Multiscale Approach Research Articles

Energy efficiency optimization by multi-scale approach

Energy efficiency optimization by multi-scale approach

A multiscale approach for network intrusion detection based on variance–covariance subspace distance and EQL v2

As an important network defense approach, network intrusion detection is mainly used to identify anomaly traffic behavior. However, dominant network intrusion detection approaches are now struggling to identify the complex and variable means of attack, leading to high false alarm rate. Additionally, the feature redundancy and class imbalance problem in the intrusion detection dataset also constrain the performance of detection methods. This paper proposes a multiscale intrusion detection approach based on variance–covariance subspace distance and Equalization Loss v2 (EQL v2). Firstly, the variance–covariance subspace distance is used for feature selection on the preprocessed dataset to determine a set of representative feature subsets that can effectively approximate the original feature space. Secondly, the loss function, EQL v2, is adopted to balance the positive and negative gradients, addressing the class imbalance problem. Finally, a pyramid depthwise separable convolution model is proposed to capture the multiscale information of the traffic, and the convolutional layer in the depthwise convolution is replaced with self-supervised predictive convolutional attention block to compensate for the performance loss caused by the parameter reduction. Extensive experiments demonstrated that the proposed approach exhibits better performance on the three datasets of NSL-KDD, UNSW_NB15, and CIC-IDS-2017, with accuracy rates of 99.19%, 97.81%, and 99.83%, respectively, effectively improve the intrusion detection performance.

Evolution of concentration distribution and removal of a solute in magnetohydrodynamics channel flow: effects of buoyancy-driven force and induced magnetic field

The dispersion of reactive solutes in various flow geometries has significant implications for ecological and environmental applications. Researchers such as Poddar et al . (Poddar et al . 2021 Proc. R. Soc. A 477 , 20200830 (doi: 10.1098/rspa.2020.0830 ) and Dhar et al . (Dhar et al . 2021 Phys. Fluids. 33 , 083609) have extensively studied solute transport in magnetohydrodynamics (MHD) channel flow, focusing on the applied magnetic field. However, the impact of the induced magnetic field on solute dispersion between two parallel plates remains poorly understood. This article presents an analytical investigation using Mei’s multi-scale homogenization approach to examine the effects of the Hartmann number, Grashof number and absorption parameter on the multi-dimensional concentration distribution and removal efficiency of a reactive solute in MHD laminar channel flow. The findings provide insights into concentration behaviour, such as the decrease in the Taylor transport coefficient with an increase in the induced magnetic field. These insights have practical implications for improving sewage and wastewater treatment by effectively separating desired particles from contaminants, contributing to advancements in environmental purification techniques.

Improving heat transfer prediction across catalytic SiC interface through a multiscale framework leveraging machine learning and data fusion techniques

Accurate aerothermal prediction under hypersonic flow conditions is crucial for any thermal protection materials design and engineering. The surface catalytic effect, where dissociated atoms recombine into their molecular forms at the air-solid interface, is an exothermic reaction and plays an important role in high-enthalpy aerodynamic environment prediction. Taking SiC, a widely recognized high-temperature thermal protection material as an example, a multiscale fusion approach for aerothermal prediction is proposed. The approach utilizes reactive molecular dynamics method to construct an interface catalysis model, and the Bayesian maximum entropy to integrate experimental and simulational data into an optimized database. Subsequently, using the radial basis function neural network algorithm, a machine learning-based reaction kinetics model with precise analysis of surface catalysis is trained. The obtained catalytic recombination efficiency is used as a boundary input for computational fluid dynamics simulation, enabling rapid and accurate prediction of hypersonic thermal environment. The result shows that the predicted surface heat flux by this novel approach is consistent with the benchmark studies, but comes with significantly reduced computation time. The stagantion heat flux predictions based on the assumptions of full catalytic wall, finite rate catalytic wall (from the proposed multiscale fusion method), and non-catalytic wall are determined to be 8.65×106 W/m2, 7.51×106 W/m2, and 4.02×106 W/m2, respectively. Comparing these with the wind tunnel benchmark value of 7.48×106 W/m2, the error from the proposed multiscale fusion method is 0.30 %, indicating significant enhancement in prediction accuracy through the proposed upscaling method. The new approach could not only reveal complex exothermic reaction processes at the interface, but also enhance the prediction accuracy at much higher computational efficiency, providing an alternative for multiscale modeling of complex flow and heat transfer at the interface.

