Realistic Characteristics Research Articles

A parametric modeling method for 3D woven composites considering realistic meso-structural characteristics

The precision of mesoscale simulation for three-dimensional woven composites (3DWCs) is intricately linked to the fidelity of the geometric representation. This paper aims to present a novel parametric modeling approach for generating representative volume element (RVE) of the 3DWCs while considering its realistic meso-structural characteristics. The real architecture of 3DWCs is defined to consider the squeezed surface warp tow. Tow geometry is specified, accounting for torsion of tow cross-section and crimp of weft tow path. The RVEs of the composites are geometrically assembled via a specific translational symmetry. The tensile response of the composites with the novel geometry is scrutinized utilizing a progressive damage model, compared with that of the ideal geometry. Additionally, the impact of weft tow size on the tensile response of the composites is explored.

Prediction of retrogressive landslide in sensitive clays by incorporating a novel strain softening law into the Material Point Method

Sensitive clays, when subjected to large strains, exhibit a unique strain-softening behavior, transforming into a remolded liquid with remarkably low shear strength. When a slope fails, this behavior leads to the remolded clay moving away from its original position, triggering subsequent failures and catastrophic outcomes. To accurately predict such scenarios, it is crucial to incorporate realistic strain-softening characteristics into the constitutive soil model. This paper presents a novel yet practical strain-softening law developed by the authors that effectively captures the post-peak behavior of sensitive clays down to their remolded strength. The softening law is implemented in an elastoplastic Mohr-Coulomb model and incorporated into Anura3D, an open-source software that uses the Material Point Method to simulate large deformations. The constitutive model is calibrated by simulating the stress-strain behavior through direct shear tests conducted at three sensitive clay landslide locations. The accuracy of the overall numerical framework is assessed by predicting the post-failure movements of these landslides. Notably, two of the landslides, Sainte-Monique (1994) and Saint-Jude (2010), have previously been analyzed using other numerical tools, allowing for a comparative analysis with the method presented here. The third landslide, the Saint-Luc-de-Vincennes landslide, representing a composite flow slide and spread, has been numerically simulated for the first time. The post-failure behavior observed in the landslide events is compared with field observations and other numerical analyses. The results show that the MPM models with the proposed strain-softening law can reasonably predict post-failure retrogression and runout distance, which are crucial parameters for determining the risk of landslides in sensitive clays.

Liver Tissue Surrogates: Development and Biomechanical Characterization

Human liver tissue often suffers extensive damage during impact-based trauma injuries, such as those that happen in vehicle accidents. However, it is challenging to study the biomechanics of human liver tissue under different loading situations through experiments in a clinical context due to ethical and biosafety concerns. In this work, biofidelic liver tissue surrogates, which exhibit realistic mechanical characteristics, have been developed for the first time. The tissue surrogates were made using a four-part elastomer material that could be cast to any size or form, mimicking the mechanical behavior of the liver tissue. The tissue's behavior was tested under six different strain rates, ranging from low to high, and its non-linear mechanical behavior was characterized using the Yeoh hyperelastic curve fit model. To the best of our knowledge, no liver tissue surrogates have been developed which possess the same biomechanical properties as real liver tissue. The use of precisely developed tissue simulants can be beneficial for surgical training, trauma research, and the development of medical models for different liver disorders such as liver cirrhosis, fatty liver, and liver fibrosis.

Balancing Submarine Landslides and the Marine Economy for Sustainable Development: A Review and Future Prospects

To proactively respond to the national fourteenth Five-Year Plan policy, we will adhere to a comprehensive land and sea planning approach, working together to promote marine ecological protection, optimize geological space, and integrate the marine economy. This paper provides a comprehensive review of the sustainable development of marine geological hazards (MGHs), with a particular focus on submarine landslides, the marine environment, as well as the marine economy. First, the novelty of this study lies in its review and summary of the temporal and spatial distribution, systematic classification, inducible factors, and realistic characteristics of submarine landslides to enrich the theoretical concept. Moreover, the costs, risks, and impacts on the marine environment and economy of submarine engineering activities such as oil and gas fields, as well as metal ores, were systematically discussed. Combined with the current marine policy, an analysis was conducted on the environmental pollution and economic losses caused by submarine landslides. Herein, the key finding is that China and Mexico are viable candidates for the future large-scale offshore exploitation of oil, gas, nickel, cobalt, cuprum, manganese, and other mineral resources. Compared to land-based mining, deep-sea mining offers superior economic and environmental advantages. Finally, it is suggested that physical model tests and numerical simulation techniques are effective means for investigating the triggering mechanism of submarine landslides, their evolutionary movement process, and the impact on the submarine infrastructure. In the future, the establishment of a multi-level and multi-dimensional monitoring chain for submarine landslide disasters, as well as joint risk assessment, prediction, and early warning systems, can effectively mitigate the occurrence of submarine landslide disasters and promote the sustainable development of the marine environment and economy.

