Structural Optimization Research Articles

Numerical simulation and structural optimization of forced convection oven

Temperature uniformity is a fundamental parameter for a forced convection oven to guarantee food baking quality. In this study, computational fluid dynamics (CFD) simulation is employed to analyze the air flow and heat transfer within a forced convection oven. Subsequent experimental measurements were conducted to explore the temperature variations inside the oven. The maximum deviation between the numerical simulation results and the experimental data was 2.70%, demonstrating the high accuracy of the simulation model. Based on the established simulation model, we can meticulously investigate the impact of fan structures on the airflow and temperature fields. Carefully designing the fan structure can significantly enhance temperature uniformity across the oven’s baking racks, reducing the standard deviation of temperatures on the second and third baking racks from 5.46°C and 6.69°C to 2.26°C and 2.46°C, respectively. This study provides a robust design strategy for forced convection ovens, yielding tangible benefits for practical application and market competitiveness.

Does Government Digital Transformation Drive High-Quality Urban Economic Development? Evidence from E-Government Platform Construction

Digitalization represents a pivotal global development trend and serves as a significant force propelling economic and social transformation. This manuscript uses the global Malmquist–Luenberger (GML) model to estimate green total factor productivity (GTFP) across 284 Chinese cities from 2003 to 2018, taking the pilot policy of “construction and application of e-government public platforms based on cloud computing” as an example to assess the impact of government digital transformation on the qualitative development of the economy by using a difference-in-differences model to explore the path of its role and driving mechanism. The results reveal that government digital transformation promotes the qualitative improvement of the city’s economic development, and its driving effect shows a marginal incremental law. Moreover, government digital transformation can contribute to the formation of a “latecomer advantage” in the lagging regions, which creates a “catch-up effect” on the regions with favorable development foundations, excellent geographical conditions, high urban ranking, and high education quality. Additionally, government digital transformation boosts economic and social development quality through both innovation spillover and structural optimization.

Optimization of Composite Wing Structure with Static, Buckling, and Flutter Constraints using the Finite Element Method

In airplane structural design, creating a strong yet lightweight structure is crucial. During the preliminary design phase, structural sizing optimization is necessary to achieve an efficient and lightweight structure that meets safety standards. Composite materials offer a high strength-to-weight ratio, enabling a lighter structure without compromising strength. This work focuses on optimizing composite structure wings by considering laminae thicknesses as design variables and minimizing structure weight as the objective, using the gradient-based method. The optimization constraints include the Tsai-Hill criterion, buckling factor, and flutter speed to ensure the structure's safety against static load, buckling phenomena, and flutter phenomena. The optimization results indicate that the buckling factor is the most critical constraint, carrying the highest weight. The weight of the wing structure decreased by 53% and 79% after optimizing with static and flutter constraints, respectively. Despite minimal changes in weight after optimizing with the buckling constraint, the structure now meets the buckling safety criteria and is safe from buckling.

A New Zero Waste Design for a Manufacturing Approach for Direct-Drive Wind Turbine Electrical Generator Structural Components

An integrated structural optimization strategy was produced in this study for direct-drive electrical generator structures of offshore wind turbines, implementing a design for an additive manufacturing approach, and using generative design techniques. Direct-drive configurations are widely implemented on offshore wind energy systems due to their high efficiency, reliability, and structural simplicity. However, the greatest challenge associated with these types of machines is the structural optimization of the electrical generator due to the demanding operating conditions. An integrated structural optimization strategy was developed to assess a 100-kW permanent magnet direct-drive generator structure. Generated topologies were evaluated by performing finite element analyses and a metal additive manufacturing process simulation. This novel approach assembles a vast amount of structural information to produce a fit-for-purpose, adaptative, optimization strategy, combining data from static structural analyses, modal analyses, and manufacturing analyses to automatically generate an efficient model through a generative iterative process. The results obtained in this study demonstrate the importance of developing an integrated structural optimization strategy at an early phase of a large-scale project. By considering the typical working condition loads and the machine’s dynamic behavior through the structure’s natural frequencies during the optimization process coupled with a design for an additive manufacturing approach, the operational range of the wind turbine was maximized, the overall costs were reduced, and production times were significantly diminished. Integrating the constraints associated with the additive manufacturing process into the design stage produced high-efficiency results with over 23% in weight reduction when compared with conventional structural optimization techniques.

