Structural Design Optimization Research Articles

Lifetime assessment of deep-sea manned cabin with PMMA pressure-resistant hull prior to micro-cracks

Fully-transparent cabin type, utilized in manned submersibles, is typically constructed using viscoelastic PMMA (polymethyl methacrylate) material. Accurate fatigue life assessment of manned cabin is of paramount importance to ensure structural safety. In this study, a calculation process for evaluating the fatigue life of PMMA manned cabins based on a continuum damage mechanics model combined with the viscoelastic constitutive relationship of the PMMA material in response to the absence of related specification is proposed. Fatigue tests of PMMA material are performed for determination of damage evolution model parameters and the calculation process is validated by the fatigue test data. Tailored subroutines are then developed by using ABAQUS software tool, respectively for structural analysis of a typical manned cabin with PMMA pressure-resistant hull, openings, hatches and penetration plates and simulating trapezoidal loading history acting on manned cabin. Subroutine coupling is accordingly employed to conduct parametric analysis on fatigue crack initiation life of the fully-transparent manned cabin with consideration of different outside pressure level and operation time and valuable results are obtained for identifying key areas of concern. This study intends to contribute to further cabins safety enhancement, as well as to design optimization and fatigue analysis of underwater structures involving PMMA materials.

A new model of thermo-mechanical actuation for twisted and coiled polymer muscles with initial curvature

Abstract The twisted and coiled polymer (TCP) artificial muscle is one type of novel soft actuator for mimicking natural skeletal muscle that can provide large linear and torsional actuation and energy density. Twisting and coiling are the pivotal steps in fabricating TCP muscles. The influence of twisting on the actuation response of TCP muscles has been extensively investigated recently. However, the influence of coiling remains unclear. Based on the finite strain theory, we establish a new thermo-mechanical actuation model for TCP muscles with initial curvature. The theoretical predictions based on the model align well with the finite element simulations, accurately capturing the actuation response of thermally-activated TCP muscles. It is revealed that twisting contributes positively to the actuation, while coiling has a passive effect. Geometrical parameters, such as the helix radius and helix angle, can effectively regulate the actuation performance of TCP muscles. Furthermore, an optimal bias angle is identified that maximizes both the recovery torque and the linear actuation. This study sheds light on the structural optimization design of TCP muscles.

Design and optimization of heat dissipation for a high-voltage control box in energy storage systems

Abstract To address the issue of excessive temperature rises within the field of electronic device cooling, this study adopts a multi-parameter optimization method. The primary objective is to explore and realize the design optimization of the shell structure of the high voltage control box, aiming to effectively mitigate the temperature rise in internal components and enhance their thermal management efficacy without altering the fan performance, environmental conditions, or spatial layout. Initially, the study employs computational fluid dynamics methods to investigate the heat dissipation characteristics of the high voltage control box, subsequently verifying the simulation parameters' accuracy through temperature rise tests. Building upon this foundation, the article conducts a thorough analysis of how the position and shape of the box's openings impact the device's temperature rise. The findings suggest that configuring circular openings on the front and rear sides can optimize the heat dissipation effect. Moreover, the SHERPA algorithm was employed to refine the size and distribution of the openings on the outer shell of the high voltage control box through multi-parameter optimization, yielding locally optimal structural parameters. Post-optimization, the temperature measurement points within the high voltage control box exhibited a maximum reduction in temperature rise of 27.16%. The pivotal contribution of this methodology is the application of a data-driven decision-making process for the enhancement of conventional heat dissipation designs. This research offers invaluable practical insights and novel perspectives on the optimization of thermal management designs for box-type electronic devices.

