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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Multiphase lattice Boltzmann simulations for porous media applications

Over the last two decades, lattice Boltzmann methods have become an increasingly popular tool to compute the flow in complex geometries such as porous media. In addition to single phase simulations allowing, for example, a precise quantification of the permeability of a porous sample, a number of extensions to the lattice Boltzmann method are available which allow to study multiphase and multicomponent flows on a pore scale level. In this work, we give an extensive overview on a number of these diffuse interface models and discuss their advantages and disadvantages. Furthermore, we shortly report on multiphase flows containing solid particles, as well as implementation details and optimization issues.

42 ENGINEERING↗
DOE OSTI2019

TOUGH-UDEC: A simulator for coupled multiphase fluid flows, heat transfers and discontinuous deformations in fractured porous media

A numerical simulator entitled TOUGH-UDEC is presented for the assessment of coupled thermal-hydraulic-mechanical processes in fractured porous media. Two existing well-established codes, TOUGH2 and UDEC, are coupled to model multiphase fluid flows, heat transfers, and discontinuous deformations in fractured porous media by means of discrete fracture representation. TOUGH2 is widely used for the modeling of heat transfers and multiphase multicomponent fluid flows, and UDEC is a well-known distinct element code for rock mechanics. The two codes are solved sequentially, with coupling parameters passed to each equation at specific intervals. After solving thermal-hydraulic equations within the TOUGH2 code, pressure and temperature information is imported into the UDEC model. Upon solving the mechanical equation within the UDEC code the calculated fracture apertures are converted to the equivalent permeability and porosity values for a TOUGH2 flow analysis. The solution is calculated by iteratively following an explicit sequence for numerical efficiency. Verifications are presented to demonstrate the capabilities of the coupled TOUGH-UDEC simulator. Three application examples of (1) shear dilation due to increased pore pressure, (2) thermal stress and (3) CO 2 injection, show that the new simulator can be an effective tool for geoengineering applications involving shear activation of fractures and faults.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Thermodynamically consistent algorithms for models of incompressible multiphase polymer solutions with a variable mobility

Here we present a general strategy for developing structure and property preserving numerical algorithms for thermodynamically consistent models of incompressible multiphase polymer solutions with a variable mobility. We first present a formalism to derive thermodynamically consistent, incompressible, multiphase polymer models. Then, we develop the general strategy, known as the supplementary variable method, to devise thermodynamically consistent numerical approximations to the models. We illustrate the numerical strategy using newly developed models of incompressible diblock copolymer solutions coupled with an electric and a magnetic field, respectively. Mesh refinement is conducted to verify convergence rates of the developed schemes. Some numerical examples are given to exhibit underlying dynamics absent from and driven by the external fields, respectively, highlighting differences between models with the variable and constant mobilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2017

Radiation damage of hollandite in multiphase ceramic waste forms

Radiation damage was simulated in multiphase titanate-based ceramic waste forms using an ion accelerator to generate high energy alpha particles (He + ) and an ion implanter to generate 7 MeV gold (Au 3+ ) particles. X-ray diffraction and transmission electron microscopy were used to characterize the damaged surfaces and nearby regions. Simulated multiphase ceramic waste forms were prepared using two processing methods: spark plasma sintering and melt-processing. Both processing methods produced ceramics with similar phase assemblages consisting of hollandite-, zirconolite/pyrochlore-, and perovskite- type phases. The measured heavy ion (Au 3+ ) penetration depth was less in spark plasma sintered samples than in melt-processed samples. Furthermore, structural breakdown of the hollandite phase occurred under He + irradiation indicated by the presence of x-ray diffraction peaks belonging to TiO 2 , BaTiO 5 , and other hollandite related phases (Ba 2 Ti 9 O 20 ). The composition of the constituent hollandite phase affected the extent of damage induced by Au 3+ ions.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Elaborating the phases and mechanical properties of multiphase alloy: Experimental two-dimensional mapping combined with theoretical calculations