Multiscale topology optimization of anisotropic multilayer periodic structures based on the isogeometric analysis method

Multiscale topology optimization of anisotropic multilayer periodic structures based on the isogeometric analysis method

Vertex-based graph neural network classification model considering structural topological features for structural optimization

Traditional surrogate models always face the challenge of low accuracy when dealing with high-dimensional problems in structural optimization, this study aims to overcome this problem and proposes a vertex-based graph neural network (GNN) classification model. In contrast to conventional machine learning models that treat design variables as independent inputs, the proposed model develops a vertex-based graph representation to transform structural topological features and critical physical information into the graph data. According to a message passing mechanism based on the graph convolutional, it can extract the correlations among design variables and enhance its capability in handling high-dimensional structural optimization problems. Three truss examples, including a 10-bar with 10 variables, a 600-bar with 25 variables, and a 942-bar with 59 variables, are utilized to investigate the performance of the proposed surrogate model. The results demonstrate that the GNN-based surrogate model outperforms traditional machine learning approaches, particularly in the two high-dimensional problems, showcasing its superior ability to capture complex variable correlations and handle high-dimensional structural optimization tasks. Moreover, the proposed method significantly reduces the computational expenses by over 60% compared to conventional metaheuristic algorithms, while yielding optimal designs with comparable quality. These results demonstrate the efficiency and effectiveness of the GNN-based surrogate model in tackling complex, high-dimensional structural optimization problems.

Directed evolution-based non-gradient method for structural topology optimization

Existing non-gradient optimizers often rely on stochastic search strategies, which require a large number of finite element analyses to evaluate the objective function. In this study, a directed evolution-based non-gradient topology optimization method is proposed to reduce the high computational cost. A directed generation strategy of load path components is presented, based on the idea of fixed-point enumeration. This strategy determines the position and direction of components by locating the region with the maximum stress and enumerating samples with different orientations at each iteration step. Then, an element removal criterion is developed to gradually eliminate the redundant elements from the components. Consequently, an adaptive adjustment technique of component size is presented to overcome the numerical instability. The applicability and effectiveness of the proposed method are illustrated through numerical examples. The results show that the proposed method can reasonably generate structural load paths with exceptional efficiency compared to existing non-gradient algorithms.

Using In situ Transmission Electron Microscopy to Study Strong Metal‐Support Interactions in Heterogeneous Catalysis

AbstractPrecisely controlling the microstructure of supported metal catalysts and regulating metal‐support interactions at the atomic level are essential for achieving highly efficient heterogeneous catalysts. Strong metal‐support interaction (SMSI) not only stabilizes metal nanoparticles and improves their resistance to sintering but also modulates the electrical interaction between metal species and the support, optimizing the catalytic activity and selectivity. Therefore, understating the formation mechanism of SMSI and its dynamic evolution during the chemical reaction at the atomic scale is crucial for guiding the structural design and performance optimization of supported metal catalysts. Recent advancements in in situ transmission electron microscopy (TEM) have shed new light on these complex phenomena, providing deeper insights into the SMSI dynamics. Here, the research progress of in situ TEM investigation on SMSI in heterogeneous catalysis is systematically reviewed, focusing on the formation dynamics, structural evolution during the catalytic reactions, and regulation methods of SMSI. The significant advantages of in situ TEM technologies for SMSI research are also highlighted. Moreover, the challenges and probable development paths of in situ TEM studies on the SMSI are also provided.

Breaking the Bottleneck of Activity and Stability of RuO2-Based Electrocatalysts for Acidic Oxygen Evolution.

