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DOE OSTI2017

Co-optimization of CO 2 -EOR and Storage Processes under Geological Uncertainty

This paper presents an integrated numerical framework to co-optimize EOR and CO 2 storage performance in the Farnsworth field unit (FWU), Ochiltree County, Texas. The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process. The reservoir flow model has been constructed based on the field geophysical, geological, and engineering data. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). A history match of primary and secondary recovery processes was conducted to estimate the reservoir and multiphase flow parameters as the baseline case for analyzing the effect of recycling produced gas, infill drilling and water alternating gas (WAG) cycles on oil recovery and CO 2 storage. A multi-objective optimization model was defined for maximizing both oil recovery and CO 2 storage. The uncertainty quantification model comprising the Latin Hypercube sampling, Monte Carlo simulation, and sensitivity analysis, was used to study the effects of uncertain variables on the defined objective functions. Uncertain variables such as bottom hole injection pressure, WAG cycle, injection and production group rates, and gas-oil ratio among others were selected. The most significant variables were selected as control variables to be used for the optimization process. A neural network optimization algorithm was utilized to optimize the objective function both with and without geological uncertainty. The vertical permeability anisotropy (Kv/Kh) was selected as one of the uncertain parameters in the optimization process. The simulation results were compared to a scenario baseline case that predicted CO 2 storage of 74%. The results showed an improved approach for optimizing oil recovery and CO 2 storage in the FWU. The optimization process predicted more than 94% of CO 2 storage and most importantly about 28% of incremental oil recovery. The sensitivity analysis reduced the number of control variables to decrease computational time. A risk aversion factor was used to represent results at various confidence levels to assist management in the decision-making process. The defined objective functions were proved to be a robust approach to co-optimize oil recovery and CO 2 storage. The Farnsworth CO 2 project will serve as a benchmark for future CO 2 –EOR or CCUS projects in the Anadarko basin or geologically similar basins throughout the world.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A dynamic microbial sulfur cycle in a serpentinizing continental ophiolite

Summary Serpentinization is the hydration and oxidation of ultramafic rock, which occurs as oceanic lithosphere is emplaced onto continental margins (ophiolites), and along the seafloor as faulting exposes this mantle‐derived material to circulating hydrothermal fluids. This process leads to distinctive fluid chemistries as molecular hydrogen (H 2 ) and hydroxyl ions (OH − ) are produced and reduced carbon compounds are mobilized. Serpentinizing ophiolites also serve as a vector to transport sulfur compounds from the seafloor onto the continents. We investigated hyperalkaline, sulfur‐rich, brackish groundwater in a serpentinizing continental ophiolite to elucidate the role of sulfur compounds in fuelling in situ microbial activities. Here we illustrate that key sulfur‐cycling taxa, including Dethiobacter , Desulfitispora and ‘ Desulforudis ’, persist throughout this extreme environment. Biologically catalysed redox reactions involving sulfate, sulfide and intermediate sulfur compounds are thermodynamically favourable in the groundwater, which indicates they may be vital to sustaining life in these characteristically oxidant‐ and energy‐limited systems. Furthermore, metagenomic and metatranscriptomic analyses reveal a complex network involving sulfate reduction, sulfide oxidation and thiosulfate reactions. Our findings highlight the importance of the complete inorganic sulfur cycle in serpentinizing fluids and suggest sulfur biogeochemistry provides a key link between terrestrial serpentinizing ecosystems and their submarine heritage.