Hybrid multiscale computational approach for heat transfer in GaN semiconductors

Abstract With ongoing advancements in semiconductor technology, understanding thermal transport in semiconductor devices—where phonons are the primary charge carriers—is crucial. This involves a multiscale process in which phenomena at different scales are governed by distinct control equations, necessitating various numerical methods. In this context, this paper introduces a novel multiscale simulation method called the Second-Order Reconstruction Operator Finite Difference Lattice Boltzmann Method (ROsFDLBM). This method effectively combines the Lattice Boltzmann Method (LBM) with the Finite Difference Method (FDM). ROsFDLBM employs reconstruction operators to accurately map the relationship between distribution functions and macroscopic physical quantities, significantly reducing errors caused by the transmission of interface information. The efficacy of this coupling scheme has been confirmed through an interface-coupled thermal diffusion model. Comparative analyses have revealed that the relative error of ROsFDLBM does not exceed 1.2%, demonstrating its superior accuracy. Further simulations of GaN devices have confirmed that traditional macroscopic methods tend to underestimate the temperature in the heat source area at micro-nano scales, with discrepancies reaching up to 60.9 K. Overall, the ROsFDLBM aligns with current research findings and offers an accurate and efficient simulation strategy for addressing complex heat transfer problems.

The role of coherent nano precipitates on stacking fault and deformation twin formation during tensile deformation of wire arc additive manufactured nickel-aluminum-bronze

The strain hardening mechanisms and dislocation interactions of a wire arc additively manufactured (WAAM) nickel-aluminum-bronze (NAB) alloy was investigated using multi-scale characterization approach cross-correlating scanning electron microscopy (SEM), SEM-based electron back-scatter diffraction (EBSD), site- and crystallographic orientation-specific focused ion beam (FIB) nanofabrication, scanning transmission electron microscopy (STEM) and STEM-based energy dispersive X-ray spectroscopy (EDS). Flat, rectangular dog bone specimens were strained under uniaxial tension conditions. To understand the micro- and nanometer scale deformation mechanisms throughout the microstructure, tensile test specimens were interrupted near the yield strength and ultimate tensile strength. STEM microstructural characterization revealed that tensile deformation occurs by planar slip and multiple slip bands interacting on different {111} planes as well as the nucleation of dislocations from the interfaces associated with grains and secondary phase particles. To the authors knowledge for the first time the Center of symmetry (COS) analysis revealed that in both samples the shearing of nanoscale precipitates led to the formation of extended stacking faults including a combination of intrinsic stacking faults and 2-layer extrinsic stacking faults. At high strain levels more dislocations and stacking faults were nucleated, as well as intersecting slip bands further facilitating the activation of the stair-rod cross-slip mechanism and thickening of an ESF to create a 3-layer nano-twin. Activated mechanisms of plastic deformation and associated role of the precipitates are discussed with respect to the microscopic strength-plasticity characteristics as well as macroscopic mechanical properties of WAAM NAB alloy.

Cross-scale study of heat transfer performance in metal rubber with complex topological structures

Metal Rubber (MR), characterized by its porous topology and metal-based entangled structure, is commonly utilized in high-temperature environments. The intricate nature of MR's structure complicates the accurate experimental investigation of its thermophysical properties, thereby restricting its broader applications. Therefore, based on numerical simulation, the three-dimensional reconstruction of the spatial topology of MR is achieved. Additionally, MATLAB-LS/DYNA-ABAQUS is coupled to replicate the heat transfer process of MR at macro-microscopic scales. Our findings elucidate the relationship between MR's density, number of contact points, and thermal conductivity, revealing a stepwise decrease in thermal conductivity along the forming direction and a spatially varying anisotropic heat transfer mechanism that involves energy feedback on non-forming surfaces. Specifically, at the material level of investigation, a molecular dynamics model for heat transfer in microscopic metal wire contacts was generated. This model enables the simulation and dynamic tracking of atomic group thermal behavior under temperature effects. Finally, equivalent thermal conductivity (ETC) tests were conducted concurrently. By integrating the thermoelectric analogy method, we introduced for the first time a hybrid series-parallel mode and the tortuosity of discontinuous materials. This approach comprehensively considers critical factors such as porosity, temperature, and interlayer thermal resistance, culminating in the development of a predictive numerical model for thermal conductivity in porous metal-based materials. In this study, a bottom-up multiscale research approach is proposed to deeply explore the spatially multidirectional heat transfer mechanisms of microporous metal-based tangled materials, from the material level to the complex topological structural level. Simultaneously, new dimensions are opened for the study of such materials with unique discontinuous structural characteristics.

