Structural Optimization Analysis Research Articles

Electronic analysis of n-propyl xanthate complexes with group 12 metals: a theoretical-experimental study.

Xanthates are organic compounds of great interest in coordination chemistry due to their different basic sites, which allow them to form complexes with different coordination modes and geometries. These compounds are relevant in the environment and act as heavy metal collectors in aqueous environments. In this theoretical-experimental work, electronic spectroscopy studies of n-propyl xanthate complexes with group 12 metals were performed. This study verified structural differences in these systems, depending on the environment in which they are inserted. In addition, structural differences were observed when the solid was changed to an n-hexane solution. Thus, it was observed that the complexes assume a mononuclear structure in solution, while they present a polymeric form in the solid phase. The electronic spectra obtained through TD-DFT calculations were compared to those of the previously synthesized complexes. In the final theoretical analysis, the main orbitals involved in these transitions were assigned using population analysis calculations. The synthesis of the complexes was confirmed through infrared (MID and FAR), UV‒Vis, Raman, and NMR-1H spectroscopic analyses. The structures of the mononuclear and polymeric complexes were optimized in vacuum and n-hexane. Under vacuum, DFT levels M06L/6-311 + + G** + LANL2TZ and M06L/def2-TZVP were used for the mononuclear complexes, and M06L/LANL2DZ + LANL2 were used for the polymer complexes. For the calculations of the mononuclear complexes in n-hexane, the same level of theory was used for the solid state. TD-DFT calculations for 300 excited states were performed with the same levels of theory and used the optimized structures of the complexes. Furthermore, population analysis was carried out on all the systems studied. Gaussian 09 software was used for the structure optimization, TD-DFT, and population analysis calculations. GaussSum software was used to evaluate the molecular orbitals and electronic spectra.

From simulation to reality: CFD-ML-driven structural optimization and experimental analysis of thermal plasma reactors

Thermal plasma reactors offer an environment of high temperature, enthalpy, and reactivity, making them highly efficient for solid waste treatment and promising for clean energy production from municipal and industrial waste. Optimal geometrical parameters of the reactor can enhance waste treatment and reactor performance. This study presents a comprehensive analysis of 11 key geometrical parameters of a thermal plasma reactor. Utilizing CFD Fluent software, numerical simulations were conducted to generate a dataset. Subsequently, a predictive model focusing on the average temperature in the core melt zone was trained using six Machine Learning (ML) algorithms. The Particle Swarm Optimisation (PSO) algorithm optimized the hyperparameters of the Gradient Booster Regression (GBR) model, which was combined with a Genetic Algorithm (GA) to identify the reactor's optimal geometrical parameters. A DC arc plasma torch-solid waste thermal plasma reactor treatment system was established on this basis. The study also explored the effects of gasification coefficient, reaction temperature, and thermal plasma jet mode on system performance. Findings indicate that the PSO-GBR model achieved the highest prediction accuracy, with the temperature in the core reaction zone reaching 3621 K. The deviation between numerical simulations and machine learning predictions was a mere 1.3%. Enhancing syngas yield and energy efficiency is achievable by controlling reaction temperature and increasing the gasification coefficient. A laminar plasma jet mode, at equal power, provides a more effective reaction environment. The accuracy and reliability of the machine learning-driven regression model and optimization results are significant in guiding the optimal design of plasma reactors and advancing waste-to-energy conversion processes.

Enhancing heat transfer efficiency in corrugated tube heat exchangers: A comprehensive approach through structural optimization and field synergy analysis

In this paper, a precise and efficient method to optimize corrugated tube heat exchangers is proposed by combining computational fluid dynamics simulation with optimization. The optimization of tubular heat exchangers involves contradictory Colburn coefficient j, and the friction coefficient f, so it is a multi-objective optimization problem. The approximate model is obtained by an extreme learning machine, and the structure parameter of the heat exchanger is optimized by the nondominated sorting genetic algorithm-Ⅱ. Compared to the results between the original and optimized tube, the optimized structure Colburn coefficient increased by 5.1 % and the friction coefficient decreased by 9.3 %. Finally, the internal flow field is compared qualitatively from temperature, pressure, and velocity. The optimization effect is further emphasized by using the field synergy theory.

