Physics-based Multiscale Modeling Research Articles

Scalable Multi-Material Additive Manufacturing of Bioinspired Polymeric Material With Metallic Structures Via Electrically Assisted Stereolithography

Abstract Heterogeneous material systems consisting of metallic structures and polymer matrixes are of significance for applications such as integrated circuits, microelectromechanical devices, antennas, sensors, actuators, and metamaterials. Scaly-foot snail which lives in the deep ocean exhibits high strength and temperature resistance due to unique shells made of metal and polymer. Recently, different multi-material structures have been fabricated with metal deposition using multiple manufacturing processes. However, using these complicated hybrid processes is challenging to construct complexthree-dimensional (3D) structures of heterogeneous material with enhanced properties, high resolution, and time efficiency. Here, we establish a novel manufacturing strategy to build bioinspired hierarchical structures with heterogeneous material systems using electrically assisted stereolithography. The photocurable printing solution that can act as an electrolyte for charge transfer was developed, and the curing characteristic of the printing solution was further investigated. A fundamental understanding of the formation mechanism of metallic structures on the polymer matrix was studied through physics-based multi-scale modeling and simulations. The correlation between metallic structures morphology, printing solution properties, and printing process parameters, and their effects in building bioinspired hierarchical structures with heterogeneous materials were identified. Demonstrative test cases were built to verify the printing performance of the proposed approach. This research work will deliver a scalable additive manufacturing (AM) process that can facilitate various interesting applications based on bioinspired heterogeneous material and structures.

(Invited) Artistic: An AI and Modeling-Supported Digital Infrastructure for Battery Manufacturing Optimization

Energy, power, lifetime and safety of Lithium ion batteries (LIBs) are determined in part by the internal microstructure of the electrodes. That microstructure is characterized by the spatial location and the resulting interfaces between the active material particles, the conductive additive, the binder and the pores. The microstructure is determined by the manufacturing process of the electrodes, which encompasses, in its traditional wet form, multiple steps and numerous parameters. Therefore, its optimization is difficult and can be time consuming and costly if carried out only on the basis of a trial and error approach.In this lecture I discuss a digital infrastructure for accelerated optimization of the manufacturing process of LIBs that we are developing since few years: ARTISTIC.1 Such digital infrastructure is supported on a hybrid approach encompassing a physics-based multiscale modeling workflow, Artificial Intelligence (AI)/machine learning models and high throughput experimental characterizations.2 ARTISTIC simulates different steps along the battery cells manufacturing process, such as the electrode slurry preparation, the coating, the drying, the calendering and the electrolyte infiltration. For that purpose, it uses a combination of sequentially-coupled 3D-resolved physical models based on experimentally-validated Coarse Grained Molecular Dynamics, Discrete Element Method and Lattice Boltzmann Method. This allows predicting the impact of the manufacturing process parameters on the final electrode microstructure in three dimensions. The predicted electrode microstructures are then injected in a continuum performance simulator capturing the influence of the electrode microstructure on the solid electrolyte interphase formation (for anodes) and the electrochemical response (of anodes vs. lithium, cathodes vs. lithium and the full cells). Machine learning models are used to unravel manufacturing parameters interdependencies from the physical models’ predictions and experimental data, and to suggest manufacturing process optimizations. The predictive capabilities and chemistry-neutrality of the ARTISTIC digital infrastructure are illustrated with results for different LIB electrode formulations and chemistries (NMC, LFP, graphite, silicon/graphite blends) and also for organic active materials for sodium ion batteries. Finally, I discuss the free online battery manufacturing simulation services (online caculator and associated databases) offered by our project and that you can use interactively from your Internet Browser.3

Real-time multiscale prediction of structural performance in material extrusion additive manufacturing

