How does graphene grow on complex 3D morphologies?

Three-dimensional chiral materials with intrinsic chirality play a crucial role in achieving a strong chiral response and flexible light manipulation. Reconfigurable chirality through the 3D morphological transformation of chiral materials is significant for greater freedom in tailoring light but remains a challenge. Inspired by the unique 3D morphological memory capability of shape memory alloys (SMAs), we demonstrate and discuss a chiral resonator in the microwave regime that can realize reconfigurable chirality through 3D morphological transformation. The introduction of heating film realizes voltage control of SMA’s morphology for utilizing the temperature sensitivity of SMA better, enabling arbitrary control of circular dichroism (CD) flip and CD intensity. The qualitative and quantitative analysis of the surface current distribution of chiral enantiomers reveals that the chirality of meta-atoms originates from the surge of electric dipole p x and electric quadrupole Q. It is worth mentioning that the proposed strategy to achieve reconfigurable chirality using 3D morphological transformations can be directly extended to other higher frequencies, such as visible, infrared, and terahertz bands. Significantly, our paradigm to study the relationship between complex 3D morphology and chirality holds potential for application in biosensing, spin detection, and spin-selective devices.

In this article we attempt to shed some light on the factors that determine whether volume-increasing reactions and growth in pores will reduce or increase permeability. We will start by describing fi eld-scale examples of reaction-driven fracturing, and use a Discrete Element Model (DEM) to analyze how the resulting pattern and the rate and progress of reaction depend on the initial porosity of the rock. Ultimately, however, stress generation is related to growth processes taking place at the pore scale. We will therefore zoom in and describe pore-scale growth processes and how these are associated with fracturing and the production of new reactive surface area and new transport channelways for migrating fl uids. Stress generation by growth in pores requires that crystals continue to grow even after having ‘hit’ the pore wall. This implies that the fl uid from which the crystals precipitate is not squeezed out from the reactive interface by the normal stress generated by the growth, but can be kept in place as a thin fi lm by opposing forces that operate at very small scales. To understand the dynamics of crystal growth against confi ning pore walls, we need to zoom in even further and examine interface processes taking place at the nanometer scale. Hence, the last part of this chapter focuses on the nanometer-scale morphology of the reacting interface and the mechanical and transport properties of the fl uids confi ned along reactive grain boundaries.

“Black mass” is the industry term used to describe a type of e-waste comprising crushed and shredded battery cells. It is an intermediate product in the recycling of spent, end-of-life, batteries. It comprises a mixture of metals including; lithium, manganese, cobalt and nickel, which are valuable recycling commodities, of increasing strategic and economic importance to meet future global demands in the “battery revolution”. The objective of this “phase characterisation” investigation was to provide an indicative profile of the morphological characteristics, grain size, composition and textural variability for a natural state (unprepared) and prepared (powdered) sample of black mass from Europe. It is envisaged that this is relevant to: (a) develop a pragmatic sampling strategy to ensure a non-biased, representative sample is provided for assaying; (b) determine the optimal processing route (e.g., hydrometallurgical, pyrometallurgical); (c) identify the presence of payable phases; (d) identify phases that may hinder metal recovery or penalty components; (e) develop assay procedures; and (f) assist in the identification of hazards and the mitigation of risks. The techniques used were: (1) visual examination and binocular microscopy; (2) manual scanning electron microscopy (SEM); (3) automated scanning electron microscopy with linked energy dispersive spectrometers (SEM-EDS), to characterise the phases present and particle types (using AMICS); (4) X-ray computed tomography (X-CT) to provide the 3D morphology of the particles; and (5) laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) to detect and quantify the phases that cannot be detected using automated SEM-EDS and X-CT. The results demonstrated that automated SEM-EDS can be used to characterise and quantify the phases present, and the phase chemistry allows the particle types to be assigned back to the primary battery components. Some phases identified are at present not linked back to the original components and further work is needed to refine the compositional groups, with the analysis of additional samples from different feeds/streams in Europe, China and USA. Interactive SEM-EDS analysis also demonstrated that there is a high-resolution chemical variability within the individual particle categories. X-CT imaging demonstrated the complex 3D textures and morphology of the particles, which is potentially significant during sample preparation and chemical analysis. Neither automated nor manual interactive analysis can determine the presence / abundance of the light elements, such as lithium. However, LA-ICP-MS was successful in detecting and quantifying lithium. Furthermore, LA-ICP-MS was able to determine contents of 67 other non-metals and metals at a 20-micron scale with ppm to ppb level detection limits.

