Search Results (19)

This paper presents a comprehensive design approach for optimizing the pole configuration of a coaxial magnetic gear (CMG) structure with an overhang to enhance torque characteristics. Five CMG models were designed, and their characteristics were analyzed. A three-dimensional finite element method analysis was conducted to account for axial leakage flux. To efficiently explore the design space, we utilized an optimal Latin hypercube sampling method to generate experimental points and constructed a kriging-based metamodel owing to its low root-mean-square error. We analyzed torque characteristics across the design variables to identify characteristic trends and performed a parametric sensitivity analysis to evaluate the influence of each variable on the torque. We derived an optimal solution that satisfied the objective function and constraints using the design variables. The characteristics of the proposed model were validated through electromagnetic field analysis, fast Fourier transform analysis of the air-gap magnetic flux density, and structural analysis. The optimal model achieved an average torque of 61.75 Nm, representing a 21.15% improvement over the initial model, while simultaneously reducing the ripple factor by 0.41%. These findings indicate that the proposed CMG design with an overhang effectively enhances torque characteristics. Full article

Occupational health risk prediction of miners is a core issue to ensure the safety of high-risk operations. Current risk assessment methodologies face critical limitations, as conventional unimodal prediction systems frequently demonstrate limited efficacy in capturing the multifactorial nature of occupational health deterioration. This study presents a novel stacked ensemble architecture employing dual-phase algorithmic optimization to address these muti-parametric interactions. The proposed framework implements a hierarchical modeling paradigm: (1) a primary predictive layer employing heterogeneous base learners (Random Forest and Logistic Regression classifiers) to establish foundational decision boundaries, and (2) a meta-modeling stratum utilizing regularized logistic regression with hyperparameter optimization via grid search-assisted k-fold cross-validation. Empirical validation through comparative analysis reveals the enhanced ensemble achieves a mean accuracy of 90%. Receiver operating characteristic analysis confirms superior discriminative capacity (AUC = 0.89), surpassing conventional ensemble methods by 23.3 percentile points. The model’s capacity to quantify nonlinear exposure–response relationships while maintaining computational tractability suggests significant utility in occupational health surveillance systems. These findings substantiate that the proposed dual-layer optimization framework substantially advances predictive capabilities in occupational health epidemiology, particularly in addressing the complex synergies between environmental hazards and physiological responses in confined industrial environments. Full article

The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)’s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos–kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness. Full article

When training a parametric surrogate to represent a real-world complex system in real time, there is a common assumption that the values of the parameters defining the system are known with absolute confidence. Consequently, during the training process, our focus is directed exclusively towards optimizing the accuracy of the surrogate’s output. However, real physics is characterized by increased complexity and unpredictability. Notably, a certain degree of uncertainty may exist in determining the system’s parameters. Therefore, in this paper, we account for the propagation of these uncertainties through the surrogate using a standard Monte Carlo methodology. Subsequently, we propose a novel regression technique based on optimal transport to infer the impact of the uncertainty of the surrogate’s input on its output precision in real time. The OT-based regression allows for the inference of fields emulating physical reality more accurately than classical regression techniques, including advanced ones. Full article

In this study, we propose an optimization method for occupant protection systems using a machine learning technique. First, a crash simulation model was developed for a Euro NCAP MPDB frontal crash test condition. Second, a series of parametric simulations were performed using a THOR dummy model with varying occupant safety system design parameters, such as belt attachment locations, belt load limits, crash pulse, and so on. Third, metamodels were developed using neural networks to predict injury criteria for a given occupant safety system design. Fourth, the occupant safety system was optimized using metamodels, and the optimal design was verified using a subsequent crash simulation. Lastly, the effects of design variables on injury criteria were investigated using the Shapely method. The Euro NCAP score of the THOR dummy model was improved from 14.3 to 16 points. The main improvement resulted from a reduced risk of injury to the chest and leg regions. Higher D-ring and rearward anchor placements benefited the chest and leg regions, respectively, while a rear-loaded crash pulse was beneficial for both areas. The sensitivity analysis through the Shapley method quantitatively estimated the contribution of each design variable regarding improvements in injury metric values for the THOR dummy. Full article

Micro-electrical discharge machining (μ-EDM) has come up as an effective material removal process for the manufacturing of miniaturized components in modern industries. The performance and quality of the μ-EDM process mainly depend on the combination of process parameters selected. This paper attempts to demonstrate the applicability of three well-known multi-criteria decision-making (MCDM) techniques, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), multi-attributive border approximation area comparison (MABAC), and complex proportional assessment (COPRAS) methods, separately hybridized with the grey wolf optimization (GWO) algorithm. The proposed hybrid optimization approaches are applied to find the optimal parametric setting of a μ-EDM process during machining on a stainless steel shim as the work material. Feed rate, capacitance, and voltage were selected as the machining control parameters, while material removal rate, surface roughness, and tool wear ratio were selected as the responses. The polynomial regression (PR) meta-models are observed as the inputs to these hybrid optimizers. The results obtained are further compared to the traditional weighted sum multi-objective optimization (WSMO) approach, which suggests that all the considered MCDM-PR-GWO approaches outperform traditional PR-WSMO-GWO approaches in obtaining better machining performance measures. Full article

It is often computationally expensive to monitor structural health using computer models. This time-consuming process can be relieved using surrogate models, which provide cheap-to-evaluate metamodels to replace the original expensive models. Because of their high accuracy, simplicity, and efficiency, Artificial Neural Networks (ANNs) have gained considerable attention in this area. This paper reviews the application of ANNs as surrogates for structural health monitoring in the literature. Moreover, the review contains fundamental information, detailed discussions, wide comparisons, and suggestions for future research. Surrogates in this literature review are divided into parametric and nonparametric models. In the past, nonparametric models dominated this field, but parametric models have gained popularity in the recent decade. A parametric surrogate is commonly supplied with metaheuristic algorithms, and can provide high levels of identification. Recurrent networks, instead of traditional ANNs, have also become increasingly popular for nonparametric surrogates. Full article

