Structural Design Optimization Research Articles

Optimization design of support structure based on 3D printing technology

Parts are often warped and deformed when they are molded using selective laser melting (SLM) technology. Thus, it is necessary to study the addition support modes of parts molded using SLM. Consequently, we designed dendritic, E-stage and conical supports, having different structural parameters and different partitions using Magics, and then, we analyzed their performances using the finite element software Abaqus. The structural parameters of the supports were optimized and finally tested using SLM molding technology. The maximum stress concentration was found for dendritic supports, followed by E-stage supports, and then conical supports. The stress concentration and deformation level of Scheme 2 were less than those of Scheme 1. The stress intensity and deformation levels for two partitions were less than those for three partitions. For parts molded by SLM, the deformation was maximum for conical supports, followed by dendritic supports, and then E-stage supports. When gradient supports of similar volumes were added, additional partitions did not effectively improve the molding quality. When supports of similar volumes were added, adding gradient supports did not effectively improve the molding quality. The results provide a basis for the application of SLM in molding high-precision parts.

Optimum design of uniform and non-uniform infill-coated structures with discrete variables

This article introduces a novel computer-aided procedure to design optimised coated structures with precise shell thickness control using the Smallest Univalue Segment Assimilating Nucleus operator and a novel augmentation-projection technique. Structures with heterogeneous sections, or coated structures, combine two different materials for the nucleus and the shell, which are generally chosen so that the material in the infill is lighter and the material in the coating is stiffer, which in this work are supposed homogeneous. Solving the interface problem requires material properties interpolation equations that consider three material phases, accurate placement of the coating over the base material, and precise control over the coating's thickness. The formation of the coating is controlled by the Smallest Univalue Segment Assimilating Nucleus, an edge detection operator developed in Digital Image Processing. The coating's thickness is controlled by an innovative methodology consisting of the projection of an augmented contour field, which is shown to create a constant thickness coating around the material domain. The optimisation problem is solved with the Sequential Element Rejection and Admission method. The validity of the procedure has been verified by solving various numerical application examples.

Large‐scale elasto‐plastic topology optimization

AbstractThis work presents large‐scale elasto‐plastic topology optimization for design of structures with maximized energy absorption and tailored mechanical response. The implementation uses parallel computations to address multi million element three‐dimensional problems. Design updates are generated using the gradient‐based method of moving asymptotes and the material is modeled using small strain, nonlinear isotropic hardening wherein the coaxiality between the plastic strain rate and stress is exploited. This formulation renders an efficient state solve and we demonstrate that the adjoint sensitivity scheme resembles that of the state update. Furthermore, the KKT condition is enforced directly into the path dependent adjoint sensitivity analysis which eliminates the need of monitoring the elasto‐plastic switches when calculating the gradients and provides a straight forward framework for elasto‐plastic topology optimization. Numerical examples show that structures discretized using several millions degrees of freedom and loaded in multiple load steps can be designed within a reasonable time frame.

Structural Optimization of a Giant Magnetostrictive Actuator Based on BP-NSGA-II Algorithm

This study introduces an integrated structural optimization design method based on a BP neural network and NSGA-II multi-objective genetic algorithm. Initially, a two-dimensional axisymmetric finite element model of the Giant Magnetostrictive Actuator (GMA) was established, and the coupling simulation of the electromagnetic field, structural field, and temperature field was conducted to obtain the GMA’s performance parameters. Subsequently, the structural parameters of the GMA magnetic circuit, including the magnetic conducting ring, magnetic conducting sidewall, magnetic conducting body, and coil, were used as inputs, and the axial magnetic induction intensity, uniformity of axial magnetic induction intensity, and coil loss on the Giant Magnetostrictive Material (GMM) rod were used as outputs to establish a back propagation (BP) neural network model. This model delineated the nonlinear relationship between structural parameters and performance parameters. Then, the BP-NSGA-II algorithm was applied to perform multi-objective optimization on the actuator’s structural parameters, resulting in a set of Pareto optimal non-dominated solutions, from which a set of optimal solutions was obtained using the entropy weight method. Finally, simulation analysis of this optimal solution was conducted, indicating that under a 5 A power supply excitation, the maximum axial magnetic induction intensity on the optimized GMM rod increased from 0.87 T to 1.12 T; the uniformity of axial magnetic induction intensity improved from 93.1% to 96.5%; and the coil loss decreased from 7.79 × 104 W/m3 to 4.97 × 104 W/m3. Based on the optimization results, a prototype actuator was produced, and the test results of the prototype’s output characteristics proved the feasibility of this optimization design method.

