Homogeneous Model Research Articles

Modeling of contact resistivity and simplification of 3D homogenization strategy for the H formulation

The finite element method (FEM) provides a powerful support for the calculations of superconducting electromagnetic responses. It enables the analysis of large-scale high-temperature superconducting (HTS) systems by the popular H formulation. Nonetheless, modeling of contact resistivity in three-dimensional (3D) FEM is still a matter of interest. The difficulty stems from the large aspect ratio of the contact layer in numerical modeling. Nowadays, an available solution is to model the contact layer with zero thickness but requires the discontinuity conditions of the magnetic field. In this paper, the energy variational method is utilized to incorporate the contribution of contact resistivity into the H formulation. From the perspective of energy transfer, the contact resistivity is related to the energy dissipation of the radial current flowing through the contact interface. In terms of applications, this method can be employed to calculate the charging delay of no-insulation coils and the current sharing behaviors of CORC cables. One advantage of this model is that the magnetic field is continuous and hence can be easily implemented in FEM. Additionally, it requires fewer degrees of freedom and hence presents advantages in computational efficiency. Moreover, this method can be employed to simplify the 3D H homogeneous model for insulated coils. The above discussions demonstrate that the proposed model is a promising tool for the modeling of contact resistivity.

FE modeling to generate composite RVEs with high volume fractions and various shapes of inclusions

This work proposes a novel multi-step dynamic FE compression method to generate Representative Volume Elements (RVEs) of composites containing a variety of inclusions. This method is actualized through a sequential and four-stage procedure: (1) Sparse and periodic inclusions exhibiting a predefined orientation distribution are generated by implementing a modified random sequential adsorption algorithm, (2) Sparse inclusions undergo biaxial compression into the region of the targeted RVE via a dynamic FE analysis, (3) Periodic inclusions are compressed into the region utilizing a dynamic FE analysis with periodic boundary conditions, and (4) Positions and orientations of the compressed inclusions are extracted and the RVE in computer-aided design format is then generated. The proposed method confers advantages from four distinct perspectives: (1) applicability to various inclusion geometries, including ellipses, lobules, polygons, kidneys and stars, (2) ability to generate periodic RVEs of composites with high inclusion volume fractions (up to 80.0% for circular inclusions), (3) simple and straightforward numerical implementation without explicitly considering inclusion intersection check and (4) capacity to predefine inclusion orientation distribution. Statistical analyses utilizing multiple spatial descriptors, i.e., inclusion orientation angle, local volume fraction, Voronoi cell area, nearest-neighbor distance and orientation, second order intensity function, radial distribution function and two-point probability function, confirm randomness of inclusion distribution in the generated RVEs. The elastic properties and damage behaviors of composites via the FE homogenization method are predicted based on the generated RVEs and are compared with those of available experimental data, the literature and the mean-field homogenization models to demonstrate effectiveness of the proposed multi-step dynamic FE compression method.

Reinforcing resilience for integrated design of green and grey infrastructure with real-time control rules by considering system failures

The mutual benefits of real-time control and green and grey infrastructure for urban drainage system (UDS) renovations have drawn great attention. However, risks emerged due to equipment and structural failures, and no studies considered such failures while optimizing the renovation scheme. To address this issue, this study proposed a multi-criteria optimization method for the integrated design of green and grey infrastructure with real-time control rules, where the economic cost and system performance under normal and exceptional conditions were optimized simultaneously. Failure probabilities of equipment and structure were quantified using homogeneous Poisson process models, and failure losses were estimated with the aid of a machine learning-based surrogate model. This approach was tested with a combined UDS in China. Results indicate that: (1) More investment does not necessarily increase system resilience to failures. Equipment and structural failures can significantly lower the effectiveness of grey infrastructure and real-time control. Therefore, solutions with more investments in grey infrastructure, which indicate higher costs, experience greater failure losses. (2) System resilience to failures can be significantly improved while maintaining other objectives when compared with the traditional design scheme. The proposed method allows for a new perspective in addition to the cost-and-resilience two-objectives design of UDS renovations, especially for systems threatened by various failures.

