Multiscale Structure Research Articles

Collaborative multiscale water resources planning in England

ABSTRACT Practitioners in water resources planning in England are navigating new multiscale planning structures. A National Framework introduced in 2020 embeds strategic cooperation across a privatized industry to meet higher resilience standards. This study presents a critical analysis of the National Framework to identify feasible solutions to navigate England’s water crisis. Findings are based on engagement with practitioners identifying successes, challenges, and lessons learned throughout 2020–2024. Recommendations include establishing a national coordination office as part of an explicit multiscale framework. Over time, the framework can continue to be built upon for a more informed transition to adaptive, collaborative, integrated planning.

Multiscale Confinement‐Modulated Cellulosic Dielectric Materials for Energy Harvesting and Self‐Powered Devices

AbstractThe rapid rise of triboelectric nanogenerators, an innovative technology for low‐frequency energy harvesting and self‐powered sensing, has increased the interest in high‐performance triboelectric materials. Enhancing the surface charge density via dielectric modulation is essential for high‐performance triboelectric nanogenerators. As the most abundant biopolymer on earth, cellulose has remarkable properties such as excellent mechanical strength, thermal stability, and tunable surface chemistry, indicating its significant application potential in the design and fabrication of triboelectric nanogenerators. Owing to its unique multiscale structure and excellent processability, cellulose holds substantial promise for dielectric modulation. This review aims to provide comprehensive insights into the rational design and tailored preparation of cellulosic materials with optimal dielectric constants. First, the multiscale structure and exceptional advantages of cellulosic materials are interpreted. A comprehensive investigation into multiscale confinement‐modulated cellulosic dielectric materials encompassing cellulosic molecules, dipoles, and fibers along with their dipoles is undertaken and the significance of interfacial polarization is explored. Furthermore, the emerging applications of cellulosic materials with superior dielectric properties in triboelectric nanogenerators, including energy harvesting, self‐powered sensing, and self‐powered medical and smart monitoring systems, are described. Finally, the challenges and future opportunities for cellulosic dielectric modulation are summarized.

Performance assessment on DES/DDES method for transient flow in a centrifugal turbomachine model subjects to rotor-stator interaction

We present an assessment of the performances of the detached eddy simulation (DES) and delayed detached eddy simulation (DDES) approaches in resolving the transient flow in a system of a centrifugal impeller and a vaned diffuser which is strongly destabilized by the rotor-stator interaction (RSI). The numerical results obtained by the DES and DDES approaches are compared against that from an independent large-eddy simulation (LES) investigation and that of a vaneless diffuser model. The time-averaged and fluctuating velocity, the nonlinear primary momentum transport term, and the two-point correlation of fluctuating pressure in the impeller, diffuser, and vaneless region are analyzed. The numerical results reveal that the diffuser vanes substantially perturb the flow inside the impeller, especially at the impeller outlet. The DDES approach cannot resolve the multi-scale flow structures in the vaneless region. The DES approach produces a time-averaged velocity field that is more consistent with the LES approach, and the DDES approach performs better for the reversed flow. For the transient behaviors, the DES approach is more sensitive to weak fluctuation, and for the pressure fluctuation in the impeller where the RSI significantly perturbs. The DDES approach gives a consistent two-point correlation of fluctuating pressure with the LES approach, while the DES approach could not give an accurate prediction.

GLCANet: Global–Local Context Aggregation Network for Cropland Segmentation from Multi-Source Remote Sensing Images

Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global–Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global–local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China.

Self-exploring automated experiments for discovery, optimization, and control of unsteady vortex-dominated flow phenomena

Typical unsteady vortex-dominated flows like those involved in bio-inspired propulsion, airfoil separation, bluff body wakes, and vortex-induced vibrations can be prohibitively expensive to simulate and impossible to measure comprehensively. These examples are governed by nonlinear interactions, and often involve moving boundaries, high-dimensional parameter spaces, and multiscale flow structures. The classical way to get around these challenges has been to reduce the experimental complexity by using canonical motions or simplified unsteady inflow conditions. A paradigm shift is emerging in the form of self-exploring automated experiments that combine the automation of the experimental pipeline with data-science tools to increase experimental throughput and expedite scientific discovery. Such automated experiments can explore and exploit higher-dimensional parameter spaces and cover more realistic and technically relevant unsteady conditions compared to what is traditionally feasible with supervised canonical experiments. This alternative approach can yield robust and generalizable models and control solutions, as well as the discovery of rare and extreme events. Here, we provide a perspective on the transformative potential of self-exploring automated experiments for the discovery, optimization, and control of unsteady vortex-dominated flow phenomena. Published by the American Physical Society 2024