Advances in Filament Structure of 3D Bioprinted Biodegradable Bone Repair Scaffolds.

Conventional bone repair scaffolds can no longer meet the high standards and requirements of clinical applications in terms of preparation process and service performance. Studies have shown that the diversity of filament structures of implantable scaffolds is closely related to their overall properties (mechanical properties, degradation properties, and biological properties). To better elucidate the characteristics and advantages of different filament structures, this paper retrieves and summarizes the state of the art in the filament structure of the three-dimensional (3D) bioprinted biodegradable bone repair scaffolds, mainly including single-layer structure, double-layer structure, hollow structure, core-shell structure and bionic structures. The eximious performance of the novel scaffolds was discussed from different aspects (material composition, ink configuration, printing parameters, etc.). Besides, the additional functions of the current bone repair scaffold, such as chondrogenesis, angiogenesis, anti-bacteria, and anti-tumor, were also concluded. Finally, the paper prospects the future material selection, structural design, functional development, and performance optimization of bone repair scaffolds.

Optimized structure design for binary particle mixing in rotating drums using a combined DEM and gaussian process-based model

Particle mixing is a crucial operation in various industrial production processes. However, phenomena like segregation or local accumulation can arise, especially when particles differ in properties like radius and density. Numerical simulation of particles using Discrete Element Method (DEM) allows for the manipulation of control variables in batches, generating a large amount of data and facilitating quantitative research. In this study, the mixing behaviors of binary particles in rotating drums are systematically investigated. The DEM model is first validated with experimental data and then rotating drums with varying obstacles, rotation speeds, particle radii, and densities are simulated. Moreover, a Gaussian process-based optimization is conducted by correlating Lacey mixing index (MI) and parameterized shape of obstacle to find the optimized mixing condition. Experimental validations are further performed on the optimized condition to verify the design. It is shown that this integrated approach holds significant potential for enhancing the efficiency, effectiveness of industrial mixing processes and the consideration of energy consumption when balancing the mixing efficiency and optimal rotating speed.

Quantile-based optimization under uncertainties for complex engineering structures using an active learning basis-adaptive PC-Kriging model

The Reliability-Based Design Optimization (RBDO) of complex engineering structures considering uncertainties has problems of being high-dimensional, highly nonlinear, and time-consuming, which requires a significant amount of sampling simulation computation. In this paper, a basis-adaptive Polynomial Chaos (PC)-Kriging surrogate model is proposed, in order to relieve the computational burden and enhance the predictive accuracy of a metamodel. The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework. Finally, five engineering cases have been implemented, including a benchmark RBDO problem, three high-dimensional explicit problems, and a high-dimensional implicit problem. Compared with Support Vector Regression (SVR), Kriging, and polynomial chaos expansion models, results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.

Optimization design and simulation of chip-level micro-gas columnar preconcentrator structure based on ANSYS

Abstract To achieve the efficient preconcentration of measured sample gas in microfluidic chips, this paper proposes a design and simulation scheme for the chip-level micro-gas columnar pre-concentrated structure based on ANSYS software. Firstly, four kinds of chip-level micro-gas columnar preconcentrators with circular, semi-circular, triangular, and rhombic shapes were designed, and the simulation analysis of gas-phase micro-fluidic dynamics was performed on the chip-level micro-gas columnar preconcentrator by ANSYS software. The influence of different design parameters on the pre-concentrated effect of level chips micro-gas columnar preconcentrator was discussed. The simulation results show that the arrangement is a triangle arranged in reverse parallel, and the preconcentration effect of the chip-level micro gas column preconcentrator is better.