A reduced order variational spectral method for efficient construction of eigenstrain‐based reduced order homogenization models

AbstractReduced order models (ROMs) are often coupled with concurrent multiscale simulations to mitigate the computational cost of nonlinear computational homogenization methods. Construction (or training) of ROMs typically requires evaluation of a series of linear or nonlinear equilibrium problems, which itself could be a computationally very expensive process. In the eigenstrain‐based reduced order homogenization method (EHM), a series of linear elastic microscale equilibrium problems are solved to compute the localization and interaction tensors that are in turn used in the evaluation of the reduced order multiscale system. These microscale equilibrium problems are typically solved using either the finite element method or semi‐analytical methods. In the present study, a reduced order variational spectral method is developed for efficient computation of the localization and interaction tensors. The proposed method leads to a small stiffness matrix that scales with the order of the reduced basis rather than the number of degrees of freedom in the finite element mesh. The reduced order variational spectral method maintains high accuracy in the computed response fields. A speedup higher than an order of magnitude can be achieved compared to the finite element method in polycrystalline microstructures. The accuracy and scalability of the method for large polycrystals and increasing phase property contrast are investigated.

Mesoscale modeling of dynamic compressive behavior of microcapsule-based self-healing concrete under impact loading

Microcapsule-based self-healing concrete (MSC) has been widely studied, with a focus on static behavior and self-healing effectiveness. However, the dynamic mechanical properties of MSC have rarely been studied. This study presents a mesoscale numerical investigation of the dynamic compressive behavior of MSC under impact loading. In mesoscale, MSC is regarded as a four-phase composite material mainly composed of coarse aggregates, interface transition zones, cement mortar, and microcapsules. A pseudo 3D numerical model is constructed by combining a slice of a detailed mesoscale model with a homogenous 3D model. The mesoscale MSC slice models with different mass fractions of microcapsules (0%, 2%, 5%, and 8%) are constructed. Different coarse aggregate shapes (i.e., circles, ellipses, and polygons) are considered. The uniaxial dynamic compressive behaviors of MSC materials under loads of different strain rates are numerically simulated and compared with those from split Hopkinson pressure bar tests previously done by the authors. The comparison results show that the present mesoscale model can accurately predict the compressive strength and failure mode of MSC. The effects of the microcapsules ratio and strain rate on the dynamic strength are studied. Results show that the MSC compressive strength decreases with the increase in microcapsules and increases with the increase in strain rate. The dynamic increase factor (DIF) of the specimen is jointly contributed by the material DIF, inertial constraints, and heterogeneity. Different aggregate shapes have little effect on the simulation results of MSC behavior. The obtained dynamic mechanical properties of MSC may assist in designing MSC to resist collisions or explosions.

A new model for predicting pressure traverse in two phase flow geothermal well

The complexity associated with the two phase or multiphase flow system have been a long-time challenge in the modelling of the flowing pressure in the production channel which includes geothermal well. The flow of hot water and its vapour ensued all throughout the geothermal system producing from the reservoir source to the wellbore and later to the surface. The performance of geothermal system is primarily dependent on the ability to accurately predict the flowing pressure in the production well. Several models derived from the fundamental thermodynamic energy equation to handle the complexity of two-phase flow in geothermal well have demonstrated diverse discrepancy when compared with real time measurement. It has been conceived in the study that the inclusion of pressure drop due to accumulation in the thermodynamic energy equation reduces the discrepancy between the predicted pressure and the field measurement. It is pertinent to the geothermal energy industry to assess the accurate model that consider all constituent terms that impact the two-phase flow behaviour in a geothermal production well. A more exact numerical model for evaluating the flowing pressure in geothermal has been derived in this study where the right constituent terms that practically impact the two-phase flow behaviour in a geothermal vertical well are considered. The present model has the capacity to predict flowing pressure accurately as the flow transit from one regime to another. Likewise, the present model was able to capture the transient period that usually occurs when the geothermal well is put to production. The present model was validated using data from KE1-22 and Mesa 6-1 wells reported in literature. The present model gave the least mean absolute relative error of 0.033 when applied to measured field data from KE1-22 Well while Hassan and Kabir, Ansari, Orkiszewski and Homogenous models gave errors of 0.12, 0.33, 0.41 and 0.38 respectively. Similarly, relative to data from Mesa 6-1 the present model gave a mean absolute relative error of 0.052 while Hassan and Kabir, Ansari, Orkiszewski and Homogenous models gave errors of 0.17, 1.46, 1.37 and 2.53 respectively. The newly derived model is reliable for assessing the performance of the geothermal system and evaluating flowing pressure at every difference transition period.

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. https://github.com/1160300611/MCDHGN.

