Search Results (29)

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method. Full article

With the rapid advancement of urbanization, urban transportation systems are facing increasingly severe congestion challenges, especially at traditional roundabouts. The rapid increase in vehicles has led to a sharp increase in pressure at roundabouts. In order to alleviate the traffic pressure in the roundabout, this paper changes the road design parameters of the roundabout, uses a CFD method combined with sensitivity analysis to study the influence of different inlet angles, lane numbers, and the outer radius on the pressure, and seeks the road design parameter scheme with the optimal mitigation effect. Firstly, the full factorial experimental design method is used to select the sample points in the design sample space, and the response values of each sample matrix are obtained by CFD. Secondly, the response surface model between the road design parameters of the roundabout and the pressure in the ring is constructed. The single-factor analysis method and the multi-factor analysis method are used to analyze the influence of the road parameters on the pressure of each feature point, and then the moment-independent sensitivity analysis method based on the response surface model is used to solve the sensitivity distribution characteristics of the road design parameters of the roundabout. Finally, the Kriging surrogate model is constructed, and the NSGA-II is used to solve the multi-objective optimization problem to obtain the optimal solution set of road parameters. The results show that there are significant differences in the mechanism of action of different road geometric parameters on the pressure of each feature point of the roundabout, and it shows obvious spatial heterogeneity of parameter sensitivity. The pressure changes in the two feature points at the entrance conflict area and the inner ring weaving area are significantly correlated with the lane number parameters. There is a strong coupling relationship between the pressure of the maximum pressure extreme point in the ring and the radius parameters of the outer ring. According to the optimal scheme of road parameters, that is, when the parameter set (inlet angle/°, number of lanes, outer radius/m) meets (35.4, 5, 65), the pressures of the feature points decrease by 34.1%, 38.3%, and 20.7%, respectively, which has a significant effect on alleviating the pressure in the intersection. This study optimizes the geometric parameters of roundabouts through multidisciplinary methods, provides a data-driven congestion reduction strategy for the urban sustainable development framework, and significantly improves road traffic efficiency, which is crucial for building an efficient traffic network and promoting urban sustainable development. Full article

Biodegradable magnesium alloys have garnered increasing attention in recent years, with magnesium alloy–based biomedical devices being clinically used. Unlike biologically inert metallic materials, magnesium-based medical devices degrade during service, resulting in a mechanical structure that evolves over time. However, there are currently few computer-aided engineering methods specifically tailored for magnesium-based medical devices. This paper introduces a structural optimization framework for Mg-1Ca interference screws, accounting for degradation using a continuum damage model (CDM). The Optimal Latin Hypercube Sampling (OLHS) technique was employed to sample within the design space. Pull-out strengths were used as the optimization objective, which were calculated through finite element analysis (FEA). Both Response Surface Methodology (RSM) and Kriging models were employed as surrogate models and optimized using the Sequential Quadratic Programming (SQP) algorithm. The results from the Kriging model were validated through FEA, and were found to be acceptable. The relationships between the design parameters, the rationale behind the methodology, and its limitations are discussed. Finally, a final design is proposed along with recommendations for interference screw design. Full article

Plastic injection molding is a fundamental manufacturing process used in various industries, accounting for approximately 30% of the global plastic product market. A significant challenge of this process lies in the need to employ sophisticated computational techniques to optimize the various phases. This review examines the optimization methodologies in injection molding, with a focus on integrating advanced modeling, surrogate models, and multi-objective optimization techniques to enhance efficiency, quality, and sustainability. Key phases such as plasticizing, filling, packing, cooling, and ejection are analyzed, each presenting unique optimization challenges. The review emphasizes the importance of cooling, which accounts for 50–80% of the cycle time, and examines innovative strategies, such as conformal cooling channels (CCCs), to enhance uniformity and minimize defects. Various computational tools, including Moldex3D and Autodesk Moldflow, are discussed due to their role in process simulation and optimization. Additionally, optimization algorithms such as evolutionary algorithms, simulated annealing, and multi-objective optimization methods are explored. The integration of surrogate models, such as Kriging, response surface methodology, and artificial neural networks, has shown promise in addressing computational cost challenges. Future directions emphasize the need for adaptive machine learning and artificial intelligence techniques to optimize molds in real time, offering more innovative and sustainable manufacturing solutions. This review is a comprehensive guide for researchers and practitioners, bridging theoretical advancements with practical implementation in injection molding optimization. Full article

