SEARCH · Search NASA

Results for “demonstration and field testing”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Contrasting the characteristics of atmospheric pressure plasma jets operated with single and double dielectric material: physicochemical characteristics and application to bacterial killing

Abstract This study reports an experimental comparison of two types of atmospheric pressure plasma jets in terms of their fundamental plasma characteristics and efficacy in bacterial sterilization. The plasma jets are fabricated by inserting a high voltage electrode inside a one-end closed (double DBD plasma jet) or both ends open (single DBD plasma jet) quartz tubes which are further enclosed inside a second quartz tube containing a ground electrode. Both plasma jets are operated in contact with water surface by using a unipolar pulsed DC power supply with helium as the working gas. Results from electrical and time-resolved imaging show that the single DBD configuration induces 3–4 times higher accumulation of charges onto the water surface with significantly faster propagation of plasma bullets. These results are accompanied by the higher discharge intensity as well as stronger emissions from short-lived reactive species which were analyzed through optical emission spectroscopy at the plasma-water interface. The rotational temperature for the single DBD configuration was observed to be higher making it unsafe for direct treatments of sensitive biological targets. These characteristics of the single DBD configuration result in the production of more than two times higher concentration of H 2 O 2 in plasma activated water. Shielding of the HV electrode reduces the plasma potential which in turn reduces the electric field & electron energy at the plasma-water interface. The reduced electric field for the double DBD configuration was lower by ≈463 Td than the single DBD configuration. The bactericidal efficacy of the two configurations of the plasma jets were tested against Escherichia coli , a well studied Gram-negative bacterium that can be commensal and pathogenic in human body. Our results demonstrate that although single DBD plasma jet result in stronger antibacterial effects, the double DBD configuration could be safer.

Physics↗

DOE OSTI2021

Evaluation of KDP Estimation Algorithm Performance in Rain Using a Known-Truth Framework

Accurate estimation of specific differential phase ( K DP ) is necessary for rain rate estimation, attenuation correction, and hydrometeor classification algorithms. There are numerous published methods to process polarimetric radar observations of propagation differential phase shift (Φ DP ) and estimate K DP , but the corresponding K DP estimate uncertainty is unquantified. This study provides guidance on how commonly used K DP estimation algorithms perform in various environments. Here, we create numerous synthetic (“true”) K DP profiles, integrate over them to obtain “smoothed” Φ DP , and then add noise typical of S-band operational weather radar measurements. Each algorithm is applied to our noisy Φ DP profiles and compared to the true K DP profile such that the errors and uncertainty are quantified. The synthetic K DP profiles are Gaussian in shape, which allows systematic variations in their magnitude and width to determine how each algorithm performs in smooth, slowly changing K DP profiles, as well as steep profiles. Results demonstrate that algorithm performance is dependent on the Φ DP field received. These results are further supported by an error analysis of each algorithm for two more complicated synthetic K DP profiles. Some K DP algorithms allow users to change various tuning parameters; a subset of these tuning parameters is tested to provide guidance on how changing these parameters impacts algorithm performance. We then provide evidence that our known-truth framework provides insight into algorithm performance in observed data through two case studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A non-hierarchical correlation discrete variable representation

The correlation discrete variable representation (CDVR) facilitates (multi-layer) multi-configurational time-dependent Hartree (MCTDH) calculations with general potentials. It employs a layered grid representation to efficiently evaluate all potential matrix elements appearing in the MCTDH equations of motion. The original CDVR approach and its multi-layer extension show a hierarchical structure: the size of the grids employed at the different layers increases when moving from an upper layer to a lower one. In this work, a non-hierarchical CDVR approach, which uses identically structured quadratures at all layers of the MCTDH wavefunction representation, is introduced. Here, the non-hierarchical CDVR approach crucially reduces the number of grid points required, compared to the hierarchical CDVR, shows superior scaling properties, and yields identical results for all three representations showing the same topology. Numerical tests studying the photodissociation of NOCl and the vibrational states of CH 3 demonstrate the accuracy of the non-hierarchical CDVR approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Development of Ti 3 SiC 2 MAX phase tubular structures for solar receiver applications

