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For over 30 years, NETL’s work in multiphase flow science has served as one of the cornerstones of the lab’s research portfolio. Multiphase flow refers to the simultaneous flow of gases, liquids and/or solid materials. The goal of the multiphase flow science team is to provide computational modeling tools to help offset the risk and cost of multiphase reactor development.
Modeling of microwave heating of multiphase systems requires resolving the electromagnetic field from the single particle to the entire cavity. We introduce a multiscale methodology for computationally affordable simulations of microwave heating of multiphase systems consisting of one phase dispersed in a continuum phase. The methodology homogenizes the original multiphase system, by taking advantage of the large separation of length scales, to calculate the effective permittivity, effective thermal conductivity, and volumetric power absorbed in each phase. The methodology is rigorously assessed against particle-resolved detailed numerical simulations of model systems. We demonstrate a significant reduction in computational cost while retaining the accuracy of detailed simulations. Lastly, the present methodology enables high throughput exploration of novel multiphase system designs utilizing microwave heating.
Microwaves provide alternative heating and allow process intensification due to their rapid, volumetric, and selective nature. Recognizing the central role of multiphase reactors in chemical industry, a recent surge in employing microwaves is observed. We review the recent experimental and modeling investigations of microwave heating of multiphase reactors with emphasis on chemical engineering applications. Here we demonstrate that there is accumulated evidence for improved performance via microwave heating and a clear opportunity for further process intensification. In most of the cases, this improved performance stems from a temperature gradient between two phases. We discuss the ongoing debate on the mechanism by which microwaves affect chemical processes exacerbated by the inability of measuring the temperature distribution in a microwave cavity. We outline recent progress in this direction in monolith reactors and needs for future work. We underscore the lack of detailed modeling and simulation tools even for single-phase systems and emphasize the imperative for multiscale predictive modeling to bridge the experimental-modeling gap. Promising results are shown by a few recently published modeling studies that can predict the experimental measurements in complex multiphase reactors. A combination of experimental and modeling tools can provide a comprehensive picture of the microwave multiphase reactors as well as a means toward scale-up and optimization.
Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.
Abstract Multiphasic scaffolds with tailored gradient features hold significant promise for tissue regeneration applications. Herein, this work reports the transformation of two‐dimensional (2D) layered fiber mats into three‐dimensional (3D) multiphasic scaffolds using a ‘solids‐of‐revolution’ inspired gas‐foaming expansion technology. These scaffolds feature precise control over fiber alignment, pore size, and regional structure. Manipulating nanofiber mat layers and Pluronic F127 concentrations allows further customization of pore size and fiber alignment within different scaffold regions. The cellular response to multiphasic scaffolds demonstrates that the number of cells migrated and proliferated onto the scaffolds is mainly dependent on the pore size rather than fiber alignment. In vivo subcutaneous implantation of multiphasic scaffolds to rats reveals substantial cell infiltration, neo tissue formation, collagen deposition, and new vessel formation within scaffolds, greatly surpassing the capabilities of traditional nanofiber mats. Histological examination indicates the importance of optimizing pore size and fiber alignment for the promotion of cell infiltration and tissue regeneration. Overall, these scaffolds have potential applications in tissue modeling, studying tissue‐tissue interactions, interface tissue engineering, and high‐throughput screening for optimized tissue regeneration.