The material extrusion additive manufacturing (AM) has been extensively used in fabricating structures with complex geometries. However, geometric defects often exist in an AM structure, which could compromise its final performance. In this paper, a real-time multiscale performance evaluation method is developed for material extrusion-based honeycomb structures. The representative cell boundary is extracted from three-dimensional (3D) point clouds obtained via an in-situ monitoring approach. The cell boundary is then used to generate the digital twin of the unit cell of the printed layer based on the finite element (FE) method. A physics-based multiscale modeling approach called mechanics of structure genome (MSG) is then employed to predict the effective material properties of the printed layer and plate stiffness matrix of the final structure. The proposed approach provides a highly efficient way to predict the real-time performance of the as-manufactured products. Moreover, the numerical example shows that the geometric defects could result in complex mechanical behaviors in the defected parts, which cannot be captured by the conventional approaches based on the shape deviations. The numerical results are validated by the three-point bending tests. The proposed method can be used in the closed-loop control of material extrusion-based manufacturing systems.

Nanoionic memristive phenomena in metal oxides: the valence change mechanism

This review addresses resistive switching devices operating according to the bipolar valence change mechanism (VCM), which has become a major trend in electronic materials and devices over the last decade due to its high potential for non-volatile memories and future neuromorphic computing. We will provide detailed insights into the status of understanding of these devices as a fundament for their use in the different fields of application. The review covers the microscopic physics of memristive states and the switching kinetics of VCM devices. It is shown that the switching of all variants of VCM cells relies on the movement of mobile donor ions, which are typically oxygen vacancies or cation interstitials. VCM cells consist of three parts: an electronically active electrode (AE), often a metal with a high work function, in front of which the switching occurs, a mixed ionic-electronic conducting (MIEC) layer consisting of a nanometer-scale metal oxide or a stack of different metal oxides, and an ohmic counter electrode (OE). After an introduction to definitions and classification, the fundamentals of solid-state physics and chemistry associated with VCM cells are described, including redox processes and the role of electrodes. The microscopic changes induced by electroforming, a process often required prior to resistive switching, are described in terms of electronic initialization and subsequent changes in chemistry, structure, and conductivity. The switching process is discussed in terms of switching polarity, geometry of the switching region, and spectroscopic detection of the valence changes. Emphasis is placed on the extreme nonlinearity of switching kinetics described by physics-based multiscale modeling, ranging from ab initio methods to kinetic Monte Carlo and finite element models to compact models that can be used in circuit simulators. The review concludes with a treatment of the highly relevant reliability issues and a description of the failure mechanisms, including mutual trade-offs.

A new urban earthquake disaster simulation method: City-scale nonlinear time-history analysis