Solar receiver tubes are key components of concentrating solar-thermal power (CSP) systems that harvest solar energy. For better efficiency, the Gen3 CSP receivers, which collect heat into a heat transfer fluid, require a temperature exceeding 700 °C during operation and need to perform under extreme conditions of high temperature and high thermal stress. Operators are seeking CSP designs using new high-temperature structural materials with high thermal conductivity and high creep resistance to achieve a design life of 30 years and thus help recover the plant capital cost sooner. MAX phase materials, which consist of an early transition metal element, an A-group element, and carbon or nitrogen, are expected to exhibit high creep resistance as well as high fracture toughness. Here, in this paper, we describe fabricating both (1) dense Ti 3 SiC 2 MAX phase disks and (2) short-length tubes using field-assisted sintering technology (FAST). First, the disk samples that we fabricated are fully dense and contain ≈90 % Ti 3 SiC 2 MAX phase materials and ≈10 % TiC phase materials. We determined a flexure strength of 519 ± 32 MPa by conducting a four-point bending test at room temperature with rectangular bar samples of ≈100 % density. The thermal conductivity of the Ti 3 SiC 2 MAX phase samples, measured by light flashing analysis, decreases linearly from a value of 41 W . m -1 . K -1 at room temperature to a value of 36 W . m -1 . K -1 at 650 °C. A solar reflectance measurement of the Ti 3 SiC 2 MAX phase revealed that, temperature increases from 400 to 1400 °C, thermal emittance increases from 0.39 to 0.49, while selectivity decreases from 1.8 to 1.4, respectively. Whereas the surface oxidized MAX phase samples after 100 h exposure to air at 1000 °C exhibit that of SiC. Next, we discuss fabrication of the crack-free Ti 3 SiC 2 MAX phase tubular structures accomplished by using FAST processing in graphite bedding. A Ti 3 SiC 2 MAX phase content of > 95 % with traceable ≈3% remaining TiC phase and ≈15 % porosity were demonstrated after high-temperature annealing. An average fracture strength of ≈250 MPa was determined with Ti 3 SiC 2 MAX phase tubes of ≈85 % density by diametral compression testing at room temperature. Our work demonstrated that using FAST processing to produce Ti 3 SiC 2 MAX phase tubular structures for CSP receiver applications is a viable approach.

14 SOLAR ENERGY↗

DOE OSTI2025

Optimizing Insulation Design for Transformers in Medium Voltage Power Conversion Systems

Medium-frequency transformers (MFTs) play a crucial role in medium-voltage (MV) solidstate transformer (SST) systems, particularly in extreme fast charging applications. Achieving partial discharge (PD)-free operation while maintaining high power density is a significant challenge due to the high electric field (E-field) stresses inherent in MV applications. This dissertation focuses on the insulation design and optimization of MFTs used in both the main power electronics circuits and auxiliary power supplies. The study begins with an overview of insulation testing methodologies, including high potential tests, basic insulation level tests, and PD tests, which are critical for evaluating MFT insulation reliability. Given the importance of PD-free operation for long-term reliability, particular emphasis is placed on understanding PD mechanisms, including void, corona, and surface discharge, and their mitigation strategies. A high voltage isolated auxiliary power supply is then introduced, utilizing a gapped transformer encapsulated in silicone gel. This design achieves PD-free insulation up to 18 kV RMS while maintaining low coupling capacitance to minimize common-mode current. The proposed solution ensures reliable operation in MV environments and offers a scalable approach for auxiliary power in cascaded SST architectures. To improve MFT insulation in main power conversion circuits, a novel structure is developed using polypropylene sheets and potting compounds to create a void-free air gap, effectively mitigating E-field intensity. A prototype transformer with this insulation structure is built and achieves PD-free operation up to 30 kV RMS. This design is experimentally validated in a resonant converter operating at 46 kW, demonstrating its feasibility for MV SST applications. Further optimization is implemented to enhance MFT performance for dual-active-bridge(DAB) converters by integrating a semiconductive shielding layer within the insulation structure. This shielding layer improves the magnetic coupling coefficient while effectively confining the E-field within high insulation materials, thereby reducing eddy current losses. The optimized MFT achieves PD-free operation at 12.6 kV RMS and is successfully tested in a DAB converter operating at 43 kW, which meets the insulation requirements for a 13.2 kV SST system. This dissertation advances MFT insulation design by introducing and experimentally validating novel approaches that improve high voltage insulation while optimizing magnetic coupling and manufacturability. The proposed insulation structures enable PD-free operation while minimizing insulation material usage and simplifying assembly, making them ideal for high power, high voltage applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Computer-aided classification of suspicious pigmented lesions using wide-field images

