Scalar Mixing Research Articles

CFD modelling and simulations of atomization-based processes for production of drug particles: A review.

Atomization-based techniques are widely used in pharmaceutical industry for production of fine drug particles due to their versatility and adaptability. Key performance measure of such techniques is their ability to provide control over critical quality attributes (CQAs) of produced drug particles. CQAs of drug particles produced via atomization critically depend on fluid dynamics of sprays; resulting mixing, heat and mass transfer; distribution of supersaturation and subsequent nucleation and growth of particles. It is essential to develop and use computational fluid dynamics (CFD) models for adequate understanding of multi-scale transport processes ranging from molecular scale mixing and particle scale processes, and from atomizer nozzle to overall spray chamber scale establishing relationships between CQAs and design and operating parameters of spray nozzle and chamber. In this work, we critically review past and current research efforts on CFD modelling of pharmaceutical atomization-based processes with an objective to provide clear assessment of the state of the art and to provide recommendations. An overview of the key atomization-based methods for producing drug particles with desired CQAs is presented. Key underlying physical processes and relevant concepts are then outlined. This discussion is related to the demands on CFD models; and state of the art is then discussed with respect to the process needs. Recommendations are provided towards higher fidelity and more efficient models of atomized multiphase flow dynamics and turbulence, drying modelling for the produced particles, and validation approaches. We conclude by highlighting a perceived need for numerical atomization studies with a pharmaceutical context; then, we deliver an outlook on current promising active control and machine learning strategies to augment the shift towards quality-by-design approaches in pharmaceutical manufacturing.

MOF-derived one-dimensional micro-nano mixed scale hierarchical porous carbon architecture for highly efficient microwave absorber

MOF-derived one-dimensional micro-nano mixed scale hierarchical porous carbon architecture for highly efficient microwave absorber

MS-UNet: A Novel Multi-scale U-shaped Network for COVID-19 CT Image Segmentation

The U-Net network has its own powerful capabilities in medical image segmentation tasks, yet still it is a challenging task to make U-Net accurately segment the infected lesions of COVID-19 CT images because these lesion areas are usually irregular in shape, various in size, and blurry in boundaries. In this paper, a novel multiscale U-shaped network based on U-Net for accurate segmentation of lesion regions in COVID-19 CT images is proposed. First, we generate two auxiliary scale features (fi0.5, fi1.5) based on the main scale feature (fi1.0) through zoom strategy. Secondly, we design the Scale Integration Module (SIM), which is capable of filtering and aggregating scale-specific features and can fully exploit multi-scale semantic information. Thirdly, the hierarchical mixed module (HMM) has successfully substituted for the down-up aggregation process of the U-Net network, which further enhances the mixed scale features. On the dataset COVID-19-CT829, compared with the recent COVID-19 segmentation model, hiformer, the Dice, Sen and F-measure of our network have increased by 2.24%, 2.83%, 3.14%, respectively; on the dataset COVID-19-CT100, the Dice, Sen and F-measure of our network have increased by 2.91%, 3.72%, 2.42%, respectively. Moreover, we have validated the generalizability and portability of our network on other medical datasets (Polyp segmentation dataset: CVC-612 and kvasir), and our network has also achieved superior results of COVID-19 CT image segmentation.

Insertion and Anchoring of the HIV-1 Fusion Peptide into a Complex Membrane Mimicking the Human T-Cell.

A fundamental understanding of how the HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a nine-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow time scale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarse-grained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the T-cell membrane, highlighting the importance of the conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.

Interactions Between Multiple Physical Particle Injection Pumps in the Southern Ocean

