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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

The bispectrum of cosmic string temperature fluctuations including recombination effects

We calculate the cosmic microwave background temperature bispectrum from cosmic strings, including the contributions from the last scattering surface, using a well-established Gaussian model for the string energy-momentum correlation functions, and a simplified model for the cosmic fluid. We check our approximation for the integrated Sachs-Wolfe (ISW) contribution against the bispectrum obtained from the full sky map of the cosmic string ISW signal used by the Planck team, obtaining good agreement. We validate our model for the last scattering surface contribution by comparing the predicted temperature power spectrum with that obtained from a full Boltzmann code treatment applied to the Unconnected Segment Model of a string network. We find that including the last scattering contribution has only a small impact on the upper limit on the string tension resulting from the bispectrum at Planck resolutions, and argue that the bispectrum is unlikely to be competitive with the power spectrum at any resolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics

Mesoscopic simulations of hydrocarbon flow in source shales are concerning, in part due to the heterogeneous shale pores with sizes ranging from a few nanometers to a few micrometers. Additionally, the sub-continuum fluid–fluid and fluid–solid interactions in nano- to micro-scale shale pores, which are physically and chemically sophisticated, must be captured. To address those challenges, we present a GPU-accelerated package for simulation of flow in nano- to micro-pore networks with a many-body dissipative particle dynamics (mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the code offloads all intensive workloads on GPUs. Other advancements, such as smart particle packing and no-slip boundary condition in complex pore geometries, are also implemented for the construction and the simulation of the realistic shale pores from 3D nanometer-resolution stack images. Our code is validated for accuracy and compared against the CPU counterpart for speedup. In our benchmark tests, the code delivers nearly perfect strong scaling and weak scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge National Laboratory’s (ORNL) Titan supercomputer. Moreover, a single-GPU benchmark on ORNL’s SummitDev and IBM’s AC922 suggests that the host-to-device NVLink can boost performance over PCIe by a remarkable 40%. Lastly, we demonstrate, through a flow simulation in realistic shale pores, that the CPU counterpart requires 840 Power9 cores to rival the performance delivered by our package with four V100 GPUs on ORNL’s Summit architecture. This simulation package enables quick-turnaround and high-throughput mesoscopic numerical simulations for exploring complex flow phenomena in nano- to micro-porous rocks with realistic pore geometries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning of hidden variables in multiscale fluid simulation

Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. For example, when solving equations related to fluid dynamics for systems with a large Reynolds number, sub-grid effects become important and a turbulence closure is required, and in systems with a large Knudsen number, kinetic effects become important and a kinetic closure is required. By adding an equation governing the growth and transport of the quantity requiring the closure relation, it becomes possible to capture microphysics through the introduction of ‘hidden variables’ that are non-local in space and time. The behavior of the ‘hidden variables’ in response to the fluid conditions can be learned from a higher fidelity or ab-initio model that contains all the microphysics. In our study, a partial differential equation simulator that is end-to-end differentiable is used to train judiciously placed neural networks against ground-truth simulations. We show that this method enables an Euler equation based approach to reproduce non-linear, large Knudsen number plasma physics that can otherwise only be modeled using Boltzmann-like equation simulators such as Vlasov or particle-in-cell modeling.