Onsite measurements of pedestrian-level wind and preliminary assessment of effects of turbulence characteristics on human body convective heat transfer

Wind is an important factor affecting outdoor thermal comfort in urban environment, but its turbulence characteristics at the pedestrian level and effects on convective heat transfer (hc) from a human body remain poorly understood. Previous studies were only conducted in wind tunnels with low turbulence intensity, small turbulence integral length scale, and fixed prevailing wind direction. To address this knowledge gap, this field study was conceived to monitor the pedestrian level wind in actual outdoor settings and to measure the hc of a thermal manikin at the same time. The results show that both longitudinal and lateral turbulence intensity significantly enhance whole body hc. It remains to be examined whether the small-scale wind direction variation can be included in the lateral turbulence intensity or vice versa. The integral length scale found at the two sites were larger than a typical manikin dimension, and the whole body hc peaked when these two scales were comparable. As the first major field study to quantify convection heat loss of a thermal manikin exposed to real-life urban boundary layer wind, it is demonstrated crucial to consider realistic turbulence characteristics in the field when evaluating hc over a human body for urban microclimate design.

Simulation of urban surface temperature and surface heat balance in the Tokyo metropolitan area

This study aimed to propose a method for simulating ground surface temperature and surface heat balance using thermal infrared remote sensing and an urban surface heat balance model and to conduct a simulation in the Tokyo metropolitan area. Previous studies have typically employed ground surface temperatures obtained through satellite remote sensing to analyze urban thermal environments. However, these studies were limited to using surface temperature data available only at the time of satellite passage. Therefore, in this study, we conducted a simulation for the Tokyo metropolitan area by integrating a one-dimensional heat balance model with ground surface temperatures observed by satellites. The novelty of this study lies in the utilization of parameters related to heat balance obtained through calculations of physical processes, as opposed to relying on empirical formulas or parameters. Consequently, realistic ground surface characteristics, including parameters such as evaporation efficiency and thermal inertia essential for the simulation, were derived by this combined method. Furthermore, diurnal variations in ground surface temperature distributions were accurately represented by using realistic surface temperature data based on satellite remote sensing, considering the effect of heat storage in urban areas—an aspect challenging to reproduce using conventional local meteorological models.

Superfluidity in neutrino clusters

We study the formation of a superfluid condensate of neutrinos inside a neutrino cluster. The attractive interaction between neutrinos is mediated by a scalar boson which is lighter than a neutrino. We consider the appearance of neutrino bound states consisting of particles with oppositely directed spins. The gap equation for such a system is derived. Based on numerical simulations of the neutrino distribution in a cluster, we find the phase transition temperature and the coherence length inside such a cluster for various parameters of the system. The constraints on the parameters of the Yukawa interaction, resulting in the neutrino superfluidity, are derived. We obtain that the cosmic neutrino background can contribute to the superfluid condensate inside a neutrino cluster having realistic characteristics. The mechanism of the neutrino cluster cooling in the early universe, based on the plasmons Čerenkov radiation, is proposed.

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

AbstractDeveloping a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

Multi-scale population analysis unit construction method considering scene feature variability and long/short-term patterns in spatiotemporal population activities

ABSTRACT Differences in the spatial scale and component elements of urban scenes affect the analysis of spatiotemporal changes in population distribution (PD) patterns. Fixed scales and geometric forms constrained previous analyses of PD and spatiotemporal changes, which neglected the realistic characteristics of urban scenarios. These limitations hinder the ability to capture the diversity and spatiotemporal representation of PD patterns across multiple spatial scales. This study developed a multi-spatial scale population analysis unit (PAU) construction method considering the scenario heterogeneity and long/short-term patterns of spatiotemporal population changes (PC). Initially, we decompose the multi-scale changes in population time series patterns and their relationships with scene features along the temporal dimension to analyze the primary and secondary factors. Starting from the temporal characteristics of the PD and PC, we established a validation and correction method for the factors. Finally, combined with a multi-feature clustering method, a multi-scale PAU construction method driven by scene feature factors is proposed. Experiments were conducted at different spatial scales in both routine and emergent scenarios. The results indicated that this method can help to obtain more homogeneous analysis regions and enhance the stability and phase pattern representations of changes in PDs, thereby improving the interpretability of analytical results.

