Multiscale Model Research Articles

Multi-scale hydrogen embrittlement prediction model of low alloy steel based on multi-dimensional defect reconstruction

Multi-scale hydrogen embrittlement prediction model of low alloy steel based on multi-dimensional defect reconstruction

Interval price predictions for coal using a new multi-scale ensemble model

Interval price predictions for coal using a new multi-scale ensemble model

Global dynamics of a multiscale malaria model with two strains

Global dynamics of a multiscale malaria model with two strains

Geometric flow control in lateral flow assays: Macroscopic two-phase modeling

Lateral flow assays (LFAs) are widely employed in a diverse range of applications, including clinical diagnostics, pharmaceutical research, forensics, biotechnology, agriculture, food safety, and environmental analysis. A pivotal component of LFAs is the porous polymeric membrane, which facilitates the capillary-driven movement of fluids, known as “imbibition,” in which a wetting fluid displaces a non-wetting fluid within the pore space of the membrane. This study presents a multi-scale modeling framework designed to investigate the imbibition process within LFAs. The framework integrates microscopic membrane characteristics into a macroscopic two-phase flow model, allowing the simulation of imbibition in membranes with different micro-scale properties and macro-scale profiles. The validity of the model was established through comparative analysis with documented case studies, a macro-scale single-phase flow model, and experimental observations, demonstrating its accuracy in simulating the imbibition process. The study also examines imbibition in various geometric configurations, including bifurcated (Y-shaped) and multi-branch geometries commonly found in multiplexed LFAs. The influence of geometric features such as length ratio, width ratio, branching angle, bifurcation point location, and asymmetry on fluid transport is investigated. Results indicate that membranes with larger branching angles exhibit slower imbibition. In addition, the influence of membrane type on macroscopic flow patterns is evaluated, showing that membranes with lower permeability require longer imbibition times. The insights gained from this research support a data-driven strategy for manipulating wetting behavior within LFAs. This approach can be leveraged to optimize the performance of LFAs and increase their effectiveness in various applications.

The principle of compromise-in-competition: understanding mesoscale complexity of different levels

This perspective is not meant to be a complete summary of the full series of our studies over decades, rather it aims to look back across the entire process to outline: What inspired this work at its beginning? How did the mindset gradually evolve from a solely engineering study to the new concept of Mesoscience? And where is the research currently being focused and to be extended? This approach elucidates the central importance of correlating precise engineering problems to be resolved with associated missing links in the underlying, fundamental science while exploring a notable commonality, namely, the universality of Mesoscience. Inspired by the physical phenomenon of the coexistence and interaction between a gas-rich dilute phase and a solid-rich dense phase with different physical mechanisms dominating the behaviour for each phase in gas–solid fluidization, it was recognized that an underlying compromise-in-competition (CIC) exists between these two physically dominant mechanisms. This, then, is the CIC principle. We believe that this is the origin of inherent structural complexity in these and other systems. We have shown that this CIC principle can be formulated as a multi-objective optimization problem through the so-called energy-minimization multi-scale model, leading to a massive improvement in both the accuracy and scalability of computation and its wide application in both academia and industries. Furthermore, this approach was extended to formulate and understand other complex systems in many apparently disparate fields, indicating the wide applicability of the CIC principle and then resulting in the proposition and advancement of the concept of Mesoscience. Furthermore, within the main body of this perspective article, future directions for identifying, developing and applying the concept of Mesoscience will be prospected. This includes extending its possible generality and applications in many different disciplines and fields, even to analysing global challenging issues and promoting CIC-informed AI. In this way, many challenging problems in engineering—identified through their underlying mesoscale complexity—will be closely correlated with the development of future fundamental science. Through this approach, we hope to illustrate that fundamental research can now tackle the intrinsic complexity existing in a multitude of engineering problems. Through the concept of Mesoscience, we aim to explore the possible commonality from complexity. The deduction of commonality from diversity is possible in resolving global challenges, shifting paradigms in science and filling in the existing gaps at the mesoscales of different levels of our knowledge, though there are difficulties and uncertainty coming from diversity.

Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform

Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations.

