Insights from high-fidelity modeling of industrial rotary bell atomization.

The global automotive industry sprayed over 2.6 billion liters of paint in 2018, much of which through electrostatic rotary bell atomization, a highly complex process involving the fluid mechanics of rapidly rotating thin films tearing apart into micrometer-thin filaments and droplets. Coating operations account for 65% of the energy usage in a typical automotive assembly plant, representing 10,000s of gigawatt-hours each year in the United States alone. Optimization of these processes would allow for improved robustness, reduced material waste, increased throughput, and significantly reduced energy usage. Here, we introduce a high-fidelity mathematical and algorithmic framework to analyze rotary bell atomization dynamics at industrially relevant conditions. Our approach couples laboratory experiment with the development of robust non-Newtonian fluid models; devises high-order accurate numerical methods to compute the coupled bell, paint, and gas dynamics; and efficiently exploits high-performance supercomputing architectures. These advances have yielded insight into key dynamics, including i) parametric trends in film, sheeting, and filament characteristics as a function of fluid rheology, delivery rates, and bell speed; ii) the impact of nonuniform film thicknesses on atomization performance; and iii) an understanding of spray composition via primary and secondary atomization. These findings result in coating design principles that are poised to improve energy- and cost-efficiency in a wide array of industrial and manufacturing settings.

Cryo-electron tomography with mixed-scale dense neural networks reveals key steps in deployment of Toxoplasma invasion machinery.

Host cell invasion by intracellular, eukaryotic parasites within the phylum Apicomplexa is a remarkable and active process involving the coordinated action of apical organelles and other structures. To date, capturing how these structures interact during invasion has been difficult to observe in detail. Here, we used cryogenic electron tomography to image the apical complex of Toxoplasma gondii tachyzoites under conditions that mimic resting parasites and those primed to invade through stimulation with calcium ionophore. Through the application of mixed-scale dense networks for image processing, we developed a highly efficient pipeline for annotation of tomograms, enabling us to identify and extract densities of relevant subcellular organelles and accurately analyze features in 3-D. The results reveal a dramatic change in the shape of the anteriorly located apical vesicle upon its apparent fusion with a rhoptry that occurs only in the stimulated parasites. We also present information indicating that this vesicle originates from the vesicles that parallel the intraconoidal microtubules and that the latter two structures are linked by a novel tether. We show that a rosette structure previously proposed to be involved in rhoptry secretion is associated with apical vesicles beyond just the most anterior one. This result, suggesting multiple vesicles are primed to enable rhoptry secretion, may shed light on the mechanisms Toxoplasma employs to enable repeated invasion attempts. Using the same approach, we examine Plasmodium falciparum merozoites and show that they too possess an apical vesicle just beneath a rosette, demonstrating evolutionary conservation of this overall subcellular organization.

The case for data science in experimental chemistry: examples and recommendations.

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

Cross-correlation analysis of X-ray photon correlation spectroscopy to extract rotational diffusion coefficients.

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering.

Modern scientific instruments are acquiring data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems-that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. Here, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We validate the method using synthetic data, demonstrating that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments.

Xi-cam: a versatile interface for data visualization and analysis.

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

A mixed-scale dense convolutional neural network for image analysis.

Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.

Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase.

Free-electron lasers now have the ability to collect X-ray diffraction patterns from individual molecules; however, each sample is delivered at unknown orientation and may be in one of several conformational states, each with a different molecular structure. Hit rates are often low, typically around 0.1%, limiting the number of useful images that can be collected. Determining accurate structural information requires classifying and orienting each image, accurately assembling them into a 3D diffraction intensity function, and determining missing phase information. Additionally, single particles typically scatter very few photons, leading to high image noise levels. We develop a multitiered iterative phasing algorithm to reconstruct structural information from single-particle diffraction data by simultaneously determining the states, orientations, intensities, phases, and underlying structure in a single iterative procedure. We leverage real-space constraints on the structure to help guide optimization and reconstruct underlying structure from very few images with excellent global convergence properties. We show that this approach can determine structural resolution beyond what is suggested by standard Shannon sampling arguments for ideal images and is also robust to noise.

Nanosurveyor: a framework for real-time data processing.

BACKGROUND: The ever improving brightness of accelerator based sources is enabling novel observations and discoveries with faster frame rates, larger fields of view, higher resolution, and higher dimensionality. RESULTS: Here we present an integrated software/algorithmic framework designed to capitalize on high-throughput experiments through efficient kernels, load-balanced workflows, which are scalable in design. We describe the streamlined processing pipeline of ptychography data analysis. CONCLUSIONS: The pipeline provides throughput, compression, and resolution as well as rapid feedback to the microscope operators.