Severity Estimation of Inter-Turn Short-Circuit Fault in PMSM for Agricultural Machinery Using Bayesian Optimization and Enhanced Convolutional Neural Network Architecture

The permanent magnet synchronous motor (PMSM) is a key power component in agricultural machinery. The harsh and variable working environments encountered during the operation of agricultural machinery pose significant challenges to the safe operation of PMSMs. Early diagnosis of inter-turn short-circuit (ITSC) faults is crucial for improving the safety of the motor. In this study, a fault diagnosis method based on an improved convolutional neural network (CNN) architecture is proposed, featuring two main contributions. First, a dilated convolutional neural network is combined with residual structures, multi-scale structures, and channel attention mechanisms to enhance the training efficiency of the model and the quality of feature extraction. Second, Bayesian optimization algorithms are applied for the automatic tuning of architecture hyperparameters in deep learning models, achieving automatic optimization of the hyperparameters for the fault diagnosis model of ITSCs. To validate the effectiveness of the proposed algorithm, 17 simulated tests of ITSC fault severities were conducted under both constant conditions and dynamic conditions. The results show that the proposed model achieves the best performance regarding the validation accuracy (98.2%), standard deviation, F1 scores, and feature learning capability compared to four other models with different architectures, demonstrating the effectiveness and superiority of the algorithm.

Insights into Dual Self-Assembly Mechanisms in Various Artocarpus altilis (Parkinson) Fosberg Starch-Endogenous Lipid-Endogenous Protein Complexes: Interactions between Digestibility Kinetics and Multiscale Structure.

Chinese seedless breadfruit is rich in starch, lipids, and protein. To explore the interactions among these macromolecules during food processing, the seedless breadfruit starch-endogenous lipid-endogenous protein complex was investigated. Native seedless breadfruit starches are categorized as low-resistant-content starch [low-resistant starch (LRS)] or high-resistant starch (HRS). After complexation, dual self-assembly mechanisms occur after complexation. Initially, for the LRS group, long chains of amylopectin and amylose participate in complexation due to the migration from the short side chain of amylopectin, leading to an increase in RS content compared to the native starch. In contrast, amylose participated in complexation in the HRS group, which showed higher digestibility than that of raw starch. According to chemometric analysis, the HRS group complex possesses a more compact external and internal nanomicrostructure, leading to its weaker digestibility kinetics compared to the LRS group complex. This study provides a fundamental basis for the comprehensive application of novel multicomponent foods.

Mechanism of starch multi-scale structural in determining the textural properties and formability of starch pearls

Starch pearls are widely used in bubble tea and desserts, yet the mechanistic understanding of the formation process of their textural properties remain unclear. To investigate the relationship between the multi-scale structure of starch and the textural properties of starch pearls, analyses of fine structure, crystal structure, rheological behavior, and textural profiling were conducted. The results showed that starch gels with a higher content of short-chain amylose (100 < X ≤ 1000) exhibited weaker formability during starch pearl preparation, leading to a lower flow behavior index (n*). This, in turn, positively influenced the hardness and resilience of the starch pearls. Cassava, potato, and yam starch pearls contained a large amount of long-chain amylopectin (24 < X ≤ 100) and long-chain amylose (5000 < X ≤ 20,000). The high proportion of long chains resulted in a weaker ordered structure, leading to increased peak viscosities and final viscosities and a higher consistency coefficient (k*). This structural feature enhanced the formability stability, viscosity, and chewability of starch pearls. Our findings reveal that variations in starch multi-scale structure significantly influence the textural quality and formability of starch pearls, providing valuable insights for optimizing starch selection and processing techniques in the food industry.

Effects of high temperature, high pressure, and high shear during extrusion on maize starch-fish protein extrudates: Based on physical properties and multiscale structure

This study explored the effects of high temperature, high pressure and high shear during extrusion on maize starch-fish protein extrudates and elucidated the interaction between starch and protein. The results showed that high temperatures promoted an increase in the β-sheets content of the extrudates and contributed to the fusion of starch and protein. High pressure disrupted the starch's agglomerate structure and protein gel network. With the increase of extrusion strength, the pasting properties of the extrudates were reduced, and the ΔH decreased from 5.87 J/g to 2.70 J/g. Moreover, high shear reduced the short-range orderliness of starch through shear-generated water migration, while promoting the mixing of starch and protein. Interestingly, the synergistic effect of high temperature, high pressure and high shear promoted new hydrogen bonding and hydrophobic interactions between starch and protein. This study provides a scientific basis for considering the unique properties and structure of maize starch-fish protein extrudates.

