Predictive Multiscale Modeling Research Articles

Determination of the linear elastic stiffness and hygroexpansion of softwood by a multilayered unit cell using poromechanics

Hygroexpansion of wood is a known and undesired characteristic in civil engineering. When wood is exposed to changing environmental humidity, it adsorbs or desorbs moisture and warps. The resulting distortions or - at restrained conditions - cracks are a major concern in timber engineering. We herein present a multiscale model for prediction of the macroscopic hygroexpansion behavior of individual pieces of softwood from their microstructure, demonstrated for spruce. By applying poromicromechanics, we establish a link between the swelling pressure, driving the hygroexpansion of wood at the nanoscale, and the resulting macroscopic dimensional changes. The model comprises six homogenization steps, which are performed by means of continuum micromechanics, the unit cell method and laminate theory, all formulated in a poromechanical framework. Model predictions for elastic properties of wood as functions of the moisture content closely approach corresponding experimental data. As for the hygroexpansion behavior, the swelling pressure has to be back-calculated from macroscopic hygroexpansion data. The good reproduction of the anisotropy of wood hygroexpansion, based on only a single scalar calibration parameter, underlines the suitability of the model. The multiscale model constitutes a valuable tool for studying the effect of microstructural features on the macroscopic behavior and for assessing the hygroexpansion behavior at smaller length scales, which are inaccessible to experiments. The model predictions deliver input parameters for the analysis of timber at the structural scale, therewith enabling to optimize the use of timber and to prevent moisture-induced damage or failure.

Multiscale prediction of wind speed and output power for the wind farm

This paper focuses on establishing the multiscale prediction models for wind speed and power in wind farm by the average wind speed collected from the history records. Each type of the models is built with different time scales and by different approaches. There are three types of them that a short-term model for a day ahead is based on the least squares support vector machine (LSSVM), a medium-term model for a month ahead is on the combination of LSSVM and wavelet transform (WT), and a long-term model for a year ahead is on the empirical mode decomposition (EMD) and recursive least square (RLS) approaches. The simulation studies show that the average value of the mean absolute percentage error (MAPE) is 4.91%, 6.57% and 16.25% for the short-term, the medium-term and the long-term prediction, respectively. The predicted data also can be used to calculate the predictive values of output power for the wind farm in different time scales, combined with the generator’s power characteristic, meteorologic factors and unit efficiency under various operating conditions.

A Japanese case of breast cancer with BRCA1 mutation

s / The Breast 20 (2011) S12–S55 S27 burden, and slower transfer of new discoveries and technologies into clinical practice. Methods: The objective of INTEGRATE, a European Commission funded project involving six European partners, is to build a data-sharing platform for all forms of data generated from clinical trials. The dynamic environment will collect, manage and provide secure and compliant access to complex and novel datasets for the biomedical community, including basic scientists, translational, and clinical researchers as well as the pharmaceutical industry to enhance cooperative research. It will be specifically designed to manage complex genomic data that can be used for molecular screening of patients for clinical trials as well as for analysis of clinical, pathology and imaging data. INTEGRATE will involve reuse and adaptation of existing openly available services and tools, as well as development of new technologies and analysis tools specifically for this data sharing environment. The INTEGRATE platform will initially be tested using data generated in neoadjuvant breast cancer clinical trials run by the Breast International Group (BIG). This will subsequently be expanded to include the setting of adjuvant and metastatic breast cancer and potentially other forms of cancer as well. Results: Experts from multiple disciplines, such as medical oncology, knowledge engineering, IT, basic bioscience, and ethical bodies have collaborated to define the scope of the platform. To ensure semantic interoperability and support reuse, the semantics of the domain will be captured in a core dataset using standardised terminologies such as SNOMED CT, ICD-O and LOINC, defined in collaboration with the end-user research community. The INTEGRATE platform will also support the development, preservation and sharing of multi-scale predictive models. The technical INTEGRATE environment will be based on an open serviceoriented architecture. An effort will be made to ensure that INTEGRATE is compatible with other similar existing platforms, and that the highest standards of data security and privacy are adopted. Conclusions: INTEGRATE will develop a standards-based environment that will support co-ordinated and collaborative biomedical research by allowing optimal exploitation of breast cancer clinical trial data, and thus potentially contribute to significant advancements in cancer research.

