Modeling Accuracy Research Articles

Improved local time-stepping schemes for storm surge modeling on unstructured grids

This paper presents improved explicit local time-stepping (LTS) schemes of both first and second order accuracy for storm surge modeling. The two-dimensional shallow water equations are numerically solved on unstructured triangular meshes using finite volume method with Roe’s approximate Riemann solver. The LTS algorithms are designed based on explicit Euler and strong stability preserving Runge–Kutta time integration methods. A single-layer interface prediction–correction scheme is adopted to combine coarse and fine time discretization, further enhancing the stability of the LTS schemes, particularly at higher LTS levels and during long time simulations. An ideal numerical test validates the efficiency of the improved LTS models, revealing their capability to improve computational speed while preserving conservation properties and reducing accuracy loss as LTS levels increase. We further apply the LTS models to cross-scale simulations of storm surges in the Northwest Pacific. Results show that compared to the global time-stepping (GTS) models, the LTS models significantly boost computational speed by up to 37%, all while delivering equally reliable computational outcomes. With expanding high-resolution coastal data and the need for high-resolution modeling, the improved LTS models show great potential for cross-scale storm surge modeling.

Stochastic evaluation of quasi-zero stiffness magnetic spring using a reluctance-corrected analytical model

—In state of the art high-precision motion systems, which utilize magnetic levitation, a quasi-zero stiffness gravity compensation is one of the essentials to improve dynamic performance, eliminate position dependency and thus simplify controller design. Special configurations of permanent magnets, such as Halbach magnet arrays, can realize magnetic levitation as a form of magnetic spring. However, unlike conventional permanent magnet machines with large stiffness, the quasi-zero stiffness magnetic spring requires higher modeling accuracy for force estimation, which is affected by the nonlinear reluctance effect of permanent magnets. In this study, we propose an accurate magnetic modeling method for the quasi-zero stiffness magnetic spring. By correcting the reluctance effect of magnets, the proposed magnetic model achieves superior accuracy estimating levitation force and stiffness to the conventional surface current model. Using the reluctance-corrected magnetic model, tolerance analysis was performed to identify dominant geometric parameters affecting the uncertainty of the Halbach array magnetic spring performance. A Monte-Carlo simulation was used to estimate the overall tolerance of magnetic forces and stiffness. The experimental results fit in the tolerances.

Data-driven atmospheric corrosion prediction model for alloys based on a two-stage machine learning approach

Alloy material corrosion is a comprehensive problem affected by multi-dimensional factors, including the time, environment, and material element. Its corrosion prediction has been facing the challenges of complex mechanism and small sample size. However, parametric methods are limited in terms of modeling accuracy and generalization performance for high-dimensional feature problems. Non-parametric machine learning algorithms, while having excellent modeling capabilities, need to be optimized in conjunction with the structure of the corrosion problem. Therefore, we develop a two-stage hybrid intelligent machine learning model for improving alloy corrosion prediction accuracy. The model takes the time, environment, and material information of the alloy corrosion process as inputs and the corrosion rate as output. The first stage of the model utilizes tree ensemble learning (TEL) models for initial prediction modeling and feature importance knowledge mining. In the second stage, the neural network (NN) is used to enhance the fusion of the multi-model prediction outputs, and it is optimized using feature importance modification (FIM). The proposed TEL-FIMNN model shows considerable performance improvement over the TEL and ANN models on real low-alloy steel corrosion dataset. Compared to other advanced multi-model fusion methods such as linear regression (LR), support vector regression (SVR), random forest (RF), and Bayesian model averaging (BMA), the proposed model also shows superior results. The TEL-FIMNN model provide a new structure from the perspectives of knowledge mining and fusion enhancement, which has good potential for multi-dimensional corrosion prediction.