Impact of rehydration on multi-scale structural transformations and starch digestibility of dried noodles.

To understand the relationship between the structure and starch digestibility of dried noodles, the changes in multi-scale structure and in vitro starch digestibility of dried noodles with different protein contents (ranging from 10 % to 15 %) during rehydration were tracked. The results indicated that the hardness of dried noodles decayed according to the first-order exponential decay function, with rapid and slow stages. This depended on near-linear protein aggregation and near-logarithmic starch gelatinization. The gelatinization degree reached 70.9 to 79.4 % in the early stage of rehydration. Water absorption kinetics and distribution analysis revealed that the moisture migrating into the noodles was initially utilized for starch gelatinization. This led to the formation of a honeycomb gel structure at the edge of the noodles, which gradually spread from the edge to the center, as observed by SEM and CLSM. As rehydration progressed, the starch digestion degree increased. The digestion rate (k1) decreased with the formation of the composite gel structure, while k2 showed an increased trend. Additionally, with the increase in the protein content of noodles, the aggregation degree and increment were enhanced. This resulted in the formation of a more compact composite gel structure, which reduced the rate and extent of starch digestion within the noodles. When the noodles were overcooked, the C∞ of the high-protein samples was reduced by approximately 10 % compared to that of the low-protein samples. Overall, the formation of the composite gel structure reduces the heterogeneity of the noodles from the edge to the center, resulting in a closer starch digestion rate in fast and slow steps. Besides, the rapid and massive aggregation of proteins at high protein content contributed to the formation of a compact gel structure, which in turn interfered with the rate and extent of starch digestion.

On unsteady flow characteristics in a water-jet pump based on proper orthogonal decomposition method under different inflow conditions

Considering the complex flow characteristics of the water-jet pump and the anisotropy of Reynolds stresses, a hybrid Reynolds-averaged Navier–Stokes/large-eddy simulation method based on von Kármán scale and nonlinear correction has been developed, while its effectiveness has also been verified. The proper orthogonal decomposition method is employed to study the unsteady flow characteristics inside a water-jet pump due to its advantages in flow field analysis. The internal and external characteristics of the water-jet pump under two kinds of inflow conditions are comparatively analyzed, and the intrinsic influence mechanism of nonuniform inflow on the performance of the water-jet pump is investigated. The results indicate that nonuniform inflow causes the overall decrease in the pump head curve, and occurs serious reverse flow at impeller outlet near blade tip region, which in turn generates a large energy dissipation. The flow state is relatively smooth under uniform inflow condition, while the nonuniform inflow makes tip leakage flow induce many unstable flow structures, causing large turbulent kinetic energy and low pressure difference between the pressure surfaces and suction surfaces of impeller blades. There are multiscale flow structures in the water-jet pump, with large scales corresponding to low-order modes, which play a dominant role in turbulent movement and can be used as an important element for evaluating hydraulic performance. The energy content of low-modes is lower and the distribution of flow structures in each-order mode is more chaotic in the water-jet pump for nonuniform inflow compared to the ones for uniform inflow.

Transient flow characteristics of single and twin circular impinging jets using particle image velocimetry and proper orthogonal decomposition

This study experimentally investigated the transient flow characteristics of single and twin parallel circular impinging jets using Particle Image Velocimetry (PIV) and Proper Orthogonal Decomposition (POD). High spatiotemporal resolution data were collected to analyze and visualize the distribution and interactions of multi-scale vortex structures. The experimental data were evaluated for PIV uncertainty and statistical convergence. The flow field distribution of the fountain that may occur in the twin impinging jets was also discussed. The results revealed that, for the oblique single jet, high-energy large-scale structures primarily concentrate in downhill regions and gradually decompose into smaller-scale structures. In the combination of twin jets, vortices in the shear layers merge in an alternating pattern. The frequencies of the inner and outer shear layers are sensitive to jet spacing with different characteristic interaction patterns repeatedly observed based on varying spacings. Three distinct cases are defined based on the interaction of the twin jets with the impingement plate. Additionally, three distinct flow field structures of fountain are defined to facilitate, indicating the degree of combination as well. The velocity profiles of the fountain are similar and adhere to a Gaussian distribution. Furthermore, a semi-empirical equation was developed to describe the centerline velocity of the fountain.

