Modeling Scheme Research Articles

Wheel Wear Modeling and Re-Profiling Strategy Optimization Based on the Maintenance Data of a Three-Axle Bogie Locomotive

Wear caused by wheel–rail contact forces is inevitable during vehicle operation, which has an important impact on the security and stability of train operation. Therefore, it is of great significance to study wheel wear patterns and optimize re-profiling strategies to extend service life. Based on the wheel wear data of three-axle bogie locomotives, this paper proposes a data-driven hybrid wheel wear model and optimization schemes of the re-profiling strategy. The wear model consists of a wheel flange thickness wear model, a wheel diameter wear model, and a re-profiling ratio coefficient model. Then, utilizing the above models, the optimization of the re-profiling strategy for different axle position wheels is raised, and the optimization of the complete vehicle re-profiling strategy is presented by considering the wheel diameter difference. Finally, a wheel data-driven analysis platform was developed to enable the management and utilization of wheel maintenance data. Analysis of extensive maintenance data indicates that the guide wheels wear the fastest, approximately 22.2% higher than the middle wheels, which wear the slowest. The re-profiling ratio coefficient model indicates that the ratio coefficient increases as the wheel flange thickness before re-profiling increases. Simulations demonstrate a longer expected wheel life with a flange thickness between 28 and 32.5 mm. Compared to measured values, the optimization strategy reduces complete vehicle re-profiling by 21.5%. Through the initial implementation at CRRC Dalian Locomotive Ltd, it has become evident that this methodology offers a viable solution to enhance the service longevity of locomotive wheels.

Implementation of a slope stability method in the CRITERIA-1D agro-hydrological modeling scheme

AbstractThis paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.

Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively.

An Effective Scheme for Modeling and Compensating Differential Age Errors in Real-Time Kinematic Positioning

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.

Geometrically nonlinear topology and fiber orientation optimization of composite structures using membrane-embedded model

The membrane-embedded model can be efficiently used in the finite element analysis of composite structures considering geometrical nonlinearity because of its practicability. This paper extends this model to the topology and fiber orientation optimization considering geometrical nonlinearity. The discrete design variables of topology and fiber orientation are continuous by the solid isotropic material with penalization method and bi-value coding parameterization method. An optimized interpolation scheme for the membrane-embedded model is constructed for geometrical nonlinearity. The filtering function is added to improve the convergence of the fiber orientation. To reduce the computational complexity, topology and fiber orientation are independently updated at each iteration during the optimization process. Numerical examples demonstrate the effectiveness of the proposed method for membrane-embedded composite considering geometrical nonlinearity.

An asymptotic preserving kinetic scheme for the M1 model of linear transport

Moment models with suitable closure can lead to accurate and computationally efficient solvers for particle transport. Hence, we propose a new asymptotic preserving scheme for the M1 model of linear transport that works uniformly for any Knudsen number. Our idea is to apply the M1 closure at the numerical level to an existing asymptotic preserving scheme for the corresponding kinetic equation, namely the Unified Gas Kinetic Scheme (UGKS) originally proposed in Mieussens (2013) and extended to linear transport in Xu and Huang (2010). A second order extension is suggested and validated. The generic nature of this method is also demonstrated in an application to the M2 model. Several test cases show the performances of this new scheme in both the M1 and M2 case.