AbstractContributions to the biological pump that arise from the physical circulation are referred to as physical particle injection pumps. A synthesized view of how these physical pumps interact with each other and other components of the biological pump does not yet exist. Here, observations from a quasi‐Lagrangian float and an ocean glider, deployed in the Southern Ocean's Subantarctic Zone for one month during the spring bloom, offer insight into daily‐to‐monthly fluctuations in the mixed layer pump (MLP) and the eddy subduction pump (ESP). Estimated independently, each mechanism contributes intermittent export fluxes of roughly several hundred milligrams of particulate organic carbon (POC) per square meter per day. The glider‐based estimates indicate sustained weekly periods of MLP export fluxes across the base of the mixed layer with a magnitude of mg POC . Potential export fluxes from the ESP, based on a mixed layer instability scaling, occasionally exceed 400 mg POC , with export elevated due to both strong inferred vertical velocities and enhanced isopycnal slopes. Significant export fluxes from the ESP are localized to the edges of mesoscale eddies and to fronts, whereas the MLP acts more broadly due to the larger scales of atmospheric forcing. Regimes occur when MLP and ESP export fluxes can have either the same or opposite sign. Simple summation of fluxes from existing parameterizations of the two pumps likely misrepresents the total physical carbon flux. Insights into how mesoscale stirring and submesoscale velocities set POC vertical structure is a key target to reduce uncertainty in global carbon export fluxes.

Flow regimes in the evolution of a hot buoyant vortex dipole

Vortices and vortex dipoles play a critical role in turbulence, facilitating scalar mixing, diffusion, and energy dissipation. When a temperature gradient is present, buoyancy effects become significant, and buoyant vortices and dipoles emerge as characteristic features of such flows. In this study, we examine the evolution of a buoyant vortex dipole (BVD) arising from a temperature difference between the vortex dipole and the surrounding fluid. Using the Oberbeck–Boussinesq approximation, we derive the governing equations in non-dimensional form to capture the essential physics. The computational domain is periodic, and simulations are performed using the open-source spectral solver Dedalus. The interplay of thermal diffusion, viscous diffusion, and buoyancy drives the evolution of various coherent structures. Based on these interactions, we identify four distinct topological features and classify the evolution of the BVD into six regimes: (a) thermal diffusion-dominated regime, (b) viscous diffusion-dominated regime, (c) balanced diffusion regime, (d) weak wake street regime, (e) buoyancy-driven transition regime, and (f) multiple tertiary wakes regime. This classification provides a comprehensive framework to understand the dynamics of BVDs under varying physical influences.

Quantitative radiography for determining density fluctuations in HED experiments.

We have developed a method to extract density fluctuation measurements from x-ray radiographs of high-energy density (HED) instability growth and turbulence experiments. We use this information to calculate density fluctuation statistics for constraining the performance of turbulent mix models in HED systems. The density calculation combines image filtering, removal of systemic effects such as backlighter variation, calculation of transmission across multiple materials, and use of tracer materials to generate an approximate single-material density field. From the density map, we calculate both average density and a variance-like moment b (density-specific-volume covariance), which we compare to our models. We infer both quantities from a single image, which is significantly more information than the historic single scalar mix width measurements. We also develop a method of analyzing simulation outputs that incorporate both the density fluctuation metric from a turbulence model and the bulk material maps from the hydrodynamic code. This analysis helps address the question of how to initialize the simulations for best comparison to data from systems with large separations of scale in the mixing perturbation initial condition. We find that our data analysis method yields 1D average density and b curves with similar morphology and amplitudes as those from preliminary simulation comparisons.

A Multistate Model Incorporating Relative Survival Extrapolation and Mixed Time Scales for Health Technology Assessment.

Multistate models have been widely applied in health technology assessment. However, extrapolating survival in a multistate model setting presents challenges in terms of precision and bias. In this article, we develop an individual-level continuous-time multistate model that integrates relative survival extrapolation and mixed time scales. We illustrate our proposed model using an illness-death model. We model the transition rates using flexible parametric models. We update the hesim package and the microsimulation package in R to simulate event times from models with mixed time scales. This feature allows us to incorporate relative survival extrapolation in a multistate setting. We compare several multistate settings with different parametric models (standard vs. flexible parametric models), and survival frameworks (all-cause vs. relative survival framework) using a previous clinical trial as an illustrative example. Our proposed approach allows relative survival extrapolation to be carried out in a multistate model. In the example case study, the results agreed better with the observed data than did the commonly applied approach using standard parametric models within an all-cause survival framework. We introduce a multistate model that uses flexible parametric models and integrates relative survival extrapolation with mixed time scales. It provides an alternative to combine short-term trial data with long-term external data within a multistate model context in health technology assessment.