活动转板式马铃薯分级装置优化设计分析

The study focused on enhancing a movable rotating plate type grading device for potatoes, prioritizing safety and weight reduction. By optimizing the body frame and movable rotating plate using ANSYS Workbench 2022, a static analysis was conducted to confirm compliance with design requirements for strength and deformation. The optimized frame experienced a remarkable 32.3% weight reduction. Additionally, the direct optimization module of ANSYS Workbench 2022 was employed to lighten the movable rotating plate, resulting in a 22.86% reduction in the total mass of the device. This research serves as an invaluable reference for the structural design and optimization analysis of potato grading devices.

Algorithms of isogeometric analysis for MIST-based structural topology optimization in MATLAB

Algorithms of isogeometric analysis for MIST-based structural topology optimization in MATLAB

A structural optimization analysis of cable-driven soft manipulator

Cable-driven soft robots hold significant potential for surgical and industrial applications, yet their performance and maneuverability can be further enhanced through design optimization. By optimizing the design, factors such as bending angles, manipulator deformation, and overall functionality can be directly influenced, leading to improved interaction with the environment and more accurate task performance. This article presents a physics-based design optimization approach for cable-driven soft robotic manipulators, aiming to enhance bending performance through structural design enhancements. Four design criteria, namely, cross-sectional shape, material, gap shape, and gap size, are considered in the optimization process. Given the inherent nonlinearity of soft materials, finite element modeling techniques are employed to analyze the effects of modifying each design parameter on displacement and bending angle. The manipulator’s design is evaluated using ABAQUS/CAE, and an analysis of variance test is conducted to identify significant performance differences among the design parameters. The results reveal that material variation has the most substantial impact, followed by gap shape and gap size. Based on subsequent parameter optimization, Dragon Skin 10 is determined to be the optimal material for bending motion, while a trapezoidal gap shape is preferred. In addition, a genetic algorithm is utilized to select a maximum gap size of 8.87 mm. These findings provide valuable insights into key design principles for cable-driven soft manipulators, aiming to enhance flexibility and reduce actuation forces. By establishing a fundamental understanding of the relationship between morphology and motion capability, this methodology demonstrates an effective simulation-driven optimization approach that incorporates the nonlinear elastic behavior of materials to improve performance. Overall, this work establishes a framework for optimizing cable-driven architectures to suit various applications in the field of soft robotics.

Multi-objective structure optimization and performance analysis of catalytic micro-reactor channel designed by an improved topology optimization model

Previous micro-reactor channel studies mainly focus on the limited design space or structure layout, rarely exploits design forms in which the procedural topology structure evolves freely. Therefore, a multi-objective topology optimization model are proposed for the inverse free evolution of micro-reactor channels, which can synergistically balance the effects among three objectives of reaction rate, flow loss and heat transfer to achieve optimal comprehensive performance. The topology optimization problem is formulated based on the material density-based design variable. The fluid dynamics response of design variables at each optimization iteration is updated by solving multi-physics governing equations, objective functions and variable constraints. Adjoint-based sensitivity analysis and GCMMA algorithm are used to obtain the gradient information of objective functions and update design variables, respectively. Additionally, the strict volume constraints and proposed pseudo-design-domain concept are introduced into the multi-objective function. In numerical examples, the influence of different Re conditions and different multi-objective weight-ratios on optimal designs is investigated, and their regular evolution mechanisms and the trade-off between performance optimization and structure evolution are analyzed. Then, the optimization principles for topology structure and performance parameters under the control of multi-objective functions are revealed. Finally, the comprehensive performance of micro-reactors under topology optimization method is compared with that of traditional micro-reactors to demonstrate the effectiveness of the improved method and reveal the physical mechanisms of multi-objective performance enhancement. The comparison results show that topology optimization designs can achieve 86.1% growth in reaction rate and 47.1% reduction in flow loss, and comprehensive performance coefficients have also been significantly optimized.