Modeling and measurement of dielectric anisotropy in materials manufactured via fused filament fabrication processes

Additive manufacturing (AM) is increasingly recognized as an enabling technology for novel devices with increased functionality and bandwidth for microwave applications. However, due to the layer-by-layer build approach employed by most AM techniques, internal patterns are introduced to printed materials, which cause an effective anisotropy in their material parameters. An electrostatic model inspired by parallel plate capacitors, describing the dielectric anisotropy of materials manufactured with fused filament fabrication (FFF) AM techniques is proposed in this work. The accuracy of the model’s predictions about the permittivity tensor of printed materials is investigated via numerical simulations. Furthermore, permittivity tensor measurements are carried out for samples printed with various materials and print settings. Measurement data is fitted to the model via a least squares approach, and excellent agreement is observed. Novel conclusions about the impact of material and print parameters on the effective permittivity of additively manufactured materials, improving the accuracy in modeling and designing additively manufactured microwave devices are drawn.

Enhancing customer retention in telecom industry with machine learning driven churn prediction

Customer churn remains a critical concern for businesses, highlighting the significance of retaining existing customers over acquiring new ones. Effective prediction of potential churners aids in devising robust retention policies and efficient customer management strategies. This study dives into the realm of machine learning algorithms for predictive analysis in churn prediction, addressing the inherent challenge posed by diverse and imbalanced customer churn data distributions. This paper introduces a novel approach—the Ratio-based data balancing technique, which addresses data skewness as a pre-processing step, ensuring improved accuracy in predictive modelling. This study fills gaps in existing literature by highlighting the effectiveness of ensemble algorithms and the critical role of data balancing techniques in optimizing churn prediction models. While our research contributes a novel approach, there remain avenues for further exploration. This work evaluates several machine learning algorithms—Perceptron, Multi-Layer Perceptron, Naive Bayes, Logistic Regression, K-Nearest Neighbour, Decision Tree, alongside Ensemble techniques such as Gradient Boosting and Extreme Gradient Boosting (XGBoost)—on balanced datasets achieved through our proposed Ratio-based data balancing technique and the commonly used Data Resampling. Results reveal that our proposed Ratio-based data balancing technique notably outperforms traditional Over-Sampling and Under-Sampling methods in churn prediction accuracy. Additionally, using combined algorithms like Gradient Boosting and XGBoost showed better results than using single methods. Our study looked at different aspects like Accuracy, Precision, Recall, and F-Score, finding that these combined methods are better for predicting customer churn. Specifically, when we used a 75:25 ratio with the XGBoost method, we got the most promising results for our analysis which are presented in this work.

Hydrological model parameter regionalization: Runoff estimation using machine learning techniques in the Tha Chin River Basin, Thailand

Understanding hydrological processes necessitates the use of modeling techniques due to the intricate interactions among environmental factors. Estimating model parameters remains a significant challenge in runoff modeling for ungauged catchments. This research evaluates the Soil and Water Assessment Tool's capacity to simulate hydrological behaviors in the Tha Chin River Basin with an emphasis on runoff predictions from the regionalization of hydrological parameters of the gauged basin, Mae Khlong River Basin. Historical data of Mae Khlong River Basin from 1993 to 2017 were utilized for calibration, followed by validation using data from 2018 to 2022.•Calibration results showed the SWAT model's reasonable accuracy, with R² = 0.85, and the validation with R² of 0.64, indicating a satisfactory match between observed and simulated runoff.•Utilizing Machine Learning (ML) techniques for parameter regionalization revealed nuanced differences in model performance. The Random Forest (RF) model exhibited an R² of 0.60 and the Artificial Neural Networks (ANN) model slightly improved upon RF, showing an R² of 0.61 while the Support Vector Machine (SVM) model demonstrated the highest overall performance, with an R² of 0.63.•This study highlights the effectiveness of the SWAT and ML techniques in predicting runoff for ungauged catchments, emphasizing their potential to enhance hydrological modeling accuracy. Future research should focus on integrating these methodologies in various basins and improving data collection for better model performance.