Purpose: To assess and compare the multiphasic computed tomography (CT) features of neuroendocrine tumor (NET) liver metastases and to investigate the possibility to predict the histologic subtype of the primary tumor. Materials and methods: Between January 2013 and December 2017 patients with biopsy proven NET with at least one liver metastasis who underwent multiphasic CT were enrolled in this study. All cases were acquired using a standardized multiphasic liver CT protocol, arterial, portal, and hepatic venous phases were obtained. Images were retrospectively analyzed in consensus by two abdominal radiologists blinded to clinical data and histologic subtype. The size, number, and location of lesions were noted. Enhancement patterns of each lesion on arterial, portal, and hepatic venous phases were assessed. For quantitative analysis, CT attenuation of tumors, liver parenchyma, and aorta were measured using a circular region of interest (ROI) on arterial, portal, and hepatic venous phases for reflecting the blood supply of the tumor. Tumor-to-aorta and tumor-to-liver ratio were calculated in all three phases. Differences between subtypes of NET liver metastases were studied using ROC analysis of clustered data. Results: A total of 255 neuroendocrine tumor liver metastases divided into 101 (39.6%) pancreatic, 60 (23.5%) gastroenteric and 94 (36.8%) lung NET liver metastases were analyzed. Contrast enhancement of lesions was homogeneous in 78% of patients (n = 199), which was significantly more frequent in patients with pancreatic group than in those with gastroenteric origin (n = 90, 89.1% vs. n = 28, 46.7%; p < 0.001). Gastroenteric NET metastases frequently showed heterogeneous enhancement, which was significantly higher than in the other two groups (50% vs. 3% and 2%). With respect to the location of the primary tumor, the difference in enhancement patterns of the liver lesions was statistically significant (p < 0.001). Pancreatic NET metastases were mostly hyperdense on arterial images and isodense on portal and hepatic venous phase images (79.2%, n = 80). Gastroenteric NET metastases were mostly hyperdense on arterial phase images and hypodense on portal and hepatic venous phase images (n = 28, 46.7%). The most frequent pattern for lung NET metastases was hypoattenuation on all three phase images (n = 44, 46.8%). ROC analysis of clustered data revealed statistically significant differences between pancreatic NET liver metastases, gastroenteric NET liver metastases, and lung NET liver metastases in terms of tumor-to-aorta (T–A) ratio and tumor-to-liver (T–L) ratio (p < 0.001). Conclusion: We observed statistically significant differences in multiphasic CT features (enhancement pattern, T–A ratio, and T–L ratio) between histologic subtypes of NET liver metastases. As the difference in histological subtypes of NET liver metastases results in a different prognosis and different management strategy, these CT features might help to identify the primary tumor when it is not known to ensure accurate tumor staging and to provide optimal treatment.
Abstract The long-term performance, or resistance to elemental release, is the defining characteristic of a nuclear waste form. In the case of multiphase ceramic waste forms, correlating the long-term performance of multiphase ceramic waste forms in the environment to accelerated chemical durability testing in the laboratory is non-trivial owing to their complex microstructures. The fabrication method, which in turn affects the microstructure, is further compounding when comparing multiphase ceramic waste forms. In this work, we propose a “designer waste form” prepared via spark plasma sintering to limit interaction between phases and grain growth during consolidation, leading to monolithic high-density waste forms, which can be used as reference materials for comparing the chemical durability of multiphase waste forms. Designer waste forms containing varying amounts of hollandite in the presence of zirconolite and pyrochlore in a fixed ratio were synthesized. The product consistency test (PCT) and vapor hydration test (VHT) were used to assess the leaching behavior. Samples were unaffected by the VHT after 1500 h. As measured by the PCT, the fractional Cs release decreased as the amount of hollandite increased. Elemental release from the zirconolite and pyrochlore phases did not appear to significantly contribute to the elemental release from the hollandite phase in the designer waste forms.
We perform Lyman- α (Ly α ) Monte-Carlo radiative transfer calculations on a suite of 2500 models of multiphase, outflowing media, which are characterized by 14 parameters. We focus on the Ly α spectra emerging from these media and investigate which properties are dominant in shaping the emerging Ly α profile. Multiphase models give rise to a wide variety of emerging spectra, including single-, double-, and triple-peaked spectra. We find that the dominant parameters in shaping the spectra include (i) the cloud covering factor, f {sub c} , which is in agreement with earlier studies, and (ii) the temperature and number density of residual H i in the hot ionized medium. We attempt to reproduce spectra emerging from multiphase models with “shell models” which are commonly used to fit observed Ly α spectra, and investigate the connection between shell-model parameters and the physical parameters of the clumpy media. In shell models, the neutral hydrogen content of the shell is one of the key parameters controlling Ly α radiative transfer. Because Ly α spectra emerging from multiphase media depend much less on the neutral hydrogen content of the clumps, the shell-model parameters such as H i column density (but also shell velocity and dust content) are generally not well matched to the associated physical parameters of the clumpy media.
