Multi-scale modeling and mechanical performance analysis of finger seals with plain woven C/C composite.

The application of carbon fibers reinforced carbon matrix (C/C) composite can solve the local wear of metallic finger seals effectively. However, the performance of C/C composite finger seal is complex and variable, which further decreases the sealing performance and life. Therefore, a method of multi-scale modeling and mechanical performance analysis for plain woven C/C composite finger seals was conducted. The circumferential finger beams of C/C composite were modeled by multi-scale structural analysis and weaving simulation. The radial static and dynamic stiffness characteristics of finger beams were investigated. The results showed that the radial static stiffness of the finger beam with three layers was about 3 times that with single layer. The radial stiffness of circumferential finger beams presented a periodic distribution pattern with a period of 90°. The radial dynamic stiffness of C/C composite finger beams increased with the excitation displacement amplitude and rotor speed. But the magnitude and fluctuation degree of dynamic stiffness were greater than those of static stiffness. A large difference in radial stiffness will lead to local wear and hysteretic leakage. This study lays a foundation for the analysis and optimization of the hysteresis and wear characteristics of C/C composite finger seals.

Probing the Linear-to-Plastic Transition in Polymer Nanocomposites via Atomistic Simulations: The Role of Interphases.

Polymer nanocomposites have found ubiquitous use across diverse industries, attributable to their distinctive properties and enhanced mechanical performance compared to conventional materials. Elucidating the elastic-to-plastic transition in polymer nanocomposites under diverse mechanical loads is paramount for the bespoke design of materials with desired mechanical attributes. In the current work, the elastic-to-plastic transition is probed in model systems of polyethylene oxide (PEO) and silica, SiO2, nanoparticles, through detailed atomistic molecular dynamics simulations. This comprehensive, multi-scale analysis unveils pivotal markers of the elastic-to-plastic transition, highlighting the quintessential role of microstructural and regional heterogeneities in density, strain, and stress fields, featuring the polymer-nanoparticle interphase region. At the atomic level, the behavior of polymer chains interacting with nanoparticle surfaces is traced, differentiating between free and adsorbed chains, and identifying the microscopic origins of the linear-to-plastic transition. The mechanical behavior of subregions are characterized within the PEO/SiO2 nanocomposites, focusing on the interphase and bulk-like polymer areas, probing stress heterogeneities and their decomposition into various force contributions. At the inception of plasticity, a disruption is discerned in isotropy of the polymeric density field, the emergence of low-density regions, and microscopic voids/cavities within the polymer matrix concomitant with a transition of adsorbed chains to free. The yield strain also emerges as an inflection point in the local versus global strain diagram, demarcating the elastic limit, and the plastic regime shows pronounced strain heterogeneities. The decomposition of the atomic Virial stress into bonded and non-bonded interactions indicates that the rigidity of the material is primarily governed by non-bonded interactions, significantly influenced by the volume fraction of the nanoparticle. These findings emphasize the importance of the microstructural and micromechanical environment at the polymer-nanoparticle interface on the linear-to-plastic transition, which is of great importance in the design of nanocomposite materials with advanced mechanical properties.

Unraveling the complexity of amorphous solid as direct ingredient for conventional oral solid dosage form: The story of Elagolix Sodium.

Conventional solid oral dosage form development is not typically challenged by reliance on an amorphous drug substance as a direct ingredient in the drug product, as this may result in product development hurdles arising from process design and scale-up, control of physical quality attributes, drug product processability and stability. Here, we present the Chemistry, Manufacturing and Controls development journey behind the successful commercialization of an amorphous drug substance, Elagolix Sodium, a first-in-class, orally active gonadotropin-releasing hormone antagonist. The reason behind the lack of crystalline state was assessed via Molecular Dynamics (MD) at the molecular and inter-molecular level, revealing barriers for nucleation due to prevalence of intra-molecular hydrogen bond, repulsive interactions between active pharmaceutical ingredient (API) molecules and strong solvation effects. To provide a foundational basis for the design of the API manufacturing process, we modeled the solvent-induced plasticization behavior experimentally and computationally via MD for insights into molecular mobility. In addition, we applied material science tetrahedron concepts to link API porosity to drug product tablet compressibility. Finally, we designed the API isolation process, incorporating computational fluid dynamics modeling in the design of an impinging jet mixer for precipitation and solvent-dependent glass transition relationships in the cake wash, blow-down and drying process, to enable the consistent manufacture of a porous, non-sintered amorphous API powder that is suitable for robust drug product manufacturing.

Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems.

Predicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales. However, physics alone is often not sufficient due to lack of knowledge on certain details of the system. With sufficient data, however, machine learning techniques may aid. If data are yet relatively cumbersome to obtain, hybrid methods may come to the rescue. We focus in this report on using various types of neural networks (NN) including NN's into which physics information is encoded (PeNN's) and also studied effects of NN's hyperparameters. We apply the networks to predict the viscosity of an emulsion as a function of shear rate. We show that using various network performance metrics as the mean squared error and the coefficient of determination ( R 2 ) that the PeNN's always perform better than the NN's, as also confirmed by a Friedman test with a p-value smaller than 0.0002. The PeNN's capture extrapolation and interpolation very well, contrary to the NN's. In addition, we have found that the NN's hyperparameters including network complexity and optimization methods do not have any effect on the above conclusions. We suggest that encoding NN's with any disciplinary system based information yields promise to better predict properties of complex systems than NN's alone, which will be in particular advantageous for small numbers of data. Such encoding would also be scalable, allowing different properties to be combined, without repetitive training of the NN's.

A supervised learning-assisted multi-scale study for thermal and mechanical behavior of porous Silica.

This paper presents a comprehensive investigation of mesoporous Silica utilizing a multi-scale modeling approach under periodic boundary conditions integrated with machine learning algorithms. The study begins with Molecular Dynamics (MD) simulations to extract Silica's elastic properties and thermal conductivity at the nano-scale, employing the Tersoff potential. Subsequently, the derived material characteristics are applied to a series of generated porous Representative Volume Elements (RVEs) at the microscale. This phase involves the exploration of porosity and void shape effects on Silica's thermal and mechanical properties, considering inhomogeneities' distributions along the X-axis and random dispersion of pore cells within a three-dimensional space. Furthermore, the influence of pore shape is examined by defining open and closed-cell models, encompassing spherical and ellipsoidal voids with aspect ratios of 2 and 4. To predict the properties of porous Silica, a shallow Artificial Neural Network (ANN) is deployed, utilizing geometric parameters of the RVEs and porosity. Subsequently, it is revealed that Silica's thermal and mechanical behavior is linked to pore geometry, distribution, and porosity model. Finally, to classify the behavior of porous Silica into three categories, quasi-isotropic, orthotropic, and transversely-isotropic, three methodologies of decision tree approach, K-Nearest Neighbors (KNN) algorithm, and Support Vector Machines (SVMs) are employed. Among these, SVMs employing a quadratic kernel function demonstrate robust performance in categorizing the thermal and mechanical behavior of porous Silica.

Review of cardiac-coronary interaction and insights from mathematical modeling.

Cardiac-coronary interaction is fundamental to the function of the heart. As one of the highest metabolic organs in the body, the cardiac oxygen demand is met by blood perfusion through the coronary vasculature. The coronary vasculature is largely embedded within the myocardial tissue which is continually contracting and hence squeezing the blood vessels. The myocardium-coronary vessel interaction is two-ways and complex. Here, we review the different types of cardiac-coronary interactions with a focus on insights gained from mathematical models. Specifically, we will consider the following: (1) myocardial-vessel mechanical interaction; (2) metabolic-flow interaction and regulation; (3) perfusion-contraction matching, and (4) chronic interactions between the myocardium and coronary vasculature. We also provide a discussion of the relevant experimental and clinical studies of different types of cardiac-coronary interactions. Finally, we highlight knowledge gaps, key challenges, and limitations of existing mathematical models along with future research directions to understand the unique myocardium-coronary coupling in the heart. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.