Structural Reliability and Optimization Using Differential Geometric Approaches

This study investigates how differential geometry ideas can be used to effectively carry out structural optimization and reliability analysis. Strong mathematical representations and methods for examining intricate surfaces and forms are provided by differential geometry. The basic ideas of differential geometry, such as tensors, manifolds, and curvature, are initially introduced in the work. Then, to account for ambiguities in the geometry, the probability theory in tangent spaces is developed. As a result, structural reliability can be determined using propagating uncertainty. To enable reliability-based design optimization, differential geometry representations are linked with optimization techniques. The proposed differential geometry-based approach is applied in a number of case studies to trusses, airplane wings, car bodies, and ship hulls. The outcomes show a significant increase in productivity and scalability compared to conventional finite element methods. The article offers new tools for dealing with uncertainty.

Design and Analysis of New Hydrogen Oxygen Pneumatic Proportional Reducing Pressure Valve

The pneumatic proportional pressure reducing valve is the main pressure control component in the fuel cell gas supply system, which directly affects the performance of the entire fuel cell system. Based on the research background of the hydrogen oxygen fuel cell gas supply system, a set of pneumatic proportional pressure reducing valves suitable for hydrogen oxygen gas environments was designed. Through structural design and multi-parameter analysis, the structural parameter design optimization and characteristic analysis of the proportional valve were proposed. It can provide high-precision and high stability gas control capabilities for fuel cell systems, and provide support for engineering applications.

Structural optimization and modal analysis of belt conveyor driving drum

Structural optimization and modal analysis of belt conveyor driving drum

Full-scale resonant bending fatigue testing of casing joints under bending moment load

The quality and reliability of oil casings play a pivotal role in the safety and efficiency of offshore oil and gas exploration. This study conducted research on resonant bending fatigue of 9-5/8-inch casing joints and established a systematic theoretical model for resonant bending fatigue based on the Euler–Bernoulli beam equation. The relationships between parameters affected by frequency and mode shape functions were deduced and solved to analyze the influence of wall thicknesses, length and weights at both ends of the specimen on the natural frequency of the resonant bending fatigue system. A full-scale fatigue test was then designed and conducted to investigate the circumferential stress distribution, reveal the stress amplitude–frequency response characteristics of the specimen, and explore resonance response behaviors under different excitation forces. This was done to establish a method for evaluating the oil casing life and obtain fatigue life characteristic curves of the specimen. A finite element model was employed to analyze the internal stress distribution of the coupling under bending moment load, considering the influence of material properties, thread lead angle and non-linear contact on casing joints. Finally, the fatigue life algorithm was summarized and compared by integrating the stress simulation results with the material properties of the specimen, elucidating the fatigue damage evolution process of casing joints. This research provides a significant engineering reference value for structural design optimization, fatigue testing and life analysis of oil casing threaded joints.

Structure-guided screening of protein-protein interaction for the identification of Myc-Max heterodimer complex modulators

De-regulation of oncogenic myelocytomatosis (c-Myc or Myc) transcription factor is one of the most common molecular anomalies encountered in human cancers, and it is typically linked to many aggressive malignancies including breast, lung, cervix, colon glioblastomas, and other haematological organs. The Myc belongs to the basic helix-loop-helix zipper protein family (bHLH-ZIP), and its dimerization with another principal interactor protein partner Myc-associated factor X (Max) is essentially required for cellular transformation, cell growth and proliferation, and transcriptional activation. Intermolecular interactions have been evaluated between hetero-dimer Myc-Max protein, which identified protein-protein interaction (PPI) specific modulators using highly précised molecular docking study followed by long-range interaction stability analyzed through molecular dynamic (MD) simulation. Moreover, ADME profile analyses have been estimated for screened hit compounds. MM-GBSA-based binding free energy (ΔG) estimations have been performed for all screened hit compounds obtained from multi-step molecular docking-based virtual screening technique. According to the employed various rigorous multi-chemometric techniques, four identified inhibitors/modulators appear to have a considerable number of intermolecular contacts with hotspot residues in the hetero-dimer interface region of the Myc-Max PPI complex. However, identified hit compounds might need further structural optimization or extensive biophysical analyses for better understanding of the molecular mechanism for exhibiting the Myc-Max PPI interface binding stability. Communicated by Ramaswamy H. Sarma

Progressive failure method and structural optimization analysis of marine sandwich L-shaped composite joints