Using a set of high-resolution hydrodynamical simulations run with the Cholla code, we investigate how mass and momentum couple to the multiphase components of galactic winds. The simulations model the interaction between a hot wind driven by supernova explosions and a cooler, denser cloud of interstellar or circumgalactic media. By resolving scales of ${\rm{\Delta }}x\lt 0.1$ pc over $\gt 100$ pc distances, our calculations capture how the cloud disruption leads to a distribution of densities and temperatures in the resulting multiphase outflow and quantify the mass and momentum associated with each phase. We find that the multiphase wind contains comparable mass and momenta in phases over a wide range of densities and temperatures extending from the hot wind ($n\approx {10}^{-2.5}$ ${\mathrm{cm}}^{-3}$, $T\approx {10}^{6.5}$ K) to the coldest components ($n\approx {10}^{2}$ ${\mathrm{cm}}^{-3}$, $T\approx {10}^{2}$ K). We further find that the momentum distributes roughly in proportion to the mass in each phase, and the mass loading of the hot phase by the destruction of cold, dense material is an efficient process. These results provide new insight into the physical origin of observed multiphase galactic outflows and inform galaxy formation models that include coarser treatments of galactic winds. Our results confirm that cool gas observed in outflows at large distances from the galaxy ($\gtrsim 1$ kpc) likely does not originate through the entrainment of cold material near the central starburst.
A self-healing polymer material that includes a multiphase copolymer, and a method of making the copolymer, are provided. The multiphase copolymer includes one or more hydrogen bond-forming copolymer segments, each segment including a polymerized acrylamide monomer and a polymerized acrylic monomer. The polymerized acrylamide monomer includes functional groups that form hydrogen bonds in the multiphase copolymer, and is present in the one or more copolymer segments in an amount sufficient for self-healing of the multiphase copolymer.
The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. Herein, to address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.
Solubility trapping is crucial for permanent CO 2 sequestration in deep saline aquifers. For the first time, a pore-scale numerical method is developed to investigate coupled scCO 2 -water two-phase flow, multicomponent (CO 2 (aq), H + , HCO 3 – , CO 3 2 – and OH – ) mass transport, heterogeneous interfacial dissolution reaction, and homogeneous dissociation reactions. Pore-scale details of evolutions of multiphase distributions and concentration fields are presented and discussed. Time evolutions of several variables including averaged CO 2 (aq) concentration, scCO 2 saturation, and pH value are analyzed. Specific interfacial length, an important variable which cannot be determined but is required by continuum models, is investigated in detail. Mass transport coefficient or efficient dissolution rate is also evaluated. The pore-scale results show strong non-equilibrium characteristics during solubility trapping due to non-uniform distributions of multiphase as well as slow mass transport process. Complicated coupling mechanisms between multiphase flow, mass transport and chemical reactions are also revealed. Lastly, effects of wettability are also studied. The pore-scale studies provide deep understanding of non-linear non-equilibrium multiple physicochemical processes during CO 2 solubility trapping processes, and also allow to quantitatively predict some important empirical relationships, such as saturation-interfacial surface area, for continuum models.
This paper presents a two-dimensional immersed boundary method for fluid–structure interaction with compressible multiphase flows involving large structure deformations. This method involves three important parts: flow solver, structure solver and fluid–structure interaction coupling. In the flow solver, the compressible multiphase Navier–Stokes equations for ideal gases are solved by a finite difference method based on a staggered Cartesian mesh, where a fifth-order accuracy Weighted Essentially Non-Oscillation (WENO) scheme is used to handle spatial discretization of the convective term, a fourth-order central difference scheme is employed to discretize the viscous term, the third-order TVD Runge–Kutta scheme is used to discretize the temporal term, and the level-set method is adopted to capture the multi-material interface. In this work, the structure considered is a geometrically non-linear beam which is solved by using a finite element method based on the absolute nodal coordinate formulation (ANCF). The fluid dynamics and the structure motion are coupled in a partitioned iterative manner with a feedback penalty immersed boundary method where the flow dynamics is defined on a fixed Lagrangian grid and the structure dynamics is described on a global coordinate. We perform several validation cases (including fluid over a cylinder, structure dynamics, flow induced vibration of a flexible plate, deformation of a flexible panel induced by shock waves in a shock tube, an inclined flexible plate in a hypersonic flow, and shock-induced collapse of a cylindrical helium cavity in the air), and compare the results with experimental and other numerical data. The present results agree well with the published data and the current experiment. Finally, we further demonstrate the versatility of the present method by applying it to a flexible plate interacting with multiphase flows.