New permeability model considering multiscale migration mechanism of deep coalbed methane and its application

Deep coalbed methane (CBM) flow exhibits various transportation mechanisms within the multiscale pore structures of reservoirs, including continuous flow, Knudsen diffusion, and surface diffusion. Current research predominantly emphasizes the effects of individual or partial flow mechanisms and single-factor influences on the multiscale migration of CBM. We proposed a new apparent permeability model that integrates multiple flow mechanisms to enhance our understanding of the factors governing CBM flow in complex fractured networks. This model accounted for stress sensitivity, adsorbed gas desorption, and matrix shrinkage. By assigning appropriate weights to different flow mechanisms, the model yielded a more accurate representation of the deep CBM apparent permeability, avoiding the overestimation resulting from the linear superposition of diverse migration mechanisms. Our findings indicated that the apparent permeability was positively correlated with compressibility and negatively correlated with the tortuosity and Poisson's ratio. In the presence of the adsorbed gas, the apparent permeability of organic matter showed heightened sensitivity to formation pressure, rock compressibility, and tortuosity. However, the impacts of these factors became less pronounced when the pressure differential was small. The proposed model was applied to the flow simulations for a multi-fractured horizontal well within a deep coal reservoir characterized by a complex fracture network. The simulation results agreed well with the production data. We found that continuous flow was the dominant contributor to the apparent permeability of organic and inorganic matter within the coal rock, followed by Knudsen diffusion and surface diffusion. This study provided insights into the evolution of apparent permeability of CBM during development and offered valuable guidance for the analysis of CBM production dynamics, productivity forecasting, and production system design.

The effects of micro-fractures and mixed wettability on oil/water imbibition in porous media

Imbibition is widely recognized as an effective process for enhancing oil recovery in shale reservoirs. However, shale formations often feature complex multi-scale laminar structures, including micro-fractures, and distinct wettability characteristics in organic and inorganic pores. To better understand the impact of micro-fractures and mixed wettability on water imbibition, we developed a pore-scale model incorporating mixed wettability and micro-fractures to simulate countercurrent imbibition. The results indicate that: (1) Counter-current imbibition exhibits complex flow characteristics and can be divided into three stages. In the early stage, two equivalent oil-water phase interfaces form, and an external force exceeding resistance is required to displace the oil phase. In the middle stage, a continuous oil phase gradually develops in the main channel, with only one phase interface. In the later stage, driving forces and resistance approach mechanical equilibrium, allowing some oil droplets to be expelled into the main channel. (2) The mixed wettability of shale pores amplifies oil phase trapping and capillary fingering during water imbibition. A concentrated distribution of oil-wet pores increases adhesion forces between the oil and pore walls, resulting in pronounced oil trapping. Conversely, a concentrated distribution of water-wet pores accelerates water flow due to capillary forces, enhancing the fingering effect. (3) Micro-fractures effectively connect matrix pores distant from the main channel. As the bifurcation angle increases, imbibition recovery initially rises and then decreases. However, mixed wettability significantly restricts the flow conductivity of micro-fractures, highlighting the importance of considering its influence in reservoir studies.

Microstructure Regulation and Fabrication of Epoxy-based Conductive Films with Excellent Electrical and Thermal Conductivity, Adhesion, Toughness, and Flexibility

The electrically conductive adhesive films (ECAFs) are new materials used for large-format bonding in microelectronics packaging. However, improving its electrical and thermal conductivity, adhesion, and flexibility simultaneously has been a challenge. In this study, silver powder with different particle sizes and poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) (PEDOT/PSS) were used to create a high-efficient multi-scale conductive bridge structure. This reduced the volume resistivity from 7.30×10-4 Ω·cm to 3.80×10-4 Ω·cm, with lower silver powder content. The thermal conductivity improved from 9.074 W/m·K to 9.124 W/m·K, and adhesion performance between the epoxy matrix and the metal increased from 6.85 MPa to 8.08 MPa. Furthermore, the addition of hydroxyl-functionalized polyurethane materials enhanced the toughness without significantly affecting its electrical, thermal, and adhesion properties. This study successfully prepared epoxy-based conductive adhesive films with a combination of electrical conductivity, thermal conductivity, adhesion, and flexibility, while significantly reducing costs due to the reduction in silver powder content.