A Semi-Markov Approach to Study a Group of Kinesin Motors.

We propose a general mathematical and computational approach to study cellular transport driven by a group of kinesin motors. It is a framework for multi-scale modeling that integrates kinetic models of single kinesin motors, including detachment and reattachment events, to study group behaviors of several motors. By formulating the problem as a semi-Markov process and applying a central limit theorem, asymptotic velocity and diffusivity can be readily calculated, which offers considerable computational advantage over Monte Carlo simulations in tasks such as parameter sensitivity analysis and model selection. We demonstrate the method with some examples. The importance of incorporating the intrinsic microscopic-level dynamics of individual motors is illustrated by showing how changes at the microscopic level propagate to the motor-cargo complex at a mesoscopic level. Particularly, we showcase an example in which changes in the second moment of single-motor characteristics gives rise to different first moment characteristics of the motor group.

Mechanistic modelling of relative biological effectiveness of carbon ion beams and comparison with experiments.

Objective.Relative biological effectiveness (RBE) plays a vital role in carbon ion radiotherapy, which is a promising treatment method for reducing toxic effects on normal tissues and improving treatment efficacy. It is important to have an effective and precise way of obtaining RBE values to support clinical decisions. A method of calculating RBE from a mechanistic perspective is reported.Approach.Ratio of dose to obtain the same number of double strand breaks (DSBs) between different radiation types was used to evaluate RBE. Package gMicroMC was used to simulate DSB yields. The DSB inductions were then analyzed to calculate RBE. The RBE values were compared with experimental results.Main results.Furusawa's experiment yielded RBE values of 1.27, 2.22, 3.00 and 3.37 for carbon ion beam with dose-averaged LET of 30.3 keVµm-1, 54.5 keVµm-1, 88 keVµm-1and 137 keVµm-1, respectively. RBE values computed from gMicroMC simulations were 1.75, 2.22, 2.87 and 2.97. When it came to a more sophisticated carbon ion beam with 6 cm spread-out Bragg peak, RBE values were 1.61, 1.63, 2.19 and 2.36 for proximal, middle, distal and distal end part, respectively. Values simulated by gMicroMC were 1.50, 1.87, 2.19 and 2.34. The simulated results were in reasonable agreement with the experimental data.Significance.As a mechanistic way for the evaluation of RBE for carbon ion radiotherapy by combining the macroscopic simulation of energy spectrum and microscopic simulation of DNA damages, this work provides a promising tool for RBE calculation supporting clinical applications such as treatment planning.

DNA supercoiling in bacteria: state of play and challenges from a viewpoint of physics based modeling.

DNA supercoiling is central to many fundamental processes of living organisms. Its average level along the chromosome and over time reflects the dynamic equilibrium of opposite activities of topoisomerases, which are required to relax mechanical stresses that are inevitably produced during DNA replication and gene transcription. Supercoiling affects all scales of the spatio-temporal organization of bacterial DNA, from the base pair to the large scale chromosome conformation. Highlighted in vitro and in vivo in the 1960s and 1970s, respectively, the first physical models were proposed concomitantly in order to predict the deformation properties of the double helix. About fifteen years later, polymer physics models demonstrated on larger scales the plectonemic nature and the tree-like organization of supercoiled DNA. Since then, many works have tried to establish a better understanding of the multiple structuring and physiological properties of bacterial DNA in thermodynamic equilibrium and far from equilibrium. The purpose of this essay is to address upcoming challenges by thoroughly exploring the relevance, predictive capacity, and limitations of current physical models, with a specific focus on structural properties beyond the scale of the double helix. We discuss more particularly the problem of DNA conformations, the interplay between DNA supercoiling with gene transcription and DNA replication, its role on nucleoid formation and, finally, the problem of scaling up models. Our primary objective is to foster increased collaboration between physicists and biologists. To achieve this, we have reduced the respective jargon to a minimum and we provide some explanatory background material for the two communities.