A Multi-functional Electronic Glove for Multidimensional Environmental Perception and Gesture Recognition

In recent years, wearable electronic gloves with rich sensing functions have garnered widespread attention. Researchers are exploring various aspects of wearable devices including structural design, material innovation, and algorithm optimization. An analysis of demand suggests that wearable electronic gloves should possess sensory functions like hand, which includes roughness of contacted objects, assessing temperature of touched objects, determining grasp force, and interpreting gesture language. This paper presents a multi-sensing wearable flexible electronic glove equipped with graphene foam material sensors that can perceive external pressure, roughness, temperature, and express gesture language. The experimental results show that this glove exhibits excellent approximately linear-range sensing characteristics for force perception (1.475 kPa-1 at 0-0.4 kPa), outstanding temperature sensing capability (temperature sensitivity is -1.22% within the range of 30-80℃), and recognition judgment of 12 different gestures. Additionally, an optimized KNN (K-nearest neighbor) algorithm based on time-domain window is proposed. Compared with traditional KNN algorithm, KNN based on time-domain window improves the accuracy of gesture intent recognition from 86.15% to 91.93%. Wearable electronic gloves represent promising developments for fields such as hand rehabilitation training for patients, intention expression for individuals suffering from language barriers, and wearable multifunctional sensing applications.

Performance-Based Design Optimization of Structures: State-of-the-Art Review

Performance-Based Design Optimization of Structures: State-of-the-Art Review

Efficiency analysis of induction heating systems with respect to electromagnetic shielding film's properties

Enhancing induction heating efficiency requires precise material selection and optimal structural design, including consideration of electromagnetic shielding materials. This study explores the impact of material properties on high-frequency resistance (Rs), inductance (Ls), and energy consumption. Metal powder-filled composites and ferrites typically exhibit lower relative permeability (μr) compared to nanocrystals, with an increase in μr leading to enhanced Ls, Rs, and heating efficiency. However, nanocrystalline shielding films show a unique trend where heating efficiency initially rises and then declines for multi-layered films, with a critical point at μr = 1500. Magnetic fragmentation induced imperfections in nanocrystalline films disrupt eddy current paths, reducing energy loss and improving heating efficiency as μr decreases. The notion of “relative conductivity (σ')” is introduced to demonstrate that a reduction in the fragmentation extent leads to the expansion of eddy current pathways as μr increases. This augmentation enhances both σ' and the imaginary permeability (μ''), leading to increased eddy current losses that affect overall efficiency. In evaluating energy efficiency for nanocrystals, it is crucial to consider μ', μ'', and σ' in conjunction, as there is a positive correlation between μ'' and σ'. Finite element analysis (FAE) confirms these observations, showing how film properties impact efficiency. Comprehending shielding mechanisms not only optimizes induction heating but also benefits applications such as wireless charging for electric vehicles and mobile communication devices.

Optimization Design and Analysis of Irregular Cross-Sectional Structure in Water Conducting Fibers

Optimization Design and Analysis of Irregular Cross-Sectional Structure in Water Conducting Fibers

Dynamic response and sound radiation of cracked asphalt pavements under moving loads and variable thermal profiles

ABSTRACT This study investigates the acoustic performance and dynamic response of cracked, viscoelastic porous asphalt pavements under moving loads and variable thermal conditions. Realistic pavement cracks are modelled using an advanced line spring model with fracture compliance coefficients. The novelty lies in the synergistic integration of a heterogeneous triple-porosity media model, a non-uniform depth-dependent temperature profile, and an advanced fracture mechanics-based line spring model with arbitrary crack orientation. Variations in temperature that are uniform, linear, and nonlinear with respect to the thickness layer axis are taken into account. The governing equations are derived by Hamilton’s principle and solved analytically via Fourier-Laplace transforms. The acoustic pressure is first-time predicted through Rayleigh integral analysis. Durbin’s numerical inversion validates the model’s accuracy versus sophisticated finite element simulations. Parametric analysis offers new insights into the effects of crack length, pore size distribution, loading frequency, and thermal gradients on noise pollution and fatigue cracking. The integrated chemo-thermo-mechanical modelling enables optimal structural design and accelerated pavement testing to limit acoustic radiation and premature failure in porous asphalt pavements. It allows for the development of noise-reducing porous asphalt mixtures by modifying aggregate gradation, binder content, and air void distribution.