42 ENGINEERING↗
DOE OSTI2017

Appendix for the Final Technical Report - DE FE0009284

Carbon capture utilization storage (CCUS) is a potential technology to store anthropogenic emissions for CO 2 . Utilization often refers to CO 2 -enhanced oil recovery (CO 2 -EOR). An important factor in the success of CO 2 storage in saline formations or CO 2 -EOR reservoirs is ensuring that the storage occurs safely and is long-term. Assessment of well integrity has become more important for CCS and CO 2 -EOR as it has become apparent that wells represent the most likely migration pathway for CO 2 to leave a CCS storage unit or a CO 2 -EOR reservoir. Although wells represent a migration pathway they are also the best vehicle for employing technology monitoring CO 2 injection and storage. This contradiction of being a potential migration path and key monitoring technology leads to a need to understand how monitoring wells may be similar or different in comparison to other types of wells with respect to migration risk. The maturation and completion US Department of Energy sponsored research projects presents an opportunity to assess the integrity of monitoring wells that have been exposed to injected CO 2 . This paper discusses an integrity assessment of two monitoring wells in an operating CO 2 -EOR flood in Mississippi, USA. The CFU31F-2 and CFU31F-3 monitoring wells were constructed to test monitoring technologies in and above a commercial CO 2 -EOR project. The materials selected and the design of the well were optimized for monitoring. Carbonation in CFU31-F2 was seen as high as 7900 ft, above what was considered top of cement based on the logs. Time-lapse comparison of cement bond amplitude data and acoustic impedance maps show a deterioration of signal that implies a deterioration of cement bond or cement along much of the cemented annulus in the long-string section. Analysis of sidewall cores using XRD and LA-ICP-MS validated the log interpretation by confirming the degradation of cement (carbonation) along the casing-cement interface. The ultrasonic image maps also clearly identify the control lines and monitoring technology attached to the outside of the of the long-string casing on each well studied. The control lines appear as microdebonded or fluid filled vertical features implying that they could act as leakage pathways. The sidewall core through the control line at 10380 ft confirms that CO 2 is migrating along the control line with heavily carbonated cement at the control line interface. LA-ICP-MS and XRD on formation interface of the sidewall cores collected in both wells indicates that CO 2 is also moving of the cement-formation interface. LA-ICP-MS and XRD indicate that the amount carbonation in the center of the cores was less than the carbonation at the interfaces. Indicating that CO 2 is reaching the center of the cores by diffusing in from the interfaces and not migrating up from the reservoir though the porous matrix of the cement. This agrees with Duguid et al. [5] and Carey et al. [1] who have found that the interfaces in the well are more conductive than the porous network of the cement. Both the materials used to construct the well and the decision to attach monitoring technology to the outside of the well may have contributed to the migration of CO 2 along the interfaces. Careful consideration should be given to material selection to ensure that it does not degrade when in contact with the fluids in the reservoir and overlying strata. The addition of the control line on the outside of the casing complicated the cement placement and likely caused no cement to bond to the casing adjacent to the control line leading out of the reservoir. Further study of other wells with external lines should be conducted to see of the results of the construction of CFU31-F2 and -F3 is normal or an exception.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Evidence for a composite organic–inorganic fabric of belemnite rostra: Implications for palaeoceanography and palaeoecology

Carbonate skeletons of fossil marine organisms are widely used to reconstruct palaeoceanographic parameters. Specifically, the geochemistry of Jurassic and Cretaceous belemnite rostra is traditionally interpreted to represent near sea-surface seawater properties. More recently, an increasing number of workers, have reported significant scatter in geochemical data (e.g., δ 18 O, δ 13 C, element/Ca ratio) when comparing rostra from the same stratigraphic level or within a single belemnite rostrum. This scatter is not explained by differential diagenetic overprint alone. Here we report petrographic evidence on the primary ultrastructure of rostra of Megateuthis (Middle Jurassic) and Belemnitella and Gonioteuthis (Late Cretaceous). The biogenic ultrastructure consists of a filigree framework of triaxial branches and tetrahedrons of variable size forming a honeycomb-like network. Data presented here suggest that these rostra yielded as much as 50 to 90% primary pore space. On the level of a working hypothesis – and in analogy with modern cephalopods – we propose that the pore space was formerly filled with body fluid and/or organic compounds during the life time of these organisms. Intra-rostral porosity was post mortem occluded by earliest diagenetic isopachous calcite cements of a non-biogenic origin. These may have been precipitated due to increased alkalinity related to the decay of organic matter. If this holds true, then the resulting fabric represents a composite biogenic/abiogenic structure. In order to optically separate the two calcite phases forming a single calcite fibre, we employed a wide range of state-of-the-art analytical tools to thin sections and ultra-thin sections of well-preserved specimens. Finally, pending a verification of these well-supported ultrastructural data by means of high-resolution geochemical analyses from biogenic and abiogenic phases, we suggest that these findings have significance for those using belemnite rostra as archives of their palaeoenvironment.