Thermal Study of Carbon-Fiber-Reinforced Polymer Composites Using Multiscale Modeling.

The layered fibers of carbon-fiber-reinforced polymer (CFRP) composites exhibit low thermal conductivity (TC) throughout their thickness due to the poor TC of the polymeric resin. Improved heat transmission inside the hydrogen storage tank during the filling process can reduce further compression work, and improved heat insulation can minimize energy loss. Therefore, it is crucial to understand the thermal properties of composites. This paper reports the thermal behavior of plain-woven CFRP composite using simulation at the micro-, meso-, and macro-scales. The TC was predicted numerically and compared to experimental findings and analytical models. Good results were found. Using the approach of multi-scale modeling, a parametric study was carried out to analyze in depth the influence of certain variables on thermal properties. The study revealed that both fiber volume fraction and temperature significantly influenced the TC of the composite, with the interphase fiber/matrix thickness following closely in terms of impact. The matrix porosity was found to have a relatively slighter impact, particularly within the porosity range of 5 to 15%.

Reconstruction of the human lower esophageal sphincter based on ultra-mill imaging for biomechanical analysis.

Introduction: The lower esophageal sphincter (LES) controls the passage into the stomach and prevents reflex of contents into the esophagus. Dysfunctions of this region typically involves impairment of muscular function, leading to diseases including gastro-esophageal reflux disease and achalasia. The main objective of this study was to develop a finite element model from a unique human LES dataset reconstructed from an ultra-mill imaging setup, and then to investigate the effect of anatomical characteristics on intraluminal pressures. Methods: A pipeline was developed to generate a mesh from a set of input images, which were extracted from a unique ultra-mill sectioned human LES. A total of 216 nodal points with cubic Hermite basis function was allocated to reconstruct the LES, including the longitudinal and circumferential muscles. The resultant LES mesh was used in biomechanical simulations, utilizing a previously developed LES mathematical model based on the Visible Human data to calculate intraluminal pressures. Anatomical and functional comparisons were made between the Ultra-mill and Visible human models. Results: Overall, the Ultra-mill model contained lower cavity (1,796 vs. 5,400 mm3) and muscle (1,548 vs. 15,700 mm3) volumes than the Visible Human model. The Ultra-mill model also developed a higher basal pressure (13.8 vs. 14.7 mmHg) and magnitude of pressure (19.8 vs. 18.9 mmHg) during contraction. Out of all the geometric transformations (i.e., uniform enlargement of volume, lengthening along the center-axis, dilation of the diameter, and increasing muscle thickness), the muscle volume was found to be the main contributor of basal and magnitude of pressures. Increases in length also caused proportional increases to pressures, while dilation of diameter had a less influential reverse effect. Discussion: The findings provide information on interindividual variability in LES pressure and demonstrates that anatomy has a large influence on pressures. This model forms the basis of more complex simulations involving food bolus transport and predicting LES dysfunctions.

Autism Spectrum Disorder: A Neuro-Immunometabolic Hypothesis of the Developmental Origins.

Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia, among the brain's non-neuronal "glia" cell populations, play a pivotal role in neurodevelopment and predisposition to and initiation of disease throughout lifespan. One of the most common neurodevelopmental disorders manifesting between 1-4 years of age is the autism spectrum disorder (ASD). A pathological glial-neuronal interplay is thought to increase the risk for clinical manifestation of ASD in at-risk children, but the mechanisms remain poorly understood, and integrative, multi-scale models are needed. We propose a model that integrates the data across the scales of physiological organization, from genome to phenotype, and provides a foundation to explain the disparate findings on the genomic level. We hypothesize that via gene-environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes that impact neurodevelopment and neurobehavior. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conducted an analysis on the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirmed 21 gene hits. Using unsupervised statistical network analysis, we then identified six clusters of probable protein-protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches. We conclude with delineation of the next steps to verify our model on the individual gene level in an assumption-free manner. The proposed model is of interest for the multidisciplinary community of stakeholders engaged in ASD research, the development of novel pharmacological and non-pharmacological treatments, early prevention, and detection as well as for policy makers.