Early identification of melanoma is conducted through whole-body visual examinations to detect suspicious pigmented lesions, a situation that fluctuates in accuracy depending on the experience and time of the examiner. Computer-aided diagnosis tools for skin lesions are typically trained using pre-selected single-lesion images, taken under controlled conditions, which limits their use in wide-field scenes. Here, we propose a computer-aided classifier system with such input conditions to aid in the rapid identification of suspicious pigmented lesions at the primary care level. 133 patients with a multitude of skin lesions were recruited for this study. All lesions were examined by a board-certified dermatologist and classified into “suspicious” and “non-suspicious”. A new clinical database was acquired and created by taking Wide-Field images of all major body parts with a consumer-grade camera under natural illumination condition and with a consistent source of image variability. 3–8 images were acquired per patient on different sites of the body, and a total of 1759 pigmented lesions were extracted. A machine learning classifier was optimized and build into a computer aided classification system to binary classify each lesion using a suspiciousness score. In a testing set, our computer-aided classification system achieved a sensitivity of 100% for suspicious pigmented lesions that were later confirmed by dermoscopy examination (“SPL_A”) and 83.2% for suspicious pigmented lesions that were not confirmed after examination (“SPL_B”). Sensitivity for non-suspicious lesions was 72.1%, and accuracy was 75.9%. With these results we defined a suspiciousness score that is aligned with common macro-screening (naked eye) practices. This work demonstrates that wide-field photography combined with computer-aided classification systems can distinguish suspicious from non-suspicious pigmented lesions, and might be effective to assess the severity of a suspicious pigmented lesions. We believe this approach could be useful to support skin screenings at a population-level.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Conceptual design of HTS magnets for fusion nuclear science facility

Second-generation high temperature superconductors (HTS) are available for producing >20 T at the magnet bore compared to 13–16 T for lower temperature superconducting (LTS) toroidal field magnets proposed in recent fusion energy systems studies (FESS) of Fusion Nuclear Science Facility (FNSF). HTS may enable higher fusion power density and smaller device size. High current density cables of multi-layered REBCO tapes have achieved >10 kA at 4–20 K operation in short sample tests for fusion. High current density cables are required for engineering design of FNSF to allow space for interior plasma components. High current density HTS magnets are particularly attractive in reducing the size of a fusion device, beneficial for compact tokamaks, due to their space constraints. Successful HTS magnet development may enable the design of smaller and cheaper fusion pilot plants with a mission of demonstrating net electricity. It may also offer significant cost and performance advantages in non-fusion applicants such as nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI). Furthermore, we developed HTS magnet design concepts for a compact FNSF radial build in order to define the coil size, winding pack mechanical loading and engineering requirements. Partnering with vendors in the US, PPPL is also testing high current cable prototypes aiming at enabling low cost cable technology toward 100 A/mm 2 engineering current density over the winding pack desired in high field model coil development for compact fusion pilot plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Machine learning a molecular Hamiltonian for predicting electron dynamics

We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density evolution, producing highly accurate results even when propagating 1,000 time steps beyond the training data. As a more rigorous test, we use the learned Hamiltonians to simulate electron dynamics in the presence of an applied electric field, extrapolating to a problem that is beyond the field-free training data. We find that the resulting electron dynamics predicted by our learned Hamiltonian are in close quantitative agreement with the ground truth. Our method relies on combining a reduced-dimensional, linear statistical model of the Hamiltonian with a time-discretization of the quantum Liouville equation within time-dependent Hartree Fock theory. In conclusion, we train the model using a least-squares solver, avoiding numerous, CPU-intensive optimization steps. For both field-free and field-on problems, we quantify training and propagation errors, highlighting areas for future development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗

DOE OSTI2024

Characterization and Optimization of Skipper CCDs for the SOAR Integral Field Spectrograph