Sabuda, Mary C.↗

DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗

DOE OSTI2016

Bypass Flow Resistance in Prismatic Gas-Cooled Nuclear Reactors

Available computational fluid dynamics (CFD) predictions of pressure distributions in the vertical bypass flow between blocks in a prismatic gas-cooled reactor (GCR) have been analyzed to deduce apparent friction factors and loss coefficients for systems and network codes. We performed calculations for vertical gap spacings "s" of 2, 6 and 10 mm, horizontal gaps between the blocks of two mm and two flow rates, giving a range of gap Reynolds numbers Re Dh of about 40 to 5300. Laminar predictions of the fully-developed friction factor f fd were about three to ten per cent lower than the classical infinitely-wide channel In the entry region, the local apparent friction factor was slightly higher than the classic idealized case but the hydraulic entry length L hy was approximately the same. The per cent reduction in flow resistance was greater than the per cent increase in flow area at the vertical corners of the blocks. The standard k-ϵ model was employed for flows expected to be turbulent. Its predictions of f fd and flow resistance were significantly higher than direct numerical simulations for the classic case; the value of L hy was about thirty gap spacings. Initial quantitative information for entry coefficients and loss coefficients for the expansion-contraction junctions between blocks is also presented. Our study demonstrates how CFD predictions can be employed to provide integral quantities needed in systems and network codes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Deep learning surrogate model for kinetic Landau-fluid closure with collision

In this work, the kinetic Landau-fluid (LF) closure with collision and periodic boundary condition is used in the development of the deep learning (DL) surrogate model. A classical neural network, namely, feedforward neural network or sometimes termed multilayer perceptron, is constructed and trained to learn the kinetic LF closure in the static limit and arbitrary mean free path in configuration space. The preliminary relation between best hyperparameters and critical parameters for data generation is found. Compared with the numerical approach (non-Fourier method) of the LF closure, the deep learning surrogate model shows an order of magnitude of improvement in terms of accuracy. Perhaps most importantly, the surrogate model closure has been integrated for the first time with fluid simulations. Our DL-enabled fluid simulations, for the first time, give the correct Landau damping rate for a wide range of wave vectors, while the Hammett–Perkins closure cannot produce the correct damping rate. We correctly connect the collisionless Hammett–Perkins closure and collisional Braginskii closure to reproduce the intrinsic nonlocal feature of the heat flux with DL techniques. We address the most concerning error accumulation problem and find that simulations with the deep learning surrogate model are as good as, if not better than, simulations with the analytic closure in terms of long-term numerical stability in the linear Landau damping test.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Subsurface stress criticality associated with fluid injection and determined using machine learning

Machine-learning methods and apparatus are disclosed to determine critical state or other parameters related to fluid-driven failure of a terrestrial locale impacted by anthropogenic activities such as hydraulic fracturing, hydrocarbon extraction, wastewater disposal, or geothermal harvesting. Acoustic emission, seismic waves, or other detectable indicators of microscopic processes are sensed. A classifier is trained using time series of microscopic data along with corresponding data of critical state or failure events. In disclosed examples, random forests and artificial neural networks are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to live data from a fluid injection locale in order to assess a frictional state, assess seismic hazard, assess permeability, make predictions regarding a future fluid-driven failure event, or drive engineering solutions for mitigation or remediation. Variations are disclosed.

Johnson, Paul Allan↗

DOE OSTI2012

CHEMISTRY IN THE FIRST HYDROSTATIC CORE STAGE BY ADOPTING THREE-DIMENSIONAL RADIATION HYDRODYNAMIC SIMULATIONS

We investigate molecular evolution from a molecular cloud core to a first hydrostatic core in three spatial dimensions. We perform a radiation hydrodynamic simulation in order to trace fluid parcels, in which molecular evolution is investigated, using a gas-phase and grain-surface chemical reaction network. We derive spatial distributions of molecular abundances and column densities in the molecular cloud core harboring the first core. We find that the total gas and ice abundances of many species in a cold era (10 K) remain unaltered until the temperature reaches {approx}500 K. The gas abundances in the warm envelope and the outer layer of the first core (T {approx}< 500 K) are mainly determined via the sublimation of ice-mantle species. Above 500 K, the abundant molecules, such as H{sub 2}CO, start to be destroyed, and simple molecules, such as CO, H{sub 2}O, and N{sub 2}, are reformed. On the other hand, some molecules are effectively formed at high temperature; carbon chains, such as C{sub 2}H{sub 2} and cyanopolyynes, are formed at temperatures >700 K. We also find that large organic molecules, such as CH{sub 3}OH and HCOOCH{sub 3}, are associated with the first core (r {approx}< 10 AU). Although the abundances of these molecules in the first core stage are comparable to or less than in the protostellar stage (hot corino), reflecting the lower luminosity of the central object, their column densities in our model are comparable to the observed values toward the prototypical hot corino, IRAS 16293-2422. We propose that these large organic molecules can be good tracers of the first cores.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Numerical Investigation of Fuel Property Effects on Mixed-Mode Combustion in a Spark-Ignition Engine