Advancing thermohydraulic performance of micro-grooved flat heat pipes through a combined numerical-experimental approach

A combined numerical-experimental model has been developed to predict the thermohydraulic characteristics of micro-grooved flat heat pipes (FHPs). This model uses a limited number of experimental data for calibration with two parameters, βexp,1 and βexp,2, which account for uncertainties in the accommodation coefficients used to determine evaporation and condensation mass fluxes. This calibration, which is a key feature of the developed model, addresses the uncertainties and challenges in determining these coefficients. Moreover, the model incorporates the effects of interfacial shear stress, axial wall conduction, heat transfer in the liquid block region, and the contact angle of the liquid–vapor interface. Furthermore, the evaporation mass flux from the curved liquid–vapor interface is calculated using a separate multiscale approach developed in two recent works for analyzing evaporation from the extended meniscus in microchannels, considering thin-film evaporation. The model demonstrates high accuracy in predicting the maximum heat transport capacity (Qmax) and wall temperature distribution across various input heat powers, with a maximum error of less than 0.5 % in wall temperature predictions, as validated against experimental data. The validated model is then used to explore a new micro-grooved wick structure featuring converging/diverging microchannels. In this structure, the channel width is constant and equal to Wf2 in the first half of the heat pipe, from the condenser end to the midpoint, and then it decreases or increases to Wf1 by the end of the evaporator section. Results indicate that a flat heat pipe with a converging micro-grooved wick structure (Wf1 = 200 µm and Wf2 = 160 µm) can enhance Qmax from 53 to 70 W, 70.8 to 91.8 W, and 91.2 to 116.5 W at working temperatures of 60 °C, 70 °C, and 80 °C, respectively. This means the new converging micro-grooved wick structure can boost flat heat pipe performance by more than 25 % compared to a flat heat pipe with a straight micro-grooved wick (Wf1 = Wf2 = 200 µm) without increasing the occupied space.

Wavelet Transforms in Psychological Science: A Multiscale Approach to Cognitive, Clinical, and Developmental Insights

Wavelet Transforms in Psychological Science: A Multiscale Approach to Cognitive, Clinical, and Developmental Insights

Cumulative Effects Assessment in Community Watersheds at Different Spatial Scales: A Review of Indicators

Cumulative effect assessments (CEA) for water systems are becoming more necessary as pressures from multiple stressors impact communities, watersheds, and basins. CEA is an important tool for shifting from fragmented to holistic, integrated management as per the Integrated Water Resources Management (IWRM) paradigm. Through a review of scientific literature, this study analyzed the patterns and use of indicators for upstream-downstream linkages. This study found that five categories of spatial scale approaches are used: the national/regional scale, basin scale, watershed scale, local scale, and the specially defined multi-scale approach. It was found that CEA has been applied using a high-level, qualitative approach for national, regional, and multi-scale studies – these undertakings inform strategic planning and sustainable decision-making. Basin, watershed, and local scale CEA studies have focused on quantitative modeling of environmental and human systems. These assessments emphasized the interconnectedness of water systems, as well as the role of policy development and stakeholders in improving system outcomes. Four indicator categories were found to span all five spatial scales: water quality (occurred in 64% of the literature), water quantity (53%), land use (56%), and landscape characteristics (67%). These indicator categories provide a common foundation for understanding stressor interactions, impact response, and upstream-downstream linkages. However, further research is needed on temporal scales and the integration of time into CEA for watersheds. This review found that CEA, a tool which is useful for a variety of scales, can help find a balance between present and future needs and further community sustainability using a holistic and integrated approach.

Chatter stability analysis for non-circular high-speed grinding process with dynamic force modelling

To avoid instability and resulting chatter during the non-circular grinding process, it is necessary to elucidate the potential occurrence of periodic chatter behavior when high-speed grinding loses stability and to define the stability boundary of the grinding system. Building upon an analysis of the geometric kinematic characteristics of non-circular profiled components, a dynamic grinding force model for non-circular high-speed grinding was derived by considering both the delay effect and the elastic yielding mechanism between the grinding wheel and workpiece. Then, A multi-factor coupled dynamic model of non-circular grinding was established. By employing a multi-scale approach, bifurcation analysis and classification of the instability process in the grinding system were conducted, using a typical non-circular profiled shaft component, such as a camshaft, for theoretical analysis and experimental validation. The research determined the stability boundary of the high-speed grinding process for non-circular profile components, validated the possibility of the existence of a conditionally stable chatter region in the non-circular high-speed grinding process, and identified differences in grinding chatter characteristics among different segments of the cam profile, with a greater propensity for chatter at the juncture of the cam fall and base circle.

