Scale Bridging Research Articles

On identifying dynamic length scales in crystal plasticity

Materials are often heterogeneous at various length scales, with variations in grain structure, defects, and composition which has a strong influence on the emergent macroscopic plastic behavior. In particular, heterogeneities lead to fluctuations in the plastic response in the form of jerky flow and ubiquitous strain bursts. One of the crucial aspects of plasticity modeling is scale bridging: In order to deliver physically correct crystal plasticity models, one needs to determine relevant microstructural length scales. In this paper we advance the idea that continuum descriptions of dislocation mediated plasticity cannot neglect dynamic correlations related to the avalanche behavior. We present an extensive weakest link analysis of crystal plasticity by means of three-dimensional discrete dislocation dynamics simulations with and without spherical precipitates. We investigate strain bursts and related length scales and conclude that while sufficiently strong obstacles to dislocation motion tend to confine the dislocation avalanches within well-defined sub-volumes, in pure dislocation systems the avalanches may span the system, implying that the dynamic length scale is, in fact, the size of the entire sample. Consequences of this finding on continuum modeling are thoroughly discussed.

Behavior of a phase field model for wetting on structured surfaces

AbstractTechnical surfaces are generally not perfectly smooth, but usually exhibit roughness. Additionally, as micro production techniques continue to evolve, geometrical surface structures at the micro scale can be manufactured, which presents a challenge for wetting models that are commonly formulated for smooth surfaces. A phase field model for surface wetting, proposed by Diewald et al., serves as a basis for this investigation. On smooth surfaces, the width of the gas‐liquid interface can be widened for scale bridging purposes, as this allows for accurate computations on coarse grids. However, it was observed that this interface scaling impacts the results when the model is applied to rough surfaces, and therefore a free choice of the interface width is not always sensible. In this work, the ability of the model to deal with sinusoidally shaped surfaces is investigated. An Allen–Cahn evolution equation is used to determine static equilibrium configurations for droplets on such surfaces.

Atomistic Simulation of HF Etching Process of Amorphous Si3N4 Using Machine Learning Potential.

An atomistic understanding of dry-etching processes with reactive molecules is crucial for achieving geometric integrity in highly scaled semiconductor devices. Molecular dynamics (MD) simulations are instrumental, but the lack of reliable force fields hinders the widespread use of MD in etching simulations. In this work, we develop an accurate neural network potential (NNP) for simulating the etching process of amorphous Si3N4 with HF molecules. The surface reactions in diverse local environments are considered by incorporating several types of training sets: baseline structures, reaction-specific data, and general-purpose training sets. Furthermore, the NNP is refined through iterative comparisons with the density functional theory results. Using the trained NNP, we carry out etching simulations, which allow for detailed observation and analysis of key processes such as preferential sputtering, surface modification, etching yield, threshold energy, and the distribution of etching products. Additionally, we develop a simple continuum model, built from the MD simulation results, which effectively reproduces the surface composition obtained with MD simulations. By establishing a computational framework for atomistic etching simulation and scale bridging, this work will pave the way for more accurate and efficient design of etching processes in the semiconductor industry, enhancing device performance and manufacturing precision.

Bayesian model-based damage detection in engineering systems using Invertible Neural Networks

This study explores the application of BayesFlow, a state-of-the-art deep learning technique for approximate Bayesian inference, in the context of Structural Health Monitoring (SHM), and specifically, model-based damage detection. BayesFlow diverges from other likelihood-free methods by not relying on predefined summary statistics, but learning optimal ones from the simulations via invertible neural networks. It is also capable of performing almost instantaneous inference for an arbitrary number of datasets, which is ideal for real-time applications such as bridge monitoring systems. The effectiveness of the proposed approach is demonstrated with a small scale bridge use case with synthetic measurements for different testing scenarios. Across all sub-cases, BayesFlow is able to determine both the damage location and severity with a high accuracy, while the credible intervals are close to those obtained under an exact Bayesian inference approach with the true likelihood.