Tidal turbine blade design optimization based on coupled deep learning and blade element momentum theory

The practical design optimization of blade structures is crucial for enhancing the power capture capability of tidal turbines. However, the significant computational costs required for directly optimizing turbine blades through numerical simulations limit the practical application of blade structure optimization. This paper proposes a framework for tidal turbine blade design optimization based on deep learning (DL) and blade element momentum (BEM). This framework employs control points to parameterize the three-dimensional geometric shape of the blades, uses convolutional neural networks to predict the hydrodynamic performance of each hydrofoil section, and couples BEM to forecast the performance of tidal turbine blades. The multi-objective non-dominated sorting genetic algorithm II is employed to optimize the geometric parameters of turbine blades to maximize the power coefficient and minimize the thrust coefficient, aiming to obtain the optimal trade-off solution. The results indicate that the prediction of the DL-BEM model agrees well with experimental data, significantly improving optimization efficiency. The optimized tidal turbine blades exhibit excellent power coefficients and reduced thrust coefficients, achieving a more balanced structural solution. The proposed optimization framework based on DL accurately and rapidly predicts the performance of tidal turbines, facilitating the design optimization of high-performance tidal turbine blades.

A hybrid machine learning and evolutionary approach to material selection and design optimization for eco-friendly structures

A hybrid machine learning and evolutionary approach to material selection and design optimization for eco-friendly structures

Ultimate strength failure mechanism and optimization design of marine composite deck grillage structures with opening

In this paper, the ultimate bending strength and the failure mechanism of the marine composite deck grillage structure are experimentally and numerically investigated, before the optimization design is conducted to enhance the ultimate bearing capacity. The ultimate strength experiment of the composite deck grillage structure is first carried out. Based on the improved progressive failure method and the cohesive damage theory, the numerical simulation considering fiber failure, matrix failure, shear failure and adhesive layer failure is conducted and the predicted strains, load–displacement curves, and failure modes of the specimen are compared with the experimental ones to verify the accuracy of the present method. Both experimental and predicted results show that the stiffeners and the laminated deck near the opening are the stress concentration areas, and the matrix failure is main failure mode. The optimization results show that the suitable panel thickness can reduce the stress concentration effect and avoid premature fracture in localized areas due to stress concentration. The increase of the stiffener heights can significantly increase the ultimate strength and the bending stiffness. These findings can provide support for the design of composite deck grillage structures in ships.

Parametric study on the decarbonization potential of structural system and concrete mix design choices for mid-rise concrete buildings

Mid-rise reinforced concrete buildings are projected to continue being the predominant typology for urban development. Thus, reducing the carbon footprint of such buildings is critical for achieving a sustainable built environment. Reducing the amount of concrete and steel in a building through structural and mix design optimization is identified as a primary resource efficiency strategy. This paper is among the first to present evidence of the decarbonization potential of these dematerialization strategies on a building level. The study combines structural design choices such as slab system design, steel reinforcement optimization and span width with materials-based strategies, such as low binder concrete and low-carbon binders. For each scenario, material quantities are calculated following design prescriptions by EN1992–1–1 while state-of-the art life cycle inventory data are adopted to calculate the carbon footprint. Results show that shifting towards more efficient structural systems (i.e., waffle slab system) could save up to 20% of the carbon footprint on the building level compared to more traditional systems, such as slab on beams and flat slabs. In addition, reducing the spans from 7.5 to 5 m can save up to 20% more. Finally, the use of low-clinker cement in low-binder concrete can save another 50% in terms of CO2 impact per built-up area. Realistically, results of the case study concluded that implementing these three strategies could reduce the typical 232 kg CO2e/m2 value of the carbon footprint of structural elements of a mid-rise building up to only 58 kg CO2e/m2, i.e., a four-fold reduction.

Benchmark problems in optimum structural design of 3D reinforced concrete frames

Benchmark problems in optimum structural design of 3D reinforced concrete frames

Effect of temperature softening on the actuation performance of twisted and coiled polymer muscles

Twisted and coiled polymer (TCP) artificial muscles represent a promising class of actuators capable of emulating natural skeletal muscle, offering substantial linear actuation and energy density. The actuation characteristics of the thermally-activated TCP muscle and twisted fiber are characterized experimentally, emphasizing the influence of the temperature-softening effect. We find that the recovered torque of twisted fiber increases monotonically with temperature while showing a non-monotonical variation with the fiber bias angle. The variations of actuation strain and end torque with three structural parameters of TCP muscles, i.e., fiber bias angle, helical angle, and spring index, are comprehensively investigated. An optimal bias angle and spring index are present. A significant temperature-softening effect of precursor fiber is observed; a polymer becomes softer and more pliable as the temperature increases. A thermo-mechanical model for predicting the effect of temperature-softening on TCP muscle is established and validated with experimental data. This work facilitates precise control and guides the structural optimization design of the thermally-activated TCP muscle.

