Finite Model Research Articles

Knowing How to Plan about Planning: Higher-order and Meta-level Epistemic Planning

Automated planning in AI and the logics of knowing how have close connections. In the recent literature, various planning-based know-how logics have been proposed and studied, making use of several notions of planning in AI. In this paper, we explore the reverse direction by using a multi-agent logic of knowing how to do know-how-based planning via model checking and theorem proving/satisfiability checking. Based on our logical framework, we propose two new classes of related planning problems: higher-order epistemic planning and meta-level epistemic planning, which generalize the current genre of epistemic planning in the literature. The former is for planning about planning, i.e., planning with higher-order goals that are again about epistemic planning, e.g., finding a plan for an agent to make sure p such that the adversary does not know how to make p false in the future. The latter is about planning at the meta-level by abstract reasoning combining knowledge-how from different agents, e.g., given that i knows how to prove a lemma and i knows j knows how to prove the theorem once the lemma is proved, we should derive that i knows how to let j knows how to prove the theorem. To make these possible, our framework features not only the operators of know-that and know-how but also a temporal operator □, which can help in capturing both the local and global knowledge-how. We axiomatize this powerful logic over finite models with perfect recall and show its decidability. We also give a PTIME algorithm for the model checking problem over finite models.

Design of a lightweight broadband vibration reduction structure with embedded acoustic black holes in viscoelastic damping materials

Viscoelastic damping materials (VDMs) are valued for their high damping characteristics in vibration and noise control. However, they typically underperform at lower frequencies and add substantial mass to structures. This study introduces an innovative approach by embedding an acoustic black hole (ABH) structure within VDMs (ABH-VDM) to achieve lightweight and broadband vibration damping. Firstly, a finite element method-based vibration model is developed to analyse the propagation and attenuation characteristics of vibrations in a plate strip embedded with ABH-VDM. This analysis provides insights into the dynamic behaviour and damping effectiveness of the proposed structure. Secondly, the study investigates the vibration reduction capabilities and mass implications of ABH-VDM on large-scale plate structures. The influence of ABH structural parameters, including the power exponent, cutoff thickness, and array configuration, is systematically investigated to optimize damping performance. Finally, experimental validation confirms that ABH-VDM achieves an additional 1.4 dB reduction in vibration across the entire frequency spectrum, with a bandwidth extension of 900 Hz. Moreover, ABH-VDM reduces mass by 8.6 %, demonstrating its potential for lightweight vibration control in structural applications. This research contributes valuable insights into advancing lightweight and broadband damping solutions for enhanced vibration management in engineering systems.

Tests of covariate effects under finite Gaussian mixture regression models

Mixture of regression model is widely used to cluster subjects from a suspected heterogeneous population due to differential relationships between response and covariates over unobserved subpopulations. In such applications, statistical evidence pertaining to the significance of a hypothesis is important yet missing to substantiate the findings. In this case, one may wish to test hypotheses regarding the effect of a covariate such as its overall significance. If confirmed, a further test of whether its effects are different in different subpopulations might be performed. This paper is motivated by the analysis of Chiroptera dataset, in which, we are interested in knowing how forearm length development of bat species is influenced by precipitation within their habitats and living regions using finite Gaussian mixture regression (GMR) model. Since precipitation may have different effects on the evolutionary development of the forearm across the underlying subpopulations among bat species worldwide, we propose several testing procedures for hypotheses regarding the effect of precipitation on forearm length under finite GMR models. In addition to the real analysis of Chiroptera data, through simulation studies, we examine the performances of these testing procedures on their type I error rate, power, and consequently, the accuracy of clustering analysis.

3D printing of asymmetric buttress plate for posteromedial tibial plateau fracture using metal fused filament fabrication