A preliminary investigation of the inverse Finite Element Method applied to bridge structures

Deflection monitoring plays a key role in bridge Structural Health Monitoring, as deflection data may offer valuable insights into potential structural issues, such as cracks and corrosion. However, monitoring bridge deflections under operative loads presents challenges, particularly in scenarios where establishing a fixed reference point for displacement sensors is impractical, such as when a bridge spans a waterway or the sensors may be vulnerable to vandalism. In recent years, the inverse Finite Element Method (iFEM) has emerged as a possible solution. This method provides a load- and material-independent means of computing structural displacements from strain measurements, in real time at a minimal computational cost. Additionally, the iFEM can serve as a virtual sensing approach to monitor strains at locations lacking sensors, enabling the assessment of the fatigue life consumption at virtually any point in the structure. Despite its potential, applications of the iFEM in the civil engineering field are very limited, with no validation on full scale bridges has been performed to date. This study addresses this gap by investingating the application of the iFEM for bridge deflection monitoring through distributed strain measurements. The proposed approach is tested on experimental case studies, involving cross-validatiion of reference-free deflections inferred by the iFEM with those obtained from displacement sensors. This work contributes to the advancements of structural health monitoring methodologies in civil engineering, demonstrating the practicality and reliability of iFEM in bridge applications.

Bridging scales with Machine Learning: From first principles statistical mechanics to continuum phase field computations to study order–disorder transitions in Li[formula omitted]CoO[formula omitted]

LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for Li-ion batteries, whose performance is strongly governed by Li composition-dependent crystal structure and phase stability. Here, we use LixCoO2 (LCO) as a model system to benchmark a machine learning-enabled framework for bridging scales in materials physics. We focus on two scales: (a) assemblies of thousands of atoms described by density functional theory-informed statistical mechanics, and (b) continuum phase field models to study the dynamics of order–disorder transitions in LCO. Central to the scale bridging is the rigorous, quantitatively accurate, representation of the free energy density and chemical potentials of this material system by coarse-graining formation energies for specific atomic configurations. We develop active learning workflows to train recently developed integrable deep neural networks for such high-dimensional free energy density and chemical potential functions. The resulting, first principles-informed, machine learning-enabled, phase-field computations allow us to study LCO cathodes’ order–disorder transitions in terms of temperature, microstructure, and charge cycling. We highlight several insights gained to the dynamics of the phase transitions, and that have been made possible by the quantitatively rigorous scale bridging. To the best of our knowledge, such a scale bridging framework has not been previously demonstrated for LCO, or for materials systems of comparable technological interest. This approach can be expanded to other materials systems and can incorporate additional physics to that studied here.

Experimental study and new-proposed characterization of burning rate and flame geometry of gasoline pool fires with different aspect ratios

This study investigated the burning rate and the flame geometry of gasoline pool fires under crosswinds in the context of bridge vehicle fires. A 1:4 scale bridge fire platform was established. A total of 45 fire experiments were conducted on 9 gasoline pool with 1–2.2 aspect ratios under 0–2 m/s cross-wind. The influence mechanism of aspect ratio n on the characteristic parameters of pool fires was investigated. The results showed that the burning rate of gasoline pool ranges from 0.028 kg‧m−2‧s−1 to 0.064 kg‧m−2‧s−1, which increases with wind speed and pool size. When the wind speed exceeds 1 m/s, the title angle of the flame on the windward side is greater than 45° and is less affected by pool size. The wall heat conduction at wind downstream increases with aspect ratio when placing the longer side facing to cross flow. The correction factor Fr·n can be used to describe the competition between the air entrainment and the buoyancy in gasoline pool fire. A general model based on aspect ratio is proposed to quantify the burning rate and flame tilt characteristics of gasoline fires. The reliability and applicability of the model are compared with the existing experimental data.

Computer vision-based real-time monitoring for swivel construction of bridges: from laboratory study to a pilot application

PurposeThe swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.Design/methodology/approachThis study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.FindingsIn the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.Originality/valueIn a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Computed entropy spectra for grain boundary segregation in polycrystals

Grain boundary solute segregation influences most bulk material properties, and understanding solute thermodynamics at grain boundaries is critical for engineering them. However, the vast grain boundary space in polycrystals is challenging to evaluate due to its size, especially for the intrinsically hard-to-compute segregation excess entropy. Here data science methods are used to generate a database of site-wise grain boundary segregation entropy spectra for 155 dilute binary alloys within the harmonic approximation. The spectral framework allows scale bridging between the calculated atomistic site-wise energy-entropy spectra and macroscopic segregation entropy estimates. The results affirm that macroscopic averaging is not sufficient: a spectral treatment of grain boundary segregation is needed to accurately model bulk temperature dependence of grain boundary solute segregation. The calculated spectral entropy database and thermodynamic framework can be applied for both understanding segregation experiments and alloy design exercises, paving the way to a finite-temperature grain boundary genome.