Electrochemical acidic oxygen evolution reaction (OER) is an important part for water electrolysis utilizing a proton exchange membrane (PEM) apparatus for industrial H2 production. RuO2 has garnered considerable attention as a potential acidic OER electrocatalyst. However, the overoxidation of Ru active sites under high potential conditions is usually harmful for activity and stability, thereby posing a challenge for large-scale commercialization, which needs effective strategies to circumvent the leaching of Ru and further activate Ru sites. Herein, a Mini-Review is presented to summarize the recent developments regarding the activation and stabilization of the Ru active sites and lattice oxygen through the modulation of the d-band center, coordination environment, bridged heteroatoms, and vacancy engineering, as well as structural protection strategies and reaction pathway optimization to promote the acidic OER activity and stability of RuO2-based electrocatalysts. This Mini-Review offers a profound understanding of the design of RuO2-based electrocatalysts with greatly enhanced acidic OER performances.

Theoretical and experimental approaches for flexible cage dynamic characteristics of four-point contact ball bearing considering multi-point contact behaviors

Multi-point contact often occurs between the balls and raceways of the four-point contact ball bearing (FPCBB) due to operating conditions and manufacturing errors, which can lead to unstable motion and wear of the cage. A dynamic analysis method for FPCBB with a flexible cage under multi-point contact is presented in this paper. Initially, the boundary conditions for different contact points (two-point, three-point, and four-point contact) are clarified through the research of the dynamic interaction and collision mechanisms between the balls and the raceways. Subsequently, based on the structure flexibility, and the collision effect among the balls, cage, and guiding ring, a dynamic model for FPCBB is constructed, and the heightened accuracy and superiority of the model are substantiated through comparative analyses with the conventional rigid cage model. Furthermore, the influence of operating conditions and manufacturing errors on the rotational stability and the wear degree of the cage are explored, and the accuracy and reliability of the model are experimentally verified. The results show that the degree of cage wear is strongly influenced by the contact state, and the maximum wear degree varies at different positions. Reasonable speeds, loads, and manufacturing error range can effectively improve the contact conditions, thereby augmenting cage motion stability and mitigating wear. This research contributes novel insights into the failure analysis and structural optimization of the FPCBB.

Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning.

Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA delivery efficiency; however, their design is suboptimal. Here, a rational method for designing LNPs is explored, focusing on the ionizable lipid composition and structural optimization using machine learning (ML) techniques. A total of 213 LNPs are analyzed using random forest regression models trained with 314 features to predict the mRNA expression efficiency. The models, which predict mRNA expression levels post-administration of intradermal injection in mice, identify phenol as the dominant substructure affecting mRNA encapsulation and expression. The specific phospholipids used as components of the LNPs, as well as the N/P ratio and mass ratio, are found to affect the efficacy of mRNA delivery. Structural analysis highlights the impact of the carbon chain length on the encapsulation efficiency and LNP stability. This integrated approach offers a framework for designing advanced LNPs and has the potential to unlock the full potential of mRNA therapeutics.

Structural and Compositional Optimization of Fe-Co-Ni Ternary Amorphous Electrocatalysts for Efficient Oxygen Evolution in Anion Exchange Membrane Water Electrolysis.

Anion exchange membrane water electrolysis (AEMWE) offers a sustainable path for hydrogen production with advantages such as high current density, dynamic responsiveness, and low-cost electrocatalysts. However, the development of efficient and durable oxygen evolution reaction (OER) electrocatalysts under operating conditions is crucial for achieving the AEMWE. This study systematically investigated Fe-Co-Ni ternary amorphous electrocatalysts for the OER in AEMWE through a comprehensive material library system comprising 21 composition series. The study aims to explore the relationship between composition, degree of crystallinity, and electrocatalytic activity using ternary contours and binary plots to derive optimal catalysts. The findings reveal that higher Co and lower Fe contents lead to increased structural disorder within the Fe-Co-Ni system, whereas an appropriate amount of Fe addition is necessary for OER activity. It is concluded that the amorphous structure of Fe-Co3-Ni possesses an optimal ternary composition and degree of crystallinity to facilitate the OER. Post-OER analyses reveal that the optimized ternary amorphous structure induces structural reconstruction into an OER-favorable OOH-rich surface. The Fe-Co3-Ni electrocatalysts exhibit outstanding performances in both half-cells and single-cells, with an overpotential of 256mV at 10mAcm- 2 and a current density of 2.0 Acm- 2 at 1.89V, respectively.