基于EDEM的滩涂养殖双螺旋推进器仿真分析与试验

Aiming at the problems of coastal ecological damage and low yield of mudflat aquaculture caused by the invasion of M. alterniflora, in order to improve the operational efficiency of mudflat wet and soft ground, and to promote the ecological balance and the development of coastal agriculture, a walking device with twin spiral propellers for muddy wet and soft ground was designed. Using EDEM simulation software to simulate and analyze, the discrete element model of muddy soil particles is established to analyze the interaction mechanism with the spiral propeller and the operation propulsion effect, and it is concluded that the spiral propeller will not produce congestion phenomenon during the operation; data are collected through several simulation tests, and the optimal parameter design of the spiral propeller structure is derived from the response surface analysis, and the spiral propeller is designed to operate at a speed of 2.416 mph in the simulation with the optimal parameter of structural design. The field test shows that the optimal height of the spiral blades is 50 mm, the total length of the drum is 2,970 mm, the helix angle of lift is 30 °, the pitch is 453 mm, and the propelling speed is 2.36 m/s. The data collected through several simulation tests are used to find the optimal parameter design of the spiral propeller structure, and the simulation speed of the spiral propeller in the optimal structural design parameter is 2.416 m/s.

Analytical models of the electric field and carrier transit time in the base of bipolar junction transistors

The conventional theory of the bipolar junction transistor (BJT) does not clarify the electric field and its effect on the carrier transit in the uniformly doped base region, although the electric field is considered to be a second-order effect at high-level injection. In this study, analytical models of the carrier distribution, electric field, and carrier transit time in the base region are developed without making additional assumptions. It is found that the electric field may change direction, from forward to reverse, as long as the injection level reaches a criterion, affecting the carrier transport behavior. The models are based on the renovated p–n junction theory, provide insight into the carrier transit and are able to cover all levels of injection without the need to handle the low- and high-level injection separately. These models hold significance in retrieving the theoretical integrity of bipolar junction devices, and can be useful for further investigation in structure design, capacitance control, and AC performance optimization of the p–n junction-based device.

Vehicle vibration test platform structure design and control strategy optimization

AbstractFor the needs of vehicle vibration test platform with high precision, large load capacity and fast response, the three‐dimensional model design and analysis of vehicle vibration test platform are carried out; in order to improve the motion performance of the platform, a vibration test plat‐form control strategy combining hybrid heuristic algorithm and PID control is proposed. Based on the designed 3D model parameters, the single‐channel mathematical model of the servo‐electric cylinder is derived and a hybrid heuristic algorithm PID optimization model is established to compare and analyze the control performance of the platform with the Ziegler‐Nichols method PID. The results show that the step system overshoot is 3.80% and the dynamic performance of the system is significantly improved when the hybrid heuristic algorithm PID control is used. The simulation system model of vehicle vibration test platform control is established, and the operation results show that the platform is closer to the input signal in the spatial position change curve when the hybrid heuristic algorithm PID control is used. Its maximum displacement error is 0.09 mm, and the motion accuracy of the system is improved by 61% compared with the Ziegler‐Nichols method PID control.