A hybrid multiscale model for fluid flow in fractured rocks using homogenization method with discrete fracture networks

Fluid flow in subsurface tight reservoirs containing pores, microcracks and macrocracks is notably influenced by the characteristics of macro/micro-cracks. A novel hybrid multiscale model is proposed to address the response of macrocracks and pores/microcracks in different spatial scales. Specifically, an equivalent macroscopic model (EMM) deduced from locally periodic representative element volume (REV) is developed using the asymptotic homogenization method to represent the poroelastic behavior of porous medium with microcracks. Simultaneously, the macrocracks are modeled explicitly using the discrete fracture model (DFM), where the hydraulic properties of cracks influenced by fluid pressure gradient is represented by the nonlinear opening/closure behavior. The obtained hybrid model takes into account the heterogeneous nature of fractured rock masses containing pores, micro/macro-cracks, which is fundamental to describe fluid flow behavior in fracture-matrix system. Specialized finite elements, regular meshing technique and adaptive time stepping algorithm are adopted to improve the computational efficiency. The hybrid multiscale model is firstly validated step by step to demonstrate the accuracy and then used to simulate fluid flow in fractured rock reservoir, shedding light on the underlying mechanisms of the enhanced flow capacity resulting from microcrack distribution, connectivity, and macrocrack stimulation.

Dopant effects on the environment-dependent chemical properties of NiO(100) surfaces

NiO is a promising material for applications such as electronic devices and catalysts due to its low cost, environmental friendliness, and optimal chemical and electronic properties. Inclusion of a dopant element into the NiO near surface structure can further tune material properties. Despite extensive studies on doped NiO (M−NiO) materials, there remains a lack of knowledge regarding the connection between dopant, environment-dependent dominant near surface structures, and overall chemical properties. Here, the dopant effects on the near surface structure and electronic properties on M−NiO(100) are systematically examined using a combination of density functional theory (DFT) and ab initio phase diagrams. The simultaneous factors tested include dopant element (Al, Mo, Nb, Sn, Ti, V, W, or Zr), dopant placement (surface or subsurface), Ni/O vacancies (surface or subsurface), and oxygen species (O* or O2*) adsorption. The results reveal the dominant structures and compositions of M−NiO(100) under a wide range of environmental conditions (i.e. varying temperature and pressure). Subsequent electronic analyses of the dominant M−NiO(100) structures shows non-uniform changes in charge redistribution, band gap, work function, and d-band center with dopant and near surface structure, emphasizing the role of dopants in tuning M−NiO(100) both geometric and electronic properties. Overall, these multiscale modeling results enable a rapid, effective, and a priori prediction of dominant M−NiO(100) structures with distinct chemical properties, crucial for guiding NiO-based materials design with enhanced performance for advanced electronics, energy storage, and catalytic systems.

Floodwater Extraction from UAV Orthoimagery Based on a Transformer Model

In recent years, remote sensing has experienced a significant transformation due to rapid advancements in deep learning technology, which have greatly outpaced traditional methodologies. This integration has attracted substantial interest within the academic community. To address the complex challenges of extracting data on intricate water bodies during disaster scenarios, this study developed a post-disaster floodwater body dataset and an enhanced multi-scale transformer model architecture. Through end-to-end training, the precision of the model in extracting floodwater contours has been significantly improved. Additionally, by utilizing the vast amounts of unannotated data in remote sensing through an unsupervised pre-training task, the model’s backbone network has been fortified, greatly enhancing its performance in remote sensing applications. Experimental analyses have shown that the multi-scale transformer-based algorithm for floodwater contour extraction proposed in this study is not only widely applicable but also excels in delivering precise segmentation results in complex environments. This refined approach ensures that the model adeptly handles the intricacies of floodwater body delineation, providing a robust solution for accurate extraction, even in disaster-stricken areas. This innovation represents a substantial leap forward in remote sensing, offering valuable insights and tools for disaster management and environmental monitoring.

A Review of the Efficient and Thermal Utilization of Biomass Waste

As a new type of energy that can meet the requirements of carbon neutrality, biomass has received wide attention in recent years, and its rational and efficient thermal utilization can help reduce greenhouse gas emissions and establish an energy-saving, low-carbon energy system to promote sustainable development. In this paper, the current utilization and research status of plant-based biomass waste is comprehensively summarized from four aspects, namely component properties, industrial thermal utilization means, experiments and theoretical calculations. In addition, this paper summarizes the research progress in several aspects, such as microscopic experimental studies, macroscopic pyrolysis characterization, and multiscale theoretical model construction of biomass waste. However, due to the diversity and heterogeneity of biomass, there are still some challenges to extending the laboratory research results to large-scale industrial production, for which we also provide an outlook on future technological innovations and development directions in this research area.