Spectropolarimetric Radio Imaging of Faint Gyrosynchrotron Emission from a CME: A Possible Indication of the Insufficiency of Homogeneous Models

The geo-effectiveness of coronal mass ejections (CMEs) is determined primarily by their magnetic fields. Modeling of gyrosynchrotron (GS) emission is a promising remote sensing technique to measure the CME magnetic field at coronal heights. However, faint GS emission from CME flux ropes is hard to detect in the presence of bright solar emission from the solar corona. With high dynamic-range spectropolarimetric meter wavelength solar images provided by the Murchison Widefield Array, we have detected faint GS emission from a CME out to ∼8.3 R ⊙, the largest heliocentric distance reported to date. High-fidelity polarimetric calibration also allowed us to robustly detect circularly polarized emission from GS emission. For the first time in the literature, Stokes V detection has jointly been used with Stokes I spectra to constrain GS models. One expects that the inclusion of polarimetric measurement will provide tighter constraints on the GS model parameters. Instead, we found that homogeneous GS models, which have been used in all prior works, are unable to model both the total intensity and circular polarized emission simultaneously. This strongly suggests the need for using inhomogeneous GS models to robustly estimate the CME magnetic field and plasma parameters.

Effects of cavitation on the vortex shedding behind a truncated hydrofoil subjected to forced oscillation

Abstract Cavitating vortex shedding behind a truncated NACA 0009 hydrofoil subjected to forced oscillation has been numerically investigated using a one degree-of-freedom (1 DOF) model coupled with a homogeneous mixture cavitation model. The pitching motion of the hydrofoil has been prescribed using a sinusoidal function with a fixed amplitude and variable frequencies ranging from lock-off values below the vortex shedding frequency to values well above it and passing through the lock-in condition. The obtained results have permitted us to study the change in the dynamics and morphology of the shed-vortex structures with varying hydrofoil oscillation frequencies when cavitation takes place. More specifically, it has been found that the frequency regime of the lock-in condition is altered due to the occurrence and development of cavitation. Also, two distinct wake patterns have been observed for oscillating frequencies below and above the vortex shedding frequency. Furthermore, the phase angle between the applied torque and the hydrofoil surface acceleration has been estimated as a function of the excitation frequency and the cavitation number, which has helped to understand the effects of cavitation on the dynamic behaviour of the shed vortices.

Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging.

The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the successful application of traditional ultrasound for brain imaging. In this study, we propose a physics-embedded neural network with deep learning based full waveform inversion (PEN-FWI), which can achieve reliable quantitative imaging of brain tissues. The network consists of two fundamental components: forward convolutional neural network (FCNN) and inversion sub-neural network (ISNN). The FCNN explores the nonlinear mapping relationship between the brain model and the wavefield, replacing the tedious wavefield calculation process based on the finite difference method. The ISNN implements the mapping from the wavefield to the model. PEN-FWI includes three iterative steps, each embedding the F CNN into the ISNN, ultimately achieving tomography from wavefield to brain models. Simulation and laboratory tests indicate that PEN-FWI can produce high-quality imaging of the skull and soft tissues, even starting from a homogeneous water model. PEN-FWI can achieve excellent imaging of clot models with constant uniform distribution of velocity, randomly Gaussian distribution of velocity, and irregularly shaped randomly distributed velocity. Robust differentiation can also be achieved for brain slices of various tissues and skulls, resulting in high-quality imaging. The imaging time for a horizontal cross-sectional imag e of the brain is only 1.13 seconds. This algorithm can effectively promote ultrasound-based brain tomography and provide feasible solutions in other fields.

Detecting local perturbations of networks in a latent hyperbolic embedding space.

This paper introduces two novel scores for detecting local perturbations in networks. For this, we consider a non-Euclidean representation of networks, namely, their embedding onto the Poincaré disk model of hyperbolic geometry. We numerically evaluate the performances of these scores for the detection and localization of perturbations on homogeneous and heterogeneous network models. To illustrate our approach, we study latent geometric representations of real brain networks to identify and quantify the impact of epilepsy surgery on brain regions. Results suggest that our approach can provide a powerful tool for representing and analyzing changes in brain networks following surgical intervention, marking the first application of geometric network embedding in epilepsy research.

Advancing primatology through ethical and scientific perspectives on rhesus monkey (Macaca mulatta) cloning.

A critical turning point was reached in research with the recent success in cloning rhesus monkeys (Macaca mulatta), a major advancement in primatology. This breakthrough marks the beginning of a new age in biomedical research, ushered by improved somatic cell nuclear transfer techniques and creative trophoblast replacement strategies. The successful cloning of rhesus monkeys presents the possibility of producing genetically homogeneous models that are highly advantageous for studying complex biological processes, testing drugs, and researching diseases. However, this achievement raises important ethical questions, particularly regarding animal welfare and the broader ramifications of primate cloning. Approaching the future of primate research with balance is critical, as the scientific world stands on the brink of these revolutionary breakthroughs. This paper aims to summarise the consequences, ethical challenges and possible paths forward in primatology arising from rhesus monkey cloning.

