative experiment involving a novel centrifugal variable-diameter type metering device. The results indicate optimal seeding performance when the ground speed is 13.2 km/h, the wind pressure is 1.2 kPa, and the feeding rate is 25 seeds/s. Under these conditions, the quality of feed index reaches 95.20%, the multi-index is 3.87%, and the miss index is 0.93%. Findings reveal that the developed seed metering device achieved a quality of feed index exceeding 93.00% across varying speeds of 12~18 km/h, aligning with the production requirements. Moreover, the actual power consumption of Type B and C is about 85.00% and 98.00% lower than Type A, standing at only 32.90 W at 18 km/h. The COP of Type C is about 86 times and 12 times that of Type A and B, respectively, meeting the demands for efficient production of maize seed metering devices. In comparison to traditional design and structural parameter optimization methods for maize seed metering device, this study is helpful to the sustainable development of maize industry and reduce environmental pollution.

MQ quasi-interpolation-based level set method for structural topology optimization

MQ quasi-interpolation-based level set method for structural topology optimization

Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence

Intelligent design technology for shear wall structures has great potential for enhancing design efficiency and addressing the challenges of tedious and repetitive design tasks. Recently, there has been a surge in the development of this technology. However, existing deep learning-based design methods for shear wall structures suffer from poor quality and usability issues. To address these challenges, this study proposes an intelligent design and optimization system for shear wall structures based on large language models (LLMs) and generative artificial intelligence (AI). The system employs an LLM as the core controller, which interacts with engineers to interpret their language descriptions and translate them into executable computer code. Subsequently, the system utilizes the corresponding structural generation and optimization methods to accomplish intelligent design tasks automatically. Furthermore, the system incorporates such critical factors as the empirical rules, mechanical performance, and material consumption into the structural optimization process. A unique three-level, two-stage optimization method is constructed based on topology, pattern, and size to enhance the overall design quality. Being able to complete the entire workflow of architectural drawing processing, structural scheme generation, and analysis model establishment, the proposed system enables automated and efficient design of shear wall structures. Through the analysis and validation of multiple cases, it was demonstrated that this system can significantly speed up the design by approximately 30 times compared to that of traditional methods whilst ensuring the safety and cost-effectiveness of the design schemes. Consequently, this study provides valuable insights for the advancement of automated structural design undertakings.

Discovery and Hit to Lead Optimization of Macrocyclic Peptides as Novel Tropomyosin Receptor Kinase A Antagonists.

Tropomyosin receptor kinases (Trks) are receptor tyrosine kinases activated by neurotrophic factors, called neurotrophins. Among them, TrkA interacts with the nerve growth factor (NGF), which leads to pain induction. mRNA-display screening was carried out to discover a hit compound 2, which inhibits protein-protein interactions between TrkA and NGF. Subsequent structure optimization improving phosphorylation inhibitory activity and serum stability was pursued using a unique process that took advantage of the peptide being synthesized by translation from mRNA. This gave peptide 19, which showed an analgesic effect in a rat incisional pain model. The peptides described here can serve as a new class of analgesics, and the structure optimization methods reported provide a strategy for discovering new peptide drugs.

Topology optimization framework for thermoelastic multiphase materials under vibration and stress constraints using extended solid isotropic material penalization

This paper proposes a multiphase material topology optimization (MMTO) method for thermal–mechanical structures that takes into account the effect of the temperature field under stress and frequency constraints. Thermoelastic coupling occurs when an engineering structure is concurrently subjected to thermal and mechanical loads. This work focuses on the following: (1) the derivation of three interpolation schemes that use stress, dynamic, and thermal loads based on the extended solid isotropic material penalization (SIMP) method; (2) the incorporation of stress and dynamic constraints of multi-material thermal-elastic structural designs; and (3) the effective control of the impact of thermal loads on structures based on stress and frequency levels. Numerical experiments show that a clear distinction exists in the final topologies for mechanical and thermal loads among materials. Coupled and uncoupled mechanical and thermal behaviors can be predicted using finite element models based on materials’ coefficients of thermal expansion. We see this method is capable of optimizing a structure’s strength and stability while solving the issue of a thermal environment. In addition, it allows for the precise management of stress and vibration levels when structures are not exposed to a thermal environment.