A new macro- and micro- coupling model of barrier dam soil

Barrier dams are widely distributed worldwide. It is highly important to accurately evaluate the safety and emergency response of barrier dams. However, the stressstrain relationship of barrier dam soil, which is an important issue for the safety evaluation of barrier dams, has not been elucidated clearly because of its complex composition and wide particle gradation. Based on the proposed macro- and micro- coupling modeling scheme, a series of CT triaxial tests were carried out on barrier dam soil to observe the macroscopic and microscopic behavior. Based on the particle displacement distribution, a fabric evolution rate was proposed as an index to describe the evolution of fabric behaviors. A significant correlation was detected between the shear band fabric evolution rate and the macroscopic mechanical properties of the soil. The fabric evolution equation, modulus evolution equation and Poisson's ratio evolution equation were proposed to set up a new macro- and micro- coupling model for barrier dam soil. The model has only five parameters that have definite physical meanings and can be easily determined by a series of triaxial tests. The validity of the proposed model was verified by different types of tests of barrier dam soil and soilrock mixtures. The model can reasonably describe the fundamental properties of barrier dam soil with wide particle gradation. The proposed model established by macro- and micro- coupling modeling considers the relationship between macroscopic mechanical properties and microscopic fabric behavior, which is suitable for barrier dam soils with wide particle gradations.

Modelling and inference for a degradation process with partial maintenance effects

AbstractThis paper proposes a new way of modelling imperfect maintenance in degradation models, by assuming that maintenance affects only a part of the degradation process. More precisely, the global degradation process is the sum of two dependent Wiener processes with drift. Maintenance has an effect of the ‐type on only one of these processes: it reduces the degradation level of a quantity which is proportional to the amount of degradation of this process accumulated since previous maintenance. Two particular cases of the model are considered: perturbed and partial replacement models. The usual model is also a specific case of this new model. The system is regularly inspected in order to measure the global degradation level. Two observation schemes are considered. In the complete scheme, the degradation levels are measured both between maintenance actions and at maintenance times (just before and just after). In the general scheme, the degradation levels are measured only between maintenance actions. The maximum likelihood estimation of the model parameters is studied for both observation schemes in both particular models. The quality of the estimators is assessed through a simulation study.

Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, “non-enhanced, and edematous” areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709–0.963) and 0.739 (0.581–0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.

Advancing Ionic Liquid Research with pSCNN: A Novel Approach for Accurate Normal Melting Temperature Predictions.

Ionic liquids (ILs), known for their distinct and tunable properties, offer a broad spectrum of potential applications across various fields, including chemistry, materials science, and energy storage. However, practical applications of ILs are often limited by their unfavorable physicochemical properties. Experimental screening becomes impractical due to the vast number of potential IL combinations. Therefore, the development of a robust and efficient model for predicting the IL properties is imperative. As the defining feature, it is of practice significance to establish an accurate yet efficient model to predict the normal melting point of IL (T m), which may facilitate the discovery and design of novel ILs for specific applications. In this study, we presented a pseudo-Siamese convolution neural network (pSCNN) inspired by SCNN and focused on the T m. Utilizing a data set of 3098 ILs, we systematically assess various deep learning models (ANN, pSCNN, and Transformer-CNF), along with molecular descriptors (ECFP fingerprint and Mordred properties), for their performance in predicting the T m of ILs. Remarkably, among the investigated modeling schemes, the pSCNN, coupled with filtered Mordred descriptors, demonstrates superior performance, yielding mean absolute error (MAE) and root-mean-square error (RMSE) values of 24.36 and 31.56 °C, respectively. Feature analysis further highlights the effectiveness of the pSCNN model. Moreover, the pSCNN method, with a pair of inputs, can be extended beyond ionic liquid melting point prediction.

A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks

Abstract. Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

State-dependent complexity of the local field potential in the primary visual cortex.

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

Training Warm‐Rain Bulk Microphysics Schemes Using Super‐Droplet Simulations

AbstractCloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano‐ and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm‐rain bulk schemes using high‐fidelity super‐droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super‐droplet simulations. We demonstrate the framework's effectiveness by calibrating a single‐moment bulk scheme, resulting in a reduction of data‐model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.