Highlights: • Mechanical modulus and hardness distribution mapping is obtained for the first time by NanoBlitz 3D. • Phases mapping and exact element ratio are characterized for the nano multielement carbides by EPMA. • Carbides types are confirmed by acid etching and XRD refinement. • Young's modulus of multiple carbides is verified by DFT calculations and Voigt-Reuss-Hill model. - Abstract: The relationship between types of phases, microstructure and mechanical properties is always a critical challenge for the research and application of multiphase alloys. We report for the first time that NanoBlitz 3D method implemented in iNano is used to investigate the distribution of micro mechanical modulus and hardness for the entire complex multiphase alloy Fe-6.0W-5.0Mo-4.0Cr-2.0V-0.83C by constructing the two-dimensional mapping. In order to build the correlation between the microstructure and properties, the carbides in this tempering state steel are confirmed to be Fe{sub 3}(W, Mo){sub 3}C, Cr{sub 15.58}Fe{sub 7.42}C{sub 6} and V{sub 8}C{sub 7} by acid etching and XRD refinement. The phases mapping is obtained by electron probe micro-analysis. Exact element ratio is characterized for the nano-scale multielement carbides precipitates. The Young's modulus and hardness of the precipitated carbides are up to 330 GPa and 22 GPa, respectively. The distribution range of Young's modulus and hardness is during 150 to 250 GPa and 2.5 to 7.0 GPa for the matrix phases, respectively. The experimental Young's modulus and hardness results are verified by first-principles calculations combined with Voigt-Reuss-Hill approximation. In fact, this work provides a successful demonstration to three-dimensionally characterize the micro mechanical properties for all the ceramics and alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Clinical analysis of the approximate, 3-dimensional, biological effective dose equation in multiphase treatment plans

A multiphase, approximate biological effective dose (BED{sub A}) equation was introduced because most treatment planning systems (TPS) are incapable of calculating the true BED (BED{sub T}). This work investigates the accuracy and precision of the multiphase BED{sub A} relative to the BED{sub T} in clinical cases. Ten patients with head and neck cancer and 10 patients with prostate cancer were studied using their treatment plans from Pinnacle{sup 3} 9.2 (Philips Medical, Fitchburg, WI). The organs at risk (OARs) that were studied are the normal brain, left and right optic nerves, optic chiasm, spinal cord, brainstem, bladder, and rectum. BED{sub A} and BED{sub T} distributions were calculated using MATLAB 2010b (MathWorks, Natick, MA) and analyzed on a voxel basis for percent error, percent error volume histograms (PEVHs), Pearson correlation coefficient, and Bland-Altman analysis. The maximum BED values that were calculated using the BED{sub A} and BED{sub T} methods were also analyzed. BED{sub A} was found to always underestimate BED{sub T}. The accuracy and precision of BED{sub A} distributions varied between the organs: for optic chiasm and brainstem, 50% of the patients had an overall BED{sub A} percent error of A percent error of A percent error of A distributions had maximum errors ranging from 2% to 11%, with the 11% error occurring for bladder. BED{sub A} produced much more accurate maximum BED values with adjacent organs such as normal brain, bladder, and rectum. This study has shown that BED{sub A} can calculate BED distributions with acceptable accuracy under certain circumstances. However, its consistency and accuracy strongly depend on the dose distributions of the different treatment phases. One should be cautious when using BED{sub A}.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

A two-phase mixing layer between parallel gas and liquid streams: multiphase turbulence statistics and influence of interfacial instability

The two-phase mixing layer formed between parallel gas and liquid streams is an important fundamental problem in turbulent multiphase flows. The problem is relevant to many industrial applications and natural phenomena, such as air-blast atomizers in fuel injection systems and breaking waves in the ocean. The velocity difference between the gas and liquid streams triggers an interfacial instability which can be convective or absolute depending on the stream properties and injection parameters. In the present study, a direct numerical simulation of a two-phase gas–liquid mixing layer that lie in the absolute instability regime is conducted. A dominant frequency is observed in the simulation and the numerical result agrees well with the prediction from viscous stability theory. As the interfacial wave plays a critical role in turbulence transition and development, the temporal evolution of turbulent fluctuations (such as the enstrophy) also exhibits a similar frequency. To investigate the statistical response of the multiphase turbulence flow, the simulation has been run for a long physical time so that time-averaging can be performed to yield the statistically converged results for Reynolds stresses and the turbulent kinetic energy (TKE) budget. An extensive mesh refinement study using from 8 million to about 4 billions cells has been performed. The turbulent dissipation is shown to be highly demanding on mesh resolution compared with other terms in TKE budget. The results obtained with the finest mesh are shown to be close to converged results of turbulent dissipation which allow us to obtain estimations of the Kolmogorov and Hinze scales. The estimated Kolmogorov scale is found to be similar to the cell size of the finest mesh used here. The computed Hinze scale is significantly larger than the size of droplets observed and does not seem to be a relevant length scale to describe the smallest size of droplets formed in atomization.