Research on the efficient process-oriented structural optimization method of the large-scale vacuum cryostat for fusion reactors

An efficient optimization design for the large and complex components of fusion reactors is crucial to address the engineering design requirements and further promote technical standardization. Based on research status, current engineering designs for fusion reactors have some deficiencies, such as time and energy wastage, inefficiency, and difficulties in covering the typical ‘multi-variable multi-objective’ design requirements. These are pressing and common problems that urgently need to be overcome. To deal with the aforementioned technical challenges, it is vitally important to design an efficient, precise, and normalized approach that is tailored for the development of future fusion reactors. Therefore, this paper proposes a process-oriented optimization design method, which involves Coupled external parameterized modeling, Experimental points design, Response surface optimization, and Structural integrity validation (CERS), to improve the currently inefficient design methods. And the vacuum cryostat, the largest and complex component of a tokamak, is taken as an example to present the basic procedures of CERS. Firstly, the functions, basic structures, load types, analysis methods, and verification criteria of the cryostat are presented in detail. Then, real-time data interaction between external global parametric variables and ANSYS via coupling is established by CERS, which achieves parametric modeling of the cryostat and efficient experimental point design and optimization analysis with multi-variables and multi-objectives in an automatic way. Subsequently, this study demonstrates the significance and sensitivity of various structural parameters of the cryostat from such objectives as maximum deformation, maximum equivalent stress, and total mass. And the optimal set of its structural parameters is obtained by establishing a mathematical optimization model. Finally, the structural integrity is verified. The results indicate that the optimized cryostat maintains a minimum safety margin of 23% and will not suffer fatigue damage under various load events during its service. Moreover, the nonlinear buckling load multiplier ∅ is 5.4, obtained by analyzing the load-displacement curve of the cryostat according to the zero-curvature criterion. This shows that the designed cryostat is stable enough. The proposed method is simple, efficient, and reliable, and can be applied to both the cryostat and other complex components of fusion reactors in engineering design fields. It has great value of practical technical reference and can further promote the standardization of engineering design technology for future fusion reactors.

An improved evolutionary structure optimization method considering stress minimization and smooth design

AbstractThe design of continuum structures often presents challenges related to stress concentration, which can cause significant structural damage. To address this issue, the current study presents a new stress minimization method that utilizes the Windowed Evolutionary Structural Optimization (WESO) framework. The method aims to improve algorithm stability by optimizing design variables with an intermediate density. The use of a P‐norm stress aggregation method improves the assessment of global stress levels and enhances computational efficiency. Furthermore, a stable element sensitivity formulation, derived from the adjoint sensitivity analysis of the global stress measure, effectively handles the nonlinear stress behavior. Mesh filtering techniques are utilized to convert sensitivity from elements to nodes, and the structural topological solution is represented using the level set function (LSF) based on element‐node sensitivity. This method addresses the singularity issue commonly found in density‐based optimization methods and facilitates the achievement of smooth topological solutions. Through 2D and 3D benchmark designs, the proposed method's feasibility, stability, and superiority are thoroughly demonstrated. A parametric study is conducted to identify the optimal parameter range for the algorithm, leading to the development of a rational method for parameter selection. The optimized topology, with its smooth boundaries, can guide the design of structures without the need for redesign or post‐processing, helping to drive innovation and development in engineering.

Kinematics analysis of trajectory planning of four-degree-of-freedom robotic arm

This article describes the use of the Open MANIPULATOR-X open robotic platform with four degrees of freedom to model the forward and inverse kinematics of the robot arm. Algorithms for forward and inverse kinematics were developed, and simulations were conducted for comparative analysis. System verification and validation were performed, along with trajectory planning, error analysis, and structural optimization methods. The results obtained serve as a theoretical basis for future research into improving trajectory planning and optimizing robot design. The importance of using the MATLAB platform for kinematic modeling, simulation, and algorithm development is emphasized, contributing to the enhancement of robotics skills and innovation.