Multi-fidelity surrogate-based methods play an important role in modern engineering design applications, aiming to improve model accuracy while reducing computational cost. One of the widely adopted approaches is the calibration-based method, which calibrates the low-fidelity model through a discrepancy model between low-fidelity and high-fidelity models. Since discrepancies between models exhibit varying characteristics across different problems, using a single surrogate for discrepancy approximation may lack stability. In practical engineering design problems, it is often hard for designers to select optimal surrogate models. To this end, a hybrid-surrogate-calibration-assisted multi-fidelity modeling (HSC-MFM) approach is proposed in this paper. Specifically, this approach integrates three representative surrogate models, including the polynomial response surface, Kriging model, and radial basis function, to comprehensively capture the discrepancy characteristics between different fidelity models. Furthermore, an adaptive weight calculation method is developed to improve the modeling accuracy. Testing results demonstrate that HSC-MFM achieves enhanced stability compared to most existing methods while maintaining good prediction accuracy. Finally, the proposed method is applied to predict the strength of the frame for a blended-wing-body underwater glider, which verifies its engineering applicability. Full article

The flexible rotor, as a crucial component of the traveling wave rotary ultrasonic motor, effectively reduces radial friction. However, issues such as uneven contact between the stator and rotor, as well as rotor-deformation-induced stress, still persist. This paper presents an optimization method that combines the Kriging response surface model with a multi-objective genetic algorithm (MOGA). Drawing on the existing rotor structure, a novel rotor design is proposed to match the improved TRUM60 stator. During the optimization process, the contact surface between the stator and rotor is taken as the optimization target, and an objective function is established. The Kriging response surface model is constructed using Latin hypercube sampling, and an MOGA is employed to optimize this model, allowing the selection of the optimal balanced solution from multiple candidate designs. Following stator optimization, the objective function value decreased from 0.631 to 0.036, and the maximum contact stress on the rotor inner ring was reduced from 32.77 MPa to 9.96 MPa. Experimental validation confirmed the reliability of this design, significantly improving the overall performance and durability of the motor. Full article

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table ${U^{*}}_7\left(7^4\right)$ was applied, considering weight percentage (1–13 wt%), the distance between needle and disk collector (2.1–3.9 mm), and applied voltage (14.5–17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 µS/cm for pure PVDF to 187.5 µS/cm. The fiber diameter decreased from 21.4 µm in PVDF/1% graphene to 9.1 µm in PVDF/13% graphene. Adding 5 wt% graphene increased the β-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. The results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis. Full article

The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck’s energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency. Full article

Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) integrating the adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process regression (GPR) with combined kernel function, and we developed it in Python. The proposed method used the Latin hypercube sampling method to generate a dataset sample of geo-mechanical parameters, constructed combined kernel functions of GPR and used GPR to establish a surrogate model of the nonlinear mapping relationship between displacements and mechanical parameters of the surrounding rock. Adam was used to optimize the hyperparameters of the surrogate model. The Bayesian inference algorithm was used to obtain the probability distribution of geotechnical parameters and the optimal surrounding rock mechanical parameters. Finally, the failure probability was computed using MCS–RAM based on the optimized surrogate model. Through the application of an engineering case, the results indicate that the proposed method has fewer prediction errors and stronger prediction ability than Kriging or XGBoost, and it can significantly save computational time compared with the traditional polynomial response surface method. The proposed method can be used in the reliability analysis of all shapes of tunnels. Full article

The metal fibre surface combustion structure has the characteristics of strong thermal matching ability, short response time, and strong shape adaptability. It has more advantages in the thermal test of complex hypersonic vehicle surface inlet, leading edge, etc. In this paper, a method of aerodynamic thermal simulation test based on metal fibre surface combustion is proposed. The aim of the study was to create a uniform target heat flow on the inner wall surface of a cylindrical specimen by matching the gas jet flow rate and the geometry of the combustion surface. The research adopted the optimisation design method based on the surrogate model to establish the numerical calculation model of a metal fibre combustion jet heating cylinder specimen. One hundred sample points were obtained through Latin hypercube sampling, and a database of design parameters and heat flux was established through numerical simulation. The kriging surrogate model and the non-dominated sequencing genetic optimisation algorithm with elite strategy were adopted. A bi-objective optimisation design was carried out with the optimisation objective of the coincidence between the predicted and the target heat flux on the inner wall of the specimen. The results showed that the average relative errors of heat flow density on the specimen surface were 8.8% and 6% through the leave-one-out cross-validation strategy and the validation of six test sample points, respectively. The relative error values in most regions were within 5%, which indicates that the established kriging surrogate model has high prediction accuracy. Under the optimal solution conditions, the numerical calculation results of the heat flow on the inner wall of the specimen were in good agreement with the target heat flow values, with an average relative error of less than 5% and a maximum value of less than 8%. These results show that the optimisation design method based on the kriging surrogate model can effectively match the thermal test parameters of metal fibre combustion structures. Full article