58 GEOSCIENCES↗
DOE OSTI2013

Driving self-assembly and emergent dynamics in colloidal suspensions by time-dependent magnetic fields

In this review we discuss recent research on driving self assembly of magnetic particle suspensions subjected to alternating magnetic fields. The variety of structures and effects that can be induced in such systems is remarkably broad due to the large number of variables involved. The alternating field can be uniaxial, biaxial or triaxial, the particles can be spherical or anisometric, and the suspension can be dispersed throughout a volume or confined to a soft interface. In the simplest case the field drives the static or quasi-static assembly of unusual particle structures, such as sheets, networks and open-cell foams. More complex, emergent collective behaviors evolve in systems that can follow the time-dependent field vector. In these cases energy is continuously injected into the system and striking °ow patterns and structures can arise. In fluid volumes these include the formation of advection and vortex lattices. At air-liquid and liquid-liquid interfaces striking dynamic particle assemblies emerge due to the particle-mediated coupling of the applied field to surface excitations. These out-of-equilibrium interface assemblies exhibit a number of remarkable phenomena, including self-propulsion and surface mixing. In addition to discussing various methods of driven self assembly in magnetic suspensions, some of the remarkable properties of these novel materials are described.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Simulating the Effect of Fracture Connectivity on Repository Performance with GDSA Framework - 18589

This work examines the possible migration of radionuclides from a deep geologic nuclear waste repository sited in fractured crystalline host rock. The key safety concern is the potential effect on waste isolation of the inter-connectivity of the fracture network, which is primarily established by the temporal evolution of the temperature and stress fields at the time of original rock intrusion and cooling. Two end members are considered in these simulations, one with a high degree of connectivity to the biosphere, such that advective transport through the fracture and fault network controls radionuclide migration, and the other with a low degree of connectivity, such that slow diffusive transport through the crystalline rock matrix is the controlling process for radionuclide migration to the biosphere. Both end members have several areally extensive, high-transmissivity deformation zones, whose distance to the repository is an important factor for waste isolation capability. Uncertainties in fracture properties (transmissivity, orientation, radius) and fracture distribution also give rise to uncertainty in waste isolation capability (i.e., the overall connectivity of the repository with the surface biosphere). These types of natural system uncertainties are generally present in any geologic site-characterization program and thus represent an important factor in assessing repository performance in hard rock environments. The waste considered in these simulations are spent fuel rod assemblies from the U.S. commercial nuclear reactor fleet. Thermal output of the waste must be considered in the simulations and can influence the rate and timing of waste package failure, waste form degradation, and fluid flux due to thermal expansion around the repository horizon. To achieve a high-fidelity representation of radionuclide transport in fractures and rock matrix, combined with thermal energy transport and fluid flow in fractures and matrix, the mathematical model is solved numerically in a parallel high-performance computing (HPC) environment on a finite volume unstructured grid consisting of approximately 5 million cells, using Geologic Disposal Safety Assessment (GDSA) Framework (https://pa.sandia.gov), an open-source performance assessment tool for deep underground disposal of nuclear waste. GDSA Framework uses PFLOTRAN to solve the balance equations on a three-dimensional grid with heterogeneous properties, using multiple processors in a parallel configuration based on domain decomposition. The fracture networks in these simulations are originally generated as discrete fracture networks (DFNs), which are sets of two-dimensional planes distributed in a three-dimensional domain. The method used in GDSA Framework maps the stochastically generated DFN to an equivalent continuous porous medium (ECPM) domain that allows for the simulation of coupled heat flow, fluid flow, and radionuclide transport, including heat conduction through the matrix of the fractured rock, which is a process not easily modeled in a DFN representation. Computational efficiency is also greatly enhanced using the ECPM method, allowing for a realistic representation and analysis of uncertainties in a multi-realization performance assessment of a deep geologic repository. The effect of fracture connectivity on the waste isolation safety function, as brought to light by these GDSA Framework simulations, points to the importance of including a realistic representation of uncertainties in fracture properties and distribution (effectively, uncertainty in spatial heterogeneity) in repository safety assessment simulations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Deep learning models for interpretation of point of care ultrasound in military working dogs