Molecular Electro(Photo)Catalysis: Triarylamine and Triarylimidazole Derivatives Mediated Oxidation Systems in Organic Electrosynthesis

AbstractOrganic electrosynthesis has become a green, efficient and sustainable alternative to traditional organic transformation for the redox reactions. In this connection, indirect electrolysis employing redox mediators is attaining increasing significance as it offers a chance to improve reaction selectivity and avoids the over‐oxidation/reduction issues encountered in direct electrolysis. Triarylamines and triarylimidazoles, as representative organic molecular mediators, have attracted widespread attention in recent years due to the advantages of well‐defined easy structural modification and tunable redox ability. Hence, in this minireview, the recent growth of triarylamine and triarylimidazole derivatives mediated electrochemical oxidation reactions were presented, highlighting the structural optimization, electrochemical performance and reaction mechanism of these organic mediators. Moreover, the photocatalytic and photoelectrocatalytic cases with these molecular mediators were also highlighted. To conclude, the current challenges and future prospects of this field are also discussed.

Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control

Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control

A progressive optimization of axial compression performance of 3D angle-interlock tubular woven composites through textile structure and yarn configuration innovations

Lightweight tubular composite materials with excellent compression performance are important structural components for load-bearing or energy absorption. Generally, outstanding performance depends on the structural design. Herein, a progressive structural optimization of 3D angle-interlock tubular woven reinforced composites (3DATWCs) is performed. The structural factors under consideration involve proportion of warp lining yarn, layers of weft yarn, surface constraint yarn and weft density. The axial compression performance and failure process of 3DATWCs with different structures are investigated through experiments and finite element method. The results indicate that increasing proportion of warp lining yarn can significantly improve the axial compression performance of 3DATWC. However, simply increasing proportion of warp lining yarn may decrease the straightness of yarn and constraint on warp yarns, leading to the performance reduction. The problem of performance reduction can be solved by introducing surface constraint yarn or increasing weft density. Finally, an optimization strategy is unveiled. Compared to ordinary structure, the ultimate load, plateau average load, ultimate stress, elastic modulus, total energy absorption and specific energy absorption of optimized 3DATWC increase by 101.88 %, 96.12 %, 77.46 %, 142.55 %, 119.06 %, and 77.39 %, respectively. Additionally, the axial compression performance is seriously affected by the fiber waviness, yarn waviness and interfacial properties, and can be further improved through reducing the waviness during the preparation of fabric preform.

Structural optimization and heat-fluid-solid simulation of a graded corundum-calcium hexaluminate purging plug by a multi-objective genetic algorithm approach

The purging plug is essential for enhancing production efficiency in molten steel refining, yet it faces challenges related to structural integrity due to its lifespan often not aligning with the ladle's repair cycle. This study introduces a novel corundum-calcium hexaluminate purging plug with gradual holes designed to alleviate internal stress concentration. Utilizing a multi-objective optimization model and fluid-solid coupling heat transfer method, the impact of structural parameters on thermal and mechanical properties was systematically investigated. The numerical simulation results indicate that increasing the number of gradient layers positively impacts temperature distribution. Notably, the D-210 (1.0 mm diameter) aperture reduces the stress gradient Δσmax by 648.06 MPa compared to D-212 (1.2 mm diameter) at the Y = 0.313 m section. Additionally, variation in inclination angle impacts tensile stress σt and shear stress τ, an inclination angle of 6° (α6) reduces maximum tensile stress by 211.41 MPa compared to an angle of 0° (α0).