Optimization design of hydro turbine support structure based on GA-FA-BP method

The rational design of the turbine support structure ensures the safe operation of the system, achieves significant economic benefits and improves the efficiency of the turbine. This paper presents a novel turbine support structure capable of vertical movement along the guide column. In addition, the GA-FA-BP method has been developed for the optimization design of the support structure, which combines genetic algorithm (GA), firefly algorithm (FA) and back-propagation neural network (BP). The use of quadratic response surface (RS) method along with NSGA-II for the result validation of the GA-FA-BP model ensures the robustness and reliability of the optimization results. A finite element model is set up to analyse the mechanical behaviour of the support structure under 10 different combined load conditions. The most critical load conditions in the static mechanical models are used to generate training data, and the mass, deformation and equivalent stress of the optimized and unoptimized support structures are analyzed and compared. The results show that the optimized support structure can maintain deformation and equivalent stress within a weight reduction of 21.83% compared to the original design. Compared to a single optimization model, the proposed GA-FA-BP model shows higher accuracy with a correlation coefficient up to 0.9989.

Effects of structure parameter and material property on thermal performance of on-board hydrogen storage tanks during fast refueling

As a power generation fuel, high-pressure hydrogen gas is widely used in transportation, and significantly contributed to development of hydrogen fuel cell electric vehicles. However, obvious temperature rise generated during fast refueling causes serious safety risk to reliable operation of fuel cell stacks. Therefore, it is necessary to conduct in-depth research on fast filling process. Based on safety reasons, the SAE J2601/ISO 15869 strictly regulates the maximum temperature limit of 85 °C in specifications for refueling hydrogen gas tanks. In this study, a 70 MPa, Type IV on-board hydrogen storage tank is adopted as the research object, and a 2-dimensional axisymmetric numerical model is established to study the temperature rise of the vapor within storage tanks during fast refueling. The Realizable k-ε turbulence model is employed to simulate high intensity turbulent fluctuation during fast filling. Considering the compressibility of vapor, the state of equation for real hydrogen gas is used by obtaining fluid thermodynamic properties from the National Institute of Standards and Technology database. Three groups of fast filling experiments are selected to validate the developed numerical model. The comparison result shows that the numerical model has good prediction ability on hydrogen fast refueling. The final mass average temperature differences between numerical results and experimental data are 2.6 K, 4.3 K, and 0.03 K, respectively. Based on the developed numerical model, the influences of structure parameters and material properties on the temperature rise of hydrogen storage tanks are investigated. The results indicate that the inlet diameter and wall thickness of the inner and outer layers of the storage tank have weaken impacts on the temperature rise during rapid charging, with the maximum temperature differences of 1.8 K, 0.74 K, and 0.54 K, respectively. Due to the influence of heat capacity, the material properties of different layers exhibit a similar variation trend. This work can deepen researchers' understanding of the temperature rise of on-board hydrogen storage tanks during fast filling, and provide technical references for the structural optimal design of tanks and rapid refueling at hydrogen stations.

Parametric Optimization of Structural Frame Design for High Payload Hexacopter

For drones, the use of which has been increasing recently for load carrying, lightweight drone frame design is significant for increased flight time and payload capacity. Drones are produced in different configurations with three, four or six rotors, and in different sizes depending on the purpose of use. While agility is more important in three and four rotor drone applications, six-rotor and relatively large-bodied drones are preferred in cases such as load carrying. When the body structure has to be large, lightening the design becomes very critical. Lightweight designs can be achieved by two commonly used methods for structural optimization: topology optimization and parametric optimization. Topology optimization is an advanced method that can significantly reduce weight but is expensive and time-consuming. Parametric optimization is a more practical approach for conventional manufacturing methods and was used in this study. This study aims to first simplifying the hexacopter frame model and defining key geometric parameters for mass-decreasing optimization. Finite element analysis simulations were used to evaluate the strength and deformation of the frame under various design scenarios. The results showed that parametric optimization successfully reduced the weight of the hexacopter frame while maintaining structural integrity. The maximum Von Mises stress was found as approximately one quarter of the yield strength of the frame material. The maximum total deformation was achieved below 0.3 mm, and deformation under 1 mm is considered safe in the literature. As a result, the optimized design offers a lighter drone structure in line with conventional manufacturing methods, providing better flight time and payload capacity while maintaining cost effectiveness. In future studies, comparisons can be made based on this study by performing weight optimizations suitable for current methods such as topology optimization or generative design. the cost factor and the availability of existing production lines should be taken into consideration when comparing the mentioned methods with parametric optimization.