A Numerical Bio-Geotechnical Model of Pressure-Responsive Microbially Induced Calcium Carbonate Precipitation

The employment of Microbially Induced Calcium Carbonate Precipitation (MICP) is of increasing interest as a technique for environmentally sustainable soil stabilisation. Recent advancements in synthetic biology have allowed for the conception of a pressure-responsive MICP process, wherein bacteria are engineered to sense environmental loads, thereby offering the potential to stabilise specific soil regions selectively. In this study, a 2D smart bio-geotechnical model is proposed based on a pressure-responsive MICP system. Experimentally obtained pressure-responsive genes and hypothetical genes with different pressure responses were applied in the model and two soil profiles were evaluated. The resulting model bridges scales from gene expression within bacteria cells to geotechnical simulations. The results show that both strata and gene expression–pressure relationships have a significant influence on the distribution pattern of calcium carbonate precipitation within the soil matrix. Among the evaluated experimental genes, Gene A demonstrates the best performance in both of the two soil profiles due to the effective stabilisation in the centre area beneath the load, while Genes B and C are more effective in reinforcing peripheral regions. Furthermore, when the hypothetical genes are utilised, there is an increasing stabilisation area with a decreased threshold value. The results show that the technique can be used for soil reinforcement in specific areas.

Quantifying fat zonation in liver lobules: an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach

Metabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.

A multiscale FEM-MD coupling method for investigation into atomistic-scale deformation mechanisms of nanocrystalline metals under continuum-scale deformation

This study aims to develop a multiscale bridging method for investigating nanocrystalline metals based on macro-scale deformation. For this purpose, we propose a hierarchical multiscale computational method that can focus on some of the elements in a finite element model for scale bridging to atomistic-scale models. This method assumes that atomistic-scale nanocrystalline models are related to the integration points in a finite element and deform based on the macro-scale deformation. Nanocrystalline aluminum was chosen for the validation of the multiscale method. The finite element method (FEM) and the molecular dynamics (MD) method were used for continuum-scale and atomistic-scale simulations, respectively. We utilized the notion of the CauchyBorn rule (CBR) for communicating deformation information from the continuum scale to the atomistic scale. We studied three different cases with two nanocrystalline models and two loading cases to compare differences resulting from crystal structures and loading. Based on the crystal structure change during relaxation, nonequilibrium grain boundaries (NEGBs) were shown to play a role as deformation mechanisms in the plastic regime and induce the onset and migration of crystal defects, including deformation twins, as reported in the experiment. Furthermore, the crystal orientation dependence of the onset of crystal defects was confirmed by the comparison of the results from the two different nanocrystalline models. The qualitative agreement of the results with experimental observations is also confirmed. The proposed ‘FEM-MD’ method can bridge a large-scale gap, for example, from a nano-scale to a continuum-scale such that an MD model can be coupled to a millimeter or centimeter scale compared to other embedding methods. The present method is ideal for investigating the dislocation behavior of nanocrystalline materials, which contain multi-grained nanostructure at finite temperature, undergoing various loading scenarios at the macro-scale.

Soft matter physics of the ground beneath our feet.

The soft part of the Earth's surface - the ground beneath our feet - constitutes the basis for life and natural resources, yet a general physical understanding of the ground is still lacking. In this critical time of climate change, cross-pollination of scientific approaches is urgently needed to better understand the behavior of our planet's surface. The major topics in current research in this area cross different disciplines, spanning geosciences, and various aspects of engineering, material sciences, physics, chemistry, and biology. Among these, soft matter physics has emerged as a fundamental nexus connecting and underpinning many research questions. This perspective article is a multi-voice effort to bring together different views and approaches, questions and insights, from researchers that work in this emerging area, the soft matter physics of the ground beneath our feet. In particular, we identify four major challenges concerned with the dynamics in and of the ground: (I) modeling from the grain scale, (II) near-criticality, (III) bridging scales, and (IV) life. For each challenge, we present a selection of topics by individual authors, providing specific context, recent advances, and open questions. Through this, we seek to provide an overview of the opportunities for the broad Soft Matter community to contribute to the fundamental understanding of the physics of the ground, strive towards a common language, and encourage new collaborations across the broad spectrum of scientists interested in the matter of the Earth's surface.

Mean-field theory approach to three-dimensional nematic phase transitions in microtubules.