PurposeThis research addresses the challenges encountered when securing bone plates in the human body to treat tibial plateau fractures, specifically focusing on preventing posterolateral fractures. The goal is to develop a 3D buttress plate that offers better stability, facilitating anatomical reduction and rigid fixation. The newly fabricated T-buttress plate enables early knee motion and reduces postoperative complications, marking a significant advancement over existing internal fixation plates.Design/methodology/approachA new buttress plate model was designed using modeling software, featuring an asymmetric curved design with three fragments. Finite element analysis was used to simulate the biomechanical performance of this new model, comparing it with symmetric flat and symmetric curved plates. Accurately predicting the biomechanical behavior of the implant posed challenges, especially during extensive simulations. Optimal parameters for the asymmetric curved plate were identified from the simulation results, and the 3D buttress plate was then fabricated using the metal fused filament fabrication (MFFF) process. This process presents challenges due to the novel nature of the asymmetric design.FindingsThe results indicate that the newly developed buttress plate exhibits superior strength and performance compared to current internal fixation plates. Biomechanical simulations show that the asymmetric curved design provides better stability and support. Moreover, the yield and ultimate tensile strengths were found to be 685 MPa and 855 MPa, respectively.Research limitations/implicationsThe study’s finite element analysis model has limitations due to its reliance on assumptions about material properties, boundary conditions and loading scenarios. It also excludes biological factors, patient variability and the bone’s heterogeneous nature, which may affect the accuracy and applicability of the results in real-life situations.Originality/valueThe development of an asymmetric curved buttress plate using MFFF is a novel innovation aimed at improving biomechanical performance and patient outcomes in orthopedic surgery, offering significant potential impact in the medical field.

Axiomatization of XPath with general data comparison

In this work, we study Hilbert-style proof systems for logics based on the data-aware language CoreDataXPath ( ↓ ) where the comparison relation between nodes is not necessarily an equivalence relation. We give a sound and complete axiomatization of the class of tree-like Kripke frames endowed with a general comparison relation between nodes. Modular extensions of this axiomatization are also discussed, including cases where the comparison relation is reflexive, symmetric, transitive and an equivalence. A notable highlight that we recover an axiomatization for CoreDataXPath ( ↓ ) over any data-tree when the comparison is by ‘equal data’. Moreover, we prove that all these systems are decidable by leveraging the finite model property of their corresponding frame classes.

Model Conversion and Application Framework for Rectangular Aqueduct Superstructures: Integrating Revit and Midas Civil

To improve the modeling efficiency of finite element analysis models for rectangular aqueducts based on the BIM information model, this study proposes a model conversion method that integrates Revit software and Midas Civil. By accessing the attribute information stored in the structural component objects of the BIM model, the finite element modeling parameters required for structural analysis are obtained. A secondary development program has been established using the C# programming language on the Visual Studio 2022 platform, resulting in the creation of a model interface program (Revit-Midas Civil) that facilitates the conversion of model information. This has saved designers time in constructing finite element models. The feasibility and reliability of the proposed model conversion method have been validated through a multi-girder rectangular aqueduct. The results indicate that the model conversion method successfully transformed structural analysis model information from Midas Civil into Revit model information, significantly enhancing the modeling efficiency and accuracy of rectangular aqueduct structures.

A systematic and optimal design method for replacing steel backrest in automotive seat with carbon fiber-reinforced polymer composites

Carbon fiber-reinforced polymer composites (CFRPC) are extensively utilized in the lightweight design of automobiles due to their superior material properties. This study focuses on the automotive seat backrest as the subject of research. To address the challenge of achieving both lightweight and performance optimization in CFRPC, subsequent to replacing the steel backrest in automotive seat, a systematic optimization design method is proposed. Firstly, the experimentally validated finite element analysis model of a steel seat, subjected to four typical working conditions, is redesigned for the CFRPC backrest in automotive seat. Building upon this foundation, the optimization design of the CFRPC backrest is segmented into two phases: optimization of layup size and optimization of layup sequence. During the optimization of layup size, a sophisticated optimization strategy is employed. This strategy combines many-objective optimization and multi-criteria decision-making. It integrates the modified extensible lattice sequence experimental design, the hybrid surrogate model utilizes the global response search method in conjunction with weighted prediction error lowering, the non-dominated sorting genetic algorithm-III, the improved double combination weighting model of game theory, and the improved VIsekriterijumsko KOmpromisno Rangiranje method, which incorporates Euclidean distance and gray relational analysis (IVIKOR-ED&GRA). This approach is utilized to ascertain the optimal count and thickness of the number of layers required for each specific angle. The simulation results demonstrate that the optimized CFRPC backrest, featuring refined layup dimensions, not only fulfills regulatory requirements but also enhances a majority of performance metrics, accompanied by a substantial weight reduction of 38.66%. Ultimately, the OptiStruct optimization technique of layup sequence is employed to further enhance the performance of the CFRPC backrest seat. This is achieved by modifying the layup stacking sequence, while maintaining the original material’s weight and the number of layups for each angle unchanged.