Introduction: Military working dogs (MWDs) are essential for military operations in a wide range of missions. With this pivotal role, MWDs can become casualties requiring specialized veterinary care that may not always be available far forward on the battlefield. Some injuries such as pneumothorax, hemothorax, or abdominal hemorrhage can be diagnosed using point of care ultrasound (POCUS) such as the Global FAST® exam. This presents a unique opportunity for artificial intelligence (AI) to aid in the interpretation of ultrasound images. In this article, deep learning classification neural networks were developed for POCUS assessment in MWDs. Methods: Images were collected in five MWDs under general anesthesia or deep sedation for all scan points in the Global FAST® exam. For representative injuries, a cadaver model was used from which positive and negative injury images were captured. A total of 327 ultrasound clips were captured and split across scan points for training three different AI network architectures: MobileNetV2, DarkNet-19, and ShrapML. Gradient class activation mapping (GradCAM) overlays were generated for representative images to better explain AI predictions. Results: Performance of AI models reached over 82% accuracy for all scan points. The model with the highest performance was trained with the MobileNetV2 network for the cystocolic scan point achieving 99.8% accuracy. Across all trained networks the diaphragmatic hepatorenal scan point had the best overall performance. However, GradCAM overlays showed that the models with highest accuracy, like MobileNetV2, were not always identifying relevant features. Conversely, the GradCAM heatmaps for ShrapML show general agreement with regions most indicative of fluid accumulation. Discussion: Overall, the AI models developed can automate POCUS predictions in MWDs. Preliminarily, ShrapML had the strongest performance and prediction rate paired with accurately tracking fluid accumulation sites, making it the most suitable option for eventual real-time deployment with ultrasound systems. Further integration of this technology with imaging technologies will expand use of POCUS-based triage of MWDs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

A Global Optimization Methodology for Rocket Propulsion Applications

While the response surface method is an effective method in engineering optimization, its accuracy is often affected by the use of limited amount of data points for model construction. In this chapter, the issues related to the accuracy of the RS approximations and possible ways of improving the RS model using appropriate treatments, including the iteratively re-weighted least square (IRLS) technique and the radial-basis neural networks, are investigated. A main interest is to identify ways to offer added capabilities for the RS method to be able to at least selectively improve the accuracy in regions of importance. An example is to target the high efficiency region of a fluid machinery design space so that the predictive power of the RS can be maximized when it matters most. Analytical models based on polynomials, with controlled level of noise, are used to assess the performance of these techniques.

Source record↗
NASA NTRS2022

On the Stability and Phase Behavior of Titan's Subsurface Liquid Columns

On Titan, liquid hydrocarbon may stay in the subsurface porous permeable crust known as the alkanofer, analogous to water in Earth’s aquifer. In addition to pressure gradient, the subsurface liquid in alkanofers is subject to vertical compositional grading due to the gravity and temperature gradient. The common wisdom is that the liquid would normally stay underground in a stability established by the pressure that increases with depth as observed in aquifers on Earth. However, Titan’s liquids consist of nitrogen and hydrocarbons, mainly methane and ethane, the behavior of which is very sensitive to temperature and pressure. Consequently, the liquid density does not always increase with depth, thus may introduce a reverse density profile that leads to vertical convective instability of the liquid column. If reverse density profiles are present, capillary pressures arising from liquid trapped within small pores in the crust can help with the column stability. The liquid held in the capillaries can seal the space below it thus helping with the stability, unless the overpressure built from underneath becomes larger than the capillary pressure, which causes leakage to allow the liquids to seep upward from the deep. This situation is analogous to hydrocarbon seeps on Earth, where oil and natural gas escape the reservoir and flow slowly through network of cracks to the surface. An algorithm based on an extended Gibbs equation commonly used in petroleum reservoir engineering is employed in this work to produce pressure, density, and compositional profiles for evaluating the stability and phase behavior of Titan’s subsurface fluids.

Sugata Tan↗
DOE OSTI2020

Data-driven selection of stiff chemistry ODE solver in operator-splitting schemes

Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. In conclusion, the models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

42 ENGINEERING↗
DOE OSTI2022

Reduced diffusion and enhanced retention of multiple radionuclides from pore structure characterization of barrier materials for enhanced repository performance

