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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 OSTI2018

Lessons Learned from a Laser Injury Event - Limitations of Skill-Of-The-Craft

A technician at INL received 2nd degree burns to the middle and ring fingers of his left hand while performing a preventive maintenance (PM) procedure on a Class 4 industrial laser on August 30, 2011. The task required placement of a “target” in the beam path to verify mirror alignment. The technician, believing the laser was in a safe mode, reached into the beam pathway to place the target, saw a flash and immediately withdrew his hand. Three INL technicians were assigned to perform a 2000 hour run time PM on a laser. A pre-job briefing was held August 29, 2011 by the acting foreman and the technicians started the PM. They had completed 25 steps in Section I of the work order by the end of the day. The laser systems were placed under LO/TO and the internal optics were cleaned and/or replaced. The LO/TO was released and the next morning, August 30, 2011, the technicians prepared to take a diagnostic shot inside the internal optics. The laser was powered up at the HMI, and the person in charge (PIC) selected the “Mirror Alignment” program from the human machine interface (HMI) screen, enabled the pendent and pressed the “Cycle Start” button. The technicians believed the laser was in “simmer” mode at this point and that no beam was being generated out of the resonator. A beam was being generated in the “Mirror Alignment” program when the “Cycle Start” button was pressed. They decided to check the #4 aperture target and see if they were aligned at that location. One of the technicians reach to place the target at the end of the Z box and a “Flash” occurred. The technician immediately retracted his hand with the target. The PIC turned off the high voltage (HV) at the pendent. The foreman and supervisor were informed of the incident and the injured technician was escorted to an onsite medical facility. The nurse evaluated the injury and sent the technician to a physician for further evaluation. A physician examined the technician’s hand. The technician’s left middle and ring fingers received 2nd degree burns to both finger tips. A prescription burn ointment and bandage was applied. The employee was returned to work with no restrictions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions

Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, general guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

13 HYDRO ENERGY↗
DOE OSTI2015

The FREYA project at VENUS-F - the next step towards MYRRHA

The VENUS-F reactor is a fast, zero-power research reactor. Since 2009 it has used 30% wt. metallic uranium as fuel and solid lead as reflector and coolant simulator. It can also be coupled to a particle accelerator with a neutron producing target and make up a subcritical Accelerator Driven System (ADS). Within the currently ongoing FREYA project, the main tasks being investigated are the validation of online reactivity monitoring of an ADS, the validation of nuclear data and neutronic codes, and the experimental characterization of fast critical and subcritical cores representative for MYRRHA. MYRRHA will be an ADS demonstrator with the option to be operated in critical mode too. So far the only critical core at VENUS-F was assembled using 97 fuel assemblies, each of them consisting of 9 U and 16 Pb rodlets. The core was thoroughly characterized: flux traverses, spectral indices, control rod worth, delayed neutron parameters were measured and compared with Monte Carlo and deterministic calculations. A sensitivity study was also performed focusing on the influence of material impurities, positioning uncertainties, structures and detectors around the core. It was a simple core without any perturbations in the active zone. In the next critical cores that are planned to be investigated in 2015 and 2016, additional elements will be introduced in order to become more representative for MYRRHA. As MYRRHA will use MOX fuel, oxygen will be added into the following core. This will be done by introducing Al{sub 2}O{sub 3} into the fuel assemblies. New fuel assemblies have been already made and are ready to be loaded. Each consists of 13 U, 8 Pb, and 4 Al{sub 2}O{sub 3} rodlets. The new critical core will consist of approximately 40 such fuel assemblies and will be loaded in January 2015. The first results of the characterization measurements will be presented. Additionally, it is planned to investigate coolant void effects. As MYRRHA will be a rather complex system using a number of in-pile sections and heterogeneous elements inside and around the active zone, the next cores will be step by step modified by loading new materials and perturbations with the aim to assemble a MYRRHA mock-up. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Task Order 22 – Engineering and Technical Support, Deep Borehole Field Test. AREVA Summary Review Report