The design and load-bearing characteristics of connection joints are the main factors restricting the load-bearing capacity of composite hull structures. In the present paper, the transition joint between the deck and the broadside of the main hull is designed and manufactured by combining bolt connection and cementing, which provides a reference for the design of large-scale composite hull structure. Based on Shokrieh-Hashin criterion and coherent zone modeling method, the simulation method of progressive failure of complex composite structures is improved and renewed. The test results show that the failure mode and ultimate load predicted by numerical method are in good agreement with the test results. Based on the numerical calculation method, the influences of the overlap width d of the hat profile and the overlap width s of the hand-pasted reinforced area on the strain, displacement and ultimate bearing capacity of the joint are quantitatively analyzed, and the optimal scheme of the full-scale deck-side joint is formed, which provides support for the design of large-scale marine composite joint.

Theoretical aspects and applications of goal‐oriented reanalysis methods

AbstractIn this contribution, a method for goal‐oriented reanalysis is presented. The process allows predicting the change in a quantity of interest due to structural modifications (design changes), for example, the shape, the topology, material properties, and so forth. The approach is based on a goal‐oriented method using primal and dual problems. Furthermore, the procedure is easy to implement in existing finite element programs because no derivatives with respect to the design variables are necessary. The proposed method can be used in classical applications with successive design steps, such as structural optimization, reliability, or structural damage analysis. The repeated structural analysis and calculating quantities of interest involve significant computational effort. Goal‐oriented reanalysis methods can reduce the overall computational cost in the numerical simulation.

Based on density functional theory: Reactions mechanism of Tan (n = 2–5) clusters and water molecules

The reaction paths of water molecules and Tan (n = 2–5) clusters were predicted by density functional theory calculations. The lowest energy structures of Tan@H2O (n = 2–5) clusters were found and their complexes were structurally optimized at the adsorption sites of clusters. Meanwhile, all reactants, products and transition states were found by structural optimization and frequency analysis. Based on NBO and IRI analysis, Water molecules adsorbed on the surface of tantalum clusters can all form a Ta-O bond. From the dissociation channel, the Tan (n = 2–5) clusters can extract H2 from water molecules and the whole reaction process is exothermic, these results help to design practical hydrogen generation schemes by these reactions.

Density functional theory studies on properties of cluster ConMoS (n=1 ~ 5): interatomic interactions, electronic properties, frontier orbitals.

To comprehend the microscopic property alterations within the ConMoS cluster (n=1-5), this study investigates its internal interactions, electronic characteristics, and orbital correlations employing density functional theory. Structural optimization and theoretical analysis of the cluster are conducted using the Gaussian09 software package, considering various spin multiplicities and employing the B3LYP/def2tzvp quantum chemical method as the computational standard. The outcomes reveal the optimization of the cluster, resulting in 21 stable configurations while continually acquiring energy from the external environment. Analysis of the interaction region indicator functions, the independent gradient model based on Hirshfeld partition, the localized orbital indicator functions, and the electron localization function reveals a trend toward chemical bonding interactions within the interatomic interaction regions. Moreover, the interatomic forces exhibit a high likelihood of engaging in covalent bonding interactions. Both Co and Mo atoms display greater electron delocalization, facilitating the exchange of electrons with the external environment. The paper discuss electron space range, hardness and softness, polarizability, dipole moment, Mulliken population analysis, density of states, HOMO-LUMO diagram, and UV-Vis spectra. Configuration 5a exhibits the broadest electron delocalization and the highest reactivity. It maintains structural stability in external conditions and displays the most polarized molecules. Metal atoms in this cluster exhibit superior mobility compared to non-metal atoms. We elucidate the electron density aggregation region within the cluster. Configuration 1a demonstrates the highest correlation with molar absorption coefficient for its peak. Analyzing the HOMO and LUMO orbital delocalization index and center-of-mass distances revealed that the front orbits of configuration 5a exhibited a broad distribution in space and the minimum center-of-mass distance. This study presents a theoretical investigation of Co-Mo-S clusters employing density functional theory (DFT). DFT is a prevalent method for exploring the electronic structure and characteristics of atoms, molecules, and solids. The paper examines cluster attributes encompassing interatomic interactions, electronic properties, and frontier orbitals. Gaussian09 software is employed for optimizing cluster structures, while the analysis is augmented using Multiwfn wave function analysis software. By harnessing these theoretical and computational tools, it aims to delve deeper into cluster properties, yielding valuable insights.