Predicting where and when asphaltene deposition will occur remains a big challenge, particularly with changes to produced fluids over the field life such as changes to GOR and water cut. Small changes in fluid properties or changes in flow dynamics can alter the plugging frequency of a well. Changes in fluid properties affect thermodynamics and, therefore, change the precipitation tendency. Moreover, changes in fluid properties can alter the interaction of asphaltene aggregates with steel surfaces and, therefore, the deposition tendency. Deposition tendency is also influenced by changes in flow dynamics, such as variations in fluid velocities and flow regimes. There is a common industry perception that deposition becomes less severe or even ceases in the presence of water. The reason for this is not entirely understood, but the presence of water might induce chemical changes in asphaltenes as well as modifying tubular wettability, both of which can influence asphaltene to surface adhesion. In our previous publication (SPE-213844) we investigated the potential causes behind the disappearance of asphaltene deposition in an onshore carbonate oilfield over a period of twenty years. A deposition severity score was calculated from the number of asphaltene related interventions, such as wireline inspection, analysis of field deposits and chemical soaks. This deposition severity score was correlated against water cut. This empirical relationship showed that deposition severity decreases with water cut and stops above 40%. The review confirmed that there was no evidence that the decrease in deposition occurred because the precipitation onset moved inside the formation, as result of changes to reservoir pressure or mixing with injection gas. In this paper the same dataset was used to evaluate if changes to asphaltene deposition severity can be explained by changes in flow conditions. This was evaluated by first correlating changes in deposition severity to changes in the volume of oil produced. Secondly, changes in deposition severity were evaluated for transitions in flow conditions (pressure, temperature, flow rate and flow pattern). The evaluation was done by calculations with historic well testing in a steady state well simulator as well as in a dynamic multiphase flow simulator. The simulations clearly indicated that changes in asphaltene deposition severity are not related to the amount of oil produced, lower asphaltene deposition over a period of time is not the result of less oil produced for that period of time. Steady state wellbore simulations showed a drop in Productivity Index (PI) after water breakthrough, then stabilizes for a period of time followed by a further drop in PI. These results might imply that the presence of water initially exacerbates the asphaltene build-up but then decreases as a result of changing hydrodynamics. Dynamic multiphase flow simulations revealed that the depths at which historical deposits have been recorded coincide with flow regime. No deposits have been recorded at depths below PSAT, where the flow regime changes to bubble flow with reduced contact between oil and tubular and increased oil film wall shear stress. The section of the well where deposits occur (from 3,000’ to 8.600’) is in annular flow, from low to high water cut. However, the oil film wall shear stress increases with water cut which could possibly contribute to the reduced deposition severity. The results of this new study indicate that changes in flow dynamics, resulting from increasing water cut affect asphaltene deposition and appear aligned with results of other laboratory and modeling studies.