In this research, lean mixed-mode combustion is numerically investigated using computational fluid dynamics (CFD) in a spark-ignition engine. A new E30 fuel surrogate is developed using a neural network model with matched octane numbers. A skeletal mechanism is also developed by automated mechanism reduction and by incorporating a NO x submechanism. A hybrid approach that couples the G-equation model and the well-stirred reactor model is employed for turbulent combustion modeling. The developed CFD model is shown to well predict pressure and apparent heat release rate (AHRR) traces compared with experiment. Two types of combustion cycles (deflagration-only and mixed-mode cycles) are observed. The mixed-mode cycles feature early flame propagation and subsequent end-gas auto-ignition, leading to two distinctive AHRR peaks. The validated CFD model is then employed to investigate the effects of NO x chemistry. The NO x chemistry is found to promote auto-ignition through the residual gas, while the deflagration phase remains largely unaffected. Sensitivity analysis is finally performed to understand effects of fuel properties, including heat of vaporization (HoV) and laminar flame speed (S L ). An increased HoV tends to suppress auto-ignition through charge cooling, while the impact of HoV on flame propagation is insignificant. In contrast, an increased S L is found to significantly promote both flame propagation and end-gas auto-ignition. The promoting effect of S L on auto-ignition is not a direct chemical effect; it is rather caused by an advancement of the combustion phasing, which increases compression heating of the end-gas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Isovalent Alloying and Heterovalent Substititution as Routes to Accelerate the Development and Optimization of Super-Efficient Halide Perovskite PV Solar Cells

Halide perovskites represent one of the fastest growing photovoltaic (PV) device technologies in the last decade, with research efficiencies among the highest confirmed. However, the research needed to advance tandem perovskite solar cells requires a specialized understanding base, but there is no such base for this young group of materials with the same depth as in semiconductor alloy research. Whereas IV-IV, II-V and II-VI semiconductors have a sophisticated science foundation refined over decades due of their use in electronics, lasers, LEDs and night vision applications, this is not yet the case for halide perovskites. At the same time, these materials behave rather differently than conventional semiconductors. Within the rapidly unfolding field of PV, the emerging halide-perovskite alloy research has largely been based on empiricism. Clearly, a concerted and comprehensive application to halide perovskite alloys of the modern quantum theory of solids brought us over the years a broad understanding of the critical factors enabling conventional inorganic PV could leapfrog the Edisonian, trial-and-error search for solutions to critical problems that are poorly understood. These theory approaches are required to accelerate the progress toward commercially ready hybrid-perovskites by providing a theory-guided experiment component to the EERE investments in this high-priority R&D. During this project we have studied three different rungs on the ladder of knowledge about hybrid perovskites. This kind of single halide perovskites represent the first level of development (rung 1) of solar cells, and includes materials such as APbI 3 , ASnI 3 , APbBr 3 , ASnBr 3 with different A cations, such as methylammonium (MA) with formamidinium (FM). Rung 2 is obtained by mixing B cations (Sn and Pb), and X anions (Cl and Br or I) producing a disordered alloy (A, A’)(M,M’)(X,X’) 3 -space. The most advanced rung corresponds to heterovalent-substitution of halide perovskites, creating an ordered double-perovskite compound. Although the focus of the project was on Rungs 2 and 3, we have made important discoveries regarding the nature of pure perovskites. We’ve observed that cubic perovskites should be modeled by a polymorphous network, instead of the basic single unit cell approach. This leads to the conclusion that perovskites have a fluid-like nature. An article regarding this work has been submitted to Nature Materials. Using the approach described above, we have also simulated alloys of halide perovskites, representing rung 2. We have focused on mixtures of [Cs,FA][Sn,Pb]I 3 with different concentrations, being able to disentangle the structural and electronic factors that determines the variation (bowing) of properties such as band gaps, mixing enthalpies and decomposition energies. For rung 3 compounds, we’ve closely interacted with experimental groups clarifying the chemical routes to obtain a certain compound, and what should be the most stable structure for them. As the final product, and consequent dissemination of the research, we’ve submitted two papers for publication, that are currently under evaluation. The interaction with experimental groups was also an important focus. We have participated in several local, national and international meetings, and reached out more closely to groups from CU Boulder and Arkansas.

