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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗
DOE OSTI2020

Spectrally reconfigurable quantum emitters enabled by optimized fast modulation

The ability to shape photon emission facilitates strong photon-mediated interactions between disparate physical systems, thereby enabling applications in quantum information processing, simulation and communication. Spectral control in solid state platforms such as color centers, rare earth ions, and quantum dots is particularly attractive for realizing such applications on-chip. Here we propose the use of frequency-modulated optical transitions for spectral engineering of single photon emission. Using a scattering-matrix formalism, we find that a two-level system, when modulated faster than its optical lifetime, can be treated as a single-photon source with a widely reconfigurable photon spectrum that is amenable to standard numerical optimization techniques. To enable the experimental demonstration of this spectral control scheme, we investigate the Stark tuning properties of the silicon vacancy in silicon carbide, a color center with promise for optical quantum information processing technologies. We find that the silicon vacancy possesses excellent spectral stability and tuning characteristics, allowing us to probe its fast modulation regime, observe the theoretically-predicted two-photon correlations, and demonstrate spectral engineering. Our results suggest that frequency modulation is a powerful technique for the generation of new light states with unprecedented control over the spectral and temporal properties of single photons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Biorefinery upgrading of herbaceous biomass to renewable hydrocarbon fuels, Part 1: Process modeling and mass balance analysis

Process simulation has long been a well-established tool to track key operational, design, and mass and energy balance metrics for pre-commercial technologies such as advanced lignocellulosic biofuels. While tools such as this are well-documented in the public literature around 2nd-generation cellulosic ethanol technologies (which have been scaled up to commercial deployment to some degree over the past decade), such models and analysis information remain more sparse for more complex biorefinery pathways focused on producing drop-in hydrocarbon fuels and blend-stocks, particularly regarding information required to support air emissions or other environmental analysis. In this work, we summarize key details for an established "design case" modeling the conversion of herbaceous lignocellulosic biomass into a renewable diesel hydrocarbon blend-stock based on a representative lipid pathway from oleaginous yeast. The process is based on a biochemical deconstruction and upgrading approach utilizing deacetylation and dilute acid pretreatment, followed by enzymatic hydrolysis, fermentation, and catalytic upgrading of hydrolysate sugars to fuels. We provide key mass and energy balance outputs from the process models, with accompanying stream tables and component-level flowrates. A total of 12 model scenarios are presented spanning two feedstocks, three biorefinery scales, and two processing approaches for the lignin/residual solids waste streams. This "Part 1" manuscript presents the resulting impacts across the 12 cases on fuel yields and key output streams, focused here on direct biorefinery air emissions for selected components including CO 2 as well as sulfur (SOx) and nitrogen oxides (NOx). In the context of cleaner production, the latter focus on selected biorefinery air emission outputs establishes an initial baseline estimate and accompanying framework of the model cases, upon which an accompanying "Part 2" study will build to refine the values for these and other air pollutants across these scenarios, also considering mitigation options to comply with applicable regulatory standards. We also highlight further optimization opportunities based on potential tradeoffs identified here between air emissions versus life-cycle greenhouse gas profiles attributed to the disposition of lignin/residual solids.

09 BIOMASS FUELS↗
DOE OSTI2024

Portable fiber optic sensor for rare earth elements and other critical metals using photoluminescence methods

Rare earth elements and other metals are vital to a range of technologies that are used in the energy and defense sectors. However, monopolistic market conditions have caused significant concern over the stability of the critical metal supply chain, and this has spurred extensive efforts in many nations to produce these metals domestically, both from conventional sources such as mining and well as from unconventional sources such as coal and its utilization byproducts. Slow and expensive characterization methods pose a significant barrier for both metals prospecting and process monitoring. A promising solution to this challenge is the development of highly sensitive luminescent sensors for metals, which can offer low costs, portability, and sensitivity. Anionic zinc adeninate metal-organic frameworks (BioMOFs) are known to distinguishing and detect part-per-billion levels of terbium, europium, samarium, and dysprosium in water by sensitizing the narrow, element-specific emission bands from these lanthanides. Here, a BioMOF material is immobilized onto a large diameter, solarization-resistant fiber optic tip integrated with a portable, low-cost spectrometer for rare earth element sensing. Immobilizing the sensing material on fiber instead of dispersing the sensing material in solution offers several advantages: it facilitates solvent removal, which enhances luminescent signal from the sensitized lanthanides, and it also allows the BioMOF to be recycled for multiple uses. The sensing system was deployed on a simulated process stream and exhibited qualitative agreement with inductively-coupled plasma mass spectrometry for terbium and europium detection, highlighting the potential for the sensing system to be deployed for real-world applications. By using different sensing materials, the same portable sensor may be deployed to detect other energy relevant metals such as cobalt, providing a cost-effective and sensitive platform for critical metal characterization.