Summary Recent developments in the modeling and prediction of pressure transient tests for various reservoir and flow conditions have indicated the similarities in response that may exist among totally different cases. Several case studies are used to point out pitfalls associated with force-fitting a particular flow regime or reservoir condition on a set of pressure vs. time data. Introduction Current diagnostic procedures used to select a reservoir flow model to interpret well tests include various plots of pressure drop or pressure recovery vs. some function of time. These plots include log ?p vs. log ?t, log-log plot; p vs. log ?t, semilog plot; p vs. ?t, linear flow plot; p vs. 4?t, bilinear flow plot; and p vs. (1/?t), spherical flow plot. The utility of these plots has been shown in a series of published papers during the past decades. A review of these was made recently by Ramey.1 With the advances in analytical and numerical modeling of flow problems in reservoirs, many papers have discussed modeling the response of an idealized reservoir geometry or flow regime under the conditions of a given wellbore flow. Because of many possibilities that may exist in real situations, many investigators are working to formulate and to obtain the pressure response in reservoirs of increasing complexity. Real-life examples of pressure data to fit a given idealized model are often nonexistent. Consequently, many authors use synthetic data to point out the use of their proposed technique or model. In fact, practicing engineers now are reading about many techniques and models for which there may never be examples of actual data to fit. Some people may even criticize the enormous effort toward prediction of pressure response in certain idealized models. One must note, however, that all the idealized cases published to data and yet to be published are opening our eyes to response similarities that may exist among the performance of completely different systems. The purpose of this paper is to present a few examples, make a comparison between the responses of various reservoir models, and point out the errors in interpretation if the radial flow model is forced on the data in all cases. Reservoir Flow Models Reservoir geometry and kz/kr are the two major factors controlling the nature of flow between the high-pressure portion of the field and the wellbore. Fig. 1 shows examples where cases of radial, linear, and spherical flow may develop. The bulk of the well testing literature is based on the assumption of a radial flow regime. Studies conducted on complex geometries, such as multilayered with or without crossflow and various wellbore and boundary conditions, have focused primarily on the main frame of a radial flow system. The case of linear flow has been emphasized mainly for fracture-controlled flow regime. Recently, the pressure behavior in a long and narrow reservoir was reviewed.2 Spherical flow has received very sporadic attention in the literature.3–5 Each of these flow regimes has its own distinct flow equation. The solution to the flow equation in the absence of wellbore effects results in a straight line on a plot of pressure vs. some function of time. The straight line is obtained for radial flow on a p vs. log ?t, for linear flow on p vs. ?t, and for spherical case on p vs. 1/?t. Selection of the proper flow system depends on what is known about the particular reservoir. For tests conducted during the development stages of a field, such a selection may be possible. The real difficulty is during the exploratory phase, when field data are too few to support a given model strongly. The question addressed here is whether the formation of a straight line on a given pressure vs. time plot is indicative of the corresponding flow regime. The answer would have been easy if the straight line plots were unique. Evidence shows that pressure vs. time data for a given system may result in pseudostraight lines on two or more characteristic plots. A pseudostraight line is defined here as a trend of data points resembling a straight line on a pressure vs. time plot with no physical condition justifying such a case. Experimentation With Theoretical Plots Consider a set of pressure vs. time data where p=f(t) results in a straight line on given plot. The entire set or a portion of the same set may result in a pseudostraight line on other plots. Figs. 2 through 7 show the results of experimentation with these plots. Each case starts with an assumed condition, and the graphical representation on other scales are presented.

This report summarizes the experimental and theoretical work leading to the scale-up of the SRC-I Demonstration Plant slurry fired heater. The scale-up involved a theoretical model using empirical relations in the derivation, and employed variables such as flow conditions, liquid viscosity, and slug frequency. Such variables have been shown to affect the heat transfer characteristics ofthe system. The model assumes that, if all other variables remain constant, the heat transfer coefficient can be scaled up proportional to D/sup -2/3/ (D = inside diameter of the fired heater tube). All flow conditions, liquid viscosities, and pipe inclinations relevant to the demonstration plant have indicated a slug flow regime in the slurry fired heater. The annular and stratified flow regimes should be avoided to minimize the potential for excessive pipe erosion and to decrease temperature gradients along the pipe cross section leading to coking and thermal stresses, respectively. Cold-flow studies in 3- and 6.75-in.-inside-diameter (ID) pipes were conducted to determine the effect of scale-up on flow regime, slug frequency, and slug dimensions. The developed model assumes that conduction heat transfer occurs through the liquid film surrounding the gas slug and laminar convective heat transfer to the liquid slug. A weighted average of these two heat transfer mechanisms gives a value for the average pipe heat transfer coefficient. The cold-flow work showed a decrease in the observed slug frequency between the 3- and 6.75-ID pipes. Data on the ratio of gas to liquid slug length in the 6.75-in. pipe are not yet complete, but are expected to yield generally lower values than those obtained in the 3-in. pipe; this will probably affect the scale-up to demonstration plant conditions. 5 references, 15 figures, 7 tables.

. This study investigates the geometric effects of 90-degree vertical elbows and flow configurations in two-phase flow. The study shows that the elbows make a significant effect on the transport characteristics of two-phase flow, which includes the changes in interfacial structures, bubble interaction mechanisms and flow regime transition. The effect of the elbows is characterized for global and local two-phase flow parameters. The global two-phase flow parameters include two-phase pressure, interfacial structures and flow regime transition. In order to characterize the frictional pressure drop and minor loss across the vertical elbows, pressure measurements are obtained across the test section over a wide range of flow conditions in both single-phase and two-phase flow conditions. A two-phase pressure drop correlation analogous to Lockhart-Martinelli correlation is proposed to predict the minor loss across the elbows. A high speed camera is employed to perform extensive flow visualization studies across the elbows in vertical upward, horizontal and vertical downward sections and modified flow regime maps are proposed. It is found that modified flow regime maps immediately downstream of the vertical upward elbow deviate significantly from the conventional flow regime map. A qualitative assessment of the counter-current flow limitation characteristics specific to the current experimental facility is performed. A multi-sensor conductivity probe is used to measure local two-phase flow parameters such as: void fraction, bubble velocity, interfacial area concentration and bubble frequency. The local measurements are obtained for six different flow conditions at ten measurement locations along axial direction of the test section. Both the vertical-upward and vertical-downward elbows have a significant impact on bubble distribution, resulting in, a bimodal distribution along the horizontal radius of the tube cross-section and migration of bubbles towards the inside of the elbow curvatures immediately downstream of the vertical-upward and vertical-downward elbows, respectively. The elbow effect decays further downstream of the elbow and bubbles migrate to more conventional distribution patterns. The axial transport of void fraction and interfacial area concentration shows that the elbows promote bubble disintegration. Preliminary comparisons between the interfacial area transport model and the experimental data for verticalupward and vertical downward section are also presented.

