Predictive Multiscale Modeling Research Articles

Multiscale model for bottom-up prediction of failure parameters of unidirectional carbon-fiber-reinforced composite lamina from the atomic to filament-scales, and its application to failure modeling of open-hole quasi-isotropic composite laminates

A multiscale model is developed to comprehensively predict the failure parameters associated with the elasto-plasticity of a unidirectional carbon-fiber-reinforced composite lamina; the prediction is performed according to the resin-matrix design. The developed model involves quantum-chemical reaction-path calculations, molecular-dynamics simulations, and micromechanical analyses at the filament scale. The presented model is further combined with an advanced numerical approach developed based on an extended finite-element method, to analyze composites at the laminate scale. Using the established four-scale model, the open-hole tension and compression of a quasi-isotropic laminate are simulated, starting from the composition of an epoxy resin. The predicted elasto-plastic properties and strengths of a unidirectional lamina are in good agreement with the previously reported experimental results. Furthermore, the strengths predicted for the open-hole tests are also plausible, as they are similar to the experimental values reported in literature. The established multiscale model is expected to be useful in composite-material development as it facilitates rapid and exhaustive analysis.

Multiscale Prediction Model for Autogenous Shrinkage of early-age Concrete Incorporating High Volume Fly Ash

Multiscale Prediction Model for Autogenous Shrinkage of early-age Concrete Incorporating High Volume Fly Ash

Microscopic Origins of Flow Activation Energy in Biomolecular Condensates.

Material properties of biomolecular condensates dictate their form and function, influencing the diffusion of regulatory molecules and the dynamics of biochemical reactions. The increasing quality and quantity of microrheology experiments on biomolecular condensates necessitate a deeper understanding of the molecular grammar that encodes their material properties. Recent reports have identified a characteristic timescale related to network relaxation dynamics in condensates, which governs their temperature-dependent viscoelastic properties. This timescale is intimately connected to an activated process involving the dissociation of sticker regions, with the energetic barrier referred to as flow activation energy. The microscopic origin of activation energy is a complex function of sequence patterns, component stoichiometry, and external conditions. This study elucidates the microscopic origins of flow activation energy in single and multicomponent condensates composed of model peptide sequences with varying sticker and spacer motifs, with RNA as a secondary component. We dissected the effects of condensate density, RNA stoichiometry, and peptide sequence patterning using extensive sequence-resolved coarse-grained simulations. We found that flow activation energy is closely linked to the lifetime of sticker-sticker pairs under certain conditions, though the presence of multiple competing stickers further complicates this relationship. The insights gained in this study should help establish predictive multiscale models for the material properties and serve as a valuable guide for the programmable design of condensates.

Prediction of the effective diffusion coefficient on sulfate ions in heterogeneous concrete based on Mori-Tanaka scheme

The interfacial transition zone (ITZ) between the aggregates and the matrix in concrete is the main reason for the low prediction accuracy of the effective diffusion coefficient of sulfate ions in concrete. To solve this problem, a new multi-scale prediction model of the effective diffusion coefficient for the transport of sulfate in concrete was established based on the Mori-Tanaka scheme, which fully reflects the characteristics of the imperfect ITZ, the morphological characteristics of the aggregates, the aggregate grading, and the time-varying characteristics of the content of the ITZ, the porosity and the tortuosity of the pore structure. The results showed that the prediction results of the model were basically consistent with the experimental and literature results, and the maximum error was only 20.43 %. In addition, taking the commonly used strength grades C30 and C50 concrete in engineering as examples, the changes in effective diffusion coefficient under different concentrations of sulfate erosion were predicted, showing a trend of first decreasing, plateauing, then increasing, and objectively reflecting the objective law of sulfate transport-reaction-filling-expansion damage in concrete. The predicted results confirmed that at the concentration of 3 % Na2SO4 solution, the starting time of expansion for C50 concrete is approximately 3 years later than that for C30 concrete, verifying that enhancing the concrete's strength grade significantly improves its sulfate corrosion resistance.

Investigation on thermal conductivities of plain-woven carbon/phenolic and silica/phenolic composites at high temperature: Theoretical prediction and experiment

Phenolic resin-based ablation materials have found widespread applications in the aerospace industry. The prediction of their thermal conductivity is of paramount importance for the optimization and evaluation for thermal protection systems. However, there is rarely reported thermal conductivity performance of phenolic composites during ablation processes. Therefore, this investigation theoretically predicts the dynamic response of thermal conductivity at high temperatures for plain-woven carbon/phenolic and high silica/phenolic composites. By combining the progressive cubic ablation model with existing composite material property formulas, a multiscale prediction model for effective thermal conductivity is developed. The thermal performance of the resin matrix, reinforcing fibers, yarns, and overall fabric composites is calculated. Additionally, mesoscale representative volume elements are implemented to investigate the overall heat transfer characteristics of carbon/phenolic and high-silica/phenolic composites, including temperature and heat flux distributions. Moreover, both composites thermal conductivities are measured in the range from 298 K to 473 K. The proposed prediction model demonstrates good reliability, with average deviations of 3.76 % and 7.36 % compared to finite element analysis results and experimental data, respectively.

MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction.

Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.

State of Health Estimation and Remaining Useful Life Prediction of Lithium-Ion Batteries by Charging Feature Extraction and Ridge Regression

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles.

Multi-scale modeling of shock initiation of a pressed energetic material III: Effect of Arrhenius chemical kinetic rates on macro-scale shock sensitivity

Multi-scale predictive models for the shock sensitivity of energetic materials connect energy localization (“hotspots”) in the microstructure to macro-scale detonation phenomena. Calculations of hotspot ignition and growth rely on models for chemical reaction rates expressed in Arrhenius forms; these chemical kinetic models, therefore, are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for commonly used energetic materials (e.g., HMX in this paper) there are a wide variety of reaction rate models available. These available reaction rate models produce reaction time scales that vary by several orders of magnitude. From a multi-scale modeling standpoint, it is important to determine which model best represents the reactive response of the material. In this paper, we examine three global Arrhenius-form rate models that span the range of reaction time scales, namely, the Tarver 3-equation, the Henson 1-equation, and the Menikoff 1-equation models. They are employed in a meso-informed ignition and growth model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker–Wasley curve) with experimental data for pressed HMX Class V microstructures. The results provide a guideline for model developers on the plausible range of time-to-ignition that are produced by physically correct Arrhenius rate models for HMX.

Regressive Multi-Scale BiGRU Model for Work Function Prediction of MXene Materials With Oppositional Learning-Based Optimization

Regressive Multi-Scale BiGRU Model for Work Function Prediction of MXene Materials With Oppositional Learning-Based Optimization

Numerical and Experimental Investigation of the Thermomechanical Loads on the Machined Surface when Cutting Inconel DA718

In metal cutting processes, the thermomechanical loads lead to modifications of the residual stress state of the workpiece rim zone. Especially for aerospace components, such as turbine disks made of nickel-based alloys, this modification affects the functionality and dimensional accuracy of the machined part. Therefore, the thermomechanical loads occurring in the orthogonal cutting of Inconel DA718 are investigated in this paper and chip formation simulations are performed and evaluated with the experimental results. The outcome of this work forms the basis for a research project, where it is intended to develop a multiscale prediction model for residual stresses.

Multiscale model for failure prediction of carbon-fiber-reinforced composites under off-axis load

Herein, a multiscale model comprising a quantum-chemical reaction path calculations, molecular dynamics (MD) simulations, and filament and laminate scale finite-element analyses (FEA) is proposed for predicting the failure of carbon-fiber-reinforced composites. In this study, the correlation between MD simulations and filament scale FEA was investigated and complemented. By this complementation, mechanical properties of a resin matrix used in FEA are evaluated by experiment-free MD simulations. The evaluated material properties, namely, failure properties and elasto-plastic properties of the resin matrix were validated using the results of compressive tests for neat resins DGEBA/4,4’-DDS, DGEBA/DETA, and TGDDM/4,4’-DDS. The predicted failure strain of the composites, under off-axis loads, were found to be in good agreement with previously reported experimental results. The FEA results revealed that both the failure and elasto-plastic properties of the resin matrix contribute to the failure of the composites, and the dominant properties vary for the laminate and load configuration.

A multi-scale model for local polarization prediction in flow batteries based on deep neural network

The side reaction of the flow battery will consume electrons, reduce efficiency, and eventually cause safety problems. Regarding gas management and removal, carefully controlling local over-polarization is a vital issue. However, among the multi-scale models that can predict local polarization, the lack of dimensionality inside the electrode makes it impractical to predict three-dimensional variations. In this work, we propose a three-dimensional multi-scale model that allows the rapid prediction of local polarization, which cooperates with deep neural networks, the pore network model, and the three-dimensional continuum model to have both the advantages of accuracy and extensibility. Parameters such as the porosity, permeability, and specific surface area of the electrode are calculated from 500 randomly generated microstructures, and the training samples for the deep neural network are calculated by the cell-scale model and pore-scale model. Through the developed model, we explore the effects of the interdigitated flow field, variable flow rate optimization strategies, and diverse operating conditions on local polarization. The results show that the proposed model can accurately predict local polarization. The research directions of future work include the collaborative optimization of the electrode's microstructure, the flow field, and the flow rate to ultimately improve the local polarization uniformity.