Large eddy simulation of aerodynamic wind pressure on a tall rectangular building by modified synthetic volume forcing method

To accurately predict wind pressures on a tall building by numerical Computational Fluid Dynamics (CFD) using the Large-Eddy Simulation (LES) turbulent model, it is essential to generate inflow with realistic turbulence characteristics. The Discretizing and Synthesizing Random Flow Generation (DSRFG) method, as one of the synthetic Fourier methods, has received significant attention in the Computational Wind Engineering (CWE) community because of its ability to strictly satisfy the continuity condition and generate turbulent wind with the desired power spectra. In traditional LES, the desired turbulent wind is normally generated and specified at the inlet. However, this treatment often leads to significant attenuation in turbulence energy in the high-frequency band of the fluctuating wind speed spectrum along the flow direction and unrealistic large-amplitude wind pressure fluctuations, resulting in unsatisfactory prediction performance. The Synthetic Volume Forcing (SVF) method effectively overcomes these limitations by introducing turbulence as volume force into the moment Navier-Stokes (N-S) equations. In this study, the DSRFG method is applied to generate a fluctuating wind field in combination with the SVF method to simulate dynamic wind pressures on a tall rectangular building through open-source software OpenFOAM. The turbulence intensity amplification factor (TIAF) is introduced in the SVF method to further improve its simulation accuracy. Comparison of results with wind tunnel test results verifies that the proposed TIAF-SVF numerical method developed in this study achieved sufficient accuracy in predicting wind pressures on tall buildings.

The lake’s image in the artistic worldview of the Northern text of Russian literature for children

INTRODUCTION. For the first time, the works of the Northern Text of Russian literature for children, in which the lake’s image arises, are systematized. The purpose of the study is to consider the artistic and semantic features of the leitmotif image of the lake in the context of the specifics of the artistic world of the North. The objectives of the research are to identify the main mythopoetic and realistic characteristics of the lake’s image in the works of the Northern text for children; to determine the motives and images with which it interacts as a structural element of the Northern text, to identify the place of the lake’s image in the artistic worldview of the North.MATERIALS AND METHODS. Using a systematic approach to the literary text, which is a set of methods (chronotopic, motivic, mythopoetic, systemic-semantic) that reveal the object as a single system, the lake’s image in the works of writers of the second half of the 20th century is considered: A. Linevsky, A. Petukhov, V. Pikul, M. Skorokhodov, A. Chlenov.RESULTS AND DISCUSSION. After analyzing the artistic material, it is concluded that there are two variants of the lake ’s image: tundra and forest lakes. The first is realized through the mythologeme of the circle and the personification method, the second through the motives of the two worlds, secrets, boundaries, changes of the hero. The lake’s image in the Northern text of Russian literature for children also approaches the images of the sea and the river and actualizes the motif of the unity of the world.CONCLUSION. This study is a particular stage in the study of the figurative-semantic structure of the Northern text of Russian literature for children as a supertext and indicates broad prospects for its further study.

Perceived rain dynamics on hydrophilic/hydrophobic lens surfaces and their influences on vehicle camera performance

Cameras are increasingly used in modern vehicles equipped with advanced driver assistance systems (ADAS) to collect environmental information. Cameras suffer performance degradation when driving in adverse weather conditions, such as rain, as precipitation droplets impact the camera lens and cause obstruction and blurring of the vision. The relationships between image quality, object detection accuracy, and surface wettability of camera lenses are investigated. This paper applies a previously developed evaluation procedure for wind tunnel testing with simulated adverse driving and rain conditions. Realistic rain characteristics perceived by a moving vehicle at different driving speeds are simulated using a novel rain simulation system implemented into a wind tunnel. Moreover, an emphasis is put on comparing the use of hydrophilic and hydrophobic surfaces to provide insights into material selection when designing camera lenses for ADAS. It is found that droplet dynamics, such as size, velocity, shape, and motion can impact the camera image quality and, subsequently, object detection accuracy. This paper demonstrates the use of various materials and evaluation metrics and their implications from a practical perspective when subjected to realistic driving-in-rain scenarios. The results suggest that the use of hydrophobic lenses promotes better performance over hydrophilic lenses in most cases with exceptions.