Iterative phasing for fluctuation X-ray scattering.

Fluctuation X-ray scattering (FXS) is an extension of small- and wide-angle X-ray scattering in which the X-ray snapshots are taken below rotational diffusion times. This technique, performed using a free electron laser or ultrabright synchrotron source, provides significantly more experimental information compared with traditional solution scattering methods. We develop a multitiered iterative phasing algorithm to determine the underlying structure of the scattering object from FXS data.

Multicellular architecture of malignant breast epithelia influences mechanics.

Cell-matrix and cell-cell mechanosensing are important in many cellular processes, particularly for epithelial cells. A crucial question, which remains unexplored, is how the mechanical microenvironment is altered as a result of changes to multicellular tissue structure during cancer progression. In this study, we investigated the influence of the multicellular tissue architecture on mechanical properties of the epithelial component of the mammary acinus. Using creep compression tests on multicellular breast epithelial structures, we found that pre-malignant acini with no lumen (MCF10AT) were significantly stiffer than normal hollow acini (MCF10A) by 60%. This difference depended on structural changes in the pre-malignant acini, as neither single cells nor normal multicellular acini tested before lumen formation exhibited these differences. To understand these differences, we simulated the deformation of the acini with different multicellular architectures and calculated their mechanical properties; our results suggest that lumen filling alone can explain the experimentally observed stiffness increase. We also simulated a single contracting cell in different multicellular architectures and found that lumen filling led to a 20% increase in the "perceived stiffness" of a single contracting cell independent of any changes to matrix mechanics. Our results suggest that lumen filling in carcinogenesis alters the mechanical microenvironment in multicellular epithelial structures, a phenotype that may cause downstream disruptions to mechanosensing.

Rapid disorganization of mechanically interacting systems of mammary acini.

Cells and multicellular structures can mechanically align and concentrate fibers in their ECM environment and can sense and respond to mechanical cues by differentiating, branching, or disorganizing. Here we show that mammary acini with compromised structural integrity can interconnect by forming long collagen lines. These collagen lines then coordinate and accelerate transition to an invasive phenotype. Interacting acini begin to disorganize within 12.5 ± 4.7 h in a spatially coordinated manner, whereas acini that do not interact mechanically with other acini disorganize more slowly (in 21.8 ± 4.1 h) and to a lesser extent (P < 0.0001). When the directed mechanical connections between acini were cut with a laser, the acini reverted to a slowly disorganizing phenotype. When acini were fully mechanically isolated from other acini and also from the bulk gel by box-cuts with a side length <900 µm, transition to an invasive phenotype was blocked in 20 of 20 experiments, regardless of waiting time. Thus, pairs or groups of mammary acini can interact mechanically over long distances through the collagen matrix, and these directed mechanical interactions facilitate transition to an invasive phenotype.

Algorithmic framework for X-ray nanocrystallographic reconstruction in the presence of the indexing ambiguity.

X-ray nanocrystallography allows the structure of a macromolecule to be determined from a large ensemble of nanocrystals. However, several parameters, including crystal sizes, orientations, and incident photon flux densities, are initially unknown and images are highly corrupted with noise. Autoindexing techniques, commonly used in conventional crystallography, can determine orientations using Bragg peak patterns, but only up to crystal lattice symmetry. This limitation results in an ambiguity in the orientations, known as the indexing ambiguity, when the diffraction pattern displays less symmetry than the lattice and leads to data that appear twinned if left unresolved. Furthermore, missing phase information must be recovered to determine the imaged object's structure. We present an algorithmic framework to determine crystal size, incident photon flux density, and orientation in the presence of the indexing ambiguity. We show that phase information can be computed from nanocrystallographic diffraction using an iterative phasing algorithm, without extra experimental requirements, atomicity assumptions, or knowledge of similar structures required by current phasing methods. The feasibility of this approach is tested on simulated data with parameters and noise levels common in current experiments.

Multiscale modeling of membrane rearrangement, drainage, and rupture in evolving foams.

Modeling the physics of foams and foamlike materials, such as soapy froths, fire retardants, and lightweight crash-absorbent structures, presents challenges, because of the vastly different time and space scales involved. By separating and coupling these disparate scales, we have designed a multiscale framework to model dry foam dynamics. This leads to a predictive and flexible computational methodology linking, with a few simplifying assumptions, foam drainage, rupture, and topological rearrangement, to coupled interface-fluid motion under surface tension, gravity, and incompressible fluid dynamics. Our computed results match theoretical analyses and experimentally observed physical effects, including thin-film drainage and interference, and are used to study bubble rupture cascades and macroscopic rearrangement. The developed multiscale model allows quantitative computation of complex foam evolution phenomena.