14 SOLAR ENERGY↗

NASA NTRS1998

Turbulent Compressible Convection with Rotation: Mean Flows and Differential Rotation - 2

The effects of rotation on turbulent, compressible convection within stellar envelopes are studied through three-dimensional numerical simulations conducted within a local f-plane model. This work seeks to understand the types of differential rotation that can be established in convective envelopes of stars like the Sun, for which recent helioseismic observations suggest an angular velocity profile with depth and latitude at variance with many theoretical predictions. This paper analyzes the mechanisms that are responsible for the mean (horizontally averaged) zonal and meridional flows that are produced by convection influenced by Coriolis forces. The compressible convection is considered for a range of Rayleigh, Taylor, and Prandtl (and thus Rossby) numbers encompassing both laminar and turbulent flow conditions under weak and strong rotational constraints. When the nonlinearities are moderate, the effects of rotation on the resulting laminar cellular convection leads to distinctive tilts of the cell boundaries away from the vertical. These yield correlations between vertical and horizontal motions that generate Reynolds stresses that can drive mean flows, interpretable as differential rotation and meridional circulations. Under more vigorous forcing, the resulting turbulent convection involves complicated and contorted fluid particle trajectories, with few clear correlations between vertical and horizontal motions, punctuated by an evolving and intricate downflow network that can extend over much of the depth of the layer. Within such networks are some coherent structures of vortical downflow that tend to align with the rotation axis. These yield a novel turbulent alignment mechanism, distinct from the laminar tilting of cellular boundaries, that can provide the principal correlated motions and thus Reynolds stresses and subsequently mean flows. The emergence of such coherent structures that can persist amidst more random motions is a characteristic of turbulence with symmetries broken by rotation and stratification. Such structure is here found to play a crucial role in defining the mean zonal and meridional flows that coexist with the convection. Though they are subject to strong inertial oscillations, the strength and type of the mean flows are determined by a combination of the laminar tilting and the turbulent alignment mechanisms. Varying the parameters produces a wide range of mean motions. Among these, some turbulent solutions exhibit a mean zonal velocity profile that is nearly constant with depth, much as deduced by helioseismology at midlatitudes within the Sun. The solutions exhibit a definite handedness, with the direction of the persistent mean flows often prescribing a spiral with depth near the boundaries, also in accord with helioseismic deductions. The mean helicity has a profile that is positive in the upper portion of the domain and negative in the lower portion, a property bearing on magnetic dynamo processes that may be realized within such rotating layers of turbulent convection.