Microtubules are dynamic intracellular fibers that have been observed experimentally to undergo spontaneous self-alignment. We formulate a three-dimensional (3D) mean-field theory model to analyze the nematic phase transition of microtubules growing and interacting within a 3D space, then make a comparison with computational simulations. We identify a control parameter G_{eff} and predict a unique critical value G_{eff}=1.56 for which a phase transition can occur. Furthermore, we show both analytically and using simulations that this predicted critical value does not depend on the presence of zippering. The mean-field theory developed here provides an analytical estimate of microtubule patterning characteristics without running time-consuming simulations and is a step towards bridging scales from microtubule behavior to multicellular simulations.

Solving the puzzle of hierarchical martensitic microstructures in NiTi by (111)-oriented epitaxial films

The martensitic microstructure decides on the functional properties of shape memory alloys. However, for the most commonly used alloy, NiTi, it is still unclear how its microstructure is built up because the analysis is hampered by grain boundaries of polycrystalline samples. Here, we eliminate grain boundaries by using epitaxially grown films in (111)B2 orientation. By combining scale-bridging microscopy with integral inverse pole figures, we solve the puzzle of the hierarchical martensitic microstructure. We identify two martensite clusters as building blocks and three kinds of twin boundaries. Nesting them at different length scales explains why habit plane variants with ⟨011⟩B19' twin boundaries and {942} habit planes are dominant; but also some incompatible interfaces occur. Though the observed hierarchical microstructure agrees with the phenomenological theory of martensite, the transformation path decides which microstructure forms. The combination of local and global measurements with theory allows solving the scale bridging 3D puzzle of the martensitic microstructure in NiTi exemplarily for epitaxial films.

Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash–Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling.

Investigation of mechanical properties of garnet structured Li7La3Zr2O12 under Al3+ and Ta5+ co-substitutions

The influence of co-substitutions on the structural and mechanical properties of garnet structured Li7La3Zr2O12 (LLZO) is investigated. Ab initio simulations of the cubic phase under Al and Ta substitutions are performed for an analysis of substitution dependencies on lattice constants, elastic moduli and hardness. The use of the differential effective medium theory methods enables a scale bridging description towards porous LLZO, with a 27% decay of Young's modulus for a porosity of 10%, compared to dense LLZO.

Assessment of steel bridges – cracking the code

This article has been developed from a presentation given at the IStructE conference on 'Structural rehabilitation' in December 2022. It gives a view, based on my career over 30 years, of the way in which our assessment codes can fall short in carrying out a structural assessment of steel bridges. I have taken three differing types of bridge form, scale and purpose, and explained the assessment problems encountered and the method by which a better solution was found using computer methods of analysis. The bridges chosen are the M5 Avonmouth Bridge, a steel box-girder structure; a single-span railway bridge over the River Wey, originally of wrought iron, but subsequently strengthened in steel; and, finally, a lattice-girder truss footbridge. Considering the ongoing pace of development in hardware and software power, and the relative falling cost of computer analysis, the suggestion is made that upcoming assessment codes should make better use of computer tools available.

Classification of liver lesions in CT images based on LivlesioNet, modified Multi-Scale CNN with bridge Scale method

Classification of liver lesions in CT images based on LivlesioNet, modified Multi-Scale CNN with bridge Scale method

Predictive capabilities of data-driven machine learning techniques on wave-bridge interactions

To explore coastal bridge safety subjected to extreme waves during coastal natural hazards, numerical simulations that combine finite element methods and experimental data have been recognized as effective in computing wave-induced loads on coastal bridges. However, the structural design and performance assessment for bridge networks require laborious efforts and massive computational resources to account for uncertain scenarios. To provide reliable wave force estimation tools and facilitate the associated risk assessment, this study performs a hydrodynamic experiment on the wave-bridge interactions and develops data-driven Long-Short-Term-Memory (LSTM) Machine Learning (ML) models for time series forecasting of wave forces. Specifically, a 1:30 scale bridge superstructure specimen is used for the wave test in the wave channel. Different solitary wave and regular wave conditions are tested. Time histories of wave profiles, wave-induced forces, and pressures are measured and served as a dataset basis for the training of LSTM models. High-performance LSTM prediction models are developed through the tuning of different hyperparameters. The well-trained models have high accuracy and could predict the wave force time series based on the excitation wave profiles in seconds. It is envisioned that LSTM models could provide more reliable estimations with the development based on more data sources, providing a fast path for structural design, analysis, and maintenance.