Junction conditions for one-dimensional network hemodynamic model for total cavopulmonary connection using physically informed deep learning technique

Abstract This paper presents a novel methodology utilizing physics-informed neural network (PINN) as a junction condition for a 1D network model of blood flow in total cavopulmonary connection generated by the Fontan procedure. The technique integrates a 3D mesh generation process based on the parameterization of the junction geometry, along with a sophisticated physically regularized neural network architecture. Synthetic datasets are produced using 3D steady Stokes simulations within fixed boundaries. We use a physically informed feedforward neural network that utilizes a physically regularized loss function, which incorporates the principle of mass conservation. Our PINN achieves a tolerance of 6% on the test set. We develop a 1D-PINN multiscale model based on a previously developed method for multiscale 1D–3D simulations. Comparison with 1D–3D Stokes based model and 3D Navier–Stokes based model verifies the 1D-PINN model. In the first and second comparison, the maximum deviations of the averaged pressures and flows do not exceed 1.48% and 12.26%, respectively.

Modeling the Swelling Behavior of Clayey Geomaterials Across Scales: Advances and Challenges

ABSTRACTMost soils and rocks contain varying fractions of clay minerals within their solid matrix. These geomaterials can exhibit a significant swelling potential toward chemo‐thermo‐hydromechanical loadings. Several multiscale modeling techniques have been developed to ascertain their swelling behavior across various scales, with molecular dynamics (MD), micromechanics‐based approaches, and double‐porosity models being the most common. MD simulation is a computational technique that applies Newton's second law of motion to depict the movement of particles within a granular system. Micromechanics‐based approaches upscale the poro‐elasticity law from the clay layer level to the sample scale through homogenization. Dual‐porosity models are generally based on elasto‐plasticity, incorporating different hydro‐mechanical laws at two distinct scales. These models have been extensively used, particularly for clayey soils and bentonites, though their application to clayey rocks has not been reported in the literature. Although their significant contribution to the understanding of clay swelling behavior, these techniques have been insufficiently reviewed, compared, and discussed mutually in the literature. This paper aims to provide a cross‐look on these multiscale approaches by presenting the theoretical background of existing formulations, highlighting breakthrough results, discussing major differences and current challenges, and proposing future perspectives.

Summer School in Computational Physiology: A Collaborative Course in Modeling Excitable Tissues

ABSTRACT Computational modeling of physiology is a multiscale, multidisciplinary field requiring a diverse set of skills and backgrounds. The Simula Summer School in Computational Physiology aims to teach graduate students from around the world practical methods for multiscale modeling of excitable tissues (specifically, the heart and brain). This joint summer school is collaboratively based on the complementary expertise and shared educational goals of three institutions: Simula Research Laboratory, the University of Oslo, and the University of California San Diego. The summer school’s core goal is to promote successful research collaboration among the host institutions and to train excellent computational researchers. Keys to the success of this type of school include sustainable funding, close mentorship, and innovative, adaptive teaching tools. Using a combination of lectures, hands-on programming modules, and real-world projects in small groups with experienced scientific mentors, this unique program is an immersive way for young scientists to develop skills and network with future peers from around the world.

Activation thresholds for electrical phrenic nerve stimulation at the neck: evaluation of stimulation pulse parameters in a simulation study

Abstract Objective. Phrenic nerve stimulation reduces ventilator-induced-diaphragmatic-dysfunction, which is a potential complication of mechanical ventilation. Electromagnetic simulations provide valuable information about the effects of the stimulation and are used to determine appropriate stimulation parameters and evaluate possible co-activation.
Approach. Using a multiscale approach, we built a novel detailed anatomical model of the neck and the phrenic nerve. The model consisted of a macroscale volume conduction model of the neck with 13 tissues, a mesoscale volume conduction model of the phrenic nerve with three tissues, and a microscale biophysiological model of axons with diameters ranging from 5 to 14 μm based on the McIntyre-Richardson-Grill-model for myelinated axons. This multiscale model was used to quantify activation thresholds of phrenic nerve fibers using different stimulation pulse parameters (pulse width, interphase delay, asymmetry of biphasic pulses, pulse polarity, and rise time) during non-invasive electrical stimulation. Electric field strength was used to evaluate co-activation of the other nerves in the neck.
Main results. For monophasic pulses with a pulse width of 150 μs, the activation threshold depended on the fiber diameter and ranged from 20 to 156 mA, with highest activation threshold for the smallest fiber diameter. The relationship was approximated using a power fit function x^−3. Biphasic (symmetric) pulses increased the activation threshold by 25 to 30 %. The use of asymmetric biphasic pulses or an interphase delay lowered the threshold close to the monophasic threshold. Possible co-activated nerves were the more superficial nerves and included the transverse cervical nerve, the supraclavicular nerve, the great auricular nerve, the cervical plexus, the brachial plexus, and the long thoracic nerve.
Significance. Our multiscale model and electromagnetic simulations provided insight into phrenic nerve activation and possible co-activation by non-invasive electrical stimulation and provided guidance on the use of stimulation pulse types with minimal activation threshold.