Mechanics↗
DOE OSTI2018

Multiphase Mechanism for the Production of Sulfuric Acid from SO 2 by Criegee Intermediates Formed During the Heterogeneous Reaction of Ozone with Squalene

Here we report a new multiphase reaction mechanism by which Criegee intermediates (CIs), formed by ozone reactions at an alkene surface, convert SO 2 to SO 3 to produce sulfuric acid, a precursor for new particle formation (NPF). During the heterogeneous ozone reaction, in the presence of 220 ppb SO 2 , an unsaturated aerosol (squalene) undergoes rapid chemical erosion, which is accompanied by NPF. A kinetic model predicts that the mechanism for chemical erosion and NPF originate from a common elementary step (CI + SO 2 ) that produces both gas phase SO 3 and small ketones. At low relative humidity (RH = 5%), 20% of the aerosol mass is lost, with 17% of the ozone-surface reactions producing SO 3 . At RH = 60%, the aerosol shrinks by 30%, and the yield of SO 3 is <5%. In conclusion, this multiphase formation mechanism of H 2 SO 4 by CIs is discussed in the context of indoor air quality and atmospheric chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

High-Speed Quantification of Pore-Scale Multiphase Flow of Water and Supercritical CO 2 in 2-D Heterogeneous Porous Micromodels: Flow Regimes and Interface Dynamics

The pore-scale flow of CO 2 and water in 2-D heterogeneous porous micromodels over a Ca range of nearly three orders of magnitude was explored experimentally. The porous geometry is a close reprint of real sandstone, and the experiments were performed under reservoir-relevant conditions (i.e., 8 MPa and 21°C), thus ensuring relevance to practical CO 2 operations. High-speed fluorescent microscopy and image processing were employed to achieve temporally and spatially resolved data, providing a unique view of the dynamics underlying this multiphase flow scenario. Under conditions relevant to CO 2 sequestration, final CO 2 saturation was found to decrease and increase logarithmically with Ca within the capillary and viscous-fingering regimes, respectively, with a minimum occurring during regime crossover. Specific interfacial length generally scales linearly with CO 2 saturation, with higher slopes noted at high Ca due to stronger viscous and inertial forces, as supported by direct pore-scale observations. Additionally, statistical analysis of the interfacial movements revealed that pore-scale events are controlled by their intrinsic dynamics at low Ca, but overrun by the bulk flow at high Ca. During postfront flow, while permeability is typically correlated with total CO 2 saturation in the porous domain (regardless of its mobility), the saturation of active CO 2 pathways in the current study correlated very well with permeability. This alternate approach to characterize relative permeability could serve to mitigate hysteresis in relative permeability curves. Taken together, these results provide unique insights that address inconsistent observations in the literature and previously unanswered questions about the underlying flow dynamics of this important multiphase flow scenario.

42 ENGINEERING↗
DOE OSTI2019

Mimicking Geologic Depositional Fabrics for Multiphase Flow Experiments

A novel laboratory technique is presented for generating two-dimensional beadpacks with reproducible natural geologic sedimentary bedforms. The heterogeneous beadpacks are deposited in a 0.6 m × 0.6 m × 0.02 m glass slab chamber using a programmable two-axis linear actuator. The method takes advantage of natural mechanical sorting processes, along with the ability to program the motion of the arm, to efficiently build cross-beds and ripple laminae similar to those seen in natural water-lain clastic deposits. By using a mixture of grain sizes (200 to 800 μm diameter), depositional fabrics with multiscale heterogeneity (mm to dm) are generated. Reproducibility tests show very high correlation among multiple fabric realizations produced with similar inputs. After introducing this first-of-a-kind capability to produce depositionally realistic fabrics at scales larger than that of rock cores, a demonstrative study is presented that illustrates gravity unstable multiphase flow which is relevant in the context of CO 2 sequestration. Preliminary results highlight the dramatic impact small scale capillary heterogeneities have on multiphase immiscible displacement and can help bridge understanding between core scale experiments and reservoir scale observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Time‐And‐Space Averaging Applied to Intermittent Multiphase Flow Experiments