Multi-scale collaborative prediction of optimal configuration for carbon fiber woven composites based on deep learning neural networks

Since carbon fiber woven composite panels contain complex fiber bundle structures, the aim of this paper is to investigate the factors influencing the macro-equivalent properties and topological flexibility of composite panels resulting from variations in fiber bundle configuration. Firstly, the structural topology optimization design of carbon fiber composite plates was achieved using a multi-scale modeling approach with the variable density method. Nonlinear relationships between carbon fiber bundle configuration parameters and macroscopic equivalent properties, as well as macroscale topological flexibility, were determined using deep learning methods, respectively. The maximum error in the predicted values of the deep learning network model does not exceed 0.5% when compared with the results of the finite element calculations. In addition, utilizing deep learning networks to predict macro-equivalent properties of composites can significantly decrease computational time. The impact of varying the number of filaments in fiber bundles and the spacing of the fiber bundles on the flexibility of the topology was determined using a deep learning network. The optimal topological flexibility, along with its corresponding fiber bundle spacing and number of fiber bundle filaments, are globally searched within the design domain using an adaptive genetic algorithm. The study in this paper can provide a reference for designing fiber bundle configurations of composites for practical applications.

Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer

The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing is established considering the ring deformation. The model was used to calculate and comparatively analyze the ring deformation and contact characteristics of thin-walled bearings under rigid and flexible conditions, on the basis of which the mechanism of ring deformation was described, and the effects of load conditions, ring thickness and radial clearance on ring deformation, flexible contact characteristics, and ultimate carrying capacity were analyzed. The results show that the distribution of contact loads is the main factor affecting the ring deformation. The ring deformation can optimize the bearing contact characteristics, and the greater the deformation, the more pronounced the optimization effect. However, excessive ring deformation makes the contact ellipse more susceptible to truncation, which, in turn, reduces the ultimate carrying capacity. This study indicates a 38.2% decrease in the carrying capacity of the flexible ring model compared to that of the rigid ring model. In this paper, the effect of ring deformation on bearing mechanical characteristics is deeply discussed. The research results have important guiding significance for the structural optimization design of thin-walled bearings.

Biopolymer Meets Nanoclay: Rational Fabrication of Superb Adsorption Beads from Green Precursors for Efficient Capture of Pb(II) and Dyes.

Renewable, green, and safe natural biopolymer-derived materials are highly desired for the purification of pollutants, but significantly improving their performance without the introduction of additional harmful chemicals remains a huge challenge. Based on the concept of "structure optimization design", environment-friendly composite beads (named SA/PASP/RE) with excellent adsorption performance and recyclability were rationally constructed through a green ionic crosslinking route, using the completely green biopolymer sodium alginate (SA), sodium salt of polyaspartic acid (PASP), and the natural nanoclay rectorite (RE) as starting materials. The nano-layered RE was embedded in the polymer matrix to prevent the polymer chain from becoming over-entangled so that more adsorption sites inside the polymer network were exposed, which effectively improved the mass transfer efficiency of the adsorbent and the removal rate of contaminants. The composite beads embedded with 0.6% RE showed high adsorption capacities of 211.78, 197.13, and 195.69 mg/g for Pb(II) and 643.00, 577.80, and 567.10 mg/g for methylene blue (MB) in Yellow River water, Yangtze River water, and tap water, respectively. And the beads embedded with 43% RE could efficiently adsorb Pb(II) and MB with high capacities of 187.78 mg/g and 586.46 mg/g, respectively. This study provides a new route to design and develop a green, cost-effective, and efficient adsorbent for the decontamination of wastewater.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

Investigations on the Johnson-Cook Constitutive and Damage-Fracture Model Parameters of a Q345C Steel

Due to the rapid development of high-speed trains, the service safety of vehicle body materials and structures has become a focal point in transport and impact engineering. Numerical simulations on the collision resistance of vehicle materials and structures are crucial for the safety assessment and optimal structural design of high-speed trains but have not been fully investigated due to the lack of damage model parameters. This study focuses on the Johnson-Cook (J-C) constitutive and damage-fracture models of a typical vehicle material, Q345C steel. A series of mechanical tests are conducted on the Q345C steel, including the quasi-static and dynamic compression/tension tests, quasi-static tension tests at different temperatures, and fracture tests along different stress paths, using the material test system and the split Hopkinson pressure/tension bar. Then, the parameters of the Johnson-Cook constitutive and damage-fracture models are calibrated based on the experimental results. In terms of the damage parameters related to stress paths, a new method of combining experiments and simulations is proposed to obtain the real, local fracture strains of the Q345C steel samples. This method allows the measurements of equivalent plastic strain and stress triaxiality histories under nonlinear stress paths, which are hardly accessible from individual experiments, and facilitates the accurate calibration of stress-path-related damage parameters. In addition, a high-speed plate penetration test is used to validate the J-C parameters, which can be directly implemented in the commercial finite element software Abaqus. The projectile trajectories from the simulation and experiment agree well with each other, demonstrating the reliability of the model parameters for impact scenarios and the efficiency of the experimental procedures utilized for calibration.