Scheme of Mathematical Modelling in Teaching Mathematics

Scheme of Mathematical Modelling in Teaching Mathematics

Microstructure and performance of carbon fiber reinforced aluminum matrix composites with molybdenum carbide coating on carbon fibers

In this work, a molybdenum carbide coating on the surface of short carbon fibers prepared using molten salt method was proposed to ameliorate the interfacial bonding and improve the performance of carbon fiber/Al composites. The carbon fiber/Al composites were produced using vacuum hot pressing. The characteristics of molybdenum carbide coating were analyzed. Besides, the microstructures, bending strength and thermal properties of the carbon fiber/Al composites were explored. Furthermore, the interfacial thermal resistance of the composites was investigated in relation to theoretical evaluations derived from the combined Maxwell-Garnett effective medium approach and acoustic mismatch model schemes. The results indicated that a homogeneous Mo2C coating with a thickness of 0.5 μm was obtained by proper molar ratio of carbon fibers, MoO3 and chloride salts mixture and heated at 1000 °C for 60 min. The Mo2C coating greatly enhanced the bond between the aluminum matrix and carbon fiber, leading to improvements in the densification behavior and bending strength of the coated composites. The enhanced thermal properties including improved thermal conductivity and reduced coefficient of thermal expansion (CTE) were also achieved by the Mo2C coating. The in-plane thermal conductivity of 60 vol.% Mo2C-coated carbon fiber/Al composite was 221 W·m−1·K−1 enhanced by 92% compared to that of uncoated composite and the CTE was 6.0 × 10–6 K−1 which was suitable for electronic packaging materials.

Robust Reconstruction of Historical Climate Change From Permafrost Boreholes

AbstractReconstructing historical climate change from deep ground temperature measurements in cold regions is often complicated by the presence of permafrost. Existing methods are typically unable to account for latent heat effects due to the freezing and thawing of the active layer. In this work, we propose a novel method for reconstructing historical ground surface temperature (GST) from borehole temperature measurements that accounts for seasonal thawing and refreezing of the active layer. Our method couples a recently developed fast numerical modeling scheme for two‐phase heat transport in permafrost soils with an ensemble‐based method for approximate Bayesian inference. We evaluate our method on two synthetic test cases covering both cold and warm permafrost conditions as well as using real data from a 100 m deep borehole on Sardakh Island in northeastern Siberia. Our analysis of the Sardakh Island borehole data confirms previous findings that GST in the region have likely risen by 5–9°C between the pre‐industrial period of 1750–1855 and 2012. We also show that latent heat effects due to seasonal freeze‐thaw have a substantial impact on the resulting reconstructed surface temperatures. We find that neglecting the thermal dynamics of the active layer can result in biases of roughly −1°C in cold conditions (i.e., mean annual ground temperature below −5°C) and as much as −2.6°C in warmer conditions where substantial active layer thickening (>200 cm) has occurred. Our results highlight the importance of considering seasonal freeze‐thaw in GST reconstructions from permafrost boreholes.

Printing path-dependent two-scale models for 3D printed planar auxetics by material extrusion

One particularly interesting class of mechanical metamaterials are those having a negative Poisson’s ratio, which are referred to as ‘auxetics’. Because of their geometrical complexity, auxetic designs cannot always be easily created. However, Additive Manufacturing (AM) methods like material extrusion in 3D printing present the opportunity to construct auxetic structures. Nevertheless, extruded 3D printed material can be highly anisotropic. Before 3D printed auxetics manufactured through material extrusion can be used in engineering applications, it is important to generate powerful simulation tools that can reliably reproduce and foretell their mechanical characteristics irrespective of their form and intricacy. In view of this, the current work proposes printing path-dependent models based on an experimentally validated multi-scale modelling scheme using the Lattice Beam Model (LBM). This is done by first representing idealized microstructures of extruded 3D printed polymers through geometric models and simulating these on the material scale. The aim is to explicitly model the inter-layer and intra-layer bonds that exist in material extruded 3D printed parts by assigning experimentally obtained interface properties that significantly differ from the bulk material. On the auxetic structure scale, two planar auxetic designs are modelled using the determined material scale relationships as input: Re-Entrant (RE) and Rotating Square (RS). In terms of mechanical response, the experimentally and numerically obtained force displacement curves agree reasonably well: the stiffness of the modelled auxetic designs fit well with the experimentally measured ones while the LBM simulations generally provide a good estimation in strength. Finally, it has been shown on both the material and auxetic structure scales that incorporation of the interfacial bond strengths in simulations of extruded 3D printed polymers is important, because neglecting these results in significant overestimation of the strength.