Crawford, Scott↗
DOE OSTI2023

Benchmarking Microscale Ductility Measurements (Final Report of the Project DE-NE0008799)

Conventional macroscale experimentation is generally considered to be straightforward with few limitations. Conversely, micro/nanoscale experimentation presents numerous challenges in loading device design, sample preparation and handling, as well as accurate understanding of grain size and local texture effects on recorded measurements. Despite these challenges, nanopillar compression, MEMs based micro-tension, and nanoindentation approaches have been able to provide fundamental contributions to the understanding of material behavior at small lengthscales. However, the overarching shortcoming of these micro/nanoscale experimentation approaches, is the inability to directly translate measurements evaluated at the nm and µm length scales (e.g., hardness) to macroscale tensile material behavior (i.e., elastic modulus, yield strength, and ductility). The objectives of the proposed study are, 1) to establish best practices for obtaining tensile microscale ductility measurements, and 2) to validate methodologies to for comparing microscale ductility measurements to macroscale ductility measurements. In order to achieve these objectives, a multi-lengthscale, multi-temperature testing protocol and simulation framework are executed first on copper as a model material to validate the following approach, and second on reactor grade Zircaloy-2. Experiments are conducted on specimens extracted from the same test piece to ensure nominally identical grain size and texture from specimens to specimen. Motivated by the need to isolate the contribution of size-effects on obtained mechanical property measurements, specimens are manufactured with thicknesses at the micro- (1-10 µm), meso- (10-100's µm), and macroscales (sub-sized ASTM E8). In-situ full-field deformation techniques (scanning electron microscopy (SEM) grid methods and optical DIC) are incorporated into testing at each specimen length-scale to capture plasticity localization and evolution. Experimental testing for all specimens is conducted at both room temperature and elevated temperatures to probe the role of thermal activation on plastic deformation accommodation processes. Simulation efforts focus on examining the mechanical behavior of microscale specimens using a finite element approach with explicitly resolved grain morphologies, and an embedded crystal plasticity model. The cost-efficient implementation method allows for the modeling of a statistically significant number of both real (i.e., digital twin) and generated microstructures to obtain an understanding of the interrelationships between specimen microstructure and geometric variables (grain size, texture, specimen geometry, etc.) on microscale mechanical behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rate expressions and kinetic parameters for metal ferrites in relation to applications of fossil fuel conversion to hydrogen: Part 1 of 2

Here, the goal of the present work was to provide the necessary reaction emulation information to enable detailed process simulation of a chemical looping H 2 production system from fossil fuels using CaFe 2 O 4 . This specifically pertained to the necessary kinetic data, reaction model development, and model rate parameters required for reaction emulation in both reducing and oxidizing environments. A logical methodology was defined, which included discretization of the reaction network, establishing a core model for reaction emulation that could be adapted based on the system phenomena, and development of a rate parameter regression tool designed around the core model. An extensive array of data sets was acquired by which parametric regressions were performed. The work presented and tabulated a comprehensive set of rate parameters for the reduction and oxidation reactions of CaFe 2 O 4 and descendent phases of Ca 2 Fe 2 O 5 , FeO, Fe 3 O 4 , Fe, and CaO to emulate reaction behavior in a looping-based process environment. This included direct reduction using CH 4 , H 2 , and CO, and direct oxidation reactions with steam, CO 2 and O 2 . Dynamic equilibrium was quantified for reactions that could utilize H 2 O and CO 2 as soft oxidants to re-saturate lattice oxygen in the depleted structure/phases. The kinetics associated with the oxidative mechanisms with the soft oxidants were quantified and compared to those of the reducing counterparts. The analysis provided critical insight to emulate reactions for a process that seeks to use natural gas (NG) or other fossil fuels as a direct reductant for the end goal of H 2 production.