The Landscape Ecological Quality of Two Different Farm Management Models: Polyculture Agroforestry vs. Conventional

Low-intensity, diversified agricultural land use is needed to counteract the current decline in agrobiodiversity. Landscape ecology tools can support agrobiodiversity assessment efforts by investigating biodiversity-related ecological functions (pattern–process paradigm). In this study, we test a toolkit of landscape ecology analyses to compare different farm management models: polyculture agroforestry (POLY) vs. conventional monoculture crop management (CV). Farm-scale analyses are applied on temperate alluvial sites (Po Plain, Northern Italy), as part of a broader multi-scale analytical approach. We analyze the landscape ecological quality through landscape matrix composition, patch shape complexity, diversity, metastability, and connectivity indices. We assess farm differences through multivariate analyses and t-tests and test a farm classification tool, namely, a scoring system based on the relative contributions of POLY farms, considering their deviation from a local CV baseline. The results showed a separate ecological behavior of the two models. The POLY model showed better performance, with significant positive contributions to the forest and semi-natural component equipment and diversity; agricultural component diversity, metastability; total farm diversity, metastability, connectivity, and circuitry. A reference matrix for the ecological interpretation of the results is provided. Farm classification provides a quick synthesis of such contributions, facilitating farm comparisons. The methodology has a low cost and quickly provides information on ongoing ecological processes resulting from specific farm management practices; it is intended to complement field-scale assessments and could help to meet the need for a partially outcome-based assessment of good farm practice.

A multiscale approach to understanding the shared blue-orange flower color polymorphism in two Lysimachia species

BackgroundPolymorphisms are common in nature, but they are rarely shared among closely related species. Polymorphisms could originate through convergence, ancestral polymorphism, or introgression. Although shared neutral genomic variation across species is commonplace, few examples of shared functional traits exist. The blue-orange petal color polymorphisms in two closely related species, Lysimachia monelli and L. arvensis were investigated with UV-vis reflectance spectra, flavonoid biochemistry, and transcriptome comparisons followed by climate niche analysis.ResultsSimilar color morphs between species have nearly identical reflectance spectra, flavonoid biochemistry, and ABP gene expression patterns. Transcriptome comparisons reveal two orange-specific genes directly involved in both blue-orange color polymorphisms: DFR-2 specificity redirects flux from the malvidin to the pelargonidin while BZ1-2 stabilizes the pelargonidin with glucose, producing the orange pelargonidin 3-glucoside. Moreover, a reduction of F3’5’H expression in orange petals also favors pelargonidin production. The climate niches for each color morph are the same between the two species for three temperature characteristics but differ for four precipitation variables.ConclusionsThe similarities in reflectance spectra, biochemistry, and ABP genes suggest that a single shift from blue-to-orange shared by both lineages is the most plausible explanation. Our evidence suggests that this persistent flower color polymorphism may represent an ancestrally polymorphic trait that has transcended speciation, yet future analyses are necessary to confidently reject the alternative hypotheses.

Mechanics of Materials: Multiscale Design of Advanced Materials and Structures

Materials can now be designed and architectured like structural components for targeted mechanical and physical properties. Structures and microstructures should not be studied independently and their design will benefit from a multiscale approach combining nonlinear continuum mechanics approaches and physical descriptions of elasticity, viscoplasticity, phase transformations and damage of microstructures, at various scales. The aim of the workshop was to gather outstanding junior and senior researchers in the various branches of mathematics, physics and engineering sciences suited to address the question of design of materials and structures by means of multiscale discrete and continuum approaches to their constitutive behavior. Examples include atomic or macroscopic lattices, random or periodic cellular materials, smart materials like shape memory alloys, 3D woven composites, acoustic and electromagnetic metamaterials, etc. Modern continuum mechanics relies on sophisticated constitutive laws for anisotropic materials exhibiting elastoviscoplastic behavior, still a field of intense research with new mathematical concepts. In particular size-dependent properties are addressed by resorting to generalized continua such as gradient or micromorphic and phase field models. The latter are attractive for the simulation of microstructure evolution coupled with mechanics, due to thermodynamic and metallurgical processes and damage. Scale transition and homogenization methods for continuous and discrete systems are required for the determination of effective material and structural behavior. Metamaterials are architectured materials specifically designed to achieve certain propagation and dispersion properties of elastic and plastic waves. Optimization strategies for the design of optimal architectures are involved in the design process. Target functions for optimization are now based on multicriteria (stiffness, strength, thermal expansion, transport properties, anisotropy etc.).