Advances in the contact mechanics theory and applications of biomimetic artificial adhesives inspired by biological multiscale hierarchical structures

Advances in the contact mechanics theory and applications of biomimetic artificial adhesives inspired by biological multiscale hierarchical structures

Mechanical properties and multiscale structure evolution of cement mortars under temperature cycling and low-humidity coupled condition

The degradation of concrete induced by wide temperature differences and drying significantly impacts the durability of constructions in plateau regions. This study systematically investigated the mechanical properties, the structure evolution from nanoscale to macroscale, and the hydration product changes under temperature cycling with low relative humidity (5–60°C cycling, 30±5 % RH). Damage gradients were meticulously discussed based on the experimental measured thermal gradient and mechanical failure modes. Pore coarsening and microcracks which increased with w/c mainly occurred between 50 nm and 50 μm, becoming key factors contributing to the mechanical degradation. Nevertheless, the relatively small damages enable the mortar to maintain much stable mechanical properties than cement paste and concrete. The hydration and the small amount of decomposition had little effect on the structure. Overall, the strength increased until a certain time window followed by a decreasing trend because of two contradictory factors: enhancement led by the increase of capillary pressure and surface energy and degradation driven by the gradient damage.

An 808 line phasor-based dehomogenisation Matlab code for multi-scale topology optimisation

This work presents an 808-line Matlab educational code for combined multi-scale topology optimisation and phasor-based dehomogenisation titled deHomTop808. The multi-scale formulation utilises homogenisation of optimal microstructures to facilitate efficient coarse-scale optimisation. Dehomogenisation allows for a high-resolution single-scale reconstruction of the optimised multi-scale structure, achieving minor losses in structural performance, at a fraction of the computational cost, compared to its large-scale topology optimisation counterpart. The presented code utilises stiffness optimal Rank-2 microstructures to minimise the compliance of a single-load case problem, subject to a volume fraction constraint. By exploiting the inherent efficiency benefits of the phasor-based dehomogenisation procedure, on-the-fly dehomogenisation to a single-scale structure is obtained. The presented code includes procedures for structural verification of the final dehomogenised structure by comparison to the multi-scale solution. The code is introduced in terms of the underlying theory and its major components, including examples and potential extensions, and can be downloaded from Github.

Multi-scale heterogeneous composite elastomer absorbers synergistically enhanced by CoNi nanospheres and carbon nanotubes

The advancement of high-performance absorbers is essential for applications in electromagnetic wave absorption (EWA) and stealth technologies. To enhance EWA performances, it is imperative to employ advanced design strategies that incorporate heterogeneous interfaces and multi-scale structures. In this study, we developed a composite elastomer demonstrating exceptional EWA characteristics using a two-step process involving liquid-phase reduction followed by thermal curing. High aspect ratio carbon nanotubes were integrated onto the surface of CoNi nanospheres, creating a multi-scale architecture with heterogeneous interfaces that ranged from zero-dimensional to two-dimensional. This innovative structural design significantly improved the EWA capabilities of the composite elastomer. Notably, even with only 15 wt% filler content, the composite elastomer achieved a minimum reflection loss of −54.4 dB at a thickness of 4 mm, alongside an adjustable effective absorption bandwidth exceeding 60 % of the 2–18 GHz frequency range. Additionally, it exhibited favorable mechanical strength and stretchability, enhancing its practical applicability. This work provides valuable insights into optimizing dielectric and magnetic properties through advanced structural design and underscores the potential for developing effective elastomer absorbers to deal with electromagnetic pollution in complex environment.

MRT-YOLO: A Fine-Grained Feature-Based Method for Object Detection

Object detection is an essential component of autonomous driving, unmanned aerial vehicle (UAV) reconnaissance, and other domains. It equips drones and vehicles with the capability to perceive and comprehend their surrounding environment, making it a crucial technology for achieving safe and reliable autonomous driving as well as UAV spot reconnaissance. This paper proposes an end-to-end, high-precision, multi-scale real-time detection algorithm called MRT-YOLO based on YOLOv8. Firstly, in the feature downsampling process of the backbone network, we extend the channel depth to enhance the model’s learning capability for fine-grained features and thereby improve its performance in retaining feature information. Secondly, we enhance the cross-stage partial layer version 2 (C2f) module in YOLOv8 by incorporating a channel self-attention mechanism within it, which optimizes performance through effective feature interaction and integration. Simultaneously, we also employ an improved bidirectional feature pyramid network (BiFPN) and introduce the proposed multi-scale feature learning (MFL) module to further enhance the model’s feature extraction ability. In this study, we fuse the feature maps (C2, C3, C4, and C5) from the backbone network to generate a new feature map C6, thus increasing cross-connections between low-level and high-level features. Lastly, a multi-scale small object detection structure is designed to enhance recognition sensitivity toward densely distributed small objects. The proposed algorithm’s effectiveness and superiority are demonstrated through experiments conducted on two datasets: VisDrone (UAV vision dataset) and BDD100K (automatic driving dataset).