Brummell, Nicholas H.↗

DOE OSTI2017

A Dual-Lattice Discrete Element Model to Understand Hydraulic Fracturing in a Naturally Fractured System

Interaction between hydraulic fractures (HF) and natural fractures (NF) may lead to complex fracture networks as a consequence of branching and merging of the natural and hydraulically-induced/reopened fractures. Conventional simulation methods, with assumptions of homogeneous and continuous media, are not able to capture realistic fracture patterns in the presence of pre-existing natural fractures (open or healed). These natural discontinuities alter the local stress field and impact the fracturing fluid pressure distribution when hydraulically-driven fractures approach natural discontinuities. Modeling the propagation of one or more propagating hydraulic fractures in naturally fractured reservoirs should carefully consider the complex mechanical and hydraulic interactions between hydraulically-driven and natural fractures. A rigorous hydraulic fracturing model based on a coupled discrete element method (DEM) and conjugate network flow model is described in this work. The coupled DEM-network flow model conveniently accounts for both continuous poroelasticity and discrete natural fractures simultaneously and provides more realistic fracture growth patterns in low-permeability rocks. This dual lattice DEM-network flow model can capture both tension-induced rock failure and shear-induced natural discontinuity sliding and dilation. Simulation results revealed that reactivation of natural fractures becomes more difficult when the following conditions are present: larger magnitude of stress difference, higher intercepting angle between the natural fracture and the propagating hydraulic fracture (higher implies more orthogonal to the natural fracture), larger natural fracture cohesion and angle of internal friction and/or lower natural fracture permeability - leading to continued growth of the hydraulic fracture. The term stress difference was colloquially used in the preceding sentence. For clarity, we will define this as ΔS = S H,max – S h,min ). The quantitative results of numerous simulations reinforced the intuitive understanding of the underlying phenomena. Other simulations reinforced insights from laboratory experimentation and field measurements that lower injection rates and treating fluid viscosities facilitate preferential fluid intake into natural fractures. Finally, this simulation capability for predicting the nature of complex fracture networks potentially enables optimizing treatment design and completion strategy in naturally fractured reservoirs.

42 ENGINEERING↗

DOE OSTI2017

High-Resolution X-Ray and Neutron Computed Tomography of an Engine Combustion Network Spray G Gasoline Injector

Given the importance of the fuel-injection process on the combustion and emissions performance of gasoline direct injected engines, there has been significant recent interest in understanding the fluid dynamics within the injector, particularly around the needle and through the nozzles. Furthermore, the pressure losses and transients that occur in the flow passages above the needle are also of interest. Simulations of these injectors typically use the nominal design geometry, which does not always match the production geometry. Computed tomography (CT) using x-ray and neutron sources can be used to obtain the real geometry from production injectors, but there are trade-offs in using these techniques. X-ray CT provides high resolution, but cannot penetrate through the thicker parts of the injector. Neutron CT has excellent penetrating power but lower resolution. Here, we present results from a joint effort to characterize a gasoline direct injector representative of the Spray G injector as defined by the Engine Combustion Network. High-resolution (1.2 to 3 µm) x-ray CT measurements from the Advanced Photon Source at Argonne National Laboratory were combined with moderate-resolution (40 µm) neutron CT measurements from the High Flux Isotope Reactor at Oak Ridge National Laboratory to generate a complete internal geometry for the injector. This effort combined the strengths of both facilities’ capabilities, with extremely fine spatially resolved features in the nozzles and injector tips and fine resolution of internal features of the needle along the length of injector. Analysis of the resulting surface model of the internal fluid flow volumes of the injector reveals how the internal cross-sectional area and nozzle hole geometry differs slightly from the design dimensions. A simplified numerical simulation of the internal flow shows how deviations from the design geometry can alter the flow inside the sac and holes. Our results of this study will provide computational modelers with very accurate solid and surface models for use in computational fluid dynamics studies and experimentalists with increased insight into the operating characteristics of their injectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗

DOE OSTI2024

Solving the Orszag–Tang vortex magnetohydrodynamics problem with physics-constrained convolutional neural networks