calcium ferrite oxygen carriers↗
DOE OSTI2025

Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation

As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

THE DESIGN OF LAYER TIME OPTIMIZATION IN LARGE SCALE ADDITIVE MANUFACTURING WITH FIBER REINFORCED POLYMER COMPOSITES

In this study, we have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale additive manufacturing (AM) via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreement with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation.

Jo, Eonyeon↗
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Kilonovae Across the Nuclear Physics Landscape: The Impact of Nuclear Physics Uncertainties on r-process-powered Emission

Merging neutron stars produce “kilonovae”—electromagnetic transients powered by the decay of unstable nuclei synthesized via rapid neutron capture (the r-process) in material that is gravitationally unbound during inspiral and coalescence. Kilonova emission, if accurately interpreted, can be used to characterize the masses and compositions of merger-driven outflows, helping to resolve a long-standing debate about the origins of r-process material in the Universe. We explore how the uncertain properties of nuclei involved in the r-process complicate the inference of outflow properties from kilonova observations. Using r-process simulations, we show how nuclear physics uncertainties impact predictions of radioactive heating and element synthesis. For a set of models that span a large range in both predicted heating and final abundances, we carry out detailed numerical calculations of decay product thermalization and radiation transport in a kilonova ejecta with a fixed mass and density profile. The light curves associated with our models exhibit great diversity in their luminosities, with peak brightness varying by more than an order of magnitude. We also find variability in the shape of the kilonova light curves and their color, which in some cases runs counter to the expectation that increasing levels of lanthanide and/or actinide enrichment will be correlated with longer, dimmer, redder emission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Understanding the performance of membrane for direct air capture of CO 2

Abstract Direct air capture (DAC) of CO 2 is becoming increasingly important for reducing greenhouse gas concentrations in the atmosphere. However, the cost and energy requirements associated with DAC make it less economically feasible than carbon capture from flue gases. While various methods like solid sorbents and gas–liquid absorption have been explored for DAC, membrane processes have only recently been investigated. The objective of this study is to examine the separation performance of a membrane unit for capturing CO 2 from ambient air. The performance of a membrane depends on several factors, including the composition of the feed gas, pressure ratio, material selectivity, and membrane area. The single‐stage separation process with the co‐current flow and constant permeability flux model is evaluated using a commercial module integrated with a process simulator to separate a binary mixture of carbon dioxide and nitrogen to assess the sensitivity of selectivity on purity and recovery of CO 2 in permeate, and power requirement. Additionally, three levels of CO 2 reduction from the feed stream to the retentate stream (25%, 50%, and 75%) are studied. A trade‐off between purity and recovery factor is observed, and achieving high purity in permeate requires high concentration in the retentate.

Panja, Palash↗
DOE OSTI2020

Interlaboratory Reproducibility of Contour Method Data Analysis and Residual Stress Calculation

While the contour method for residual stress assessment has developed rapidly, no published study documents its interlaboratory reproducibility. Objective: Here we report an initial reproducibility experiment focused on contour method data analysis and residual stress calculation. The experiment uses surface topography data from a physical process simulation of elastic-plastic beam bending. The simulation provides surface topography, for input to the contour method data analysis, as well as a known residual stress field with 130 MPa peak magnitude. Additionally, to increase realism, noise and specific artifacts are added to the topography data. A group of participants received the topography data (without the known residual stress), independently analyzed the data, and submitted results as a two-dimensional residual stress field. Analysis of submissions provides a group average residual stress field and the spatial distribution of reproducibility standard deviation. The group average residual stress agrees with the known stress in magnitude and spatial trend. The reproducibility standard deviation ranges from 2 to 54 MPa over the measurement plane, with an average of 5.4 MPa. Reproducibility standard deviation is smaller in the cross-section interior (≤ 5 MPa), modest near local extrema in the stress field (5 to 10 MPa), and larger near the cross-section boundaries (10 to 30 MPa). Overall, the largest values of reproducibility standard deviation (up to 54 MPa) occur in limited areas where artifacts had been added to the topography data; while some participants identified and removed these artifacts, some did not, leading to systematic differences that elevated the standard deviation.