Earthquakes can cause severe damage and huge economic loss to modern cities. Accurate and efficient seismic damage simulations of buildings have therefore become an indispensable part of earthquake hazard mitigation effort worldwide. Rational simulation results can help to illustrate potential seismic damage and can further provide an essential reference for city planning and post-earthquake rescue work. Motivated by the above, a new urban earthquake disaster simulation method, i.e., city-scale nonlinear time-history analysis (THA), is performed in this research work. The key scientific outcomes achieved are addressed below: (1) Physics-based multi-scale modeling method for urban buildings. Urban buildings can be classified into three types: Ordinary multi-story buildings, ordinary tall buildings, and special buildings. Corresponding analysis methods are proposed: Multi-story buildings can be simulated using nonlinear multiple-degree-of-freedom (MDOF) shear model; tall buildings can be simulated using nonlinear MDOF flexural-shear model; special buildings, such as large-span spatial structures and super tall buildings, can be established using the refined finite element models based on the fiber beam-column and multi-layered shell elements. (2) CPU/GPU collaborative parallel computing method. Seismic damage simulations often result in excessive computational workload. Therefore, a CPU/GPU collaborative parallel computing method is proposed: CPU will assign tasks to GPU; GPU will perform the nonlinear THA of each building; CPU will then copy result data from GPU for post-processing. Case study results show that, the proposed method can achieve a speed up of 39, compared with traditional CPU computing method with similar hardware cost. (3) Numerical coupling scheme for site-city interaction simulations. Interactions among densely distributed buildings in a city and the site, i.e., the site-city interaction (SCI) effects, can significantly influence the seismic behavior of buildings. Therefore, a numerical coupling scheme is proposed: Nonlinear MDOF models are used to represent different buildings above the ground, while an open-source spectral element program, SPEED, is used to simulate wave propagation in soil layers. The proposed numerical scheme is validated and proved useful for investigating the SCI effects. (4) High-fidelity visualization method using 3D urban polygon models and oblique aerial photography. Seismic simulation of urban buildings often produces a large number of computational results, which would be difficult to comprehend without appropriate visualizations. Thus, a high-fidelity visualization method using 3D urban polygon model is proposed, which covers three major components: Building identification, floor plan generation and displacement interpolation. In addition, to achieve a more realistic seismic damage scenario, a photo-realistic visualization method based on oblique aerial photography is proposed, including lightweight modeling, building segmentation, and dynamic visualization. Using these methods, high-fidelity visualization of seismic simulation results can be achieved. (5) Seismic loss prediction and secondary disaster simulation methods for urban buildings. Based on FEMA P-58 (next generation seismic performance assessment method of buildings) and the city-scale nonlinear THA, a practical approach for refined regional seismic loss prediction is proposed. In addition, the secondary disaster simulation for falling debris and fire following earthquake are proposed. Such a simulation will provide a useful reference for seismic loss assessment, site selection of emergency shelters, evacuation plan design, urban fire safety planning, and post-disaster emergency planning. The proposed disaster simulation method has been adopted by the SimCenter supported by the National Science Foundation of the United States to perform a seismic damage simulation and loss prediction for 1.8 million buildings in the San Francisco Bay Area. The proposed method has also been successfully applied to the seismic damage prediction of other cities, such as Beijing and Tangshan. Combined with the real-time recorded ground motions, the proposed method has been successfully used for the prompt assessment of seismic damage in 32 domestic and 42 international earthquakes. The assessment results (e.g., Jiuzhaigou Earthquake) agreed very well with the actual building damage. In summary, the city-scale nonlinear THA takes accurate consideration of the characteristics of ground motions and those of buildings, and the analysis outcomes will provide an important reference for city planning, post-earthquake rescue work, seismic damage assessment, and earthquake scenario simulation.

Multiscale modeling of compartmentalized reservoirs using a hybrid clustering-based non-local approach

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach.A novel and rigorous non-local formulation for flow in porous media is presented, in which the reservoir is represented by an adjacency matrix describing the connectivities of comprising compartments. We automatically divide the reservoir into a number of distinct compartments, in which the direction-dependent multiphase flow communication is a function of non-local phase potential differences. Our proposed clustering framework begins with a mixed-type raw dataset which can be categorical/numerical, spatial/temporal, and discrete/continuous. The dataset can contain noisy/missing values of different data types including but not limited to well production/injection history, well location, well type, geological features, PVT measurements, perforation data, etc. Unsupervised clustering techniques suited to the input data types (e.g. k-prototypes, spectral, Gaussian Mixtures, and hierarchical clustering), and appropriate distance measures (such as Euclidean distance, soft dynamic time warping, and mode) are used. The input data is standardized, and upon convergence check, the best clustering representation is obtained. Finally, Support-Vector-Machine technique is utilized in the kernel space to trace a demarcating hyperplane for the clusters.In a specific case study reported here, the proposed workflow is applied to a major field with a couple of hundreds of wells with more than 40 years of production history. Leveraging the fast forward model, an efficient ensemble-based history matching framework is applied to reduce the uncertainty of the global reservoir parameters such as inter-blocks and aquifer-reservoir communications, fault transmissibilities, and block-based oil-in-place. The ensemble of history matched models are then used to provide a probabilistic forecast for different field development scenarios. In addition, the clustering framework enables us to treat missing data and use the augmented dataset for improving the clustering accuracy.In summary, in this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome.