We study the 2D Orszag–Tang vortex magnetohydrodynamics (MHD) problem through the use of physics-constrained convolutional neural networks (PCNNs) for forecasting the density, ρ, and the magnetic field, B, as well as the prediction of B given the velocity field v of the fluid. In addition to translation equivariance from the convolutional architecture, other physics constraints were embedded: absence of magnetic monopoles, non-negativity of ρ, use of only relevant variables, and the periodic boundary conditions of the problem. The use of only relevant variables and the hard constraint of non-negative ρ were found to facilitate learning greatly. The divergenceless condition ∇·B=0 was implemented as a hard constraint up to machine precision through the use of a magnetic potential to define B=∇×A. Residual networks and data augmentation were also used to improve performance. This allowed for some of the residual models to function as surrogate models and provide reasonably accurate simulations. For the prediction task, the PCNNs were evaluated against a physics-informed neural network, which had the ideal MHD induction equation as a soft constraint. Several models were able to generate highly accurate fields, which are visually almost indistinguishable and have low mean squared error. Only methods with built-in hard constraints produced physical fields with ∇·B=0. The use of PCNNs for MHD has the potential to produce physically consistent real-time simulations to serve as virtual diagnostics in cases where inferences must be made with limited observables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Fracture-permeability behavior of shale

The fracture-permeability behavior of Utica shale, an important play for shale gas and oil, was investigated using a triaxial coreflood device and X-ray tomography in combination with finite-discrete element modeling (FDEM). Fractures generated in both compression and in a direct-shear configuration allowed permeability to be measured across the faces of cylindrical core. Shale with bedding planes perpendicular to direct-shear loading developed complex fracture networks and peak permeability of 30 mD that fell to 5 mD under hydrostatic conditions. Shale with bedding planes parallel to shear loading developed simple fractures with peak permeability as high as 900 mD. In addition to the large anisotropy in fracture permeability, the amount of deformation required to initiate fractures was greater for perpendicular layering (about 1% versus 0.4%), and in both cases activation of existing fractures are more likely sources of permeability in shale gas plays or damaged caprock in CO₂ sequestration because of the significant deformation required to form new fracture networks. FDEM numerical simulations were able to replicate the main features of the fracturing processes while showing the importance of fluid penetration into fractures as well as layering in determining fracture patterns.

02 PETROLEUM↗

DOE OSTI2014

Parallel Infrastructure Modeling and Inversion Module for E4D

Electrical resistivity tomography ERT is a method of imaging the electrical conductivity of the subsurface. Electrical conductivity is a useful metric for understanding the subsurface because it is governed by geomechanical and geochemical properties that drive subsurface systems. ERT works by injecting current into the subsurface across a pair of electrodes, and measuring the corresponding electrical potential response across another pair of electrodes. Many such measurements are strategically taken across an array of electrodes to produce an ERT data set. These data are then processed through a computationally demanding process known as inversion to produce an image of the subsurface conductivity structure that gave rise to the measurements. Data can be inverted to provide 2D images, 3D images, or in the case of time-lapse 3D imaging, 4D images. ERT is generally not well suited for environments with buried electrically conductive infrastructure such as pipes, tanks, or well casings, because these features tend to dominate and degrade ERT images. This reduces or eliminates the utility of ERT imaging where it would otherwise be highly useful for, for example, imaging fluid migration from leaking pipes, imaging soil contamination beneath leaking subsurface tanks, and monitoring contaminant migration in locations with dense network of metal cased monitoring wells. The location and dimension of buried metallic infrastructure is often known. If so, then the effects of the infrastructure can be explicitly modeled within the ERT imaging algorithm, and thereby removed from the corresponding ERT image. However, there are a number of obstacles limiting this application. 1) Metallic infrastructure cannot be accurately modeled with standard codes because of the large contrast in conductivity between the metal and host material. 2) Modeling infrastructure in true dimension requires the computational mesh to be highly refined near the metal inclusions, which increases computational demands. 3) The ERT imaging algorithm requires specialized modifications to accommodate high conductivity inclusions within the computational mesh. The solution to each of these challenges was implemented within E4D (formerly FERM3D), which is a parallel ERT imaging code developed at PNNL (IPID #30249). The infrastructure modeling module implement in E4D uses a method of decoupling the model at the metallic interface(s) boundaries, into several well posed sub-problems (one for each distinct metallic inclusion) that are subsequently solved and recombined to form the global solution. The approach is based on the immersed interface method, with has been applied for similar problems in other fields (e.g. semiconductor industry). Comparisons to analytic solutions have shown the results to be very accurate, addressing item 1 above. The solution is implemented about an unstructured mesh, which enables arbitrary shapes to be efficiently modelled, thereby addressing item 2 above. In addition, the algorithm is written in parallel and shows excellent scalability, which also addresses equation 2 above. Finally, because only the boundaries of metallic inclusions are modeled, there are no high conductivity cells within the modeling mesh, and the problem described by item 3 above is no longer applicable.