Multiscale Cancer Modeling

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.

Generalized framework for interatomic potential design: Application to Fe–He system

Radiation damage phenomena plays an important role in the lifetime of structural materials for future fusion power reactors. Developing predictive multiscale models for material behavior under irradiation conditions in a fusion reactor requires understanding the mechanisms associated with radiation damage phenomena, the He interaction with microstructures, and quantifying the associated uncertainties. Nanoscale simulations and interatomic potentials play an important role in exploring the physics of nanoscale structures. However, while interatomic potentials are designed for a specific purpose, they are often used for studying mechanisms outside of the intended purpose. Hence, a generalized framework for interatomic potential design is designed such that it can allow a researcher to tailor an interatomic potential towards specific properties. This methodology produces an interatomic potential design map, which contains multiple interatomic potentials and is capable of exploring different nanoscale phenomena observed in experiments. This methodology is efficient and provides the means to assess uncertainties in nanostructure properties due to the interatomic potential fitting process. As an initial example with relevance to fusion reactors, an Fe–He interatomic potential design map is developed using this framework to show its profound effect.

A mechanism-based multi-scale model for predicting thermo-oxidative degradation in high temperature polymer matrix composites

This paper describes a mechanism-based multi-scale model for life prediction of high temperature polymer matrix composites (HTPMC) under thermo-oxidative aging conditions. The multi-scale model incorporates molecular level damage such as inter-crosslink chain scission in a thermoset polymer due to thermo-oxidative aging of the polymer resin. The degradation of inter-laminar stress depends on remaining inter-crosslink density of thermo-set polymer in fiber/matrix interface region subjected to thermo-oxidative aging environment. The degradation of inter-laminar shear stress of thermo-oxidatively aged unidirectional IM-7/PETI-5 composite specimens at 300 °C was modeled using an in-house test-bed FEA code (NOVA-3D). A micromechanics based viscoelastic cohesive layer model was used to model delamination. The model is fully rate dependent and does not require a pre-assigned traction-separation law. Viscoelastic regularization of the constitutive equations of the cohesive layer used in this model not only mitigates numerical instability, but also enables the analysis to follow load–deflection behavior beyond peak failure load. The model was able to successfully simulate delamination failure in thermo-oxidatively aged unidirectional IM-7/PETI-5 composite, and the model predictions were verified using test data.

Embracing the Open-Source Movement for Managing Spatial Data: A Case Study of African Trypanosomiasis in Kenya

The past decade has seen an explosion in the availability of spatial data, not only for researchers, but the public as well. As the quantity of data increases, the ability to effectively navigate and understand the data becomes more challenging. Here we detail a conceptual model for a spatially explicit database management system that addresses the issues raised with the growing data management problem. We demonstrate utility with a case study in disease ecology: to develop a multiscale predictive model of African trypanosomiasis in Kenya. International collaborations and varying technical expertise necessitate a modular open-source software solution. Finally, we address three recurring problems with data management: scalability, reliability, and security.

An Integrative Systems Biology Approach to Understanding Pulmonary Diseases

Chronic inflammatory pulmonary diseases such as COPD and asthma are highly prevalent and associated with a major health burden worldwide. Despite a wealth of biologic and clinical information on normal and pathologic airway structure and function, the primary causes and mechanisms of disease remain to a large extent unknown, preventing the development of more efficient diagnosis and treatment. We propose to overcome these limitations through an integrative systems biology research strategy designed to identify the functional and regulatory pathways that play central roles in respiratory pathophysiology, starting with severe asthma. This approach relies on global genome, transcriptome, proteome, and metabolome data sets collected in cross-sectional patient cohorts with high-throughput measurement platforms and integrated with biologic and clinical data to inform predictive multiscale models ranging from the molecular to the organ levels. Working hypotheses formulated on the mechanisms and pathways involved in various disease states are tested through perturbation experiments using model simulation combined with targeted and global technologies in cellular and animal models. The responses observed are compared with those predicted by the initial models, which are refined to account better for the results. Novel perturbation experiments are designed and tested both computationally and experimentally to arbitrate between competing hypotheses. The process is iterated until the derived knowledge allows a better classification and subphenotyping of severe asthma using complex biomarkers, which will facilitate the development of novel diagnostic and therapeutic interventions targeting multiple components of the molecular and cellular pathways involved. This can be tested and validated in prospective clinical trials.