Heterogeneidad no observada en los ingresos de los trabajadores independientes en el Perú urbano

The research estimates that socioeconomic factors influenced the income of self-employed workers during the periods 2017-2019 and 2020-2022, using data from the National Household Survey conducted by the National Institute of Statistics and Informatics. A finite mixture quantile panel regression model was applied, which allowed estimating the heterogeneous effects of socioeconomic factors and modeling the individual characteristics of people, known as unobserved heterogeneity. It was found that, by using two locations or mixtures in the quantile regressions, there is a wide range of effects of years of education across different quantiles for both women and men. Moreover, these effects differ when comparing the periods before and during COVID-19.

Machine Learning–Finite Element Mesh Optimization-Based Modeling and Prediction of Excavation-Induced Shield Tunnel Ground Settlement

Ground settlement prediction for shield construction is highly important and challenging. This study introduces a machine learning algorithm combined with finite element numerical simulation, i.e., machine learning–finite element mesh optimization. For surface subsidence prediction, 16 combination models of ANN, KNN, RF and SVR were optimized by PSO, GA, BT and BO, involving raw data preprocessing, principal component analysis, hyperparameter selection and prediction accuracy evaluation. A subway shield tunneling project was analyzed, in which the meshes of finite element numerical models were discretized into different sizes from 1.0[Formula: see text]m to 2.0[Formula: see text]m. In total, 360 sets of data points were extracted from the simulation results, including stress, strain, shield jacking force, internal friction angle, cohesion force, and settlement, of which 252 data points were used as the input parameters of machine learning model. Analysis of average error rate of finite element–machine learning coupling models showed that the finite element model had the highest accuracy of settlement prediction when the mesh size of the finite element model was 1.4[Formula: see text]m, and the GA-SVR model had the highest accuracy and generalization ability in ground settlement prediction. This study highlights the uniqueness of machine learning–finite element mesh optimization model in application.

Multiscale Modeling of Nanoparticle Precipitation in Oxide Dispersion-Strengthened Steels Produced by Laser Powder Bed Fusion.

Laser Powder Bed Fusion (LPBF) enables the efficient production of near-net-shape oxide dispersion-strengthened (ODS) alloys, which possess superior mechanical properties due to oxide nanoparticles (e.g., yttrium oxide, Y-O, and yttrium-titanium oxide, Y-Ti-O) embedded in the alloy matrix. To better understand the precipitation mechanisms of the oxide nanoparticles and predict their size distribution under LPBF conditions, we developed an innovative physics-based multiscale modeling strategy that incorporates multiple computational approaches. These include a finite volume method model (Flow3D) to analyze the temperature field and cooling rate of the melt pool during the LPBF process, a density functional theory model to calculate the binding energy of Y-O particles and the temperature-dependent diffusivities of Y and O in molten 316L stainless steel (SS), and a cluster dynamics model to evaluate the kinetic evolution and size distribution of Y-O nanoparticles in as-fabricated 316L SS ODS alloys. The model-predicted particle sizes exhibit good agreement with experimental measurements across various LPBF process parameters, i.e., laser power (110-220 W) and scanning speed (150-900 mm/s), demonstrating the reliability and predictive power of the modeling approach. The multiscale approach can be used to guide the future design of experimental process parameters to control oxide nanoparticle characteristics in LPBF-manufactured ODS alloys. Additionally, our approach introduces a novel strategy for understanding and modeling the thermodynamics and kinetics of precipitation in high-temperature systems, particularly molten alloys.