INCREASING THE ACCURACY OF THE POWER LINES MODELING BASED ON PMU

The article discusses the modeling of power lines based on PMU data to assess the state of overhead lines. Current approaches to solving the problem are analyzed. The advantages and disadvantages of these approaches are presented. It has been established that when modeling power lines are traditionally represented in the form of a - circuits scheme, which leads to methodological modeling errors. To reduce the methodological error of modeling, overhead lines (OHLs) can be presented in the form of equations with distributed parameters, which allows for maximum accuracy. However, distributed line equations contain hyperbolic functions and reduce performance in real-time simulations. Based on this, it is noted that the selection of an adequate mathematical model of overhead lines with the appropriate accuracy of synchronized vector measurements in real time when assessing the state of power lines is an important stage of modeling. The article discusses overhead line models using equations with distributed parameters, a -circuits, an equivalent - circuits and chain -circuits depending on the number of links. The results of calculating the mode based on the modeling accuracy using the example of a 500 kV overhead line are presented, as well as the results of comparing various equivalent circuits. It is shown that when modeling overhead line modes based on the results of PMU measurements, representing power lines as chain circuits with links 50 km or less in length is appropriate, since it makes it possible to obtain a model accuracy that is adequate to the PMU measurements. Methodological errors in modeling ultra-high voltage transmission lines using the equations of long lines have been studied. The obtained results can be used for monitoring, analysis and operational management of the electric power system

Effects of periodic sequential arrangement of subscale miura-foldcore under quasi-static compression

This study presents experimental and numerical investigations on the quasi-static compressive responses of various subscale Miura-foldcore composites. A series of quasi-static compression tests were conducted on standard Miura foldcore specimens fabricated using carbon/epoxy woven fabric prepregs. Representative volume element (RVE) models, incorporating periodic boundary conditions (PBCs), were developed to predict the size-dependent compressive response of subscale Miura foldcores. The effective properties of the carbon/epoxy woven fabric composite used in this study were calculated using the NASA multiscale analysis tool (NASMAT) via two-step homogenization process. The FE model exhibited comparable agreement with experimental results, showcasing variations of less than 7% and 12% in maximum compressive load and compressive stiffness, respectively. The implementation of PBC in the foldcore RVE models improved modeling accuracy by <4%, but drastically increased total computational time (>50%). The periodic pattern of foldcore unit-cells, where two single foldcore unit-cells were placed in parallel or perpendicular, imposed geometric constraints, resulting in small variations in predicted stress and strain distribution contours. The key findings highlighted in this study suggest that a 1 × 1 foldcore unit-cell model without PBC is sufficient to predict accurate quasi-static compressive responses of foldcore composites. This study advances the understanding of subscale Miura-foldcore composites and provides valuable insights into the limitations associated with the use of PBC in foldcore RVE models. The findings also offer a practical guide for fabricating and analyzing traditional Miura folding patterns, promoting a more efficient and accurate approach for optimizing foldcore composite designs considering both structural performance and manufacturability.

Comparative study of moisture and stress dependent unbound material modulus for flexile pavement response modeling

Moisture can significantly impact the resilient modulus of pavement base and subgrade. Although various resilient modulus models exist, their accuracy in predicting pavement responses have not been examined and verified. This study aims to compare the accuracy of pavement response modeling using different resilient modulus, considering moisture and stress dependent unbound material modulus. Comprehensive pavement information including pavement structure, viscoelastic property of asphalt concrete, unbound material properties, moisture gradient, water table, temperature gradient and FWD deflections were collected from Long-Term Pavement Performance (LTPP) program. Based on collected data, finite element models were developed to predict pavement deflections under FWD load and pavement responses under traffic loading, respectively. The results show that the moisture-suction-stress dependent (MSS) model has the best prediction accuracy as compared to field measurements, followed by the Witczak model, while the generalized constitutive model displayed the largest deviation. Under traffic loading, the critical pavement responses predicted from Witczak and generalized constitutive model can have a significant deviation from those predicted from MSS model. The generalized constitutive model was found to constantly overestimate the responses, and the deviation of Witczak model depends on other factors such as pavement structure. Using results from MSS as the baseline, these deviations were found to result in up to 50% errors in service life of selected pavements in this study. These findings highlight the importance of taking matric suction into account for accurate modeling of flexible pavement responses.

Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling

While supervised learning models have shown remarkable performance in various natural language processing (NLP) tasks, their success heavily relies on the availability of large-scale labeled datasets, which can be costly and time-consuming to obtain. Conversely, unsupervised learning techniques can leverage abundant unlabeled text data to learn rich representations, but they do not directly optimize for specific NLP tasks. This paper presents a novel hybrid approach that synergizes unsupervised and supervised learning to improve the accuracy of NLP task modeling. While supervised models excel at specific tasks, they rely on large labeled datasets. Unsupervised techniques can learn rich representations from abundant unlabeled text but don't directly optimize for tasks. Our methodology integrates an unsupervised module that learns representations from unlabeled corpora (e.g., language models, word embeddings) and a supervised module that leverages these representations to enhance task-specific models [4]. We evaluate our approach on text classification and named entity recognition (NER), demonstrating consistent performance gains over supervised baselines. For text classification, contextual word embeddings from a language model pretrain a recurrent or transformer-based classifier. For NER, word embeddings initialize a BiLSTM sequence labeler. By synergizing techniques, our hybrid approach achieves SOTA results on benchmark datasets, paving the way for more data-efficient and robust NLP systems.

Seismic modeling by combining the finite-difference scheme with the numerical dispersion suppression neural network

Seismic finite-difference (FD) modeling suffers from numerical dispersion including both the temporal and spatial dispersion, which can decrease the accuracy of the numerical modeling. To improve the accuracy and efficiency of the conventional numerical modeling, I develop a new seismic modeling method by combining the FD scheme with the numerical dispersion suppression neural network (NDSNN). This method involves the following steps. First, a training data set composed of a small number of wavefield snapshots is generated. The wavefield snapshots with the low-accuracy wavefield data and the high-accuracy wavefield data are paired, and the low-accuracy wavefield snapshots involve the obvious numerical dispersion including both the temporal and spatial dispersion. Second, the NDSNN is trained until the network converges to simultaneously suppress the temporal and spatial dispersion. Third, the entire set of low-accuracy wavefield data is computed quickly using FD modeling with the large time step and the coarse grid. Fourth, the NDSNN is applied to the entire set of low-accuracy wavefield data to suppress the numerical dispersion including the temporal and spatial dispersion. Numerical modeling examples verify the effectiveness of my proposed method in improving the computational accuracy and efficiency.

Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.

Modeling and mapping solar energy production with photovoltaic panels on Politecnico di Torino university campus

Educational institutions have significant impacts on the society and environment they are inhabiting, and they can have a big role in influencing various development fields, including sustainability. The environmental sustainability of universities was critically analyzed recently. These bodies can contribute to the sustainability of cities due to their social role in shaping the future generations. The aim of this work is to analyze Urban Building Energy Modeling with a place-based approach using the open-source software QGIS in predicting energy production with photovoltaic solar technologies on the rooftops of the central university campus of Politecnico di Torino. This modeling can help in assessing the energy security and affordability of current and future sustainable scenarios considering their impact on climate change. This study evaluates the accuracy of urban scale QGIS-based energy modeling with a comparison of measured data available from the monitoring activity of LivingLab of Politecnico di Torino, the free tool PVGIS, and the web tools of ENEA. The QGIS modeling accuracy depends on the different precisions of the Digital Surface Model used to describe the built environment (i.e., 1 m or 5 m) and the climate input data (monthly and annual diffuse-to-global radiation and Linke turbidity factor). Moreover, this assessment can be used to map the results of new photovoltaic systems improving the energy and environmental performance of university campuses. The results of this work shed light on the significance of different input data for energy simulation tools at neighborhood-urban scale. The result shown accuracies in PV production of 10 to 37% with different spatial resolutions of the 3D built environment and of 14 to 15.2% for temporal resolution of solar irradiation variables.