To improve the efficiency of the multidisciplinary design optimization of underwater vehicles, this paper proposes a combined proxy model with adaptive dynamic sampling. The radial basis function model (RBF), Kriging model, and polynomial response surface model (PRS) are used to construct the proxy model. Efficient sample points are collected based on the synthetic minority oversampling technique (SMOTE) algorithm and the lower confidence bound (LCB) criterion. The proxy model process is integrated after dynamic sampling. The collaborative optimization framework is used, which considers the coupling between the main system set and the subsystem set. The hierarchical analysis method is used to transform the multidisciplinary optimization problem into a single-objective optimization problem. Computational fluid dynamics (CFD) numerical simulation is utilized to simulate underwater submarine navigation. The optimization strategy is applied to the underwater vehicle SUBOFF to optimize resistance and energy consumption. Three dynamic proxy models and three static proxy models are compared. The results show that the optimization efficiency of the underwater vehicle has been improved. To prove the generalization performance of the proposed combined proxy model, a reducer example is investigated for comparison. The results show that the combined proxy model (CPM) is highly accurate and has excellent generalization performance. Full article

As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10⁻³ m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10⁻⁴ m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory. Full article

Underwater unmanned robots are an essential tool for human underwater exploration and detection and are widely employed in a variety of underwater operational settings. One of the hottest issues in this field is applying bionic notions to the creation of underwater unmanned robots by simulating fish swimming or cephalopod crawling. Using the tentacle suction cup adsorption technique during octopus’ predation as a model, underwater magnetic adsorption robots with the opening and closing claws were studied in this paper. First, the robot’s general structural design is presented. The claw mechanism is demonstrated by mimicking the octopus’s tentacle action during feeding, which primarily consists of an opening and closing claw that replicates the octopus’s tentacle and a magnetic adsorption unit that replicates the octopus’s suction cup adsorption. Then, the Kriging response surface optimization method is used to optimize the design of the claw mechanism to obtain excellent mechanical properties, and simulation software is used to verify. Finally, a robot prototype was built and its pool tests were conducted, with some experimental results presented. The experimental results show that after the robot reaches the predetermined position through pneumatic ejection and secondary propulsion launch, it can quickly open its claws within 0.11 s and apply 462.42 N adsorption force to complete the adsorption of the target. Full article

The airworthiness standards of the transport category airplanes stipulate that the high energy rotor equipment must be of the sufficient containment capacity. It is of great importance to study the containment and weight reduction for the air turbine starter. In this paper, based on an OSF design, Kriging response surface model and MOGA algorithm, a neck structure optimal design method was proposed for the air turbine wheel. Using the optimal design method, the optimal structural parameters were suggested as the design parameters, and verified by the over-speed burst test. The maximum errors of the burst speeds between the experimental and design values are less than 2%, and the neck structure turbine wheel breaks in the neck as expected, validating the accuracy of the optimal design method. Then, the effects of turbine wheel burst modes on the containment were investigated quantitatively, and verified by the containment tests. Based on the experimental and simulation results, the containment design method was proposed for the neck structure turbine wheel. The results show that compared with the trisection wheel burst, the rim burst dramatically decrease the mass and initial kinetic energy of burst released fragments by 63.3% and 24.8%, thereby greatly reducing the thickness and the mass of the containment ring by 29.5% and 29.1%. Full article

In a Cassegrain optical system, the surface precision of the primary mirror is an important factor in the quality of the image. The design of a lightweight primary mirror with a high-quality optical surface is crucial. In this thesis, an integrated mirror light engine design optimization process is proposed for an aviation optoelectronic device. It is based on the Kriging surrogate model and nests the topology optimization algorithm, which constructs the mirror RMS value response surface and obtains the dominant relationship between mirror structure and surface accuracy. The optimal surface figure lightweight structure of the mirror is obtained by optimizing the surrogate model with an additive criterion and multi-objective optimization analysis. The root mean square value (RMS) of the corresponding primary mirror is 10.41 nm, which is better than 1/40 $\lambda$ ($\lambda$ = 632.8 nm). This meets the optical design specifications. The optimal primary mirror structure is analyzed by using the finite element method, which verifies the precision of the Kriging surrogate model. It has an error of 0.28%. The kinetic analysis of the primary mirror shows that the primary mirror does not yield to plastic deformation or even failure under a three-way 20 g acceleration load. This meets the environmental suitability requirements. Full article