RADARSAT-2 DInSAR and GNSS-Derived Finite Fault Model of the 2012 Mw 7.8 Haida Gwaii Earthquake

The Mw 7.8 Haida Gwaii earthquake occurred on 28 October 2012, generating up to 13 m tsunami waves and 3 m run-up along the British Columbia coastline. Despite the magnitude of the earthquake and tsunami, damages were minor due to the lack of vulnerable infrastructure in the remote area. Previous finite fault models were derived from GNSS, seismic and tsunami data, but the uncertainty remained high due to the limited number of seismic and GNSS stations near the epicenter. In this study, finite fault models were developed using RADARSAT-2 interferograms and previously published GNSS data. These models defined the location of the fault and provided a detailed slip distribution with a high degree of certainty. The results confirmed that the main slip was located on the subduction fault interface between the Pacific and North American plates, west of the Queen Charlotte Fault. The estimated moment magnitude of 7.88–7.93 is slightly larger than the previously reported moment magnitude of around 7.8, due to the capture of postseismic deformation in the interferograms. Overall, the study provides an improved finite fault slip model for the Haida Gwaii earthquake and highlights the importance of utilizing remote sensing data for studying earthquakes in remote areas.

Theoretical Model and Experimental Application for Solute Diffusion in Leaching Test With Time-Dependent Boundary Conditions

Abstract Measuring the diffusion coefficient of clay-based liner materials is important in estimating and predicting long-term barrier performance in waste containment facilities. Various theoretical models, including the finite cylindrical model, have been commonly used to determine the diffusion properties of clay-based liner materials in leaching tests. However, the assumption of zero-concentration boundary conditions of the traditional finite cylindrical model contradicts the measured variation of concentration in real leaching tests, likely resulting in (1) underestimated and unconservative diffusion coefficient, or (2) requirement of a relatively large liquid-to-soil ratio and frequent leachate replacement in the experiment to maintain the zero-concentration boundary condition. In this study, a theoretical model was developed to evaluate the solute diffusion process within a soil specimen under arbitrary, time-dependent concentration boundary conditions. The proposed model, incorporating the time-dependent boundary conditions, provides efficient calculations of the concentration distribution and the cumulative fraction leached of solute across the soil specimen. The example application of the proposed model to experimental data demonstrates the capability of the proposed model to determine apparent diffusion coefficients of clay-based liner materials without introducing errors associated with the assumption of a zero concentration boundary condition. The proposed model provides a comprehensive method to investigate the dynamic transport behaviors of solutes through clay-based liner materials in future studies.

Equivalent Heat Source Model of Thermal Relay Contact Based on Surface Roughness of Silver-Magnesium-Nickel Contact.

In a sealed electromagnetic relay, the change in the surface roughness mainly depends on the collision wear between the contact and the moving reed and the ablation effect of the arc on the contact surface based on the strong correlation between the contact resistance and the surface roughness of the Ag-Mg-Ni contact. With a change in contact resistance, the contact temperature increase in a hermetically sealed electromagnetic relay (HSER) is greatly affected. Under extreme overload conditions, the contact surface is severely ablated by the arc, and the roughness increases rapidly with the number of cycles, which greatly affects the contact resistance of the contact surface and the reliability of the relay. A thermal model of a relay contact system based on the surface roughness of Ag-Mg-Ni contacts was established in this paper by analyzing the effect of an arc on the surface roughness of Ag-Mg-Ni contacts under heavy overload conditions. The arc image of the Ag-Mg-Ni contact was recorded using a double-axis arc photographing platform, and the moving track of the arc center under overload conditions was drawn. This paper explored the patterns of arc center movement on the contact surface and the effects of the arc on the surface roughness of the contacts by analyzing the probabilities of the arc center appearing in various locations. A mathematical model correlating the number of contact cycles with contact resistance was established. Subsequently, a finite element simulation model for the equivalent heat source of the contact was developed. The theoretical model error was less than 10%. The accuracy of the equivalent heat source model was verified by comparing the measured data with the simulation results.

Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach

Summary The spatial correlation of coseismic slip is a necessary input for generating stochastic seismic rupture models, which are commonly employed in seismic and tsunami hazard assessments. To date, the spatial correlation of individual earthquakes is characterized using finite fault models by finding the combination of parameters of a von Kármán autocorrelation function that best fits the observed autocorrelation function of the finite fault model. However, because a priori spatial correlation conditions (i.e., not in the data) are generally applied in finite fault model generation, the results obtained using this method may be biased. Additionally, robust uncertainty estimates for spatial correlations of coseismic slip are generally not performed. Considering these limitations in the classic method, here, a method is developed based on a Bayesian formulation of Finite Fault Inversion (FFI) with positivity constraints. This method allows for characterizing the spatial correlation of coseismic slip and its uncertainties for an earthquake by using samples of coseismic slip from a posterior probability density function (PDF). Furthermore, a Bayesian model selection criterion called Akaike Bayesian Information Criterion (ABIC) is applied to objectively choose between different prior spatial correlation schemes before computing the posterior, to reduce subjectivity due to this prior condition. The ABIC is calculated using an approximate analytical expression of Bayesian evidence. The method is applied to simulated P-waves, demonstrating that model selection allows for objectively estimating the most suitable prior spatial correlation scheme in FFI. Additionally, the target spatial correlation of coseismic slip is accurately recovered using samples from the posterior PDF, as well as their uncertainties. Moreover, in the simulated experiment, it is shown that a non-robust choice of the prior spatial correlation scheme can significantly bias the estimated spatial correlations of coseismic slip. We apply our method to observed P-waves from the 2015, Illapel earthquake (Mw = 8.3), finding that the spatial correlation of coseismic slip of this earthquake is better described by a von Kármán ACF, with mean correlation lengths of around 47 km and Hurst parameter of 0.58. We conclude that using our method reduces biases associated with prior spatial correlation conditions and allows for robust estimation of spatial correlations of coseismic slip and their uncertainties.

Three-dimensional numerical calculation and case application of IP five-directional well logging

Abstract Conventional well logging can effectively detect the vertical distribution range of ore bodies near the well by placing detectors in the well. However, its detection capability for the horizontal extension of ore bodies is limited. Therefore, starting from the differential equations satisfied by the point source in three-dimensional structures, this paper deduced the variational problem satisfied by the total potential under three-dimensional complex conditions. By focusing on well logging, tetrahedral cross-grid partitioning technology was employed to achieve high-precision three-dimensional finite element forward modeling. Simulations were carried out for different forms of ore bodies using the induced polarization five-direction observation method, considering the presence of ore bodies near the well and the case where the measurement well passes through the ore body. The paper analyzed and summarized the induced polarization anomaly patterns formed by ore bodies with low resistance and high polarization in different horizontal extension directions under induced polarization five-direction logging with power supply measurements from different directions. Based on this, a human-computer interaction system was constructed to perform forward fitting and inversion interpretation of the measured induced polarization data, resulting in a good application effect.

Development of an FEM for the Combined Electromagnetic and Hydraulic Forming Process Based on Experimental Data

Electrohydraulic forming (EHF) which demonstrates reduced bouncing effect, formation in narrow areas, and no effect on the electrical conductivity of the blank can overcome the shortcomings of deep drawing and electromagnetic forming. However, considerable time is involved in evaluating the possibility of forming a specific part through experiments. Developing an accurate finite element model can reduce the opportunity costs of an experiment by reducing unnecessary trial and error in forming a specific part. In this study, the chamber, die, and blank components of the EHF experimental equipment in our laboratory were reverse-modeled using CATIA V5R18. Subsequently, the IGES format of the components was imported into LS-DYNA R12, and an FEM model to simulate the EHF experiment was constructed. The experimental and simulation results of nine cases, based on the SUS430 material, input voltage, and blank thickness, were compared for model verification. The forming results for all cases in the constructed finite element analysis model nearly matched the experimental results. Moreover, the linear increase in the blank thickness with input voltage and thickness was simultaneously confirmed. In a computing environment using a 4.3 GHz, 24-Core CPU and 64 GB memory, the time required for one finite element analysis was approximately 1 h.

Comparison of vibration values of rotating discs with variable parameters obtained by finite element analysis modeling with different machine learning algorithms

PurposeRotating functionally graded (FG) disks of variable thickness generates vibration. This study aims to analyze the vibration generated by the rotating disks using a finite element program and compare the results obtained with the regression methods.Design/methodology/approachTransverse vibration values of rotating FG disks with variable thickness were modeled using different regression methods. The accuracies of the obtained models are compared. In the context of comparing regression methods, multiple linear regression (MLR), extreme learning machine (ELM), artificial neural networks (ANNs) and radial basis function (RBF) were used in this study. The error graph between the observed value and the predicted value of each regression method was obtained. The error values of the regression methods used with scientific error measures were calculated.FindingsThe analysis of the transverse vibration of rotating FG disks with the finite element program is consistent with the studies in the literature. When the variables and vibration value determined on the disk are modeled with ELM, MLR, ANN and RBF regression methods, it is concluded that the most accurate model order is RBF, ANN, MLR and ELM.Originality/valueThere are studies on the vibration value of rotating discs in the literature, but there are very few studies on modeling. This study shows that ELM, MLR, ANN and RBF, which are machine learning methods, can be used in modeling the vibration value of rotating discs.