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data. Consequently, a method for cleaning wind power anomaly data by combining image processing with community detection algorithms (CWPAD-IPCDA) is proposed. To precisely identify and initially clean anomalous data, wind power curve (WPC) images are converted into graph structures, which employ the Louvain community recognition algorithm and graph- theoretic methods for community detection and segmentation. Furthermore, the mathematical morphology operation (MMO) determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning. The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines (WTs) in two wind farms in northwest China to validate its feasibility. A comparison was conducted using density-based spatial clustering of applications with noise (DBSCAN) algorithm, an improved isolation forest algorithm, and an image-based (IB) algorithm. The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms, achieving an approximately 7.23% higher average data cleaning rate. The mean value of the sum of the squared errors (SSE) of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms. Moreover, the mean of overall accuracy, as measured by the F1-score, exceeds that of the other methods by approximately 10.49%; this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Implications of digital elevation models on habitat analysis of Golden Mahseer in the Upper Ganga Basin, India

Aquatic habitat analysis is crucial in determining the relationship between river flow and habitat availability for aquatic species. This helps in identifying the environmental flow requirements of rivers. However, conducting habitat analysis in Indian rivers is challenging because of the unavailability of reliable and high-resolution terrain data. To address this challenge, a study was conducted to explore the possibilities of using Digital Elevation Models (DEMs) to extract required hydraulic data and to evaluate their accuracies. The extracted data were coupled with ecological data of a keystone fish species, namely Golden Mahseer (Tor putitora), to develop habitat analysis models for the Upper Ganga Basin (UGB) in India. The study adopted a hierarchical three-step approach for evaluating the accuracy and performance of DEMs as surrogate data sources. Firstly, cross sections at selected sites in the UGB were extracted from three different DEMs (SRTM, ASTER, and CARTOSAT) and evaluated against surveyed cross-section data with turning point tests and correlation coefficients. These data were then used to establish hydraulic and habitat analysis models. Four parameters (top width, flow cross-sectional area, hydraulic mean depth, and wetted perimeter) were evaluated using five error estimators to determine the accuracy and performance of hydraulic modelling. Finally, the hydraulic parameters were coupled with ecological requirements to develop a habitat model for different life stages of Golden Mahseer, namely fingerling, juvenile, and adult stages.We found that the SRTM predictions were better than those of the other DEMs, indicating its suitability to replicate channel geometry with higher accuracy, thus better predicting hydraulic parameters at all flow ranges. In habitat area estimation for adult Golden Mahseer, all the DEMs performed reasonably well (within ±20%) within the flow range of 100 ​m3/s, which covers the low to average flow season. Beyond this flow range, ASTER and CARTOSAT resulted in considerable underestimations, averaging 22% and 54%, respectively. It is important to note that DEM-based cross sections lack high-resolution channel information, resulting in unstable habitat predictions for younger life stages like fingerling. However, overall, the study established that DEM-based data can be relied upon for habitat modelling-based assessment of environmental flows with some precautions for sensitive cases. Remote sensing presents a promising avenue for habitat analysis studies of Indian species, offering the potential to unlock significant progress in environmental flows (E-Flows) assessments and thus providing ecological benefits.

State of charge estimation for lithium batteries based on multi-timescale FFRLS-UKF

Abstract State of charge (SOC) can represent the remaining battery capacity, and accurately estimating SOC is very useful for the drivers of electric vehicles. This study proposes a lithium battery SOC estimation technique based on multi-timescale forgetting factor recursive least squares as well as an unscented Kalman filter (MTS-FFRLS-UKF). The MTS-FFRLS-UKF uses a circuit with a 2-RC network as the battery model, whose parameters are continuously updated at a timescale that is different from SOC estimation. The experimental results under a dynamic test prove the availability and accuracy of MTS-FFRLS-UKF. Continuous parameter update enhances the accuracy of battery modelling and furthermore the SOC estimation accuracy. The mean absolute error (MAE) and root-mean square error (RMSE) of SOC estimation decrease to 0.58% and 0.62%, respectively.