We present results from the characterization and optimization of Skipper charge-coupled devices (CCDs) for use in a focal plane prototype for the Southern Astrophysical Research Integral Field Spectrograph (SIFS). We tested eight Skipper CCDs and selected six for SIFS based on performance results. The Skipper CCDs are 6k × 1k, 15 μm pixels, thick, fully depleted, p-channel devices that have been thinned to ~250 μm, backside processed, and treated with an anti-reflective coating. We demonstrate a single-sample readout noise of <4.3 e - rms pixel -1 in all amplifiers. We optimize the readout sequence timing to achieve a readout noise of 0.5 e - rms pixel -1 after 74 non-destructive measurements, which can be accomplished in a region covering 5% of the detector area in a readout time of <4 minutes. We demonstrate single-photon-counting in all 24 amplifiers (four amplifiers per detector) with a readnoise of σ N ~ 0.18 e - rms pixel -1 after N samp = 400 samples, and we constrain the degree of nonlinearity to be ≲1% at low signal levels (0 e - to 50 e - ). Clock-induced charge (CIC) remains an important issue when the Skipper CCD is configured to provide a large full-well capacity. We achieve a CIC rate of <1.45 × 10 -3 e - pixel -1 frame -1 for a full-well capacity of ~900 e - , which increases to a CIC rate of ~3 e - pixel -1 frame -1 for full-well capacities ~40,000-65,000 e - . We also perform conventional CCD characterization measurements such as charge transfer inefficiency (3.44 × 10 -7 on average), dark current (~2 × 10 -4 e - pixel -1 s -1 ), photon transfer curves, cosmetic defects (<0.45% “bad” pixels), and charge diffusion (point-spread function < 7.5 μm) to verify that these properties are consistent with expectations from conventional p-channel CCDs used for astronomy. Furthermore, we provide the first measurements of the brighter-fatter effect and absolute quantum efficiency (≳80% between 450 and 980 nm; ≳90% between 600 and 900 nm) using Skipper CCDs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Predicting Failure Using Deep Learning SAND Report

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

eMosaic: Electrification Mosaic Platform for Grid Informed Smart Charging Management (Final Scientific/Technical Report)

ABB (Prime Contractor), in collaboration with its partners at the Utah State University (USU), Idaho National Laboratory, Rocky Mountain Power (RMP), and Electric Power Engineers (EPE), have performed research, development, and wide scale demonstration of a scalable and resilient Electrification Mosaic (eMosaic) platform for Smart Charge Management (SCM) for Electric Vehicle Infrastructure. Work was completed under DE EE0009194, titled “eMosaic Electrification Mosaic Platform for Grid Informed Smart Charging Management”, funded by the US Department of Energy. The project members developed algorithms that provide localized and bulk grid services and that reduce and stabilize costs all the way down the supply chain to the PEV owner through SCM. This platform aggregates telemetry from multiple data sources as pieces of the larger picture including personal, private fleet or transportation EVs, fast chargers and other supply equipment, weather service information, and geographically distributed charging sites such as public lots, garage and retail, and private or shared usage depots. ABB and the project team designed, tested, and improved a charging management system at local/edge and cloud levels. The ultimate objective of the project was to convincingly demonstrate that the developed secure eMosaic plat-form can be readily and favorably adopted by diverse utilities and site owners at scale. This was achieved through a demonstration plan with field deployment at several physical sites across 4 states and additional scalable simulation from high fidelity charging models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

A Comparative Analysis of Infrastructure-Based Perception Sensors for Intelligent Transportation Systems

The rise of privatized and public investment in smart city infrastructure and intelligent transportation systems has generated a heightened demand for perception sensors that effectively track and detect objects while being reliable in diverse weather and lighting conditions. This growing demand for perception sensors has accelerated their development and enhanced their capabilities. With these new capabilities, it is challenging to determine the most suitable sensing unit to use in each situation. Therefore, it is essential to have a comprehensive understanding of the benefits and limitations of each sensing unit to effectively leverage their capabilities. The purpose of this paper is to provide a detailed evaluation of various perception sensors. Additionally, this paper will demonstrate the benefits of combining multiple perception sensors, which complement each other by addressing data gaps inherent to single-sensor systems, to facilitate the creation of a digital twin that models the real world. The Infrastructure, Perception, and Control (IPC) team will conduct data analysis using data collected through field testing at traffic intersections in Colorado Springs, Colorado, to make comparisons between sensors. This research aims to provide clear and concise information about modern perception systems, which will support the development of intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

A Data Quality-Aware Framework to Reliably Forecast Photovoltaic Generation and Consumer Load for an Improved Resilience of Microgrids