Electron Beam Additive Manufacturing of SS316L with a Stochastic Scan Strategy: Microstructure, Texture Evolution, and Mechanical Properties

This study examines the microstructure, crystallographic texture evolution, and mechanical properties of stainless steel 316L fabricated through electron beam melting using a stochastic scan strategy at a preheat temperature of 1123 K. X-ray diffraction confirmed the presence of a pure austenitic phase in the fabricated material. Equiaxed cellular structures were observed in the center of the melt pool regions and elongated cellular structures observed at the melt pool overlap regions. A finite element-based numerical model was employed to estimate the thermal gradients and solidification rates within the melt pool of an electron beam spot. Microstructural analysis indicated a generation of columnar grains from the bottom to the top of the build owing to high thermal gradients. A crystallographic texture investigation showed a generation of strong <110> fiber texture along the build direction of the material and reported that the stress distributions within the melt pool led to a strong crystallographic texture driven by the stress evolution observed from thermokinetic computational modelling of the electron beam-melting process. Mechanical properties were assessed using profilometry-based indentation plastometry, demonstrating strain hardening at a high temperature of 773 K.

Influence of the variation of concrete properties in determining the vibration transmission index Kij

In this work, the influence of the modulus of elasticity, the loss factor and the density of reinforced concrete on the vibration transmission index ([Formula: see text]) of a reduced concrete bench was evaluated. An experimental modal analysis based on the Operating Deflection Shape (ODS) methodology is carried out on a reinforced concrete slab (1.20 × 0.80 × 0.20 m) supported on four concrete pillars, applying four sources of distinct impacts: mallets of 1 and 4 kg, impact machine and one person jumping. The experimental results served to validate the finite element bench model (FEM) considering both the first four modes of slab vibration and the adjustment of the elasticity and shear modulus, the loss factor and the density of the concrete using the NSGA genetic algorithm-II. The [Formula: see text] index between the slab and the columns was determined experimentally, numerically and analytically, according to the Gerretsen model. The FEM model showed a percentage difference in the frequencies of the vibration modes less than 10% concerning the tests for the [Formula: see text] index. The influence of density and the modulus of elasticity mainly in the direction perpendicular to the plane of the structure modify the values of the frequencies of the vibration modes by up to 10% without interfering in the modal forms of the structure. In addition, the internal loss factor alters the amplitude of the [Formula: see text] results by an average of 10 dB. Finally, it is shown that the stiffness varies according to the orthotropy of the material. It was possible to conclude that the computational model showed good convergence in results ([Formula: see text]%) when compared with the [Formula: see text] experiments, with the same varying along the frequency, different from what is proposed in the analytical and normative models. This fact proves the hypothesis raised that structural couplings in practice generate significant interference in the results, as happens in residential buildings, with the test bench being an important experiment for initial predictions with little interference from energy transmission to the environment.

A review of finite control set model predictive control for linear machines

AbstractLinear machines (LMs) have become excellent candidates for linear motion applications due to their abilities of generating thrust without any transmissions, and have been widely adopted in transportation, industries etc. Among the numerous control methods of LM drive systems, finite control set model predictive control (FCS‐MPC) method has been given special attention due to its clear concept, fast response performance, and ability to handle constrained multivariate non‐linear control problems. It is widely used to improve the performance and efficiency of overall LM drive systems. Firstly, based on the three elements of FCS‐MPC, the advantages and shortcomings of the FCS‐MPC method in applications are fully investigated. The research status in FCS‐MPC has been summarised, such as multistep MPC, parameters robustness improvement, fast optimisation, and multi‐vector MPC. Then, the key issues in FCS‐MPC such as delay compensation, parameters design, and stability analysis are discussed, and their typical research methods and current research status are explained. Finally, the future development trends of FCS‐MPC in LM drive systems are discussed.