Development of the Multi-scale Analysis Model to Simulate Strain Localization Occurring During Material Processing

A detailed description of possibilities given by the developed Cellular Automata—Finite Element (CAFE) multi scale model for prediction of the initiation and propagation of micro shear bands and shear bands in metallic materials subjected to plastic deformation is presented in the work. Particular emphasis in defining the criterion for initiation of micro shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of these phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi scale model of strain localization phenomena. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the work. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is a subject of this work as well. In the developed model remeshing becomes possible and difficulties limiting application of the CAFE method to simple deformation processes are solved. Obtained results of numerical simulations are compared with the experimental results of cold rolling process to show good predicative capabilities of the developed model.

How Protein Materials Balance Strength, Robustness And Adaptability

Proteins constitute crucial building blocks of life, forming biological materials such as hair, bone, spider silk or cells, which play an important role in providing key mechanical functions to biological systems. We present molecular dynamics simulations combined with theoretical modeling, used here to develop predictive multi-scale models of the deformation and fracture behavior of protein materials, capturing atomistic, molecular, meso- and microscopic scales. Through explicitly considering the hierarchical architecture of protein constituents, including the details of their chemical bonding, our models are capable of predicting their mechanical behavior across multiple length- and time-scales, thereby providing a rigorous structure-property relationship. We exemplify the approach in the analysis of two model protein materials, spider silk and intermediate filaments, representing beta-sheet and alpha-helix rich protein structures, respectively. Spider silk is a protein material that can reach the strength of steel, despite the predominant weak hydrogen bonding. Intermediate filaments are an important class of structural proteins responsible for the mechanical integrity of eukaryotic cells, which, if flawed, can cause serious diseases such as the rapid aging disease progeria or muscle dystrophy. For both examples, we present a multi-scale analysis that enables us to understand the structural basis of how these materials balance strength, robustness and adaptability through formation of hierarchical molecular and supermolecular features, and how structural flaws associated with genetic diseases contribute to the failure of these materials to provide biological function. We explain why the utilization of hierarchical features in protein materials is vital to their ability to combine seemingly incompatible properties such as strength and robustness, and adaptability. We discuss the implications of our work for the science of multi-scale interactions in biophysics, and how this knowledge can be utilized to develop de novo bioinspired nanomaterials via a bottom-up design.

Simulation and Visualization of Self-Assembled Nanodevice Networks Synthesized via Plasma–Surface Interaction

Using advanced visualization techniques, a comprehensive visualization of all the stages of the self-organized growth of internetworked nanostructures on plasma-exposed surface has been made. Atomistic kinetic Monte Carlo simulation for the initial stage of deposition, with 3-D visualization of the whole system and half-tone visualization of the density field of the adsorbed atoms, makes it possible to implement a multiscale predictive modeling of the development of the nanoscale system.