Photovoltaic (PV) power and consumer load forecasting plays a critical role to ensure operational resilience of the electric grid. Most data-driven forecasting algorithms rely heavily on the continuous availability of good quality data for periodic training and validation. When deployed at the grid’s edge, prolonged disruptions to communications during extreme events degrade data quality. Factors such as missing observations, epistemic uncertainties, data drift, and concept drift are manifestations of data quality that impact the generalization of such field-deployed forecasting models. Currently, there exists no mechanism in the literature to dynamically switch between models under varying degrees of data quality as quantified by certain metrics for each factor highlighted above. This paper addresses this shortcoming by conceptually introducing a data qualityaware framework for reliable PV generation and consumer load forecasting. The framework’s design incorporates components of missing values, divergence tests, and continuous monitoring of generalization performance to detect changes in data quality caused by communications disruptions and trigger specific classes of forecasting models grouped under three use cases (UC1- UC3). As a first step towards validating this framework, real data collected from an actual field microgrid system is used to demonstrate the viability of the three use cases. Results show that the performance is the best in UC1 with an unadjusted R-square value of 0.954, followed by 0.939 for UC2 and 0.757 for UC3.

Sundararajan, Aditya↗

DOE OSTI2026

Extracting $B_s\to D_s^*\ellν_\ell$ form factors

Semileptonic $B_{(s)}$ decays are of great phenomenological interest because they allow to determine e.g. CKM matrix elements or test lepton flavor universality. Taking advantage of already existing lattice data, we demonstrate the analysis steps to extract the four form factors describing exclusive semileptonic $B_s\to D_s^*\ellν_\ell$ decays using the narrow width approximation. Our data are based on RBC/UKQCD's set of 2+1 flavor gauge field ensembles with Shamir domain-wall fermion and Iwasaki gauge field action featuring inverse lattice spacings of $a^{-1}=$1.785, 2.383, and 2.785 GeV as well as pion masses between 268 and 433 MeV. Light, strange and charm quarks are simulated using domain-wall fermions, whereas bottom quarks are generated with the relativistic heavy quark (RHQ) action.

Boushmelev, Anastasia [Siegen U.] (ORCID:000000033↗

DOE OSTI2021

SECARB Regional Project Assessment

The SECARB Cranfield Early Test Project was designed to safely demonstrate large-scale, long-term CO 2 injection and storage in a CO 2 -enhanced oil recovery (EOR) operation and associated saline reservoir. Storage during EOR operations has the potential to offset commercial costs, holding a significant promise for future large-scale deployment in the southeastern United States. Denbury Onshore LLC began CO 2 -EOR operations in July 2008 at the Cranfield oilfield located east of Natchez, Mississippi (Figure 1). The location offered an ideal setting to investigate storage monitoring at a commercial CO 2 -EOR operation. The field team, led by the Gulf Coast Carbon Center of the Bureau of Economic Geology at the University of Texas at Austin, deployed the largest suite of monitoring, verification, accounting (MVA) technologies to collect data to assess long-term CO 2 utilization and storage. The CO 2 injection interval is within the 3,000-meter-deep fluvial lower Tuscaloosa Formation, a large and regionally extensive saline reservoir, at a broad four-way structural closure that defines the Cranfield oilfield. Tests were conducted in the oil-producing area as well as in the non-oil-producing brine aquifer. The CO 2 used during EOR operation was transported via pipeline from the Jackson Dome, a natural source of CO 2 . The study focused on four integrated research areas: (1) the High-Volume Injection Test area (HiVIT); (2) the Detailed Area of Study (DAS); (3) the Geomechanical area; and (4) the near-surface observatory, also called the “P-site.” CO 2 injection occurred at the HiVIT and the DAS (Figure 2 and Figure 3). The DAS was located down-dip of the oil-water interface of the Cranfield oilfield. The well layout included one injection well and two down-dip observation wells. The team was the first of the regional partnerships to monitor more than 1 million metric tons of CO 2 . At the end of the project, the team monitored more than 11 million metric tons of CO 2 injected and 5 million metric tons of CO 2 stored. The Cranfield Project was the fifth project worldwide, and the first such project in the United States, to reach this CO 2 injection volume while being monitored to demonstrate storage effectiveness.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Resource Recovery and Environmental Protection in Wyoming’s Greater Green River Basin Using Selective Nanostructured Membranes (Final Report)

