Improved phase-field models of melting and dissolution in multi-component flows.
Ontology highlight
ABSTRACT:
SUBMITTER: Hester EW
PROVIDER: S-EPMC7655769 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations while reproducing key flow quantities. We believe that SciMARL creates unprecedented capabilities for the simulation of turbulent flows.
| S-EPMC8931082 | biostudies-literature
Project description:An advection-diffusion-limited dissolution model of an object being eroded by a two-dimensional potential flow is presented. By taking advantage of the conformal invariance of the model, a numerical method is introduced that tracks the evolution of the object boundary in terms of a time-dependent Laurent series. Simulations of a variety of dissolving objects are shown, which shrink and collapse to a single point in finite time. The simulations reveal a surprising exact relationship, whereby the collapse point is the root of a non-analytic function given in terms of the flow velocity and the Laurent series coefficients describing the initial shape. This result is subsequently derived using residue calculus. The structure of the non-analytic function is examined for three different test cases, and a practical approach to determine the collapse point using a generalized Newton-Raphson root-finding algorithm is outlined. These examples also illustrate the possibility that the model breaks down in finite time prior to complete collapse, due to a topological singularity, as the dissolving boundary overlaps itself rather than breaking up into multiple domains (analogous to droplet pinch-off in fluid mechanics). The model raises fundamental mathematical questions about broken symmetries in finite-time singularities of both continuous and stochastic dynamical systems.
| S-EPMC4786035 | biostudies-literature
Project description:Spergualin is a natural product that exhibits immunosuppressive, anti-tumor and anti-bacterial activities. Its derivatives, such as 15-deoxyspergualin (15-DSG), have been clinically approved for acute allograft rejection. However, the reported syntheses are cumbersome (>10 steps) and they suffer from low overall yields (?0.3% to 18%). Moreover, spergualin and its derivatives are chemically unstable and rapidly hydrolyzed in aqueous buffer. Here, we have re-explored these issues and report a modified synthetic route with significantly improved overall yield (?31% to 47%). The key transformation is a microwave-accelerated Ugi multi-component reaction that is used to generate the peptoid core in a single step. Using the products of this route, we found that modifications of the hemiaminal significantly increased chemical stability. Thus, we anticipate that this synthetic route will improve access to biologically active 15-DSG derivatives.
| S-EPMC3088103 | biostudies-literature
Project description:Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between-voxel interactions in high-dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi-subject fMRI datasets. A flexible cross-validation approach that assesses the ability of the models to predict voxel-wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting-state fMRI data. It was demonstrated that highly flexible subject-specific component subspaces, as well as very constrained average models, are poor predictors of whole-brain functional connectivity, whereas the best-generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882-899, 2017. © 2016 Wiley Periodicals, Inc.
| S-EPMC6866892 | biostudies-literature
Project description:Abstract Herein, we demonstrate how to predict and experimentally validate phase diagrams for multi?component systems from a high?dimensional virtual space of all possible phase diagrams involving several elements based on small existing experimental data. The experimental data for bulk phases for known systems represents a sampling from this space, and screening the space allows multi?component phase diagrams with given design criteria to be built. This approach uses machine learning methods to predict phase diagrams and Bayesian experimental design to minimize experiments for refinement and validation, all within an active learning loop. The approach is proven by predicting and synthesizing the ferroelectric ceramic system (1??)(Ba0.61Ca0.28Sr0.11TiO3)??(BaTi0.888Zr0.0616Sn0.0028Hf0.0476O3) with a relatively high transition temperature and triple point, as well as the NiTi?based pseudo?binary phase diagram (1??)(Ti0.309Ni0.485Hf0.20Zr0.006)??(Ti0.309Ni0.485Hf0.07Zr0.068Nb0.068) designed for high transition temperature (? ? 1). Each phase diagram is validated and optimized through only three new experiments. The complexity of these compounds is beyond the reach of today's computational methods. A machine learning?based approach is proposed to predict all possible phase diagrams of a given multi?component system from a high?dimensional virtual space. By quickly screening the space, a specific phase diagram with given design criteria can be constructed. Bayesian experimental design is then employed to refine the phase diagram with as few experiments as possible.
| S-EPMC7788591 | biostudies-literature
Project description:Spontaneous C4-symmetry breaking phases are ubiquitous in layered quantum materials, and often compete with other phases such as superconductivity. Preferential suppression of the symmetry broken phases by light has been used to explain non-equilibrium light induced superconductivity, metallicity, and the creation of metastable states. Key to understanding how these phases emerge is understanding how C4 symmetry is restored. A leading approach is based on time-dependent Ginzburg-Landau theory, which explains the coherence response seen in many systems. However, we show that, for the case of the single layered manganite La0.5Sr1.5MnO4, the theory fails. Instead, we find an ultrafast inhomogeneous disordering transition in which the mean-field order parameter no longer reflects the atomic-scale state of the system. Our results suggest that disorder may be common to light-induced phase transitions, and methods beyond the mean-field are necessary for understanding and manipulating photoinduced phases.