Neuroinformatics Grand Challenges

The conference on Neural Circuit Reconstruction recently held at the Janelia Farm research campus of the Howard Hughes Medical Institute (www.hhmi.org/janelia/conf-007. html) dramatically exposed the need and urgency to advance towards the automated digital tracing of neural arbors. The scientific problem was agreed to be of great importance and a bottleneck in fundamental research endeavors. At the same time, a mismatch became apparent between the confidence of computational scientists that the problem is very solvable (if not already solved) and the frustration of experimental neuroscientists with the practical limits and inadequacy of the existing solutions. In practice, despite several commercially and academically available programs for automated reconstruction, the vast majority of axons and dendrites are still traced manually. The idea was thus launched to “challenge” algorithm developers with data provided by potential users. The resulting collegial competition would bring together computational scientists and neuroscientists to join forces and solve the problem. Such an unconventional interaction would likely lead to significant scientific advancements and would certainly result in an explicit assessment of the remaining obstacles. In fact, neuroscience is a field rich in examples of problems that are supposed to be solved in principle, but that in practice encounter many “exceptions.” An old joke describes the difference between theory and practice by stating that, in theory, there is no difference. A “grand challenge” format may be a proper empirical approach to make those differences explicit. Two recent implementations of this idea gave rise to the open-ended Neural Prediction Challenge (http://neuralprediction.berkeley.edu) and to the Competitions for Predicting Single-Neuron Behavior (http://icwww.epfl.ch/~gerstner/QuantNeuron Mod2007/challenge.html). While both of these initiatives deal with electrophysiological records, the former has a functional twist as it consists of responses to sensory stimulation. In contrast, the latter is based on direct neuronal stimulation and aims to predict with high precision spike timing and subthreshold membrane potential for arbitrary inputs. The identification of other grand challenges on the road to understanding neural computation was also the topic of a recent workshop (http://cnls.lanl.gov/neuralcomp). The National Institutes of Health have recently issued a new program on Predictive Multiscale Models of the Physiome in Health and Disease (http://grants.nih.gov/grants/guide/pafiles/PAR-08-023.html). Neuroinformatics and computational neuroscience clearly provide fertile ground for these applications, with the emphasis on data sharing, mining, and analysis (e.g. http://cosyne.org/wiki/Cosyne08_Data_ sharing_and_data_analysis_challenges_in_neuroscience) as also discussed by Teeters et al. (2008) in this issue. To flesh out this idea in more detail, let us consider again the digital representation of neuronal structure. The morphology of dendrites and axons constitutes a cardinal element of neuronal phenotype, and plays a determinant role in cellular excitability, synaptic integration, and network connectivity. Whole dendritic and axonal trees can be visualized with various combinations of histological and microscopic techniques, such as e.g. intracellular injection of biocytin and brightfield imaging, or expression of a fluorescent marker (e.g. green fluorescent protein (GFP)) and confocal microscopy. Starting from Cajal, Neuroinform (2008) 6:1–3 DOI 10.1007/s12021-008-9010-5

Multi-scales modelling of dynamic behaviour for discontinuous fibre SMC composites

The requirements of passive security, notably in the transport industry, impose to maximize the dissipation of the energy and to minimize the decelerations undergone by a vehicle and thus passengers due to violent shocks (crash). This paper aims at establishing efficient expected answers towards the preoccupations mainly emanating from transport industry. Currently, the behaviour laws implemented in the dynamic explicit schemes (RADIOSS, PAM-CRASH and LS-DYNA) do not integrate sufficiently the physical aspects in the material degradation, mainly the damage process, their kinetics, the variability and especially the heterogeneity of the composite materials microstructure. This paper deals with the development of a multi-scale predictive model coupling specific experimental methodologies and the micromechanical formulation of damage mechanisms in order to build constitutive laws for discontinuous fibre reinforced composites materials. The developed micromechanical modelling is based on an experimental methodology conducted over a range of strain rates from quasi static to 250 s −1. The latter has enabled identifying local probabilistic damage criterion formulated through the Weibull’s statistical integrating the strain rate effect and describing the progressive interfacial debonding under rapid loading. The developed model has been validated to predict the stiffness reduction and the overall elastic visco-damage behaviour for SMC composite material. The model simulations agree well with high speed tensile tests and confirm that the damage threshold and kinetic in the SMC are mainly strain rate sensitive.

Multi-scale rheological model for discontinuous phenomena in materials under deformation conditions

A study of possibilities given by the developed Cellular Automata–Finite Element (CAFE) multi-scale model for prediction of the initiation and propagation of micro-shear bands and shear bands in metallic materials subjected to plastic deformation is described in the paper. Particular emphasis in defining the criterion for initiation of micro-shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of those phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi-scale model of strain localization. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the paper. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is presented in the paper as well. In this approach remeshing becomes possible and mesh distortion, which limits application of the CAFE method to simple deformation processes, is eliminated.