Numerical study on the melting performance of PCM by fractal fins in three-tube heat storage system

In order to solve the problems of low heat storage efficiency and uneven melting in the PCM area of the traditional heat accumulator, this paper takes the three-tube phase change heat accumulator with eight longitudinal fins as the basic structure to ensure that the volume of the fins in the heat accumulator is unchanged. The longitudinal fins are fractal in the shape of ' Y ', and the influence of the length and branch angle of the Y-shaped fins on the heat storage performance of the three-tube phase change heat accumulator is simulated by FLUENT software. The results show that the addition of fractal fins to the triple-tube phase change heat accumulator increases the heat transfer area and facilitates the development of natural convection compared with the addition of ordinary longitudinal straight fins, which effectively shortens the time required for the complete melting of PCM. After comprehensive analysis, the root and branch of Y-shaped fins account for 25% and 75% of the total length respectively, and the branch angle is 60°, which is the best Y-shaped fin structure. The complete melting rate of PCM is obviously improved. Compared with the ordinary straight fin heat accumulator, the heat storage rate is increased by 16.1%, and the overall heat storage performance is improved. By optimizing the design, it is found that the eccentricity has a significant effect on the melting process of the PCM. When the eccentricity is 11.5%, the overall heat storage time is the least. Compared with the original structure without eccentricity, the heat storage speed is increased, and the heat storage time is saved by about 14%. It provides a basis for the structural optimization design of phase change heat system.

Research on Optimization Design of Front and Side Structures under the 24th Edition of C-NACP Automotive Collision Standard

Structural crashworthiness plays a core role in vehicle safety design, mainly focusing on the maximum impact force that the body structure can withstand in collision accidents, and striving to maximize collision energy absorption to ensure that passengers have sufficient living space. The design of structural crashworthiness needs to consider the deformation mechanism and energy absorption characteristics of vehicles under different collision modes, and improve crashworthiness through material selection, structural optimization, and other means. In addition, the structural crashworthiness is closely connected to the passenger restraint system, which together constitute the key to the passive safety performance of the vehicle. In vehicle safety design, the improvement of structural crashworthiness can significantly enhance the vehicle's ability to protect passengers in collision accidents and reduce the risk of injury. Therefore, in-depth research on structural crashworthiness and the development of efficient crashworthiness structures are important directions for improving vehicle safety performance.

A generalized model for a triboelectric nanogenerator energy harvesting system

A triboelectric nanogenerator (TENG) energy harvesting system involves at least three components: the mechanical source that inputs mechanical energy to drive TENGs, the TENG device itself as an energy converter, and an external electrical circuit that output the energy from TENGs. Multiple theoretical models have been developed for structural optimization design and maximum power output, yet there has been less emphasis on dynamic modelling of the entire energy harvesting system. This work presents attempts to establish such system-level models that encompass both mechanical and electrical systems. Special consideration is paid to the dynamic contact problem in this generalized model, since dynamic response of impacts can significantly affect the electric outputs. To address contact constraint problems, the penalty function method is utilized to handle non-smoothness and discontinuity. Subsequently, this research specifies how to improve the maximum energy conversion efficiency, and suggest optimal executive strategies for maximizing the energy output through a thorough parametric study. The anticipated outcome is that the generalized model will not only guide optimization design and predict the dynamic characteristics of the energy harvesting system but also assess the potential feasibility of mechanical energy harvesting technology across diverse application domains.