Workload Control in Flow Shops with Bottleneck Shifting and Process Time Variability

Abstract Manufacturing industries struggle to devise precise planning and scheduling solutions due to unpredictable business situations. Additionally, uncertainties in production such as machine breakdowns, labour absenteeism, cycle time deviations, etc., would further deteriorate production plans and lead to uncertainty in decision-making processes. Flow shops with bottlenecks are particularly susceptible to these disturbances. Moreover, the random variations in cycle time variations can cause the bottleneck to shift between different stages. Literature indicates that conventional job release methods are ineffective in addressing these difficulties. In contrast, workload control methods would provide better solutions. Hence, a flow shop model has been developed and simulated using the variables like process time variations and bottleneck shifting on the discrete-event simulation software. The flow shop model incorporates realistic shop characteristics which are subjected to random process time variations, so as to assess the performance. The outcomes of the experimentation demonstrate that order release methods play a pivotal role in improving the performance of flow shops in more volatile situations.

Noise tailoring, noise annealing, and external perturbation injection strategies in memristive Hopfield neural networks

The commercial introduction of a novel electronic device is often preceded by a lengthy material optimization phase devoted to the suppression of device noise as much as possible. The emergence of novel computing architectures, however, triggers a paradigm shift in noise engineering, demonstrating that non-suppressed but properly tailored noise can be harvested as a computational resource in probabilistic computing schemes. Such a strategy was recently realized on the hardware level in memristive Hopfield neural networks, delivering fast and highly energy efficient optimization performance. Inspired by these achievements, we perform a thorough analysis of simulated memristive Hopfield neural networks relying on realistic noise characteristics acquired on various memristive devices. These characteristics highlight the possibility of orders of magnitude variations in the noise level depending on the material choice as well as on the resistance state (and the corresponding active region volume) of the devices. Our simulations separate the effects of various device non-idealities on the operation of the Hopfield neural network by investigating the role of the programming accuracy as well as the noise-type and noise amplitude of the ON and OFF states. Relying on these results, we propose optimized noise tailoring and noise annealing strategies, comparing the impact of internal noise to the effect of external perturbation injection schemes.

Implicit model to capture electrostatic features of membrane environment.

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Decision-making for solar panel selection using Sugeno-Weber triangular norm-based on q-rung orthopair fuzzy information

Solar power is an alternative energy derived from the sun. Solar power is more environmentally friendly and sustainable than burning fossil fuels which releases harmful greenhouse gas emissions. Therefore, this study aims to evaluate a reliable solar panel based on certain characteristics by incorporating the theory of the decision-making process. To serve this goal, this study discusses a well-known aggregation model of the q-rung orthopair fuzzy set, which is a broader and flexible environment of fuzzy sets and intuitionistic fuzzy sets used to handle unpredictable information of human opinions. The key components of this article are to demonstrate some realistic operations of Sugeno–Weber triangular norms considering q-rung orthopair fuzzy information. These operations provide authentic estimated information during the decision-making process. We developed a class of new aggregation operators using the q-rung orthopair fuzzy information system, including q-rung orthopair fuzzy Sugeno–Weber power weighted average and q-rung orthopair fuzzy Sugeno–Weber power weighted geometric operators. Some realistic characteristics and special cases are also demonstrated to show the compatibility of the proposed methodologies. An innovative approach to the multi-attribute decision-making problem is utilized to resolve different real-life applications considering various criteria or attributes. To show the intensity and applicability of the proposed approaches, we explored a numerical example for efficient solar panel selection based on the proposed methodologies. Furthermore, we presented a comprehensive comparison technique to compare the findings of the existing methods with the proposed aggregation approaches. Finally, the proposed research work is summarized, and the future prospects are discussed.