Multi-scale modeling to predict ligand presentation within RGD nanopatterned hydrogels

The adhesion ligand RGD has been coupled to various materials to be used as tissue culture matrices or cell transplantation vehicles, and recent studies indicate that nanopatterning RGD into high-density islands alters cell adhesion, proliferation, and differentiation. However, elucidating the impact of nanopattern parameters on cellular responses has been stymied by a lack of understanding of the actual ligand presentation within these systems. We have developed a multi-scale predictive modeling approach to characterize the adhesion ligand nanopatterns within an alginate hydrogel matrix. The models predict the distribution of ligand islands, the spacing between ligands within an island and the fraction of ligands accessible for cell binding. These model predictions can be used to select pattern parameter ranges for experiments on the effects of individual parameters on cellular responses. Additionally, our technique could also be applied to other polymer systems presenting peptides or other signaling molecules.

Multiscale Modeling of Interactive Diffusion Processes in Concrete

Deterioration of concrete is governed by coupled diffusion processes such as heat conduction, moisture transfer, and chloride ion penetration. The diffusion processes can be characterized by multiscale modeling. At the macroscopic level, a thermodynamic approach is used in the present study as a framework to derive coupled diffusion equations and constitutive equations. In the case of hygrothermal coupling, it is found that there are additional coupling effects in the stress-strain relationships aside from the conventional thermal expansion and drying shrinkage. At the mesoscopic level, composite theories are used to develop the material models between effective transport properties (macroscopic) and transport properties of constituents (mesoscopic). The result shows that, even for noncoupled processes, the effective properties are not simply the volumetric average over the constituent phases. An analytical multiscale prediction model for chloride diffusion is developed based on an assumption that both chloride diffusivity and binding capacity are independent of free chloride concentration. The analytical solution is used as an example to compare with available tests results, showing that the model is quite accurate for regular concrete cured 28 days or longer. The multiscale model can accurately predict the effect of mesoscopic parameters (e.g., aggregate content) and microscopic parameters (e.g., water-to-cement ratio)on chloride penetration.

Multiscale Modeling of Dislocation Processes in Bcc Tantalum: Bridging Atomistic and Mesoscale Simulations

AbstractPlastic deformation in bcc metals at low temperatures and high-strain rates is controlled by the motion of a/2<111> screw dislocations, and understanding the fundamental atomistic processes of this motion is essential to develop predictive multiscale models of crystal plasticity. The multiscale modeling approach presented here for bcc Ta is based on information passing, where results of simulations at the atomic scale are used in simulations of plastic deformation at mesoscopic length scales via dislocation dynamics (DD). The relevant core properties of a/2<111> screw dislocations in Ta have been obtained using quantum-based interatomic potentials derived from model generalized pseudopotential theory and an ab-initio data base together with an accurate Green's-function simulation method that implements flexible boundary conditions. In particular, the stress-dependent activation enthalpy for the lowest-energy kink-pair mechanism has been calculated and fitted to a revealing analytic form. This is the critical quantity determining dislocation mobility in the DD simulations, and the present activation enthalpy is found to be in good agreement with the previous empirical form used to explain the temperature dependence of the yield stress.

Multiscale Modeling of Dislocation Processes in Bcc Tantalum: Bridging Atomistic and Mesoscale Simulations

Plastic deformation in bcc metals at low temperatures and high-strain rates is controlled by the motion of a/2 screw dislocations, and understanding the fundamental atomistic processes of this motion is essential to develop predictive multiscale models of crystal plasticity. The multiscale modeling approach presented here for bcc Ta is based on information passing, where results of simulations at the atomic scale are used in simulations of plastic deformation at mesoscopic length scales via dislocation dynamics (DD). The relevant core properties of a/2 screw dislocations in Ta have been obtained using quantum-based interatomic potentials derived from model generalized pseudopotential theory and an ab-initio data base together with an accurate Green's-function simulation method that implements flexible boundary conditions. In particular, the stress-dependent activation enthalpy for the lowest-energy kink-pair mechanism has been calculated and fitted to a revealing analytic form. This is the critical quantity determining dislocation mobility in the DD simulations, and the present activation enthalpy is found to be in good agreement with the previous empirical form used to explain the temperature dependence of the yield stress.