Span-level bidirectional retention scheme for aspect sentiment triplet extraction

The objective of the Aspect Sentiment Triplet Extraction (ASTE) task is to identify triplets of (aspect, opinion, sentiment) from user-generated reviews. The current study does not extensively integrate the interaction between word pairs and aspect-opinion pairs during the learning process at the granularity of sentence analysis. Furthermore, the bidirectional inference for the triplet, along with the parallel computing approach for long-span texts, also fail to achieve efficient unification. We introduce a new perspective: Span-level Bidirectional Retention Scheme(SBRS) for Aspect Sentiment Triplet Extraction model. The model comprises two pathways. The first pathway involves extracting effective aspect-opinion pair outcomes via two progressive submodules that operate on words and word pairs at varying scales. Building on the first pathway, the second pathway senses the interaction information of word pairs through bidirectional recursion and combines an efficient parallel computing approach. This combination allows the model to utilize three features – context, semantics, and relationship – to accurately identify the sentimental orientation. Thus, the two pathways facilitate the learning of relation-aware representations of word pairs. We carried out experiments on two public datasets, showing an average enhancement of 3.34% and 1.72% in F1 scores compared to the most recent baselines models, and multiple experiments from diverse angles proved the model’s superiority.

Correlating mechanical properties of Al/Al bimetal composite and its constituents using combined experiment and microstructure-based multiscale modeling approach

A combined experiment and multiscale modeling approach was presented to correlate the mechanical properties of a roll-bonded AA1050/AA3004 bimetal clad sheet and constituents. The mechanical responses of the 2-ply AA1050/AA3004 and constituents were measured under various loading conditions. For the measurements, each constituent was separated from the clad sheet using electrical discharge machining and uniaxial tensile tests. Continuum-scale yield functions such as isotropic von Mises and anisotropic/non-quadratic Yld2000-2d were developed for the two constituents and the bimetal clad sheet. A grain-scale crystal plasticity finite element (CPFE) simulations were also performed incorporated with newly developed representative volume element considering measured microstructural attributes. These CPFE predictions were used to identify the anisotropy coefficients for Yld2000-2d. Using the identified yield functions, finite element modeling was followed in which the mechanical properties of each constituent sheet were addressed separately. This is a new approach developed in the current work and it enables a fundamental correlation between multilayered material and its constituents. The mechanical properties of the bimetal clad sheet under the uniaxial tensions were predicted using those of the constituents and compared with calculations based on known rule of mixture (ROM) approach. The two approaches well reproduced stress–strain curves and r–value evolutions. The two FE modeling approaches were also applied to an independent test (i.e., the limiting dome height). The predictions were in good agreement with the experimental data. Finally, the mechanical properties of the bimetal clad sheet and constituents were correlated, and proper modeling scheme for multilayered materials is proposed.

Revisiting the dry friction-like magnetic vector hysteresis model

Physically correct simulations of magnetic devices require precise and energy-consistent hysteresis models. Electrical steel sheets of, e.g., power transformers or rotating machines represent one crucial element in such a simulation since the hysteresis is revealed uni- and multi-axial, and the steel sheets may have anisotropy. A dedicated vector hysteresis model based on dry friction-like pinning is revisited and derived in terms of thermodynamic state equations in a theoretical manner. The model is then linked to an incremental energy minimization procedure used in elasto-plasticity theory. A numerically efficient two-dimensional solution scheme of the hysteresis model is extended to the three-dimensional case. Rotational losses are also in the scope of this paper since the model cannot describe this loss type by nature. Therefore, numerical adaptations are discussed to account for vanishing rotational losses in saturation.