Topological design of microstructured materials using energy-based homogenization theory and proportional topology optimization with the level-set method

In this study, we propose a novel approach to designing microstructures of porous material with extreme mechanical properties to maximize the bulk or shear modulus. The approach integrates energy-based homogenization theory and proportional topology optimization with the level-set method (HPTO-LSM). A level-set function (LSF) evolution strategy based on the nodal element mutual energies (EME) proportion is proposed and drives the LSF’s evolution. An EME proportion filtering technique is proposed and reduces the redundant branch structure. And a linear scaling strategy is applied to the HPTO-LSM to enhance the topological change ability. The effectiveness of the HPTO-LSM for designing microstructures of cellular materials to maximize the bulk or shear modulus is demonstrated using several 2D and 3D cases. The effects (changes in objective function values or topologies) of the EME proportion filtering scheme and scaling strategy on the HPTO-LSM are discussed. In addition, we compared the HPTO-LSM method with the solid isotropic material with penalization (SIMP) and bi-directional evolutionary structural optimization (BESO) methods through numerical examples. As shown by the results, effective objective function values and 2D and 3D microstructural topologies with clear interfaces and smooth boundaries can be acquired using the new approach. Applying the EME proportion filtering scheme and scaling strategy in the HPTO-LSM not only enhanced the topological variation of microstructures but also facilitated the acquisition of microstructures conducive to engineering applications. Compared to the SIMP and BESO methods, the HPTO-LSM method has significant advantages in obtaining better objective function values and better microstructural topologies.

Isogeometric Topology Optimization of Multi-patch Shell Structures

Shell structures refer to structural elements that derive strength and load-bearing capacity from their thin and curved geometry. In practical applications, shell structures are commonly composed of multiple patches to represent intricate and diverse architectural configurations faithfully. Nevertheless, the design of multi-patch shell structures holds considerable promise. However, most of the previous work is devoted to the numerical analysis of multi-patch shell structures without further optimization design. The work proposes an inverse design framework, specifically focusing on multi-patch configurations based on Reissner–Mindlin theory. First, reparameterization and global refinement operations are employed on the provided multi-patch shell structures. Renumbering the indices of control points with shared degrees of freedom at the interface naturally ensures C0-continuity between patches. Subsequently, this study investigates the amalgamation of Isogeometric Analysis (IGA) and the Solid Isotropic Material with Penalization (SIMP) method for topology optimization of shell structures. The proposed approach is validated through numerical examples, emphasizing its capacity to enhance multi-patch shell structure design, showcasing robustness and efficiency.

A topology, material and beam section optimization method for complex structures

This article presents an engineering method for optimizing the topology, materials and beam cross-sectional types of complex structures comprising bars, beams or shells, or their combinations. The optimization problem is established based on an extended ground structure where each location contains multiple components or cross-sectional beams with different materials. This problem is solved through explicit sequence approximate problems involving discrete 0/1 variables to determine the optimal topology, materials and beam section types of components, and continuous size variables. The 0/1 variables are determined with a genetic algorithm, in which size variables are optimized with a dual method after determining 0/1 variables in each generation. The final design is restricted to a condition where at most one component can exist per location, realized through modified operators in the genetic algorithm. Structural analyses are conducted before establishing approximate problems in iteration cycles. Numerical examples, including an engineering application, demonstrate the efficiency of this method.