Abstract Various researchers have studied fluctuations in pore‐scale phase occupancy during multiphase flow in porous media using synchrotron‐based X‐ray microcomputed tomography (micro‐CT). However, the impact of these fluctuations on the concept of a representative volume is not yet fully understood. In this study, we performed spatial and temporal averaging of multiphase flow experiments visualized with synchrotron‐based micro‐CT, focusing on oil saturation as the key parameter to determine a representative time‐and‐space average. Our findings revealed that a saturation value representative of both time and space was achieved during fractional flow experiments in drainage mode with fractional flows of 0.8, 0.5, and 0.3. Furthermore, we computed a range of relative permeabilities on the basis of whether momentaneous saturation or time‐and‐space averaged saturation was utilized for direct simulation. Our results highlighted the importance of time‐and‐space averaging in determining a representative relative permeability and indicated that the temporal and spatial scales covered in a typical micro‐CT flow experiment were sufficient to obtain a representative saturation value for sandstone rock under intermittent flow conditions.

Environmental Sciences & Ecology↗
DOE OSTI2025

A Multiscale Approach to Simulate Non‐Isothermal Multiphase Flow in Deformable Porous Materials

Coupled thermal, hydraulic, and mechanical processes in porous materials play important roles in several energy and environmental technologies. The Darcy-Brinkman-Biot (DBB) framework has proven effective in modeling multiphase fluid flow in deformable porous solids across both pore and Darcy scales, including in systems where fractures coexist with a porous matrix. In this study, we extend the DBB framework, originally designed for isothermal conditions, to address non-isothermal problems by incorporating an energy conservation equation. The resulting solver, hybridBiotThermalInterFoam, enables simulations of coupled multiphase fluid flow, heat transfer, and solid deformation in hybrid-scale systems containing both solid-free regions and ductile porous domains. The new solver is validated through comparisons with analytical solutions and, also, against established heat transfer solvers chtMultiRegionFoam and compressibleInterFoam. Further, a series of 2D and 3D case studies, including two-phase heat transfer in solid-free, static, or deformable porous media, highlights the solver's capacity to simulate complex flow dynamics and heat transport in systems involving high mobility ratios, viscous fingering, and fracture propagation. Our results establish the feasibility of incorporating thermal effects in simulations of a wide variety of energy geotechnics and environmental applications, including enhanced hydrocarbon recovery, soil remediation, and enhanced geothermal energy systems.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

Investigating the effect of heterogeneities across the electrode|multiphase polymer electrolyte interfaces in high-potential lithium batteries

Polymer electrolytes hold great promise for safe and high-energy batteries comprising solid or semi-solid electrolytes. Multiphase polymer electrolytes, consisting of mobile and rigid phases, exhibit fast ion conduction and desired mechanical properties. However, fundamental challenges exist in understanding and regulating interactions at the electrode|electrolyte interface, especially when using high-potential layered oxide active materials at the positive electrode. Here we demonstrate that depletion of the mobile conductive phase at the interface contributes to battery performance degradation. Molecular ionic composite electrolytes, composed of a rigid-rod ionic polymer with nanometric mobile cations and anions, serve as a multiphase platform to investigate the evolution of ion conductive domains at the interface. Chemical and structural characterizations enable the visualization of concentration heterogeneity and spatially resolve the interfacial chemical states over a statistically significant field of view for buried interfaces. We report that concentration and chemical heterogeneities prevail at electrode|electrolyte interfaces, leading to phase separation in polymer electrolytes. In conclusion, understanding the hidden roles of interfacial chemomechanics in polymer electrolytes enables us to design an interphase tailoring strategy based on electrolyte additives to mitigate the interfacial heterogeneity and improve battery performance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys

Abstract Zirconium alloys are widely used in harsh environments characterized by high temperatures, corrosivity, and radiation exposure. These alloys, which have a hexagonal closed packed (h.c.p.) structure thermo-mechanically degrade, when exposed to severe operating environments due to hydride formation. These hydrides have a different crystalline structure, than the matrix, which results in a multiphase alloy. To accurately model these materials at the relevant physical scale, it is necessary to fully characterize them based on a microstructural fingerprint, which is defined here as a combination of features that include hydride geometry, parent and hydride texture and crystalline structure of these multiphase alloys. Hence, this investigation will develop a reduced order modeling approach, where this microstructural fingerprint is used to predict critical fracture stress levels that are physically consistent with microstructural deformation and fracture modes. Machine Learning (ML) methodologies based on Gaussian Process Regression, random forests, and multilayer perceptrons (MLP) were used to predict material fracture critical stress states. MLPs, or neural networks, had the highest accuracy on held-out test sets across three predetermined strain levels of interest. Hydride orientation, grain orientation or texture, and hydride volume fraction had the greatest effect on critical fracture stress levels and had partial dependencies that were highly significant, and in comparison hydride length and hydride spacing have less effects on fracture stresses. Furthermore, these models were also used accurately predicted material response to nominal applied strains as a function of the microstructural fingerprint.

36 MATERIALS SCIENCE↗
DOE OSTI2016

An approach towards tailoring interfacial structures and properties of multiphase renewable thermoplastics from lignin–nitrile rubber

Lignin-derived thermoplastics and elastomers with both versatile performance and commercialization potential have been an elusive pursuit for the past several decades. Lignin content has been limited to about 30 wt %, often requiring chemical modification, solvent fractionation of lignin, or prohibitively expensive additives. Each of these factors is a deterrent to industrial adoption of lignin-based polymers, limiting the potential of this renewable resource. Herein we describe high-performance multiphase thermoplastics made with a blend of 41 wt % unmodified industrial lignin and low-cost additives in a matrix of general-purpose acrylonitrile-butadiene rubber (NBR). Hardwood soda lignin (HSL) and softwood kraft lignin (SKL) were blended under high shear conditions with NBR, carbon black (CB), polyethylene oxide (PEO), boric acid (BA), and dicumyl peroxide (DCP). This combination with SKL lignin in the proper proportions resulted in a thermoplastic with a tensile strength and failure strain of 25.2 MPa and 9 %, respectively; it exhibited an unexpected tensile yield, similar to that of ABS, a commodity thermoplastic. The analogous HSL lignin compositions are tough materials with tensile strengths of 7.3 16.7 MPa and failure strain of 80 140 %. The contrasting ductility and yield stress behavior were analyzed based on the compositions morphology and interfacial structure arising from the nature of each lignin studied. Lastly, the roles of CB as a reinforcement in the rubbery phase, DCP and BA as cross-linkers to create multiphase networks, and PEO to promote the adhesion and compatibility of lignin in commercial-grade NBR are also discussed in detail.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Active motion of multiphase oil droplets: emergent dynamics of squirmers with evolving internal structure

Synthetic soft matter systems, when driven beyond equilibrium by active processes, offer the potential to achieve dynamical states and functions of a complexity found in living matter. Emulsions offer the basis of a simple yet versatile system for identification of the physicochemical principles underlying active soft matter, but how multiple internal phases within emulsion droplets (e.g., Janus morphologies) organize to impact emergent dynamics is not understood. Here, we create multiphase oil droplets with ultralow interfacial tensions but distinct viscosities, and drive them into motion in aqueous micellar solutions. Preferential solubilization of select components of the oil both drives the droplet motion and yields a progression of internal phase morphological states with distinct symmetries. In this work, we find the active droplets to exhibit five dynamical states during morphogenesis. By quantifying microscopic flow fields, we show that it is possible to map the diverse droplet behaviors to squirmer models of spherical microswimmers in Stokes flow, thus showing that multiphase droplets offer the basis of a versatile platform with which to study and engineer the hydrodynamics of microswimmers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A physics-constrained neural network for multiphase flows

The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations. The multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is next implemented to correct the predicted order parameters. This enforces the predicted order parameters to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by another NN with the same network structure, but the conservation of momentum is included in the loss function to shrink the parameter space. The proposed PCNN for MPFs sequentially performs (NN)-(MCBOM)-(NN), which avoids nonphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.

Mechanics↗