We report that over the past few decades, tremendous progress has been made in the development of particle-based discrete simulation methods versus the conventional continuum-based methods. In particular, the lattice Boltzmann (LB) method has evolved from a theoretical novelty to a ubiquitous, versatile and powerful computational methodology for both fundamental research and engineering applications. It is a kinetic-based mesoscopic approach that bridges the microscales and macroscales, which offers distinctive advantages in simulation fidelity and computational efficiency. Applications of the LB method are now found in a wide range of disciplines including physics, chemistry, materials, biomedicine and various branches of engineering. The present work provides a comprehensive review of the LB method for thermofluids and energy applications, focusing on multiphase flows, thermal flows and thermal multiphase flows with phase change. The review first covers the theoretical framework of the LB method, revealing certain inconsistencies and defects as well as common features of multiphase and thermal LB models. Recent developments in improving the thermodynamic and hydrodynamic consistency, reducing spurious currents, enhancing the numerical stability, etc., are highlighted. These efforts have put the LB method on a firmer theoretical foundation with enhanced LB models that can achieve larger liquid-gas density ratio, higher Reynolds number and flexible surface tension. Examples of applications are provided in fuel cells and batteries, droplet collision, boiling heat transfer and evaporation, and energy storage. Lastly, further developments and future prospect of the LB method are outlined for thermofluids and energy applications.
Synchrotron-based radiation techniques are increasingly used in the field of flow condition measurements. As a complicated multiphase flow, cavitation has been investigated in a wide variety of industrial fields, to prevent damages on spillways at large dams or intensify the bacteria eradication rate during water treatments. To control the detrimental effects of cavitating flows, the dynamics of and mechanisms affecting cavitation development shall be visualized and identified in real-time. Due to the large reflection and scattering effects of multiphase flow, standard visible-light imaging technologies cannot penetrate vapor bubbles and provide extremely limited information. In this study, the synchrotron source emitted high-flux X-ray pulses are used to capture flow motion and visualize internal structures with sufficiently high spatial and temporal resolutions. The image processing procedures on raw x-ray images are developed using 2D Fourier transform (FT) and wavelet transform (WT) to implement the contrast enhancement and de-noising. Through the edge detection algorithm, tracking either seeded particles or phase interfaces inside the opaque multiphase flow can be achieved to perform the particle image velocimetry. Further, with decomposing particle-only and particle-eliminated subplots from the X-ray phase-contrast image, the time-resolved velocity and void fraction fields are obtained simultaneously, paving a way for further flow condition analysis such as densitometry.
Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.
This paper considers the interaction of a shock wave with a multiphase particle-gas system which creates an instability somewhat similar to the Richtmyer-Meshkov instability but with a larger parameter space. Because this parameter space is large, we only present an introductory survey of the effects of many of these parameters. We highlight the effects of particle-gas coupling, incident shock strength, particle size, effective system density differences, and multiple particle relaxation time effects. We focus on dilute flows with mass loading up to 40% and do not attempt to cover all parametric combinations. Instead, we vary one parameter at a time leaving additional parametric combinations for future work. In this work, the simulations are run with the Ares code, developed at Lawrence Livermore National Laboratory, which uses a multiphase particulate transport method to model two-way momentum and energy coupling. A brief validation of these models is presented and coupling effects are explored. It is shown that even for small particles, on the order of 1μm, multi-phase coupling effects are important and diminish the circulation deposition on the interface by up to 25%. These coupling effects are shown to create large temperature deviations from the dusty gas approximation, up to 20% greater, especially at higher shock strengths. It is also found that for a multiphase instability, the vortex sheet deposited at the interface separates into two sheets. In conclusion, depending on the particle and particle-gas Atwood numbers, the instability may be suppressed or enhanced by the interactions of these two vortex sheets.
Casper is a process-based asynchronous progress model for MPI one-sided communication on multi-and many-core architectures. The one-sided communication is not truly one-sided in most MPI implementations: the target process still relies on software progress to complete incoming operations. Casper allows the user to specify an arbitrary number of cores dedicated to background ghost processes and transparently redirects the RMA operations to ghost processes by utilizing the PMPI redirection and MPI-3 shared-memory technologies. Although Casper benefits applications that suffer from lack of asynchronous progress, the operation redirection design might not support complex multiphase applications effectively, which often involve dynamically changing communication density and computing workloads. In this paper, we present an adaptive mechanism in Casper to address the limitation of static asynchronous progress in multiphase applications. For this we exploit two adaptive strategies, a user-guided strategy and a fully transparent and automatic strategy based on self-profiling and prediction, to dynamically reconfigure the asynchronous progress in Casper according to real-time performance characteristics during multiphase execution. We evaluate the adaptive approaches in both microbenchmarks and a real quantum chemistry application suite, NWChem, on the Cray XC30 supercomputer and an Intel Omni-Path cluster.