42 ENGINEERING↗
DOE OSTI2021

A new procedure for implementing the modified inherent strain method with improved accuracy in predicting both residual stress and deformation for laser powder bed fusion

As a metal additive manufacturing (AM) process, laser powder bed fusion (L-PBF) has been widely used to produce parts with complex geometries. The large thermal gradient caused by the fast, intense, and repeated laser scanning induces significant residual deformation and stress to the as-built parts, which increase manufacturing difficulty and geometrical inaccuracy as a result. The modified inherent strain (MIS) method exploiting multiscale process simulations was developed to simulate residual deformation accurately and efficiently. However, the existing procedure of implementing the MIS method is found to give inaccurate residual stress prediction. Here in this work, a new implementation procedure for the MIS method is proposed to improve the simulation accuracy of residual stress without degrading the residual deformation prediction. The new procedure concerns the application of inherent strains to the part-scale layer-by-layer finite element model to obtain residual stress and deformation field. While the existing implementation of the part-scale MIS model involves only mechanical properties at ambient temperature, the new procedure adds one more solution step employing mechanical properties at an elevated temperature determined from the inherent strain extraction step. Both numerical and experimental studies are conducted to validate the proposed new implementation procedure. It shows that by using the new procedure, the MIS-based simulation can predict both residual stress and deformation of as-built L-PBF metal parts with good accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Techno-economic prospects for producing Fischer-Tropsch jet fuel and electricity from lignite and woody biomass with CO 2 capture for EOR

This work explores the prospective techno-economic performance of facilities that produce low- and net-negative-carbon liquid transportation fuels and electricity with CO 2 capture for enhanced oil recovery. The lignite and biomass-to-jet fuel process is based on KBR’s TRIG gasifier, Rectisol (for sulfur removal and CO 2 capture), fixed-bed low temperature Fischer-Tropsch synthesis of liquid fuels, and Brayton/Rankine combined cycles to convert synthesis/refining off-gases and waste heat to electricity. This work leverages a recent, highly-detailed assessment of a prospective first-of-a-kind (FOAK) demonstration facility to develop highly detailed Aspen Plus process simulations for nine prospective N th -of-a-kind (NOAK) plant equipment configurations. Component-level capital costs from the FOAK study are scaled and adjusted to reflective prospective learning-by-doing to estimate capital costs for the NOAK designs. NOAK plant economic performance is found to be largely insensitive to variations in plant configurations and electricity output fraction, but biomass input fraction significantly affects profitability. Facilities that consume only carbon–neutral biomass, with no lignite co-feed, have significantly net-negative carbon emissions and the most favorable prospective economics when carbon emissions are priced. For these facilities, the crude oil price required for plant economic viability falls rapidly from $\$$100/bbl as carbon emission prices increase above $\$$120/tonne CO 2eq . In general, plants that co-fire lignite with biomass are less profitable (than 100% biomass plants) due to their higher net greenhouse gas emissions.

09 BIOMASS FUELS↗
DOE OSTI2022

Cryogenic energy storage: Standalone design, rigorous optimization and techno-economic analysis

Energy storage allows flexible use and management of excess electricity and intermittently available renewable energy. Cryogenic energy storage (CES) is a promising storage alternative with a high technology readiness level and maturity, but the round-trip efficiency is often moderate and the Levelized Cost of Storage (LCOS) remains high. The complex flowsheets with intricate thermodynamics at cryogenic temperatures as well as the presence of multiple loops and refrigeration cycles pose considerable challenges for rigorous model-based design and optimization of CES systems. We present an optimization strategy that couples rigorous process simulation and Bayesian optimization with flowsheet decomposition and identification of hidden coupling constraints to optimally design standalone CES systems. Further refinement is done via a local search using the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. Here our results indicate that it is possible to achieve more than 52% round-trip efficiency and an LCOS of $153/MWh for a standalone 100 MW/400 MWh CES system limited to short-term storage with daily charging–discharging. However, a detailed techno-economic assessment reveals that the LCOS considering total capital investment may exceed $267/MWh when all direct and indirect costs of installation and operation are considered.