Preserving large-scale features in simulations of elastic turbulence

Simulations of elastic turbulence, the chaotic flow of highly elastic and inertialess polymer solutions, are plagued by numerical difficulties: the chaotically advected polymer conformation tensor develops extremely large gradients and can lose its positive-definiteness, which triggers numerical instabilities. While efforts to tackle these issues have produced a plethora of specialized techniques – tensor decompositions, artificial diffusion, and shock-capturing advection schemes – we still lack an unambiguous route to accurate and efficient simulations. In this work, we show that even when a simulation is numerically stable, maintaining positive-definiteness and displaying the expected chaotic fluctuations, it can still suffer from errors significant enough to distort the large-scale dynamics and flow structures. We focus on two-dimensional simulations of the Oldroyd-B and FENE-P equations, driven by a large-scale cellular body forcing. We first compare two positivity-preserving decompositions of the conformation tensor: symmetric square root (SSR) and Cholesky with a logarithmic transformation (Cholesky-log). While both simulations yield chaotic flows, only the latter preserves the pattern of the forcing, i.e. its fluctuating vortical cells remain ordered in a lattice. In contrast, the SSR simulation exhibits distorted vortical cells that shrink, expand and reorient constantly. To identify the accurate simulation, we appeal to a hitherto overlooked mathematical bound on the determinant of the conformation tensor, which unequivocally rejects the SSR simulation. Importantly, the accuracy of the Cholesky-log simulation is shown to arise from the logarithmic transformation. We also consider local artificial diffusion, a potential low-cost alternative to high-order advection schemes. Unfortunately, the artificially enhanced diffusive smearing of polymer stress in regions of intense stretching substantially modifies the global dynamics. We then show how the spurious large-scale motions, identified here, contaminate predictions of scalar mixing. Finally, we discuss the effects of spatial resolution, which controls the steepness of gradients in a non-diffusive simulation.

On the origin of counter-gradient transport in turbulent scalar flux: physics interpretation and adjoint data assimilation

The present study offers a twofold contribution on counter-gradient transport (CGT) of turbulent scalar flux. First, by examining turbulent scalar mixing through synchronized particle image velocimetry and planar laser-induced fluorescence on an inclined jet in cross-flow, we clarify the previously unexplained phenomenon of CGT, revealing key flow structures, their spatial distribution and modelling implications. Statistical analysis identifies two distinct CGT regions: local cross-gradient transport in the windward shear layer and non-local effects near the wall after injection. These behaviours are driven by specific flow structures, namely Kelvin–Helmholtz vortices (local) and wake vortices (non-local), suggesting that scalar flux can be decomposed into a gradient-type term for gradient diffusion and a term for large-eddy stirring. Second, we propose a new approach for reconstruction of turbulent mean flow and scalar fields using continuous adjoint data assimilation (DA). By rectifying model-form errors through anisotropic correction under observational constraints, our DA model minimizes discrepancies between experimental measurements and numerical predictions. As expected, the introduced forcing term effectively identifies regions where traditional models fall short, particularly in the jet centreline and near-wall regions, thereby enhancing the accuracy of the mean scalar field. These enhancements occur not only within the observation region but also in unseen regions, underscoring present DA approach's reliability and practicality for reproducing mean flow behaviours from limited data. These findings lay a solid foundation for adjoint-based model-consistent data-driven methods, offering promising potential for accurately predicting complex flow scenarios like film cooling.

Competitive influence of interface effect, scale effect and mixed salt ratio on thermal conductivity of mesoporous complex nitrate