Development of a synergistic damage mechanics model to predict evolution of ply cracking and stiffness changes in multidirectional composite laminates under creep

A physics-based multi-scale model that couples viscoelasticity and time-dependent damage evolution for general multidirectional laminates subjected to long-term creep loading is developed. The viscoelastic ply behavior is evaluated using a nonlinear Schapery-type viscoelastic model, while a methodology employed within the framework of classical laminate theory is used to predict the corresponding laminate time-dependent response. The evolution of microscopic ply cracks in multiple plies with different orientations during creep loading is predicted using an energy-based approach, and the corresponding laminate stiffness degradation is evaluated using a synergistic damage mechanics-based model that relies on computational micromechanics in lieu of costly experimental data. The developed model is used to predict the evolution of ply cracks and the viscoelastic stress–strain response of various cross-ply and multidirectional laminates under quasi-static and creep loading. Predicted strains, compliance changes and crack density evolutions show excellent agreement with available experimental creep data for different laminate stacking sequences, providing validation for the model. Predictions are also made for two additional multidirectional laminates in order to investigate the effect of off-axis ply angle on the creep response, which demonstrates the versatility of the model and its usefulness in assessing the long-term durability of general multidirectional composite laminates.

Combining material and model pedigree is foundational to making ICME a reality

AbstractWith the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing-internal structure-property-performance envelope, along with the capturing and storing of the associated material and model information across its life cycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality, and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Fortunately, materials information management systems and physics-based multiscale modeling methods have kept pace with the growing user demands. Herein, recent efforts to identify best practices associated with these user demands and key principles for the development of a robust materials information management system will be discussed. The goals are to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets and, ultimately, to enable ICME to become a reality. In particular, the NASA Glenn Research Center efforts towards establishing such a database (for combining material and model pedigree) associated with both monolithic and composite materials as well as a multiscale, micromechanics-based analysis toolset for such materials will be discussed.

Stabilized interface methods for mechanical joints: Physics-based models and variationally consistent embedding

This paper presents the application of a new method for interfacial modeling utilizing a merger of continuous Galerkin and discontinuous Galerkin concepts to simulate the behavior of mechanical joints. The interfacial flux terms arising naturally from the discontinuous Galerkin treatment provide a mechanism to embed friction models in a variationally consistent fashion. Due to the unbiased implementation of the interface, facilitated by avoiding the master–slave concept, the deformation of the two interacting surfaces conforms to the local material and geometric attributes of the surfaces. This results in a better preservation of physics in interface mechanics. Additionally, the method is incorporated into a Variational Multiscale framework that comes equipped with a built-in error estimation module, providing numerical estimation of convergence and distinguishing discretization errors from modeling errors. A series of quasi-static numerical simulations of a lap joint under fretting conditions are conducted to compare the performance of two friction models: (i) classical Coulomb friction model and (ii) physics-based multiscale model. Hysteresis study of a three-dimensional double-bolted lap joint for the two friction models is also presented and the computed results are shown to be consistent between conforming and nonconforming meshes.

A multidisciplinary approach to structural health monitoring and damage prognosis of aerospace hotspots

AbstractThe health monitoring and damage prognosis of aerospace hotspots is important for reducing maintenance costs and increasing in-service capacity of aging aircraft. One of the leading causes of structural failure in aerospace vehicles is fatigue damage. Based on the physical mechanism of damage nucleation and growth, a physics-based multiscale model is considered for fatigue damage assessment in metallic aircraft structures. A guided-wave based sensing approach is utilised to enable effective damage detection in a common structural hotspot: a lug joint. Finite element analysis is carried out with piezoelectric wafers bonded to the host structure and the simulated sensor signals are analysed. A damage classification strategy is developed, which integrates physically motivated time-frequency approaches with advanced stochastic modelling techniques. In particular, a variational Bayesian learning scheme is used to estimate the optimal model complexity automatically from the data, adapting the classifier for real-time use. Classification performance is studied as a function of signal-to-noise ratio and results are reported for the detection of fatigue crack damage in the lug joint. An adaptive hybrid prognosis model is proposed, which estimates the residual useful life of structural hotspots using damage condition information obtained in real-time.