Spatially varying associations between community-level predictors and COVID-19 outcomes in NYC

Abstract Background The cumulative impacts of COVID-19 on hospitalization and mortality were not uniformly distributed across New York City (NYC). To better understand potential drivers of this observed geospatial disparity, we explored the associations between community-level sociodemographic characteristics and cumulative COVID-19 hospitalizations and mortality using geographically weighted Poisson regression (GWPR). Methods Cumulative COVID-19 hospitalization and mortality rates in 177 NYC modified ZIP code tabulation areas as of Dec 2022 were taken from the NYC Department of Health and Mental Hygiene, and sociodemographic predictors were taken from the 2018 American Community Survey. GWPR was applied using both non-multiscale (single bandwidth) and multiscale (flexible bandwidth) models to assess which predictors were significant and which associations varied spatially (allowing them to potentially act as both risk and protective factors, depending on the location). Results Multiscale GWPR models outperformed non-multiscale models in rendering residual spatial autocorrelation insignificant. Although multiscale GWPR allowed flexible bandwidths for each predictor, most yielded global bandwidths, suggesting geographically consistent effects. For mortality, the percent of residents without health insurance acted solely as a risk factor. Similarly, for hospitalizations, the percent of residents with a disability acted solely as a risk factor. The percent of residents with a bachelors degree or higher acted solely as a protective factor against both outcomes. Conclusions Even when associations were allowed to vary spatially in GWPR models, we still found geographically consistent associations between many sociodemographic predictors and cumulative COVID-19 outcomes in NYC. Consistent risk factors, such as prevalence of disability, or protective factors, such as prevalence of higher education, highlight potential areas for city-wide policies to reduce the burden of future epidemics. Key messages • For cumulative COVID-19 outcomes across NYC communities, geographically consistent risk factors include disability prevalence for hospitalization and lack of health insurance for mortality. • Increasing prevalence of higher education acted as a geographically consistent protective factor against both hospitalization and mortality due to COVID-19.

SMARTINI3 parametrization of multi-scale membrane models via unsupervised learning methods

In this study, we utilize genetic algorithms to develop a realistic implicit solvent ultra-coarse-grained (ultra-CG) membrane model comprising only three interaction sites. The key philosophy of the ultra-CG membrane model SMARTINI3 is its compatibility with realistic membrane proteins, for example, modeled within the Martini coarse-grained (CG) model, as well as with the widely used GROMACS software for molecular simulations. Our objective is to parameterize this ultra-CG model to accurately reproduce the experimentally observed structural and thermodynamic properties of Phosphatidylcholine (PC) membranes in real units, including properties such as area per lipid, area compressibility, bending modulus, line tension, phase transition temperature, density profile, and radial distribution function. In our example, we specifically focus on the properties of a POPC membrane, although the developed membrane model could be perceived as a generic model of lipid membranes. To optimize the performance of the model (the fitness), we conduct a series of evolutionary runs with diverse random initial population sizes (ranging from 96 to 384). We demonstrate that the ultra-CG membrane model we developed exhibits authentic lipid membrane behaviors, including self-assembly into bilayers, vesicle formation, membrane fusion, and gel phase formation. Moreover, we demonstrate compatibility with the Martini coarse-grained model by successfully reproducing the behavior of a transmembrane domain embedded within a lipid bilayer. This facilitates the simulation of realistic membrane proteins within an ultra-CG bilayer membrane, enhancing the accuracy and applicability of our model in biophysical studies.

Air quality modeling intercomparison and multiscale ensemble chain for Latin America

Abstract. A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.

Microdosing of alteplase leads to thrombolysis in an in vitro flow model of cardiac microvascular obstruction