Fluid flow and chemical transport in porous media are the macroscopic consequences of pore structure, which integrates geometry (e.g., pore size and surface area, pore-size distribution) and topology (e.g., pore connectivity). Low-permeability geological media whose pores are poorly interconnected will exhibit the characteristics of anomalous diffusion and sample size-dependent effective porosity, which will strongly impact long-term net diffusion and retention of radionuclides in geological repository settings involving different host rocks and barrier materials. A suite of innovative and complementary experimental approaches is utilized to study the microscopic pore structure and macroscopic fluid flow & chemical transport for a range of host rocks and barrier materials, in addition to standard clay minerals and reference rocks. With a particular focus on quantifying the presence and magnitude of “isolated” pores for a reduced effective porosity in low-permeability geomedia, the integrated methodologies for basic properties and pore structure characterization of these geomedia include X-ray diffraction, thin section petrography, grain size distribution, water immersion porosimetry after vacuum-pulling for full saturation, mercury intrusion porosimetry, nitrogen physisorption, scanning electron microscopy, X-ray computed tomography, and (ultra-)small angle neutron (X-ray) scattering. In addition, custom-designed gas diffusion, tracer recipe involving a range of anionic and cationic chemicals with subsequent analyses by laser ablation and inductively coupled plasma-mass spectrometry, along with batch sorption, column transport, and imbibition tests were conducted for coupled effects of pore structure and chemical retention/transport. From the perspectives of pore structure in conjunction with multiple and complementary approaches to examining a range of sample sizes under different observational scales, we find that the poor pore connectivity is prevalent in low-permeability media (mudstone and crystalline rock) that is related to geological processes (e.g., compaction, diagenesis and thermal maturation). For example, the deep and organic matter-rich mudstones have a much smaller effective porosity than the total porosity (as a result of poor pore connectivity) and associated diffusion coefficient, and the effective porosity & diffusion coefficients are also dependent upon the sample sizes used in the measurement. Similarly, most of the pore space in the shallow mudstone is also controlled by pore-throat diameters in the 5-50 nm range of intergranular pore types from its fine-grained nature, but with an overall good pore connectivity. However, the nm-sized pore space (physically pore-network architecture) and strong sorption capacities (chemical retention from clay minerals) of both shallow and deep mudstones lead to the synergistic retention of cationic radionuclides and their utilities as effective host rocks and barrier materials. Our unique approaches of studying how the micro-scale pore structure affect macro-scale fluid flow, diffusion & retention, and chemical transport produce improved mechanistic understanding, and realistic quantification, of diffusion and retention of typical radionuclides in a range of generic host rocks and barrier materials (clay/shale, salt, crystalline rock, and tuff), with the overall results leading to scientifically-based understanding of enhanced isolation (from both diffusion and retention) of radionuclides and improved confidence on the long-term performance of geological repository to store high-level radioactive wastes. In addition to the training of 25 undergraduates, graduates, and postdocs of UTA, the scientists (organizations) involved in performing this work (e.g., discussion, sample sharing, and operation of SANS and SAXS instruments) include Ed Matteo, Yifeng Wang, and Kristopher Kuhlman (Sandia National Laboratories), Jens Birkholzer, Liange Zheng, Tim Kneafsey, and Sharon Borglin (Lawrence Berkeley National Laboratory), Mavrik Zavarin (Lawrence Livermore National Laboratory), Yukio Tachi and Yuta Fukatsu (Japan Atomic Energy Agency), Mieke de Craen (Euridice, Belgium), Markus Bleuel (NIST), Wei-Ren Chen, Gergely Nagy, Changwoo Do, William Heller, Larry Anovitz, and Kenneth Littrell (ORNL), as well as Jan Illvsky, Ivan Kuzmenko, Ju-Sang Park and Jon Almers (ANL). Key deliverables include a total of 13 peer-reviewed journal articles (nine published and three under review), 23 presentations at scientific conferences (AAPG, AAPG Southwest Section, AGU, Asian Clay Conference, GSA, GSA South-Central Section, IHLRWM, InterPore, International Conference on Chemistry and Migration Behavior of Actinides and Fission Products in the Geosphere, International Conference on Coupled Processes in Fractured Geological Media: Observation, Modeling and Application), and academic institutions (UTA, New Mexico State University; University of Poitiers, France; University of Helsinki, Finland; Uppsala University, Sweden; Istanbul Technical University, Turkey) and other organizations (Andra, France; Posiva Oy, Finland).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Finite Element Modeling of Diffusion in Fractured Porous Media by Using Hierarchical Material Properties

Abstract Fractured porous media challenge modeling approaches due to high computational costs and excessive mesh refinement imposed by the extreme scale variability of fractures and the heterogeneity of the surrounding porous rock. To overcome such difficulties, we utilize the hierarchical finite element method ( Hi ‐FEM) that has been developed previously to simulate the electrical potential distribution in complex geologic environments. The method employs the hierarchical basis functions in classical finite element analysis to enable representation of material properties on each dimensional component of a given 3D unstructured finite element, thereby inherently allowing for interactions at the boundary between fracture and a host rock. In this study, we extend its application to transient fluid flow and heat conduction in the Laplace domain. Time‐domain flow solutions are obtained by numerical inverse Laplace transform. We evaluate the accuracy of the method using different flow models and demonstrate its robustness for large‐scale, rock mass models featuring complex fracture networks. Moreover, for the computation of nodal Darcian velocity fields in fractured porous media where the fractures are represented as 2D features, a new approach that employs the Yeh's Galerkin model for both volume and facet elements is proposed. Results show that Hi ‐FEM can produce accurate flow solutions for fractured porous media without any need of coupling or transfer mechanism while still being computationally economical and numerically robust, even for large‐scale simulations.