25 ENERGY STORAGE↗
DOE OSTI2022

Smoothed boundary method for simulating incompressible flow in complex geometries

Simulating flow through porous media with explicit considerations of complex microstructures is very challenging using conventional sharp-interface methods because of the difficulties in generating meshes conformal to complex geometries. In this work, a diffuse interface embedded boundary method known as the Smoothed Boundary Method (SBM) is utilized to facilitate simulations of fluid dynamics involving complex geometries. In diffuse-interface methods, the geometry is described by a domain parameter. The SBM allows the straightforward reformulation of the time-dependent Navier-Stokes equations in terms of this domain parameter, using only algebraic identities. Thus, enforcing the appropriate boundary conditions at the irregular embedded boundary is greatly simplified. Adaptive mesh refinement is used to increase the accuracy of the diffuse interface method by allowing thinner interfacial thickness to be used in the domain parameter. Here, the SBM-formulated Navier-Stokes equations are solved with the Finite Difference Method on refined mesh systems. Sharp-interface Finite Element Method simulations using the commercial software COMSOL on body-conforming meshes are also provided for comparison. Favorable agreement between the two methods is observed. Since it is no longer necessary for the mesh to conform to the complex geometry, the grid system for the SBM simulations can be generated rapidly and without additional manual interventions, making the entire simulation process more expedient.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advanced supercritical water-based process concepts for treatment and beneficial reuse of brine in oil/gas production

Produced water generated from oil/gas reservoirs is a cost burden for oil/gas producers, with over 21 billion barrels of the waste generated in 2019. Average disposal costs range from 4.00–8.00·bbl -1 , with up to 20·bbl -1 spent on transportation alone. Dissolved solids content is a contributing factor which limits economical treatment options for this waste stream. This work proposes a novel technique that can handle high salinity waste, employing favorable properties past the critical point of water. In this study, process simulation of two supercritical water desalination (SCWD) scenarios was completed. Zero liquid discharge (ZLD) and brine concentration operating scenarios were compared, weighing the associated economics and benefits for each case. The results were shown to be economically feasible for brines with a high dissolved solids content, ranging from 3.49 to 17.28·bbl -1 in an expanded sensitivity analysis.

42 ENGINEERING↗
DOE OSTI2022

Thermodynamic modeling of adsorption at the liquid-solid interface

Adsorptive separation techniques are significantly energy efficient in comparison to conventional thermal separation techniques such as distillation. Despite extensive research and development activities undertaken for mixed-gas adsorption, the use of adsorption techniques for the separation of multicomponent liquid mixtures is still limited. A major barrier is the lack of accurate adsorption thermodynamic models, which form the scientific foundation of process simulation of such systems, making the translation to industrial scale challenging. In this work, we have rigorously computed the surface excess of adsorption for six binary liquid mixtures on silica gel at 303 K using the frameworks of the generalized Langmuir isotherm (gL) and the adsorbed solution theory (AST). The six binary liquid mixtures were formed by the pair-wise combinations of four components: benzene, 1,2-dichloroethane, cyclohexane, and n-heptane. We have based our calculations by considering simultaneous equilibria of three phases: saturated vapor phase, bulk liquid phase, and adsorbed phase. Further, the composition of the corresponding saturated vapor phase was estimated by the Nonrandom Two-Liquid activity coefficient model and experimental vapor-liquid equilibria data. The activity coefficients of the adsorbed phase, the central issue of multicomponent adsorption thermodynamics, were calculated using the adsorption Nonrandom Two-Liquid activity coefficient model. Devoid of simplifying assumptions, gL and AST provide rigorous thermodynamic frameworks for adsorption equilibria of multicomponent liquid mixtures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