The Voronoi Implicit Interface Method for computing multiphase physics.

We introduce a numerical framework, the Voronoi Implicit Interface Method for tracking multiple interacting and evolving regions (phases) whose motion is determined by complex physics (fluids, mechanics, elasticity, etc.), intricate jump conditions, internal constraints, and boundary conditions. The method works in two and three dimensions, handles tens of thousands of interfaces and separate phases, and easily and automatically handles multiple junctions, triple points, and quadruple points in two dimensions, as well as triple lines, etc., in higher dimensions. Topological changes occur naturally, with no surgery required. The method is first-order accurate at junction points/lines, and of arbitrarily high-order accuracy away from such degeneracies. The method uses a single function to describe all phases simultaneously, represented on a fixed Eulerian mesh. We test the method's accuracy through convergence tests, and demonstrate its applications to geometric flows, accurate prediction of von Neumann's law for multiphase curvature flow, and robustness under complex fluid flow with surface tension and large shearing forces.

Automatic Structure Analysis in High-Throughput Characterization of Porous Materials.

Inspection of the structure and the void space of a porous material is a critical step in most computational studies involving guest molecules. Some sections of the void space, like inaccessible pockets, have to be identified and blocked in molecular simulations. These pockets are typically detected by visual analysis of the geometry, potential or free energy landscapes, or a histogram of an initial molecular simulation. Such visual analysis is time-consuming and inhibits characterization of large sets of materials required in studies focused on identification of the best materials for a given application. We present an automatic approach that bypasses manual visual analysis of this kind, thereby enabling execution of molecular simulations in an unsupervised, high-throughput manner. In our approach, we used a partial differential equations-based front propagation technique to segment out channels and inaccessible pockets of a periodic unit cell of a material. We cast the problem as a path planning problem in 3D space representing a periodic fragment of porous material, and solve the resulting Eikonal equation by using Fast Marching Methods. One attractive feature of this approach is that the to-be-analyzed data can be of varying types, including, for example, a 3D grid representing the distance to the material's surface, the potential or free energy of a molecule inside the material, or even a histogram (a set of snapshots) from a molecular simulation showing areas which were visited by the molecule during the simulation.

Exact subgrid interface correction schemes for elliptic interface problems.

We introduce a nonconforming finite-element method for second order elliptic interface problems. Our approach applies to problems in which discontinuous coefficients and singular sources on the interface may give rise to jump discontinuities in either the solution or its normal derivative. Given a standard background mesh and an interface that passes between elements, the key idea is to construct a singular correction function that satisfies the prescribed jump conditions, providing accurate subgrid resolution of the discontinuities. Utilizing the closest point extension and an implicit interface representation by the signed distance function, an algorithm is established to construct the correction function. The result is a function that is supported only on the interface elements, represented by the regular basis functions, and bounded independently of the interface location with respect to the background mesh. In the particular case of a constant second-order coefficient, our regularization by a singular function is straightforward, and the resulting left-hand side is identical to that of a regular problem without introducing any instability. The influence of the regularization appears solely on the right-hand side, which simplifies the implementation. In the more general case of discontinuous second-order coefficients, a normalization is invoked which introduces a constraint equation on the interface. This results in a problem statement similar to that of a saddle-point problem. We employ two-level iteration as the solution strategy, which exhibits aspects similar to those of iterative preconditioning strategies.

Simulating complex tumor dynamics from avascular to vascular growth using a general level-set method.

A comprehensive continuum model of solid tumor evolution and development is investigated in detail numerically, both under the assumption of spherical symmetry and for arbitrary two-dimensional growth. The level set approach is used to obtain solutions for a recently developed multi-cell transport model formulated as a moving boundary problem for the evolution of the tumor. The model represents both the avascular and the vascular phase of growth, and is able to simulate when the transition occurs; progressive formation of a necrotic core and a rim structure in the tumor during the avascular phase are also captured. In terms of transport processes, the interaction of the tumor with the surrounding tissue is realistically incorporated. The two-dimensional simulation results are presented for different initial configurations. The computational framework, based on a Cartesian mesh/narrow band level-set method, can be applied to similar models that require the solution of coupled advection-diffusion equations with a moving boundary inside a fixed domain. The solution algorithm is designed so that extension to three-dimensional simulations is straightforward.