Beskardes, G. D.↗
DOE OSTI2025

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗
DOE OSTI2018

Present Early Test Results at SECARB Industry Briefing [Slides]

Contents include the following presentations: 1) SECARB Phase III Early Test Cranfield, MS; 2) SECARB Phase III Early Test at Cranfield, March 9, 2010; 3) Update on Results of SECARB "Early" Test of Monitoring Large Volume Injection at Cranfield; 4) Progress Update SECARB "Early" Test Monitoring 3.5 Million Tons at Cranfield, March 7, 2012; 5) Cranfield Geophysical Monitoring Status and Results, March 8, 2012; 6) Large Volume CO 2 Injection at Cranfield, Early Field Test of the SECARB Phase III–Near-Surface Monitoring, March 7, 2012; 7) Shallow Groundwater Study at the Cranfield Site, March 12, 2013; 8) Subsurface Monitoring Studies–Results and Integration, March 12, 2013; 9) Review of the Petrophysics for the Cranfield Phase III Project, March 12, 2013; 10) Geomechanics at Cranfield, March 5, 2014; 11) Gulf Coast Carbon Center Near-Surface Update, March 4-5, 2014; 12) Monitoring Design Best Practices, March 5, 2014; 13) Assessing Impacts of CO 2 Leakage on Groundwater Quality and Monitoring Network Efficiency–Case Study at a CO 2 -EOR site, March 11, 2013; 14) Near-surface Stratigraphy and Geophysics, March 11, 2015; 15) Update on Reservoir Modeling and Final Testing and P&A Underway, March 11, 2013; 16) SECARB Early Test Retrospective; 17) Fluid Source Attribution at CCS and CO 2 -EOR Sites, March 8, 2016; 18) SECARB "Early Test" at Cranfield, March 8-9, 2017; 19) Analysis of Seismic Time-Lapse Observations at Cranfield; 20) SECARB Phase III Cranfield Project (Early Test), March 8, 2018; 21) Enhanced Analytical Simulation Tool (EASiTool) Dynamic Capacity Estimation; 22) Net Carbon Negative Oil Mass Accounting–Cranfield Case Study; 23) Knowledge Sharing; 24) Advancing Toward Commercialization Final Stages of the Early Test; 25) CO 2 EOR Feasibility in SECARB Region Limited by CH 4 ; 26) Understanding of CO2 Migration and Entrapment–Cranfield Site

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Implementation of Chemistry in the Athena++ Code

Chemistry plays a key role in many aspects of astrophysical fluids. Atoms and molecules are agents for heating and cooling, determine the ionization fraction, serve as observational tracers, and build the molecular foundation of life. We present the implementation of a chemistry module in the publicly available magnetohydrodynamic code Athena++ . We implement several chemical networks and heating and cooling processes suitable for simulating the interstellar medium (ISM). A general chemical network framework in the KIDA format is also included, allowing users to easily implement their own chemistry. Radiation transfer and cosmic-ray ionization are coupled with chemistry and solved with the simple six-ray approximation. The chemical and thermal processes are evolved as a system of coupled ordinary differential equations with an implicit solver from the CVODE library. We perform and present a series of tests to ensure the numerical accuracy and convergence of the code. Many tests combine chemistry with gas dynamics, including comparisons with analytic solutions, 1D problems of the photodissociation regions and shocks, and realistic 3D simulations of the turbulent ISM. We release the code with the new public version of Athena++ , aiming to provide a robust and flexible code for the astrochemical simulation community.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1996

Fluid Stochastic Petri Nets: Theory, Applications, and Solution

In this paper we introduce a new class of stochastic Petri nets in which one or more places can hold fluid rather than discrete tokens. We define a class of fluid stochastic Petri nets in such a way that the discrete and continuous portions may affect each other. Following this definition we provide equations for their transient and steady-state behavior. We present several examples showing the utility of the construct in communication network modeling and reliability analysis, and discuss important special cases. We then discuss numerical methods for computing the transient behavior of such nets. Finally, some numerical examples are presented.

Horton, Graham↗