Experimental and multi-scale investigation of the mechanical behavior of mechanically recycled glass fiber reinforced thermoplastic composites

Fiber reinforced thermoplastic polymer composites have gained a lot of attention over the past two decades, due to their excellent mechanical performance and their lightweight resulting in lower CO2 emissions for airplanes and vehicles. However, with increased demand for these materials, research regarding environmentally friendly recycling routes has become a central environmental issue. Mechanical recycling by exploiting the melting properties of thermoplastic polymers has proven an excellent way of increasing the value of recycled composites, especially compared to other mainstream techniques. This research aims at investigating the relationship between the microstructure of these materials and their resulting mechanical properties. The studied material is processed by compression molding chopped woven composites chips, made from a polyamide 6 matrix reinforced with glass fibers. A novel microstructural investigation for these types of materials was conducted using multiple destructive and non-destructive techniques, along with tensile and flexural tests on specimens made from different chips sizes. This investigation revealed a hierarchical fiber structure with a mixture of intact woven chips and randomly oriented unidirectional fiber strands. This microstructure causes complex damage propagation mechanisms and variability in mechanical performance. This hinders development and large-scale commercialization of these materials and favors other less eco-friendly recycling strategies. Therefore, developing accurate predictive models for the mechanical response of these materials is important, thus enabling fast design optimization. A multi-scale predictive model is hence proposed based on extensive qualitative and quantitative microstructural investigation and is able to capture the anisotropy of the material. This approach is validated on experimental data from a recycled PA6/Glass fiber composite and can be applied for other recycled materials in the same manner.

RadioTransNet, Radiotherapy Translational and Preclinical Research Network: Results from the dedicated French cancer institute (INCa) call for projects

PurposeThe RadioTransNet project is a French initiative structuring preclinical and translational research in radiation therapy for cancer at national level. The network's activities are organized around four chosen priorities, which are: target definition, normal tissue, combined treatments and dose modelling. The subtargets linked to these four major priorities are unlimited. They include all aspects associated with fundamental radiobiology, preclinical studies, imaging, medical physics research and transversal components clearly related to these scientific areas, such as medical oncology, radio-diagnostics, nuclear medicine and cost-effectiveness considerations. MethodDuring its first phase of activity, four workshops following the consensus conference model and based on scientific and medical state of the art in radiotherapy and radiobiology were organized on the four above-mentioned objectives to identify key points. Then a road map has been defined and served as the basis for the opening in 2022 of a dedicated call, SEQ-RTH22, proposed by the French cancer national institute (INCa). ResultsFour research projects submitted by RadioTransNet partners have been selected to be supported by INCa: the first by Professor Anne Laprie from Oncopole Claudius-Regaud and Inserm ToNic in Toulouse on neurocognition and health after pediatric irradiation, the second submitted by Fabien Milliat from IRSN aims to study decryption and targeting of endothelial cell–immune cells interactions to limit radiation-induced intestinal toxicity, the third project, submitted by Yolanda Prezado from institut Curie–CNRS on proton minibeam radiotherapy as a new approach to reduce toxicity, and the latest project proposed by R. de Crevoisier from centre Eugène-Marquis in Rennes on predictive multiscale models of head and neck radiotoxicity induced for optimized personalized radiation therapy. Topics of each of these projects are presented here. ConclusionRadioTransNet project has been launched in 2018, supported by INCa, in order to structure and promote preclinical research in oncology radiotherapy and to favor collaboration between the actors of this research. INCa relied on RadioTransNet initiatives and activities, resulting in the opening of dedicated call for projects. Beyond its first main goals, RadioTransNet network is able to help to fund the human and technical resources necessary to conduct optimal translational and preclinical research in radiation oncology.

Predicting the Effect of Hardener Composition on the Mechanical and Fracture Properties of Epoxy Resins Using Molecular Modeling

Improving the toughness of brittle epoxy while keeping its high strength-to-weight ratio is challenging, as these two properties work against each other. Fracture processes are difficult to ascertain with experiments, as they occur at nanoscopic lengths and time scales and require higher efficiency than what can be attained with atomistic simulations. To overcome this challenge, we utilize a recently developed chemistry specific coarse-grained model to examine two different hardeners, diamine 4,4′-methylenebis(cyclohexylamine) (PACM) and propylene oxide diamine (Jeffamine), to cure bisphenol A diglycidyl ether (DGEBA) at varying stoichiometries and understand how hardener composition influences the elasticity, yield strength, and fracture toughness of epoxy resins. The results indicate that PACM mainly contributes to the modulus and that long Jeffamine chains increase fracture toughness by making the epoxy ductile, whereas short Jeffamine chains significantly improve the yield strength. Longer Jeffamines also lead to larger voids and the formation of fibrils that carry a significant amount of stress and contribute to toughness. Interestingly, the Ashby plots reveal that epoxies with intermediate-length Jeffamine chains (D800 and D2000) outperform other systems, as the toughness enhancement from flexible Jeffamine chains and the stiffness due to PACM help to overcome the strength–toughness trade-off. Our modeling framework and findings establish a path toward resin design from predictive multiscale models with no empirical input and reveal new insights into the molecular failure mechanisms of epoxy resins.