From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

Uncertainty propagation in fused filament fabrication process: a multiscale approach

Uncertainty propagation in fused filament fabrication process: a multiscale approach

Effect of compressive stress on piezoelectric coefficients: A multiscale modeling approach

The effect of compressive stress on linearized coefficients for ferroelectric ceramics has been extensively reported experimentally. However, there is no proper model for this effect which could be pertinent to designers of force sensors. This paper introduces a model based on a ferroelectric multiscale model in order to predict the effect of compressive stress on the piezoelectric constitutive laws. The comparison with experimental data shows an excellent qualitative agreement and the use of this model gives new insights on this nonlinear and hysteretic effect.

Multiscale Analysis of Impact-Resistance in Self-Healing Poly(ethylene-co-methacrylic acid) (EMAA) Plain Woven Composites.

A study combining multiscale numerical simulation and low-velocity impact (LVI) experiments was performed to explore the comprehensive effects on the impact-resistance of EMAA filaments incorporated as thermoplastic healing agents into a plain woven composite. A multiscale micro-meso-macro modeling framework was established, sequentially propagating mechanical performance parameters among micro-meso-macro models. The equivalent mechanical parameters of the carbon fiber bundles were predicted based on the microscopic model. The mesoscopic representative volume element (RVE) model was crafted by extracting the actual architecture of the monolayer EMAA filaments encompassing the plain woven composite. Subsequently, the fiber and matrix of the mesoscopic model were transformed into a monolayer-equivalent cross-panel model containing monolayers aligned at 0° and 90° by local homogenization, which was extended into a macroscopic equivalent model to study the impact-resistance behavior. The predicted force-time curves, energy-time curves, and damage profile align closely with experimental measurements, confirming the reliability of the proposed multiscale modeling approach. The multiscale analysis reveals that the EMAA stitching network can effectively improve the impact-resistance of plain woven composite laminates. Furthermore, there exist positive correlations between EMAA content and both impact-resistance and self-healing efficiency, achieving a self-healing efficiency of up to 98.28%.

Mapping the landscape of social behavior.

Social interaction is integral to animal behavior. However, we lack tools to describe it with quantitative rigor, limiting our understanding of its principles and neuropsychiatric disorders, like autism, that perturb it. Here, we present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals, solving the challenging subject occlusion and part assignment problems using 3D geometric reasoning, graph neural networks, and semi-supervised learning. We collected over 140 million 3D postures in interacting rodents, featuring new monogenic autism rat lines lacking reports of social behavioral phenotypes. Using a novel multi-scale embedding approach, we identified a rich landscape of stereotyped actions, interactions, synchrony, and body contact. This enhanced phenotyping revealed a spectrum of changes in autism models and in response to amphetamine that were inaccessible to conventional measurements. Our framework and large library of interactions will greatly facilitate studies of social behaviors and their neurobiological underpinnings.

Catalysis in Extreme Field Environments: A Case Study of Strongly Ionized SiO2 Nanoparticle Surfaces.

High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized nanoscale excitations. Recently, strong field excitation of dielectric nanoparticles has emerged as an avenue for studying catalysis in highly ionized environments, producing extreme electric fields. While the dynamics of laser-driven surface ion emission has been extensively explored, understanding the molecular dynamics leading to fragmentation has remained elusive. Here, we employ a multiscale approach performing nonadiabatic quantum molecular dynamics (NAQMD) simulations on hydrogenated silica surfaces in both bare and wetted environments under field conditions mimicking those of an ionized nanoparticle. Our findings indicate that hole localization drives fragmentation dynamics, leading to surface silanol dissociation within 50 fs and charge transfer-induced water splitting in wetted environments within 150 fs. Further insight into such ultrafast mechanisms is critical for the advancement of catalysis on the surface of charged nanosystems.