Abstract Background Cardiac microvascular obstruction (MVO) is a frequent injury of the myocardial microcirculation after successful recanalisation of the occluded coronary artery in myocardial infarction. At least in part, MVO is caused by embolised microthrombi of less than 200 µm diameter [1]. MVO leads to hypoperfusion of the myocardium and negatively affects patient outcomes. However, there is no effective treatment for MVO [2, 3]. Thrombolytic drugs are a promising approach, but they are difficult to deliver to the MVO area, and an increased risk of bleeding limits their use. In a previous study using an in vitro fluidic multi-scale model for MVO [4, 5], it was shown that controlled flow infusion (CFI) with a novel drug-delivering catheter could be used to effectively deliver highly concentrated intracoronary microdoses of a thrombolytic drug to the MVO area [6]. We now aim to investigate how such locally administered drug microdoses can induce thrombolysis as a possible therapeutic approach for MVO treatment. Methods A microfluidic chip (Fig.1) was used to study thrombolysis under flow using different microdoses of the thrombolytic drug alteplase. The chip consists of a tapered straight channel, which traps injected porcine microthrombi without causing complete channel occlusion (Fig. 1A). To mimic intracoronary CFI (consisting of temporary balloon occlusion of a coronary vessel for 90 seconds while drug is injected distally through a catheter) in vitro, microthrombi were incubated (without flow) directly in the chip for 90 seconds with various microdoses of alteplase (Fig. 2A, C). The chip was subsequently perfused with porcine plasma for 25 min (matched for physiological shear stress of 30 dyn/cm²). Conventional intravenous (IV) drug infusion (10 mg dose) resulting in a low systemic alteplase concentration, was mimicked by perfusing microthrombi with porcine plasma containing 2 µg/ml alteplase (Fig. 2B, C). Results Thrombolysis after CFI was concentration dependent, with the lowest microdose (0.3 mg) achieving 24% lysis of the thrombus area and the highest microdose (1.5 mg) achieving 60% lysis. In contrast, conventional IV drug infusion (corresponding to 10 mg alteplase IV dose) only achieved 10% lysis and was not significantly higher than the control perfused with pure porcine plasma. Conclusion Our findings suggest that intracoronary CFI is more efficient at achieving porcine microthrombus lysis than the current standard of IV bolus infusion (2 µg/ml alteplase in the circulating blood). Intracoronary CFI microdosing of fibrinolytic drugs using a drug-delivering catheter is a promising therapeutic approach for treating embolising MVO with a reduced systemic drug burden. Importantly, our intracoronary CFI approach achieves locally 6–40 times higher drug concentrations than a conventional IV drug infusion while reducing the total drug dose 60–300 times (Fig. 2C).

A Multi-Scale Evaluation Model of Sustainable Development Goal 11.7 for Problem-Solving at Different Levels

Sustainable Development Goal 11.7 (SDG 11.7) aims to promote the improvement of urban public spaces. However, the localization process of SDG 11.7 mainly relies on a bottom-up problem-solving approach, which fails to fully encompass the connotation of SDG 11.7. Additionally, existing evaluations primarily focus on a single scale, neglecting the impact of scale issues. These limitations can lead to imbalanced development or misallocation of responsibilities when guiding governments at different levels in promoting the sustainable development of public spaces. Therefore, this article introduces a multi-scale assessment model of SDG 11.7. It employs a top-down problem-solving approach to construct a sustainable development indicator framework, setting appropriate sustainable development indicators for various levels of government based on the connotation of SDG 11.7, and generates city-scale results by integrating three scales: apartment complexes, street blocks, and counties. Testing this model in Xi’an, China, revealed that it adequately captures four key aspects of SDG 11.7—safety, inclusiveness, accessibility, and greenness—through 11 indicators. The evaluation outcomes at the apartment complex, street block, and county levels effectively guide future development directions for various levels of government. Ultimately, the synthesis of these scales reveals the spatial pattern of SDG 11.7 at the city scale and identifies focal areas for development. Overall, this exploratory model demonstrates high accuracy and robustness, providing a comprehensive understanding of the essence of SDG 11.7. It also alleviates challenges posed by scale issues, offering decision support for monitoring SDG 11.7 across different levels of government in Chinese cities and promoting the process of sustainable development.

Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg-Based Implants.

Computational models of electrochemical biodegradation of magnesium (Mg)-based implants are uncertain. To quantify the model uncertainty, iterative evaluations are needed. This presents a challenge, especially for complex, multiscale models as is the case here. Approximating high-cost and complex models with easy-to-evaluate surrogate models can reduce the computational burden. However, the application of this approach to complex degradation models remains limited and understudied. This work provides a workflow to quantify different types of uncertainty within biodegradation models. Three surrogate models-Kriging, polynomial chaos expansion, and polynomial chaos Kriging-are compared based on the minimum number of samples required for surrogate model construction, surrogate model accuracy, and computational time. The surrogate models are tested for three computational models representing Mg-based implant biodegradation. Global sensitivity analysis and uncertainty propagation are used to analyze the uncertainties associated with the different models. The findings indicate that Kriging proves effective for calibrating diverse computational models with minimal computational time and cost, while polynomial chaos expansion and polynomial chaos Kriging exhibit greater capability in predicting propagated uncertainties within the computationalmodels.