Simulation Analysis and Image Processing of the Exhaust Silencing Chamber and Coil

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body.

Meniscal Allograft versus Synthetic Graft in Treatment Outcomes of Meniscus Repair: A Mini-review and Meta-analysis.

Meniscal injuries are highly correlated with osteoarthritis (OA) onset and progression. Although meniscal allograft transplantation (MAT) is a therapeutic option to restore meniscal anatomy, a shortage of donor material and the donor-derived infectious risk may be concerns in clinics. This review summarizes the literature reporting meniscus repair status in preclinical models and clinical practice using allografts or synthetic grafts. The advantages and limitations of biodegradable polymer-based meniscal scaffolds, applied in preclinical studies, are discussed. Then, the long-term treatment outcomes of patients with allografts or commercial synthetic scaffolds are compared. A total of 47 studies are included in our network meta-analysis. Compared with the meniscal allografts, the commercial synthetic products significantly improved clinical treatment outcomes in terms of the Knee Injury and Osteoarthritis Outcome Score (KOOS), Visual Analog Scale (VAS) scores, and Lysholm scores. In addition, development strategies for the next generation of novel synthetic scaffolds are proposed through optimization of structural design and fabrication, and selection of cell sources, external stimuli, and active ingredients. This review may inspire researchers and surgeons to design and fabricate clinic-orientated grafts with improved treatment outcomes.

Improved genetic algorithm based on reinforcement learning for electric vehicle front-end structure optimization design

Improved genetic algorithm based on reinforcement learning for electric vehicle front-end structure optimization design

Structure optimization design approach of friction pairs for low-temperature rise wet brake in hydraulic motor

Cam-lobe radial-piston hydraulic motors are widely used in large heavy-duty machinery as direct-drive components of hydraulic systems. Due to their extremely high output torque and power density, the wet brake equipped with the hydraulic motors are necessary to achieve ultra-high braking torque in a compact space. However, due to a large number of internal friction pairs and small fit clearance between multiple friction pairs in wet brake, the rotation of the friction pairs in non-braking state will cause large friction torque loss and obvious temperature rise, thereby leading to the thermal failure of wet brake. How to optimize the structure of friction pairs to reduce the temperature rise of wet brake in non-braking state is a persistent challenge. Existing structure optimization design approaches of friction pairs in brakes mainly focus on the structure of each friction pair, which ignore the effect of fit clearance between the friction pairs on the temperature rise of brakes. Nevertheless, the fit clearance between friction pairs is an important factor that affect the temperature rise. For that, this study proposes a two-step structure optimization design approach of friction pairs for low-temperature rise wet brake in hydraculic motor, which simultaneously considers the fit clearance between the friction pairs and the structure of each friction pair. In this approach, the design process could be divided into two steps: firstly, the influence of fit clearance between multiple friction pairs on the temperature rise of wet brake in non-braking state is analyzed and optimized by the temperature rise theoretical model; secondly, in order to further reduce the temperature rise of wet brake, the oil groove structure of each friction plate are introduced and optimized by fluid-solid-thermal coupling simulation method. By this approach, the low-temperature rise structure of friction pairs with the optimized fit clearance between multiple friction pairs and oil grooves of each friction plate. Finally, a series of experiments are conducted to verify the effectiveness of the design approach. The result show that the temperature rise of wet brake could be effectively reduced 12 % by optimizing the structure of wet brake, demonstrating that the structure optimization design approach could provide a theoretical guidance to design low-temperature rise wet brake for large torque hydraulic motors.