The rising prominence of energy security and climate change issues necessitates the advancement of sustainable energy development and energy storage technologies. Enhancing the capabilities of mesoporous composite phase change materials (CPCMs) has gained considerable research interest. CPCM’s heat storage and release performance is largely determined by its thermal conductivity. While the existing literature has documented the individual effects of the composition ratio of mixed molten salts, scale effects, and interfacial effects on thermal conductivity, no studies have been found that investigate the competitive interplay among these three factors with respect to their impact on thermal conductivity. This study aims to gain a deeper insight into the thermal conductivity changes of CPCM consisting of mixed molten salts as the phase change material (PCM) and mesoporous skeleton. A combination of molecular dynamics simulations and experimental investigations was employed to explore how interface effects, scale effects, and the proportion of mixed salts contribute to the thermal conductivity of CPCM. The findings indicate that augmented thermal conductivity, resulting from interface effects, supersedes the diminishing effects of interionic interactions. Compared to mixed salt ratios, interface effects primarily result in variations in the thermal conductivity of CPCM. The thermal conductivity of mixed nitrates escalates alongside scale increases. In the 3–4 nm range, the scale effect and mixed nitrate proportions do not notably compete in terms of influence on thermal conductivity, interface effects are more profound than scale effects. In the 4–9.5 nm range, the scale effect is more profound than mixed nitrate ratios. If the skeleton transitions from SiO2 to Al2O3, the impact of the interface effect on thermal conductivity is greater influential than the scale effect. While transitioning the interface from Al2O3 to ceramic, the effect of the interface is less than or equal to that of the scale effect on the thermal conductivity.

Co-evolutionary dynamics for two adaptively coupled Theta neurons.

Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple network of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, a, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity a, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.

Scalar mixing and entrainment in an axisymmetric jet subjected to external turbulence

The present study aims to understand the process of turbulent entrainment into a jet, as affected by background turbulence, using scalar statistics. Planar laser-induced fluorescence was employed to capture the orthogonal cross sections of the jet at a fixed downstream station with varying background turbulence intensities and length scales. The conditional scalar profiles revealed that the thickness of the scalar turbulent/turbulent interface is greater than that of the traditional turbulent/non-turbulent interface, and the interfacial thickness is an increasing function of the background turbulence intensity. Although nibbling remains the primary entrainment mechanism in the far field, increased occurrence of concentration “holes” within the interfacial layer in the presence of ambient turbulence suggests a more significant role of large-scale engulfment in the turbulent/turbulent entrainment process (although still below 1% of the total mass flux). Enhanced contribution of the area of detached jet patches (i.e., “islands”) to that of the main jet is hypothesized to be evidence of intense detrainment events in the background turbulence. This can potentially contribute to a reduced net entrainment into the jet, which manifests as less negative values of scalar skewness within the jet core.

Large Eddy Simulation of an impinging Jet for Cooling of Blades

Abstract The impinging jet shows extremely high performance in local blade cooling and has been widely applied. In this study, high precision Large Eddy Simulation (LES) was employed to simulate compressible fully turbulent impinging jet under specific conditions, using three subgrid-scale (SGS) models - the Smagorinsky model (SM), the Coherent-structure Smagorinsky Model (CSM), and the Selective Mixed Scale Model (SMSM). The predictive accuracy and computational efficiency of the three SGS models were evaluated. Compared with existing DNS and experimental results, the results indicate that there is no significant difference among the three models in computational efficiency. However, subgrid models have a crucial impact on heat transfer. The SMSM model, which utilizes small-scale turbulence information, can better simulate the heat transfer in the impingement region compared with the SM model and the CSM model.

Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment

Abstract. Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds.

Synthesis, performance and application of environmental protection scale inhibitorβ-CD-MA-SSS copolymer——in electric power industry

The issue of water scaling in the treatment of cooling water has become an imperative problem that should be solved urgently for the sake of safe operation in large-scale flexible DC converter stations. However, the coexistence of Ca2+ and Mg2+ in high content in this sort of pending raw water is quite hard to tackle. β-CD-MA-SSS, an eco-friendly ternary co-polymer antiscalant with good performance of inhibiting the formation of scale, was applied to address this conundrum in this paper. The efficacy of the antiscalant β-CD-MA-SSS was investigated in cooling water systems under the conditions of low hardness, low alkalinity, a temperature not exceeding 80 °C, and a pH range of 6–8. After optimization, this reagent dem onstrated an excellent scale inhibition rate up to 80% for both calcium carbonate and calcium-magnesium mixed scales. Scanning electron microscopy (SEM) analysis revealed a noticeable reduction in the size of CaCO3 particles when subjected to the action of β-CD-MA-SSS. It was postulated from FTIR analysis that nanoinpurity and chelation effects mainly contributed to the mixed scale inhibition. Further, the effect of β-CD-MA-SSS on the actual production wastewater was comprehensively evaluated from the technical, economic and environmental aspects.