The heterogeneity and historical complexity of interventions on built heritage are testified by the constant development of the conservation discipline. The purpose of the research is the development of a digital workflow of parametric modelling for the analysis and conservation of historical buildings, by applying visual programming language (VPL) to support the Heritage Building Information Modelling (HBIM) methodology. VPL represents a tool for explicit parametric modelling that can be used to enhance geometric and information enrichment of HBIM models. The paper describes the integration, within an HBIM-VPL process, of the Index of Masonry Quality, widely used for seismic structural analysis, and its application to a case study in Cornillo Nuovo, a village damaged by the earthquake of Amatrice in 2016. Similar approaches could enhance HBIM modelling to support different knowledge domains associated with built heritage. Full article

Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid–structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system. Full article

An important prerequisite for the generation of realistic material behavior with the Discrete Element Method (DEM) is the correct determination of the material-specific simulation parameters. Usually, this is done in a process called calibration. One main disadvantage of classical calibration is the fact that it is a non-learning approach. This means the knowledge about the functional relationship between parameters and simulation responses does not evolve over time, and the number of necessary simulations per calibration sequence respectively per investigated material stays the same. To overcome these shortcomings, a new method called Metamodel-based Global Calibration (MBGC) is introduced. Instead of performing expensive simulation runs taking several minutes to hours of time, MBGC uses a metamodel which can be computed in fractions of a second to search for an optimal parameter set. The metamodel was trained with data from several hundred simulation runs and is able to predict simulation responses in dependence of a given parameter set with very high accuracy. To ensure usability for the calibration of a wide variety of bulk materials, the variance of particle size distributions (PSD) is included in the metamodel via parametric PSD-functions, whose parameters serve as additional input values for the metamodel. Full article

For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners. Full article

Data-driven modeling provides an efficient approach to compute approximate solutions for complex multiphysics parametrized problems such as induction hardening (IH) process. Basically, some physical quantities of interest (QoI) related to the IH process will be evaluated under real-time constraint, without any explicit knowledge of the physical behavior of the system. Hence, computationally expensive finite element models will be replaced by a parametric solution, called metamodel. Two data-driven models for temporal evolution of temperature and austenite phase transformation, during induction heating, were first developed by using the proper orthogonal decomposition based reduced-order model followed by a nonlinear regression method for temperature field and a classification combined with regression for austenite evolution. Then, data-driven and hybrid models were created to predict hardness, after quenching. It is shown that the results of artificial intelligence models are promising and provide good approximations in the low-data limit case. Full article

The impact of the equation of state (EoS) crust-core matching procedure on neutron star (NS) properties is analyzed within a meta-modeling approach. Using a Taylor expansion to parametrize the core equation of state (EoS) and the SLy4 crust EoS, we create two distinct EoS datasets employing two matching procedures. Each EoS describes cold NS matter in a $\beta$ equilibrium that is thermodynamically stable and causal. It is shown that the crust-core matching procedure affects not only the crust-core transition but also the nuclear matter parameter space of the core EoS, and thus the most probable nuclear matter properties. An uncertainty of as much as 5% (8%) on the determination of low mass NS radii (tidal deformability) is attributed to the complete matching procedure, including the effect on core EoS. By restricting the analysis, imposing that the same set of core EoS is retained in both matching procedures, the uncertainty on the NS radius drops to 3.5% and below 1.5% for $1.9M_{\odot }$. Moreover, under these conditions, the crust-core matching procedure has a strong impact on the Love number $k_2$, of almost 20% for $1.0M_{\odot }$ stars and 7% for $1.9M_{\odot }$ stars, but it shows a very small impact on the tidal deformability $\mathrm{\Lambda }$, below 1%. Full article

A turbocharger’s radial turbine has a strong impact on the fuel consumption and transient response of internal combustion engines. This paper summarizes the efforts to design a new radial turbine aiming at high efficiency and low inertia by applying two different optimization techniques to a parametrized CAD model. The first workflow wraps 3D fluid and solid simulations within a meta-model assisted genetic algorithm to find an efficient turbine subjected to several constraints. In the next step, the chosen turbine is re-parametrized and fed into the second workflow which makes use of a gradient projection algorithm to further fine-tune the design. This requires the computation of gradients with respect to the CAD parametrization, which is done by calculating and combining surface sensitivities and design velocities. Both methods are applied successfully, i.e., the first delivers a well-performing turbine, which, by the second method, is further improved by 0.34% in efficiency. Full article

In this paper, the non-probabilistic steady-state dynamics of a dual-rotor system with parametric uncertainties under two-frequency excitations are investigated using the non-intrusive simplex form mathematical metamodel. The Lagrangian formulation is employed to derive the equations of motion (EOM) of the system. The simplex form metamodel without the distribution functions of the interval uncertainties is formulated in a non-intrusive way. In the multi-uncertain cases, strategies aimed at reducing the computational cost are incorporated. In numerical simulations for different interval parametric uncertainties, the special propagation mechanism is observed, which cannot be found in single rotor systems. Validations of the metamodel in terms of efficiency and accuracy are also carried out by comparisons with the scanning method. The results will be helpful to understand the dynamic behaviors of dual-rotor systems subject to uncertainties and provide guidance for robust design and analysis. Full article