3D Hybrid modeling for the ultrasonic phased array inspection of porosity in heavy plates: Simulation and experimental validation

Quantitative NDT modeling and simulation tools are important in design and fabrication of ultrasonic phased array probes with optimum characteristics and reduce the number of experimental efforts and development time. In order to achieve high accuracy (Amplitude deviation < 1 dB) with a low computational time in the simulation, the modeling tools require not only the high-frequency approximation of the ultrasonic wave propagation but also the high-precision electro-acoustic coupling model. This paper describes a computationally efficient 3D time-dependent hybrid model for the simulation of ultrasonic phased array inspection of porosity in heavy plates. This method combines the results of both 3D finite element analysis (PZFLex-FEA) and the semi-analytical ultrasonic simulation “Ray-Tracing”. As a first step, we demonstrate the FEA modeling of an ultrasonic phased array probe, which includes the full 1-3 piezocomposite design with the arrangement of array elements, conducting electrodes, multi-phase backing material, acoustical matching layer and electrical circuit of a 50 Ohm coaxial cable. In the current FEA simulation, we considered the acoustical and electrical cross-talk between the adjacent elements of an ultrasonic array sensor. In the second step, the FEA-simulated ultrasonic pulse-echo signal and its frequency spectrum of an array element in the farfield region are further used as the reference input signal in the CIVA-UT software to simulate the imaging of porosity in heavy plates, quantitatively. The design of the 3D wedge-geometry of the application specific array sensor is modeled and optimized using the CIVA raytracing and multi-phase backing models. The real excitation pulse of the industrial ultrasonic testing equipment “ROMIS (ROSEN Modular Inspection System)” is also included in the simulation to achieve high accuracy in the current hybrid modeling. The dependency of the detectability of the porosity on the frequency of the array sensor and the active element group so-called “Virtual Probe” are quantitatively analyzed. Finally, the 3D hybrid model simulated Time Corrected Gain (TCG) curves of the porosity like defects in the reference steel plates are compared with the real experiments and a good quantitative agreement is achieved.

MODELING THE DYNAMICS OF DEFORMABLE OBJECTS BASED ON VOLUMETRIC PATCHES OF FREE FORMS

A method for solving nonlinear implicit numerical integration problems based on volumetric patches of free forms for modeling deformable objects with high-speed dynamics is proposed. This method improves the accuracy, consistency and controllability of deformation modeling and animation. The method is characterized by speed, accuracy, stability and uses optimization with region decomposition. The method is well suited for modeling deformable bodies with a large time step, in a wide range of deformation dynamics. We propose decomposed optimization, an optimization method based on free-form patches with region decomposition to minimize incremental potentials at each time step. The method uses quadratic matrix decomposition to combine non-overlapping subregions. The Hessian is evaluated once at the beginning of the time step. The advantages of the method are as follows. Geometric primitives and their mathematical models are proposed, which allow the reasonable application of these primitives to solve problems of volume-oriented modeling. Such requirements are met by volumetric patches of free forms based on analytical perturbation functions relative to the base triangles. The decomposed Hessian is constructed for each subregion and calculated using a set of vertices taken from a complete non-decomposed grid. The weights add the missing second-order Hessian data to the vertices of the subregions from the neighbors along the decomposition boundaries. Thanks to this, the descent is performed along the grid coordinates. There is no need to add gradients. During the descent, the gradient is determined. The Hessians under the region are calculated and factorized in parallel once per time step. They are used as an initializer at each iteration. Then the results are mixed together. This ensures stable and continuous high-quality modeling. An automated and reliable optimization method adapted for modeling nonlinear materials, high-speed dynamics and large deformations is proposed.

Integrating Dynamic State Information to Enhance Gas Concentration Modeling Accuracy

Integrating Dynamic State Information to Enhance Gas Concentration Modeling Accuracy

Research on the temperature measurement point selection technology for multi-degree-of-freedom thermal error compensation of lathe spindle

Abstract In order to solve the problem of difficulty in selecting temperature measurement points caused by the inconsistent axial and radial thermal deformation trends of CNC lathes, a multi-degree-of-freedom joint point selection method is proposed. The Ant Colony Optimization (ACO) algorithm is employed to identify a combination of temperature measurement points. Based on this combination, thermal errors in both directions are modeled, with modeling accuracy serving as the condition for updating ant pheromones. The process iterates until the optimal temperature measurement point combination is determined. Experimental results demonstrate that the temperature measurement points selected using this method exhibit significantly improved fitting accuracy and stability for various operating conditions compared to traditional point selection methods.