Structural optimization and electrocatalytic hydrogen production performance of carbon-based composites: A mini-review

The energy demand has increased significantly in recent years and it is urgent to develop a renewable energy system that is highly efficient and non-noble metal-based. Hydrogen energy is an environmentally friendly energy source with abundant resources, which can be used to solve the problem of high energy demand without greenhouse gas emissions. However, the development of catalysts for hydrogen production technology by electrolysis of water is slow, mainly due to the complexity of the electrolysis hydrogen generation process, low hydrogen production efficiency, weak electrode material activity and high cost. Among the non-noble metal-based catalysts, carbon-based materials have high conductivity, tunable chemical bonding, and easily modified morphology, making them beneficial to achieving efficient hydrogen production, though pure carbon composites suffer from few surface-active sites and unmoderated hydrogen bonding energy, which need to be further optimized. The principle of electrocatalytic hydrogen production from the perspectives of reaction thermodynamics and kinetics is analyzed and discussed in this paper. Thermodynamics of electrocatalytic hydrogen production is reflected by the Gibbs free energy of hydrogen adsorption (ΔGH*) and electrode potential (E). Reaction kinetics of the electrocatalytic hydrogen production process are reflected by overpotential, Tafel slope and exchange current density. Structural optimization methods of carbon-based composite materials and hydrogen production performance after structural optimization are also summarized. Structural optimization methods of carbon-based composite materials mainly include introducing active sites, improving conductivity, increasing specific surface area and introducing self-supporting materials. Finally, prospects are proposed for the development direction and existing problems of electrocatalytic hydrogen production performance of carbon-based composites.

Nonlinear dynamical modeling and response analysis of complex structures based on assumed mode weighting

The assumed mode method (AMM) is widely used for dynamical modeling but faces increasing challenges in developing low-dimensional and high-precision models of complex structures for investigations of nonlinear dynamical responses and active vibration control. This is mainly because the current complex structure has an increasing number of components, each of which needs to be discretized by multiple assumed modes in dynamical modeling, leading to increasing degrees of freedom for the system. To address this issue, this paper proposes a dimension-reduced method based on the assumed mode weighting method, referred to as Mode Weighting Method (MWM). The modeling process of a multi-beam structure is fully presented as an example to show the strategy of the MWM. During this process, the assumed mode (AM) model (the dynamic model derived in terms of a set of assumed modes) is developed first where the joints of the structure between its components are considered as artificial springs and each component is discretized by its vibration modes without constraints. The components of the eigenvector of the characteristic equation derived from the AM model are taken as the weighting coefficients to get the approximate analytical global modes of the structure by multiplying the corresponding assumed modes. Consequently, a nonlinear dynamic model with a few degree of freedom is obtained by discretizing the structure with the weighted modes. Moreover, a technique for simply obtaining the weighted mode (WM) models by directly processing the AM model through a transformation matrix is presented. To validate the proposed method, the simulations on the natural characteristics and the dynamical responses for both linear and nonlinear models, are performed respectively based on the WM and the finite element models, where both results are well matched each other. This work provides a new way of developing advanced dynamical modeling methods for structural dynamics design, optimization, and active vibration control.

Structure optimization method on heat convection enhancement of unilateral-heated channel using gradient descent

Structure optimization method on heat convection enhancement of unilateral-heated channel using gradient descent

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.

Application of automatic few-group structure optimization based on perturbation theory to VHTR cores

A new automatic group structure optimization method based on the perturbation theory was proposed for the few-group structure in two-step nuclear design procedure for VHTR. It applies the sensitivity coefficient of the perturbation theory which includes not only the effect of the cross section on the multiplication factor but also the adjoint weighted reaction rate. The sensitivity coefficient of the fine group for the multiplication factor was calculated and the group boundary for a few-group can be determined so that the summation of the fine group sensitivity for a few-group should be evenly distributed over every few-group. This method was successfully implemented in the ABGO code. VHTR-350 and MiHTR 2D core were used to investigate the performance and applicability of the proposed method. The code generated the new group structures for two cores and the error of the multiplication and reaction rate by the new group structure was compared with the result by the fine group structure. The comparisons indicate that the new group structure by the proposed method can provide the multiplication factor and reaction rates comparable to the existing group structure and more accurate results than the group structure obtained using the Contributon theory.