Cumulative ordinal quasi-symmetry model and its separation for square contingency tables with ordered categories

Summary This study deals with square contingency tables, which are two-way contingency tables in which the row and column variables consist of the same classification. When the categorical variables are grouped into ordered categories from quantitative variables by cut points, a score that approximates the distance between the midpoints of the quantitative scale categories may be used to reflect the characteristics of the data. As an asymmetry model based on scores, this study proposes the cumulative ordinal quasi-symmetry model and ordinal marginal homogeneity model based on cumulative probabilities. The asymmetric parameter in the proposed models would be useful for making inferences such as that a row variable is stochastically less than a column variable or vice versa. This study also gives a separation of the cumulative ordinal quasi-symmetry model, that is, the cumulative ordinal quasi-symmetry model holds if and only if both the cumulative quasi-symmetry and ordinal marginal homogeneity models hold. The usefulness of the proposed models and the proposed separation is demonstrated through real data analysis.

A modified J model for efficiently calculating the electromagnetic fields of ReBCO no-insulation pancake coils using an explicit–implicit hybrid algorithm

Rare-earth (Re)Ba2Cu3O7−x (ReBCO) no-insulation (NI) coil is widely concerned due to its excellent electromagnetic and thermal properties. However, the presence of the turn-to-turn shunts in NI coils leads to that complexity of numerical simulation is increased. In this paper, a modified J model is proposed and the corresponding explicit–implicit hybrid algorithm is designed to calculate NI coils. The numerical results are in good agreement with the experimental data and the circuit model. The homogenization model is also proposed to simulate the large-scale NI coils in the background magnets. The modified J model has good accuracy and fast calculation speed, which can also be used to solve electromagnetic fields of insulation coils efficiently.

Investigation of dark energy scenarios in f(Q) gravitation theory

In order to solve the mystery of the accelerating and expanding universe model first, various dark energy candidates, such as Quintessence, Tachyon, k-essence, Phantom and DBI-essence, were investigated in detail in the [Formula: see text] modified symmetric teleparallel gravitation theory for homogeneous and isotropic FRLW universe model. The [Formula: see text] model is used to obtain solutions in [Formula: see text] theory. Also, the physical behaviors of some cosmological parameters such as pressure and density were studied. Dark energy candidates were analyzed for different values of n with various graphs.

Tree-based Machine Learning Ensembles and Feature Importance Approach for the Identification of Intrusions in UNR-IDD Dataset

Detection of intrusions from network data with the use of machine learning techniques has gained great attention in the past decades. One of the key problems in the network security domain is the availability of representative datasets for testing and evaluation purposes. Despite several efforts by researchers to release datasets that can be used for benchmarking attack detection models, some of the released datasets still suffer from one limitation or the other. Thus, some researchers at the University of Nevada released a dataset named UNR-IDD dataset which was argued to be free from some of the limitations of the past datasets. This study proposed Tree-based ensemble approaches for building binary intrusion identification models from the UNR-IDD dataset. Decision Tree algorithms are used as base classifiers in the Extra Trees, Random Forest and AdaBoost-based intrusion detection models. The results of the experimental analyses carried out indicated that the three ensembles performed excellently when feature selection was used compared to when all features were applied. For instance, Extra Trees model achieved an accuracy of 0.97, precision of 0.98, recall of 0.98 and f1-score of 0.98. Similarly, Random Forest model achieved an accuracy of 0.98, precision of 0.98, recall of 0.99 and f1-score of 0.98. Adaboost-based model had an accuracy of 0.96, precision of 0.96, recall of 0.99 and f1-score of 0.98. It was deduced that Random Forest intrusion classification model achieved slight overall best results when compared to the other models built. It is concluded that the three homogeneous ensemble models achieved very promising results while feature importance was used as attribute selection method.