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of protein information, including sequence, structure and interaction network. Among them, protein sequences have gained significant attention where methods are investigated to extract the information from different views of features. However, how to fully exploit the views for effective protein sequence analysis remains a challenge. In this regard, we propose a multi-view, multi-scale and multi-attention deep neural model (MMSMA) for protein function prediction. First, MMSMA extracts multi-view features from protein sequences, including one-hot encoding features, evolutionary information features, deep semantic features and overlapping property features based on physiochemistry. Second, a specific multi-scale multi-attention deep network model (MSMA) is built for each view to realize the deep feature learning and preliminary classification. In MSMA, both multi-scale local patterns and long-range dependence from protein sequences can be captured. Third, a multi-view adaptive decision mechanism is developed to make a comprehensive decision based on the classification results of all the views. To further improve the prediction performance, an extended version of MMSMA, MMSMAPlus, is proposed to integrate homology-based protein prediction under the framework of multi-view deep neural model. Experimental results show that the MMSMAPlus has promising performance and is significantly superior to the state-of-the-art methods. The source code can be found at https://github.com/wzy-2020/MMSMAPlus.

A 3D Multiscale Model for Prediction of the Microstructure Evolution in Ti6Al4V Components Produced by Laser Powder Bed Fusion

A transient 3D multiscale model has been created to simulate the evolution of solidification microstructures rapidly and effectively in Ti6Al4V parts produced through Laser Powder Bed Fusion (LPBF). The microstructure simulation tool has been enhanced to account for rapid solidification conditions in Ti6Al4V alloys during processing of multi-layer multi-track LPBF parts. The simulation tool can evaluate the impact of part geometry, lase power input, laser speed, and laser beam shape on the formation of the microstructure in LPBF-processed Ti6Al4V alloy. The multiscale model considers several factors, including preferential crystallographic growth direction, isomorphism, epitaxy, melt pool motion, and temperature gradients to generate the observed texture and morphology of the microstructure in Ti6Al4V components. The model can evaluate various microstructure characteristics, such as grain size and texture. Consequently, it could aid in controlling the formation of solidification microstructures in Additive Manufacturing (AM) processed parts. The simulation tool has been previously validated by using IN625 laser remelting experiments made by National Institute of Standards and Technology. The 3D simulation tool can also be utilized to predict the microstructure formation in Ti6Al4V components produced by the Electron Beam AM processes.

A multiscale finite element model for prediction of tensile strength of concrete

Concrete is a highly heterogeneous composite material on the microscopic length scale (10 −6 m) to the mesoscopic length scale (10 −1 m). The heterogeneous structure of concrete influences its macro mechanical properties. The multiscale approach is an effective method to analyze the mechanical properties of composite material and support the design of material. In this paper, a 3D multiscale model for prediction of tensile strength of concrete is presented. The microstructure of H ydrated C ement P aste (HCP) is generated by the HYMOSTURC and exported to the Abaqus by using Python program. The local background grid method is used to directly generate the meso-scale models of mortar and concrete. An uncoupled multiscale method is applied to transfer parameters from a smaller scale to a larger scale model. In the case of scale overlapping, parameter transfer is carried out through a simplified uncoupled averaging method. Finally, the multiscale model is verified by flexural test of mortar and splitting tensile test of concrete. • A multiscale model for concrete is constructed. • The mechanical properties of material in the larger scale model are obtained from the smaller scale model. • The multiscale model is verified by experiments. • The multiscale model can predict tensile strength of concrete based on its composition.

A sequence-to-sequence based multi-scale deep learning model for satellite cloud image prediction

A sequence-to-sequence based multi-scale deep learning model for satellite cloud image prediction

Statistical Mechanical Design Principles for Coarse-Grained Interactions across Different Conformational Free Energy Surfaces.

Systematic bottom-up coarse-graining (CG) of molecular systems provides a means to explore different coupled length and time scales while treating the molecular-scale physics at a reduced level. However, the configuration dependence of CG interactions often results in CG models with limited applicability for exploring the parametrized configurations. We propose a statistical mechanical theory to design CG interactions across different configurations and conditions. In order to span wide ranges of conformational space, distinct classical CG free energy surfaces for characteristic configurations are identified using molecular collective variables. The coupling interaction between different CG free energy surfaces can then be systematically determined by analogy to quantum mechanical approaches describing coupled states. The present theory can accurately capture the underlying many-body potentials of mean force in the CG variables for various order parameters applied to liquids, interfaces, and in principle proteins, uncovering the complex nature underlying the coupling interaction and imparting a new protocol for the design of predictive multiscale models.