Produced water (PW) represents a sizable waste stream that is co-generated with oil and natural gas production. In 2021 Wyoming ranked 8th and 9th, respectively in domestic oil and natural gas production. In 2017 Wyoming ranked as the 4th highest generator of PW in the U.S, accounting for 7% of the total volume generated. In the context of being the 3rd most arid state in the U.S., the value of water reuse becomes obvious. PW reuse, and resource recovery, in any form requires some level of treatment to remove particulates, residual (free, dispersed) hydrocarbons, organics, and salts. The level of treatment depends on the requirements of the reuse, or resource recovery, application. PW management systems in Wyoming employ in order of volume of PW managed the following management strategies: reinjection for enhanced oil recovery, surface discharge, deep well injection, evaporation ponds (impoundments), and commercial management/treatment. Complicating treatment efforts are the relatively high concentrations of organics (natural and synthetic), dispersed/free hydrocarbons, benzene-toluene-ethylbenzene, and xylenes (BTEX) compounds, biologicals, salts, and minerals. Hydrocarbons (dispersed/dissolved crude oils) and BTEX compounds, as well as synthetic organics, present economic and environmental concerns. The former represents lost revenue, while the latter results in negative environmental impacts like emissions from surface impoundments. The overall objective of this proposal was to synthesize superhydrophilic/oleophobic and superhydrophobic/oleophilic membranes for selectively concentrating and then separating BTEX compounds and oil and grease (O&G) from PW originating from the Greater Green River Basin (GGRB) in Wyoming. Three specific research aims were pursued to accomplish this overall objective. This final report details the development of the superhydrophobic and superhydrophilic membranes, as well as the design of the membrane module prototypes specifically. The technoeconomic assessment is separately reported in another document. 1. Aim #1 – Material optimization and performance evaluation of superhydrophilic/oleophobic and superhydrophobic/oleophilic membranes made by electrospinning/spraying. 2. Aim #2 – Design and construction of cross-flow membrane modules for selectively concentrating and then separating BTEX/oil from GGRB produced water. 3. Aim #3 – Techno-economic assessment of BTEX/oil recovery, and clean water production, using superhydrophilic/oleophobic and superhydrophobic/oleophilic membrane separation for GGRB PW. Superhydrophobic membranes were synthesized by electrospinning poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) nanofibers onto polyester (PET) substrates and electrospraying nano-carbon black/PVDF-HFP onto the nanofibrous layer. These membranes were characterized by high (>8000 liters per square meter per hour per bar (LMH/bar)) permeance values for pure hydrocarbon phases and a high hydrocarbon selectivity (>96%) when treating GGRB PW. All results were obtained when operating the membrane in a crossflow configuration representative of actual field operating conditions. Solvent/oil properties, specifically viscosity and total surface energy/tension, affected permeance across the membrane, which resulted in light mineral oil (394 LMH/bar) and o-xylene (1834 LMH/bar) being characterized by lower permeance values in the pure phase tests. Mixed phase fluxes between 40 to 80 LMH were obtained for the PW when operating the membrane at a feed pressure of 0.3 bar. Flux decreased as the mixed phase concentration in the feed decreased pointing to the importance of maximizing the collision efficiency between the emulsion and the membrane surface and maximizing the emulsion concentration in the feed and the turbulence within the feed channel. These tests demonstrated that the superhydrophobic membranes developed here are a viable hydrocarbon recovery method for GGRB PWs and should be pursued for testing in pilot-scale trials. Superhydrophilic membranes were successfully synthesized via electrospinning/spraying using polyacrylonitrile (PAN) nanofibers as a base nanofibrous matrix. Integration of polyaniline (PANI) into the nanofibrous matrix produced a superior membrane, for water filtration applications, relative to PAN alone and reduced graphene oxide (RGO) when integrated into the nanofibrous matrix. This conclusion was based on the PANI-PAN resistance to flux loss (fouling) when treating model solvent/oil solutions representative of GGRB PWs and field collected PW from the GGRB. The synthesized PAN membranes outperformed a commercially available PAN membrane designed for oil/water separation. This finding indicates that the surface chemical and physical characteristics of the electrospun membranes presents improved properties for filtration of challenging waters like GGRB PWs. The electrospun membranes therefore show promise overall as a substitute for conventionally polymerized membranes in PW treatment applications. The PANI-PAN membrane specifically presents superior performance characteristics for concentration O&G prior to treatment by the hydrocarbon recovery membrane and producing high-quality filtrate for reuse and/or additional treatment (desalination).

02 PETROLEUM↗