Thermal-hydraulic modeling and simulation are heavily dependent on the knowledge of flow regimes, which sort multiphase flow patterns into classifications with static boundaries that display characteristic features of flow patterns and phase distributions. Flow regime maps heretofore have been primarily developed mainly for vertical or horizontal pipe orientations by capturing images of flows at varying conditions and assigning them to different flow regimes based on visualization studies. As such, this method is inherently subjective. Furthermore, classifying two-phase flows into regimes with static boundaries is not physical, as two-phase flows undergo gradual changes as they transition from one regime to another. Therefore, such a static approach is not only unphysical but also inaccurate, resulting in the erroneous analysis of two-phase flows. Additionally, subjective observations at such flow conditions are prone to disagreement and inconsistencies in labeling. These difficulties are exacerbated in inclined two-phase flows. In this study, a novel two-phase flow pattern map employing the dynamic red, green, and blue (RGB) color model is presented. The model contains the embedded information of three normalized time-averaged statistics from signals obtained by a dual-ring impedance meter. A database of impedance meter signals from a 25.4-mm-inner-diameter adiabatic air-water inclinable test facility is used to compute statistics for each experimental flow condition. Multiple statistical measures are evaluated, and a principal component analysis is performed for feature selection. By mapping each parameter to intensities of red, green, and blue, a dynamic RGB flow pattern map can be produced in which groups of similar colors are representative of various flow regimes and the dynamic transition characteristics between the flow regimes are characterized physically. Therefore, this new method makes viable the observation of how flow patterns gradually change with respect to angle, as well as the fuzzification or quantification of the uncertainty surrounding experimental transition boundaries.

Predicting aerodynamic forces on bluff bodies remains to be a challenging task due to the unpredictable flow behavior, specifically at higher Reynolds numbers. Experimental approaches to determine aerodynamic coefficients could be costly and time consuming. In the meantime, use of numerical techniques could also require a considerable computational cost and time depending on complexity of the flow behavior. The research focusses on developing an effective deep learning technique to predict aerodynamic force coefficients acting on elliptical bluff bodies for a given aspect ratio and given flow condition. Collecting data for drag and lift coefficients of several aspect ratios for flow conditions starting from onset of vortex shredding to verge of subcritical region is conducted by an accurate full order model. The specified region will provide a transient flow behavior and thus lift coefficient will be represented in terms of root mean square value and drag coefficient in terms of a mean value. With variations in flow behavior and vortex shredding frequencies, it requires to select an appropriate turbulence model, optimum discretization of fluid domain and time step to obtain an accurate result. Flow simulations are conducted primarily using Unsteady Reynolds Averaged Navier-Stokes Equations (URANS) model and Detached Eddy Simulations (DES) model. Effectiveness in using different turbulence models for specified flow regimes are also explored in comparison to available experimental results. At lower Reynolds numbers, aerodynamic force coefficients for a specified body will only depend on Reynolds number. But after a certain specific Reynolds number, aerodynamic forces are dependent on the Mach number in addition to Reynolds number. Therefore, for higher Reynolds numbers, aerodynamic force coefficients are recorded for multiple Mach numbers with same Reynolds number and will be fed to the neural network. With the development of the machine learning and neural network modelling, many of the fields have nourished and created effective and efficient technologies to ease complex functions and activities. Our goal is to ease the complexity in the computational fluid dynamic field with a deep neural network tool created to predict drag and lift coefficient of elliptical bluff bodies for a given aspect ratio with an acceptable accuracy level. Researchers have developed deep neural network tools to predict various flow conditions and have succeeded with sufficient accuracy and a satisfying reduction of computational cost. In our proposed deep learning neural network, we have chosen to model the network with inputs as the geometry setup and the flow conditions with validated drag and lift coefficients. The model will extract the necessary flow features into filters with the convolution operation performed on the inputs. Our main directive is to create a deep learned neural network tool to predict the target values within an acceptable range of accuracy while minimizing the computation cost.