Irregular array optimization for beamforming with a polar coordinate-based partition coding approach

Abstract An innovative irregular array configuration optimization method for enhancing beamforming is introduced. This study presents partition coding to optimize sensor positioning and quantity of a non-uniform concentric circular array. This novel approach transcends traditional techniques by integrating structural partitioning and performance optimization to quantify the array’s geometry-performance correlation. Sensor candidate positions are mapped in polar coordinates, with each configuration translated into a sensor position matrix form. A significant innovation lies in the adaptation of the partition coding genetic algorithm to enhance the encoding of candidate positions and to refine crossover and mutation operations, underpinned by an elite retention strategy for selecting the optimal array configuration. Both simulation and experimental results substantiate the method’s effectiveness, achieving high-resolution acoustic mapping with commendably low computational complexity.

Structural optimization of the NiFe2O4 spinel catalyst aimed at efficient electrocatalytic C-O bond cleavage of lignin

The intricate structure of lignin poses significant challenges to its high-value utilization. Electrocatalytic conversion of lignin into valuable aromatic compounds using spinel catalysts under mild conditions is a promising approach. The electrochemical conversion of lignin and its model compounds was conducted using Ni-Fe spinel catalysts, achieving selective cleavage of the C-O bond in lignin model with 72 % phenol selectivity and 82 % conversion. Additionally, efficient electrochemical cleavage of the C-O bond was achieved using alkali lignin. Structural characterization and theoretical calculations revealed that the Ni-Fe spinel catalyst exhibited a uniform electron distribution, enhancing the adsorption of the C-O bond and lowering the activation free energy associated with C-O bond cleavage. Moreover, the heterogeneous interface of NiFe2O4 further accelerated the reaction kinetics. This study represents a significant breakthrough in active site identification and mechanistic analysis, facilitating the selective conversion of lignin and providing an efficient method for lignin depolymerization.

Nature's Load-Bearing Design Principles and Their Application in Engineering: A Review.

Biological structures optimized through natural selection provide valuable insights for engineering load-bearing components. This paper reviews six key strategies evolved in nature for efficient mechanical load handling: hierarchically structured composites, cellular structures, functional gradients, hard shell-soft core architectures, form follows function, and robust geometric shapes. The paper also discusses recent research that applies these strategies to engineering design, demonstrating their effectiveness in advancing technical solutions. The challenges of translating nature's designs into engineering applications are addressed, with a focus on how advancements in computational methods, particularly artificial intelligence, are accelerating this process. The need for further development in innovative material characterization techniques, efficient modeling approaches for heterogeneous media, multi-criteria structural optimization methods, and advanced manufacturing techniques capable of achieving enhanced control across multiple scales is underscored. By highlighting nature's holistic approach to designing functional components, this paper advocates for adopting a similarly comprehensive methodology in engineering practices to shape the next generation of load-bearing technical components.

Recent advantages on mass transfer structure construction in transition metal‐based cost‐effective catalyst toward alkaline oxygen evolution

The electrochemical oxygen evolution reaction (OER) can be combined with various reactions to fabricate electrochemical energy conversion and storage devices while the slow kinetics and poor mass transfer capability at high current densities were the key constraints to its large‐scale application. Therefore, this review primarily focuses on design and optimization of mass transfer structures of TM‐metal‐based OER catalysts. Nanostructuring, porous design, and the creation of hierarchical architectures have been applied during catalyst synthesis to enhance the surface area and accessibility, thereby improving mass transfer and catalytic OER efficiency. Strategies including doping, substrate invitation, soft/hard templating has been utilized to accelerate mass transfer as well as the ion/electron conduction efficiency for the overall improvement of OER performance of the catalysts. These developments underline the critical role of advanced material design in achieving high‐performance OER catalysts and highlight the potential of TM‐based materials in cost‐effective and scalable applications.