The Influence of Aortic Valve Disease on Coronary Hemodynamics: A Computational Model-Based Study.

Aortic valve disease (AVD) often coexists with coronary artery disease (CAD), but whether and how the two diseases are correlated remains poorly understood. In this study, a zero-three dimensional (0-3D) multi-scale modeling method was developed to integrate coronary artery hemodynamics, aortic valve dynamics, coronary flow autoregulation mechanism, and systemic hemodynamics into a unique model system, thereby yielding a mathematical tool for quantifying the influences of aortic valve stenosis (AS) and aortic valve regurgitation (AR) on hemodynamics in large coronary arteries. The model was applied to simulate blood flows in six patient-specific left anterior descending coronary arteries (LADs) under various aortic valve conditions (i.e., control (free of AVD), AS, and AR). Obtained results showed that the space-averaged oscillatory shear index (SA-OSI) was significantly higher under the AS condition but lower under the AR condition in comparison with the control condition. Relatively, the overall magnitude of wall shear stress was less affected by AVD. Further data analysis revealed that AS induced the increase in OSI in LADs mainly through its role in augmenting the low-frequency components of coronary flow waveform. These findings imply that AS might increase the risk or progression of CAD by deteriorating the hemodynamic environment in coronary arteries.

Fates of Emitted Particles Depending on Mask Wearing Using an Approach Validated Across Spatial Scales.

The spread of emitted potentially virus-laden aerosol particles is known to be highly dependent on whether a mask is worn by an infected person and on the emission scenario, i.e., whether the person is coughing, speaking, or breathing. The aim of this work is to investigate in detail the fates of particles emitted by a person wearing a perfectly fitting, a naturally fitted mask with leakage, and no mask depending on the emission scenario. Therefore, a two-scale numerical workflow is proposed where parameters are carried through from a micro-scale where the fibers of the mask filter medium and the aerosol particles are resolved to a macro-scale and validated by comparison to experimental measurements of fractional filtration efficiency and pressure drop of the filter medium as well as pressure drop of the mask. It turns out that masks reduce the number of both emitted and inhaled particles significantly even with leakage. While without a mask, the person opposite of an infected person is generally at the highest risk of being infected, a mask worn by an infected person speaking or coughing will deflect the flow leading to the fact that the person behind the infected person might inhale the largest number of aerosol particles.

Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications.

Indoor superspreading events are significant drivers of transmission of respiratory diseases. In this work, we study the dynamics of airborne transmission in consecutive meetings of individuals in enclosed spaces. In contrast to the usual pairwise-interaction models of infection where effective contacts transmit the disease, we focus on group interactions where individuals with distinct health states meet simultaneously. Specifically, the disease is transmitted by infected individuals exhaling droplets (contributing to the viral load in the closed space) and susceptible ones inhaling the contaminated air. We propose a modeling framework that couples the fast dynamics of the viral load attained over meetings in enclosed spaces and the slow dynamics of disease progression at the population level. Our modeling framework incorporates the multiple time scales involved in different setups in which indoor events may happen, from single-time events to events hosting multiple meetings per day, over many days. We present theoretical and numerical results of trade-offs between the room characteristics (ventilation system efficiency and air mass) and the group's behavioral and composition characteristics (group size, mask compliance, testing, meeting time, and break times), that inform indoor policies to achieve disease control in closed environments through different pathways. Our results emphasize the impact of break times, mask-wearing, and testing on facilitating the conditions to achieve disease control. We study scenarios of different break times, mask compliance, and testing. We also derive policy guidelines to contain the infection rate under a certain threshold.

Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions.

The stomach is extensively innervated by the vagus nerve and the enteric nervous system. The mechanisms through which this innervation affects gastric motility are being unraveled, motivating the first concerted steps towards the incorporation autonomic regulation into computational models of gastric motility. Computational modeling has been valuable in advancing clinical treatment of other organs, such as the heart. However, to date, computational models of gastric motility have made simplifying assumptions about the link between gastric electrophysiology and motility. Advances in experimental neuroscience mean that these assumptions can be reviewed, and detailed models of autonomic regulation can be incorporated into computational models. This review covers these advances, as well as a vision for the utility of computational models of gastric motility. Diseases of the nervous system, such as Parkinson's disease, can originate from the brain-gut axis and result in pathological gastric motility. Computational models are a valuable tool for understanding the mechanisms of disease and how treatment may affect gastric motility. This review also covers recent advances in experimental neuroscience that are fundamental to the development of physiology-driven computational models. A vision for the future of computational modeling of gastric motility is proposed and modeling approaches employed for existing mathematical models of autonomic regulation of other gastrointestinal organs and other organ systems are discussed.

Modeling Reactive Hyperemia to Better Understand and Assess Microvascular Function: A Review of Techniques.

Reactive hyperemia is a well-established technique for the non-invasive evaluation of the peripheral microcirculatory function, measured as the magnitude of limb re-perfusion after a brief period of ischemia. Despite widespread adoption by researchers and clinicians alike, many uncertainties remain surrounding interpretation, compounded by patient-specific confounding factors (such as blood pressure or the metabolic rate of the ischemic limb). Mathematical modeling can accelerate our understanding of the physiology underlying the reactive hyperemia response and guide in the estimation of quantities which are difficult to measure experimentally. In this work, we aim to provide a comprehensive guide for mathematical modeling techniques that can be used for describing the key phenomena involved in the reactive hyperemia response, alongside their limitations and advantages. The reported methodologies can be used for investigating specific reactive hyperemia aspects alone, or can be combined into a computational framework to be used in (pre-)clinical settings.

Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries.

One of the key challenges preventing the breakthrough of magnesium-ion batteries (MIB) is the formation of a passivating boundary layer at the Mg anode. To describe the initial steps of Mg anode degradation by O2 impurities, a Mg/O ReaxFF (force field for reactive systems) parameter set was developed capable of accurately modeling the bulk, surface, adsorption, and diffusion properties of metallic Mg and the salt MgO. It is shown that O2 immediately dissociates upon first contact with the Mg anode (modeled as Mg(0001), Mg(10 1 ‾ $\bar 1$ 0)A, and Mg(10 1 ‾ $\bar 1$ 1)), heating the surface to several 1000 K. The high temperature assists the further oxidation and forms a rock salt interphase intersected by several grain boundaries. Among the Mg surface terminations, Mg(10 1 ‾ $\bar 1$ 0)A is the most reactive, forming an MgO layer with a thickness of up to 25 Å. The trained force field can be used to model the ongoing reactions in Mg-air batteries but also to study the oxidation of magnesium metal in general.

A Static Damage Constitutive Model of Concrete Based on Microscopic Damage Mechanism.

In this article, a microscopic constitutive model is established that includes friction, plastic, and spring elements and has clear physical meaning. The friction unit reflects the mutual friction between crack surfaces, the plastic unit reflects the development of concrete plasticity, and the fracture of the spring unit reflects the formation and expansion of interior cracks in concrete. In addition, the integration of the random field theory into this model uncovers the physical underpinnings of the relationship between concrete's nonlinearity and randomness. The multi-scale modeling of the concrete static damage constitutive model is then realized once the parameters of the random field are discovered using the macro test results. In order to apply the model's applicability in finite element programs, a subroutine was ultimately constructed. The experimental data and the anticipated values from the numerical simulation are in good agreement, supporting the model's realism.