Structural Optimization and Stability Analysis for Supercavitating Projectiles

Structural Optimization and Stability Analysis for Supercavitating Projectiles

Comprehensive kinetostatic modeling and morphology characterization of cable-driven continuum robots for in-situ aero-engine maintenance

In-situ maintenance is of great significance for improving the efficiency and ensuring the safety of aeroengines. The cable-driven continuum robot (CDCR) with twin-pivot compliant mechanisms, which is enabled with flexible deformation capability and confined space accessibility, has emerged as a novel tool that aims to promote the development of intelligence and efficiency for in-situ aero-engine maintenance. The high-fidelity model that describes the kinematic and morphology of CDCR lays the foundation for the accurate operation and control for in-situ maintenance. However, this model was not well addressed in previous literature. In this study, a general kinetostatic modeling and morphology characterization methodology that comprehensively contains the effects of cable-hole friction, gravity, and payloads is proposed for the CDCR with twin-pivot compliant mechanisms. First, a novel cable-hole friction model with the variable friction coefficient and adaptive friction direction criterion is proposed through structure optimization and kinematic parameter analysis. Second, the cable-hole friction, all-component gravities, deflection-induced center-of-gravity shift of compliant joints, and payloads are all considered to deduce a comprehensive kinetostatic model enabled with the capacity of accurate morphology characterization for CDCR. Finally, a compact continuum robot system is integrated to experimentally validate the proposed kinetostatic model and the concept of in-situ aero-engine maintenance. Results indicate that the proposed model precisely predicts the morphology of CDCR and outperforms conventional models. The compact continuum robot system could be considered a novel solution to perform in-situ maintenance tasks of aero-engines in an invasive manner.

Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization

Leave-one-out cross-validation (LOOCV) is a widely used technique in model estimation and selection of the Kriging surrogate model for engineering problems, such as structural optimization and reliability analysis. However, the traditional LOOCV method has some disadvantages in terms of accuracy and efficiency. This paper proposes an enhanced-LOOCV method that incorporates hyperparameters from the Kriging model based on the complete training dataset (i.e., the complete Kriging model) into the LOOCV error calculation. By keeping the model hyperparameters in LOOCV consistent with the complete Kriging model, it reduces the number of hyperparameter optimizations and significantly increases the accuracy and efficiency of the LOOCV process. Additionally, a decremental calculation is proposed to reduce the computational costs of the correlation matrix inversion without sacrificing accuracy, resulting in an improved time complexity of the traditional LOOCV from O(n4) to O(n3). Experimental results with thirty test functions verify that the enhanced-LOOCV has better estimation performance than the Kriging model with significantly higher efficiency compared to the traditional LOOCV. Numerical experiments and an engineering case in optimization demonstrate that the enhanced-LOOCV can reduce the number of samples needed for infilling in the Kriging model, and it is more suitable for expensive optimizations in engineering.

System reduction-based approximate reanalysis method for statically indeterminate structures with high-rank modification

Efficient structural reanalysis for high-rank modification plays an important role in engineering computations which require repeated evaluations of structural responses, such as structural optimization and probabilistic analysis. To improve the efficiency of engineering computations, a novel approximate static reanalysis method based on system reduction and iterative solution is proposed for statically indeterminate structures with high-rank modification. In this approach, a statically indeterminate structure is divided into the basis system and the additional components. Subsequently, the structural equilibrium equations are rewritten as an equation system with the stiffness matrix of the basis system and the pseudo forces derived from the additional elements. With the introduction of the spectral decomposition, a reduced equation system with the element forces of the additional elements as unknowns is established. The approximate solutions of the modified structure can then be obtained by solving the reduced equation system through a pre-conditioned iterative solution algorithm. The computational costs of the proposed method are compared with those of two other reanalysis methods, and numerical examples including static reanalysis and static nonlinear analysis are presented. The results demonstrate that the proposed method has excellent computational performance for both structures with homogeneous material and structures composed of functionally graded beams. Furthermore, the superiority of the proposed method suggests that the combination of system reduction and pre-conditioned iterative solution technology is an effective approach to develop high-performance reanalysis methods.