| S-EPMC8752725 | biostudies-literature
Project description:Polarizable colloids are expected to form crystalline equilibrium phases when exposed to a steady, uniform field. However, when colloids become localized this field-induced phase transition arrests and the suspension persists indefinitely as a kinetically trapped, percolated structure. We anneal such gels formed from magneto-rheological fluids by toggling the field strength at varied frequencies. This processing allows the arrested structure to relax periodically to equilibrium--colloid-rich, cylindrical columns. Two distinct growth regimes are observed: one in which particle domains ripen through diffusive relaxation of the gel, and the other where the system-spanning structure collapses and columnar domains coalesce apparently through field-driven interactions. There is a stark boundary as a function of magnetic field strength and toggle frequency distinguishing the two regimes. These results demonstrate how kinetic barriers to a colloidal phase transition are subverted through measured, periodic variation of driving forces. Such directed assembly may be harnessed to create unique materials from dispersions of colloids.
| S-EPMC3479562 | biostudies-literature
Project description:This paper represents a model for microstructure formation in metallic foams based on the multi-phase-field approach. The model allows to naturally account for the effect of additives which prevent two gas bubbles from coalescence. By applying a non-merging criterion to the phase fields and at the same time raising the free energy penalty associated with additives, it is possible to completely prevent coalescence of bubbles in the time window of interest and thus focus on the formation of a closed porous microstructure. On the other hand, using a modification of this criterion along with lower free energy barriers we investigate with this model initiation of coalescence and the evolution of open structures. The method is validated and used to simulate foam structure formation both in two and three dimensions.
| S-EPMC7673141 | biostudies-literature
Project description:Muscle relaxant and pain reliever metaxalone (MET) is a biopharmaceutical classification systems (BCS) class II drug with poor aqueous solubility and high permeability. The presence of an aromatic skeleton and cyclic carboxamate moiety are the probable reasons for the decreased aqueous solubility, which impacts on its low bioavailability. A high dose (800 mg) of the drug often creates adverse side effects on the central nervous system that needs urgent remedy. Cocrystallization of MET with nicotinamide (NAM), salicylamide (SAM), and 4-hydroxybenzoic acid (HBA) resulted in multicomponent solids that were characterized by PXRD, DSC and single crystal X-ray diffraction. Cocrystals with SAM and NAM form 2D isostructural cocrystals, whereas with HBA the result is a differently packed cocrystal hydrate (or anisole hemisolvate) depending upon the crystallization medium. Similar to the reported MET cocrystals, these cocrystals also confirm the preference for an imide⋯imide homosynthon in the drug. The dominance of the drug-drug homodimer over drug-coformer heterodimers was demonstrated based on binding energy calculations. Further, powder dissolution experiments in pH 6.8 phosphate buffer indicate that the cocrystals improved the apparent solubility compared to the native drug by 3-9 fold. The absence of stronger heterosynthons between MET and the coformers, their lower melting points and the high solubility of the coformers are the probable reasons for the enhanced solubility of the bioactive component. The MET-NAM cocrystal exhibited the highest solubility/dissolution rate among the three binary solid forms, which may offer improved bioavailability and a lower dose with minimal side effects.
| S-EPMC9041148 | biostudies-literature
Project description:The manipulation of fluids in micro/nanofabricated systems opens new avenues to engineer the transport of matter at the molecular level. Yet the number of methods for the in situ characterization of fluid flows in shallow channels is limited. Here we establish a simple method called nanoparticle velocimetry distribution analysis (NVDA) that relies on wide field microscopy to measure the flow rate and channel height based on the fitting of particle velocity distributions along and across the flow direction. NVDA is validated by simulations, showing errors in velocity and height determination of less than 1% and 8% respectively, as well as with experiments, in which we monitor the behavior of 200 nm nanoparticles conveyed in channels of ~1.8 μm in height. We then show the relevance of this assay for the characterization of flows in bulging channels, and prove its suitability to characterize the concentration of particles across the channel height in the context of visco-elastic focusing. Our method for rapid and quantitative flow characterization has therefore a broad spectrum of applications in micro/nanofluidics, and a strong potential for the optimization of Lab-on-Chips modules in which engineering of confined transport is necessary.
| S-EPMC4431396 | biostudies-literature