The bulk heterojunction (BHJ) nanomorphology in organic solar cells strongly affects the final efficiency of the device. Progress in experimental techniques now allows visualization of the complex 3D BHJ morphology. It is, therefore, important to characterize the topological properties of the morphology in order to quantify the link between morphology features and performance. Here, we introduce a suite of morphology descriptors which encode the complex nature of the multi-stage photovoltaic process in the BHJ. These morphology descriptors are easily determined using an approach based on converting the morphology into an equivalent weighted, labeled, undirected graph. We show how these descriptors can be used to interrogate BHJ morphologies, allow identification of bottlenecks in the photovoltaic process, and conduct quantitative comparison between morphologies with respect to each sub-process in the photovoltaic phenomena. This framework provides a simple and easy-to-use characterization tool that can be used to unravel the impact of morphology on complex transport phenomena.

A numerical study of aqueous droplet generation in a high Reynolds (Re) number air flow was performed in a microfluidic flow-focusing geometry. Droplet breakup mechanisms, flow regime mapping, droplet morphology, and droplet generation frequency were studied in a high initial air flow under various flow conditions. Several flow regimes were identified including dripping, unstable dripping, plugging, stratified flow, multi-satellite droplet formation, and unstable jetting. Unstable dripping, multi-satellite droplet formation, and unstable jetting have been observed as new flow regimes in our study. We found that the high inertial air flow remarkably induces the formation of these new flow regimes by retaining unique droplet generation mechanisms and morphology. In particular, the polydisperse spray of tiny droplets is formed at the junction in the multi-satellite droplet formation regime, while at the end of a jet in the unstable jetting regime. On the other hand, stable droplet generation occurs in the dripping and plugging regimes, while generated droplets in the unstable dripping, unstable jetting, and multi-satellite droplet formation regimes are unstable. The maximum generation frequency of ~ 1900Hz was obtained under the unstable dripping regime. It was found that increasing Re number results in droplet size reduction, while higher capillary (Ca)number leads to bigger droplets.

Dynamic surface water wetting and drying are characteristic of most headwater stream networks, influencing ecological function and downstream water quality. While topography and subsurface properties have been shown to influence spatial surface water persistence, it is unclear how sensitive non‐perennial stream network flow regimes are to persistent drought conditions. To address this unknown, we monitored surface water presence and absence for three consecutive drought years at 31 locations across a 0.25 km 2 non‐perennial headwater stream network in central coastal California. We coupled these observations with landscape characteristics and climatic conditions to examine both physical and climatic drivers of spatiotemporal flow activation and persistence. We observed non‐stationarity in flow regimes throughout the stream network; reaches that were characterised by seasonal flow in 1 year became ephemeral as the drought conditions progressed. We observed declining spatial variability in surface water persistence and declining correlations between persistence and landscape attributes with ongoing drought. From year one to year three, as drought conditions intensified, correlation coefficients between surface water persistence and topographic wetness index declined from r = 0.60 to r = 0.17, stream channel slope declined from r = 0.47 to r = 0.21, profile curvature declined from r = −0.51 to r = −0.21, and elevation declined from r = −0.33 to r = −0.06. A principal component analysis suggests that, unlike prior years, flow activation events after several years of drought were more closely associated with precipitation event characteristics than antecedent storage states. This work suggests semi‐arid landscapes such as central coastal California may see shifts in the flow regimes of aquatic systems as landscape aridity intensifies with climate change. As predictive indicators of flow may shift from physical to climatic factors, the timing, intensity and frequency of individual storm events may play increasingly larger roles in driving annual‐scale flow conditions.

Flow regime behavior in upwards oil-water flow has been studied in a large diameter, inclinable, multiphase flow loop. With the pipe vertical, bubble flow occurred for all conditions tested. When the pipe was inclined by 5 or 15° from vertical, a new flow regime, countercurrent bubble flow, was observed over a wide range of flow conditions. Theoretical flow regime maps developed for gas-liquid flow did not properly predict oil-water flow regime behavior.

A quantitative method for the characterisation of karst aquifers based on spring hydrograph analysis

Machine learning for multiphase flowrate estimation with time series sensing data