Johnson, TimC.↗

DOE OSTI2023

Analysis of ducted fuel injection at high-pressure transcritical conditions using large-eddy simulations

Ducted fuel injection (DFI) is a proposed fuel injection concept for achieving substantial reductions in emissions. In this concept, the fuel is injected through a coannular duct, resulting in increased fuel-air mixing and minimized formation of soot and other unwanted combustion products. Apart from comprehensive experimental investigations on DFI, so far computational studies have been limited to single-point Reynolds-averaged Navier Stokes simulations. Therefore, the objective of this work is to complement these studies by performing large-eddy simulations using a diffuse-interface method to examine the physical mechanisms and combustion processes of DFI, specifically focusing on the mixing process and the effect of fuel-ducting on combustion and pollutant emissions. To this end, finite-rate chemistry simulations are performed of the DFI configuration corresponding to the Engine Combustion Network Spray A injector at transcritical conditions (n-dodecane fuel, 60 bar pressure and 1000 K temperature chamber conditions). A two-equation soot model is employed for the qualitative analysis of soot emissions. Direct comparisons of averaged and instantaneous flow field results with the Spray A configuration are performed to assess the effect of DFI on the first- and second-stage ignition and soot formation. Compared to the free-spray condition, the results show that the DFI case exhibits a combination of (i) increased mass flow rate and entrained air, (ii) larger pressure drop magnitude and flow velocity, and (iii) a closer-to-stoichiometric mixture composition (both globally and locally), each of which is conjectured to contribute toward reduced soot production.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1993

Characterization of voids formed during liquid impregnation of nonwoven multifilament glass networks as related to composite processing

A technique based on matching the refractive index of an invading liquid to that of a fiber mat was used to study entrapment of air ('voids') that occurs during forced in-plane radial flow into nonwoven multifilament glass networks. The usefulness of this technique is demonstrated in quantifying and mapping the air pockets. Experiments with a series of fluids with surface tensions varying from 28 x 10(exp -3) to 36 x 10(exp -3) N/m, viscosities from 45 x 10(exp -3) to 290 x 10(exp -3) Pa.s, and inlet flow rates from 0.15 x 10(exp -6) to 0.75 x 10(exp -6) m(exp 3)/s, showed that void content is a function of the capillary number characterizing the flow process. A critical value of capillary number, Ca = 2.5 x 10(exp -3), identifies a zone below which void content increases exponentially with decreasing capillary number. Above this critical value, negligible entrapment of voids is observed. Similar experiments carried out on surface treated nonwoven mats spanning a range of equilibrium contact angles from 20 deg to 78 deg showed that there is a critical contact angle above which negligible entrapment is observed. Below this value, there is no apparent effect of contact angle on the void fraction - capillary number relationship described earlier. Studies on the effect of filament wettability, and fluid velocity and viscosity on the size of the entrapment (voids) were also carried out. These indicate that larger sized entrapments which envelop more than one pore are favored by a low capillary number in comparison to smaller, pore level bubbles. Experiments were carried out on deformed mats - imposing high permeability spots at regular intervals on a background of low permeability. The effect of these spatial fluctuations in heterogeneity of the mat on entrapment is currently being studied.

Mahale, Anant D.↗