Three-dimensional displacement and slip distribution of the 2021 Mw 7.4 Maduo (Tibetan Plateau) earthquake determined by GNSS and InSAR

On May 22, 2021, an Mw 7.4 earthquake occurred on the Maduo-Jiangcuo fault, located in Maduo County, Qinghai Province (China). According to the co-seismic deformation and the three-dimensional displacement reached from the interferometric synthetic aperture radar (InSAR) and the global navigation satellite system (GNSS), the rupture trace in the NWW direction was mapped, spanning a length of approximately 160 km and the earthquake happened on a typical sinistral strike-slip fault. In three-dimensional displacement, the displacement in E-W was bigger than that in N-S and vertical, with the RMSE 3.01 mm, 15.28 mm, and 5.16 mm respectively. Moreover, the slip distribution inverted with the homogeneous half-space model and layered half-space model, indicated that the corresponding magnitude was 7.3 Mw and 7.4 Mw, and the corresponding peak slip was 5.53 m and 5.96 m. The correlation coefficients of observed and predicted both exceeded 0.791, meeting the need for slip inversion. What is more, the peak inversion depth was no more than 15 km with strike angle and dip angle of 269° and 82.9° in the homogeneous half-space model and 265° and 83.7° in the layered half-space model, respectively. In addition, the Coulomb stress changes decreased on the Maduo-Jiangcuo fault and accumulated on the East Kunlun fault, which might cause aftershocks and future earthquakes were still possible. In short, the 2021 Mw7.4 Maduo earthquake made it possible to unravel seismic behavior in the Tibet plateau, which is of great significance for seismic hazard research.

A Reliability Assessment Method for Complex Systems Based on Non-Homogeneous Markov Processes.

The Markov method is a common reliability assessment method. It is often used to describe the dynamic characteristics of a system, such as its repairability, fault sequence and multiple degradation states. However, the "curse of dimensionality", which refers to the exponential growth of the system state space with the increase in system complexity, presents a challenge to reliability assessments for complex systems based on the Markov method. In response to this challenge, a novel reliability assessment method for complex systems based on non-homogeneous Markov processes is proposed. This method entails the decomposition of a complex system into multilevel subsystems, each with a relatively small state space, in accordance with the system function. The homogeneous Markov model or the non-homogeneous Markov model is established for each subsystem/system from bottom to top. In order to utilize the outcomes of the lower-level subsystem models as inputs to the upper-level subsystem model, an algorithm is proposed for converting the unavailability curve of a subsystem into its corresponding 2×2 dynamic state transition probability matrix (STPM). The STPM is then employed as an input to the upper-level system's non-homogeneous Markov model. A case study is presented using the reliability assessment of the Reactor Protection System (RPS) based on the proposed method, which is then compared with the models based on the other two contrast methods. This comparison verifies the effectiveness and accuracy of the proposed method.

Integrated internal-external thermal modeling for packed pebble beds

In this study, an integrated external-internal thermal DEM model is introduced in a GPU environment. The external model considers contact conduction and radiation, exhibiting accuracy validated against effective thermal conductivity (ETC) under packed bed conditions. The effective radiation range for a mono-sized packed bed is determined to be 3.5rp, providing accurate predictions of ETC. The internal model integrates FDM-based discretization to solve heat generation, heat conduction and thermal expansion. The model well predicts the temperature gradient of a sphere with partial heat generation. Additionally, preliminary analysis is conducted on the postulated heat generation of packed beds under a complete coolant-loss condition. The results reveal that fission-induced expansion may impact local porosity variations exceeding 2.5%. It is also observed that internally homogeneous models may overestimate heat transfer in packed beds, critical for safety. The model's accuracy and feasibility are demonstrated, providing insights for future research.

Group-Based Federated Knowledge Distillation Intrusion Detection

Intrusion detection based on federated learning allows the sharing of more high-quality attack samples to improve the intrusion detection performance of local models while preserving the privacy of local data. Most research on federated learning intrusion detection requires local models to be homogeneous. However, in practical scenarios, local models often include both homogeneous and heterogeneous models due to differences in hardware capabilities and business requirements among nodes. Additionally, there is still room for improvement in the accuracy of recognizing novel attacks in existing researches. To address the challenges mentioned above, we propose a Group-based Federated Knowledge Distillation Intrusion Detection approach. First, through a step-by-step grouping method, we achieve the grouping effect of intra-group homogeneity and inter-group heterogeneity, laying the foundation for reducing the aggregation difficulty in intra-group homogenous aggregation and inter-group heterogeneous aggregation. Second, in intra-group homogenous aggregation, a dual-objective optimization model is employed to quantify the learning quality of local models. Weight coefficients are assigned based on the learning quality to perform weighted aggregation. Lastly, in inter-group heterogeneous aggregation, the group leader model’s learning quality is used to classify and aggregate local soft labels, generating global soft labels. Group leader models utilize global soft labels for knowledge distillation to acquire knowledge from heterogeneous models. Experimental results on NSL-KDD and UNSW-NB datasets demonstrate the superiority of our proposed method over other algorithms.