Prevalence, Symptom Profiles, and Correlates of Mixed Anxiety-Depression in Male and Female Autistic Youth.

Relatively little attention has been given to mixed anxiety and depression in autistic youth, particularly how this differs between males and females. This study investigated sex-based differences in the prevalence and correlates of mixed anxiety and depression in a sample of 51 autistic males (M age = 10.16 yr, SD = 2.81 yr, and range = 6 yr to 17 yr) and 51 autistic females (M age = - 10.07 yr, SD = 2.76 yr, and range = 6 yr to 17 yr), matched for age, IQ, and autism severity. Self-reports on generalised anxiety disorder and major depressive disorder, morning salivary cortisol, ADOS-2 scores, and WASI-II full-scale scores were collected from these autistic youth, and data on the ASD-related symptoms of these youth were collected from their parents. The data were analysed for total anxiety-depression score levels, for the underlying components of this scale, and for the individual items used in the scale. The results indicate no significant sex differences for the prevalence of mixed anxiety and depression total scores or the underlying components of anxiety and depression or for the individual items of the mixed anxiety-depression scale. There were sex differences in the significant correlates of mixed anxiety and depression: morning cortisol and ASD-related difficulties in social interaction for females, and ASD-related behaviour for males. Males' feelings of being restless or edgy were correlated with their social interaction and repetitive and restricted behaviour. Females' difficulties in social interaction were correlated with their concerns about their abilities and their sleeping problems. Females' sleeping problems, their tendency to talk about dying, and feeling worthless, were correlated with their morning cortisol. These findings suggest that, while mixed anxiety and depression is experienced similarly by autistic males and females at the global, component, and individual item levels, specific aspects of the symptomatology of mixed anxiety and depression are differently associated with aspects of their ASD-related symptomatology and their levels of chronic physiological stress for males and females.

A variable turbulent Schmidt number model in Jet-in-Crossflow simulation and its applications

This study proposes a variable turbulent Schmidt number (Sct) model to improve the precision of Reynolds-Averaged Navier-Stokes (RANS) simulations for Jet-in-Crossflow (JICF) scalar mixing phenomena, with a particular focus on hydrogen/air mixing characterized by strong density variations. By incorporating a correction term associated with turbulent kinetic energy based on a selected Sct value, the model aims to amplify the diffusion rate of the jet, particularly in regions where the interaction between the mainstream flow and the jet is significant. This enhancement in simulation accuracy is validated against Large Eddy Simulation (LES) results, which serve as the benchmark for analysis. Statistical analysis of LES results reveals a notable increase in the turbulent diffusion coefficient in regions characterized by strong interactions between the jet and mainstream flow. Meanwhile, minimal variation in turbulent viscosity is observed, resulting in a reduced Sct in areas with high turbulent kinetic energy. The proposed model is validated within a open-space JICF case, a micro-tube JICF case and a free jet case. In micro-tube JICF scenarios, initial simulations with a fixed Sct value were conducted. Following comparison, a value of 0.7 was identified as the basis for correction. Subsequently, the variable Sct model was implemented, thereby enhancing the accuracy of RANS simulations in micro-tubes.

Software application profile: tpc and micd-R packages for causal discovery with incomplete cohort data.

The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc, fill these gaps. micd and tpc packages are R packages. The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd; https://github.com/bips-hb/tpc).

Mixing in two-dimensional shear flow with smooth fluctuations.

Chaotic variations in flow speed up mixing of scalar fields via intensified stirring. This paper addresses the statistical properties of a passive scalar field mixing in a regular shear flow with random fluctuations against its background. We consider two-dimensional flow with shear component dominating over smooth fluctuations. Such flow is supposed to model passive scalar mixing, e.g., inside a large-scale coherent vortex forming in two-dimensional turbulence or in elastic turbulence in a microchannel. We examine both the decaying case and the case of the continuous forcing of the scalar variances. In both cases dynamics possesses strong intermittency, which can be characterized via the single-point moments and correlation functions calculated in our work. We present general qualitative properties of pair correlation function as well as certain quantitative results obtained in the framework of the model with fluctuations that are short correlated in time.