Electrochemical sulfate production from sulfide-containing wastewaters and integration with electrochemical nitrogen recovery

Electrochemical methods can help manage sulfide in wastewater, which poses environmental and health concerns due to its toxicity, malodor, and corrosiveness. In addition, sulfur could be recovered as fertilizer and commodity chemicals from sulfide-containing wastewaters. Wastewater characteristics vary widely among wastewaters; however, it remains unclear how these characteristics affect electrochemical sulfate production. In this study, we evaluated how four characteristics of influent wastewaters (electrolyte pH, composition, sulfide concentration, and buffer strength) affect sulfide removal (sulfide removal rate, sulfide removal efficiency) and sulfate production metrics (sulfate production rate, sulfate production selectivity). We identified that electrolyte pH (3 × difference, i.e., 25.1 to 84.9 μM h−1 in average removal rate within the studied pH range) and sulfide concentration (16 × difference, i.e., 82.1 to 1347.2 μM h−1 in average removal rate) were the most influential factors for electrochemical sulfide removal. Sulfate production was most sensitive to buffer strength (6 × difference, i.e., 4.4 to 27.4 μM h−1 in average production rate) and insensitive to electrolyte composition. Together, these results provide recommendations for the design of wastewater treatment trains and the feasibility of applying electrochemical methods to varying sulfide-containing wastewaters. In addition, we investigated a simultaneous multi-nutrient (sulfur and nitrogen) process that leverages electrochemical stripping to further enhance the versatility and compatibility of electrochemical nutrient recovery.

SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism

AbstractIncreased availability and quality of surface and subsurface observations provide the chance to improve the perception of hydrological phenomena. An important factor that hinders our understanding of hydrological structures is the characterization of heterogeneous patterns with continuous attributes inside the structures (e.g., porosity, permeability, fluid saturation, etc.). Unlike categorical attributes, continuous attributes convey more realistic characteristics but require more computational resources to characterize such complex earth systems. In this work, we propose a novel deep learning approach for the characterization of complex hydrological realism with continuous attributes based on generative adversarial networks (GANs) and self‐attention mechanism, named SA‐RelayGANs. To address the complexity of heterogeneous hydrological structures, we divide the modeling process into two stages: facies construction stage and property reconstruction stage. In the first stage, we employ an improved GAN with self‐attention mechanism to construct the heterogeneous structures while adhering to hard conditioning constraints. In the second stage, we utilize another GAN with an attribute enhancement term to reconstruct realizations based on the constructed structures and observations. SA‐RelayGANs can successfully predict the statistical distributions of heterogeneous structures with continuous attributes by using limited observations. This study highlights the effectiveness of using GANs to characterize the heterogeneous patterns of hydrological realism and the application over large geoscience fields.

How realistic features affect the stability of an Arctic marine food web model.

Rapid sea-ice decline and warmer waters are threatening the stability of Arctic ecosystems and potentially forcing their restructuring. Mathematical models that support observational evidence are becoming increasingly important. We develop a food web model for the Southern Beaufort Sea based on species with high ecological significance. Generalized modeling is applied to study the impact of realistic characteristics on food web stability; a powerful method that provides a linear stability analysis for systems with uncertainty in data and underlying physical processes. We find that including predator-specific foraging traits, weighted predator-prey interactions, and habitat constraints increase food-web stability. The absence of a fierce top predator (killer whale, polar bear, etc.) also significantly increases the portion of stable webs. Adding ecosystem background noise in terms of a collective impact of latent, minor ecosystem members shows a peak in stability at an optimum, relatively small background pressure. These results indicate that refining models with more realistic detail to account for the complexity of the ecological system may be key to bridge the gap between empirical observations and model predictions in ecosystem stability.

Spatial variability characterization and modelling of 2.5D woven SiO2f/SiO2 composites

2.5D woven SiO2f/SiO2 composites possess geometric variabilities across scales, such as the spatial fluctuations of fibers and yarns or the random distributions of defects and pores formed in the manufacturing process, which induces a complex stress and strain distribution in material testing. In this study, a multivariate cross-correlated non-Gaussian random field based on Vine Copula is proposed to characterize the spatial variability of mechanical properties. A database of material mechanical properties is acquired by stochastic representative volume element (SRVE) simulation results which contains realistic meso-geometry characteristics and spatially random voids. Based on this database, the cross- and auto-correlations of the mechanical properties are quantified by Vine Copula and non-Gaussian random field model. In a comparison of different numerical models and experimental results, the proposed method is verified, which shows great advantages in characterizing the spatial variability and big potential abilities in damage or reliability analysis.