Under Task Order 22 of the industry Advisory and Assistance Services (A&AS) Contract to the Department of Energy (DOE) DE-NE0000291, AREVA has been tasked with providing assistance with engineering, analysis, cost estimating, and design support of a system for disposal of radioactive wastes in deep boreholes (without the use of radioactive waste). As part of this task order, AREVA was requested, through a letter of technical direction, to evaluate Sandia National Laboratory’s (SNL’s) waste package borehole emplacement system concept recommendation using input from DOE and SNL. This summary review report (SRR) documents this evaluation, with its focus on the primary input document titled: “Deep Borehole Field Test Specifications/M2FT-15SN0817091” Rev. 1 [1], hereafter referred to as the “M2 report.” The M2 report focuses on the conceptual design development for the Deep Borehole Field Test (DBFT), mainly the test waste packages (WPs) and the system for demonstrating emplacement and retrieval of those packages in the Field Test Borehole (FTB). This SRR follows the same outline as the M2 report, which allows for easy correlation between AREVA’s review comments, discussion, potential proposed alternatives, and path forward with information established in the M2 report. AREVA’s assessment focused on three primary elements of the M2 report: the conceptual design of the WPs proposed for deep borehole disposal (DBD), the mode of emplacement of the WP into DBD, and the conceptual design of the DBFT. AREVA concurs with the M2 report’s selection of the wireline emplacement mode specifically over the drill-string emplacement mode and generically over alternative emplacement modes. Table 5-1 of this SRR compares the pros and cons of each emplacement mode considered viable for DBD. The primary positive characteristics of the wireline emplacement mode include: (1) considered a mature technology; (2) operations are relatively simple; (3) probability of a radiological release due to off-normal events are relatively low; (4) costs are relatively low; and (5) maintenance activities are relatively simple. The primary drawback associated with the wireline emplacement mode for DBD is the number of emplacement trips-in to the borehole, which results in a relatively higher probability for a drop event. Fortunately, the WPs can be engineered with impact limiters that will minimize the likelihood of a breach of the WP due to a drop. The WP designs presented in the M2 report appear to be focused on compatibility with the drill-string emplacement mode (e.g., the threaded connections). With the recommendation that the wireline emplacement mode be utilized for the DBFT, some changes may be warranted to these WPs. For example, the development of a WP release connection that is more reliable than the currently credited connection, which is considered to have a high failure probability, and the integration of an impact limiter into its design. The M2 report states the engineering demonstration of the DBFT will occur in the FTB over a 4-year period. AREVA recommends development and testing of the WP emplacement handling equipment occur separately (but concurrently, if not earlier) from the FTB at a mock-up facility. The separation of this activity would prevent schedule interference between the science and engineering thrusts of the project. Performing tests in a mock-up facility would allow additional control and observation compared to the FTB. The mock-up facility could also be utilized as a training facility for future operations. Terminal velocity and impact limiter testing would require the FTB for testing, since these areas would be difficult to reproduce in a limited depth mock-up. Although only at the end of the conceptual stage of design development, DBD appears to be a viable solution for some waste forms produced by the nuclear industry. However, regulatory requirements have yet to be established for pre- and post-closure performance of DBD and should be established as soon as possible. Some of the main areas of focus from a regulatory perspective include: (1) establishing acceptable performance requirements for the long-term behavior of DBD; (2) determining acceptable borehole abandonment criteria; (3) establishing retrievability requirements; (4) developing a consensus on the factor of safety (FoS) for the emplacement mode and WP; and (5) establishing safety and safeguards performance requirements for DBD. Although conservative requirements have been utilized to provide the foundation for the conceptual design of DBD, regulatory requirements and feedback are necessary to confirm recommendations made herein and to ensure the long-term performance of DBD is acceptable. The combination of the M2 report and this SRR is intended to facilitate the completion of the conceptual design for DBD for the Cs and Sr capsules and calcined waste forms. Using the conceptual design, preliminary design activities (the second stage of a three-stage process described in the M2 report) can proceed and the DBFT utilized to support, demonstrate, and confirm engineering elements of this design.

42 ENGINEERING↗
DOE OSTI2016

Exe-Guard Project

The exe-Guard Project is an alliance between Dominion Virginia Power (DVP), Sandia National Laboratories (SNL), Dartmouth University, and Schweitzer Engineering Laboratories (SEL). SEL is primary recipient on this project. The exe-Guard project was selected for award under DE-FOA-0000359 with CFDA number 81.122 to address Topic Area of Interest 4: Hardened platforms and Systems. The exe-Guard project developed an antivirus solution for control system embedded devices to prevent the execution of unauthorized code and maintain settings and configuration integrity. This project created a white list antivirus solution for control systems capable of running on embedded Linux® operating systems. White list antivirus methods allow only credible programs to run through the use of digital signatures and hash functions. Once a system’s secure state is baselined, white list antivirus software denies deviations from that state because of the installation of malicious code as this changes hash results. Black list antivirus software has been effective in traditional IT environments but has negative implications for control systems. Black list antivirus uses pattern matching and behavioral analysis to identify system threats while relying on regular updates to the signature file and recurrent system scanning. Black list antivirus is vulnerable to zero day exploits which have not yet been incorporated into a signature file update. System scans hamper the performance of high availability applications, as revealed in NIST special publication 1058 which summarizes the impact of blacklist antivirus on control systems: Manual or “on-demand” scanning has a major effect on control processes in that they take CPU time needed by the control process (Sometimes close to 100% of CPU time). Minimizing the antivirus software throttle setting will reduce but not eliminate this effect. Signature updates can also take up to 100% of CPU time, but for a much shorter period than a typical manual scanning process. Control systems are vulnerable to performance losses if off-the-shelf blacklist antivirus solutions aren’t implemented with care. This investment in configuration in addition to constant decommissioning to perform manual signature file updates is unprecedented and impractical. Additionally, control systems are often disconnected or islanded from the network making the delivery of signature updates difficult. Exe-Guard project developed a white list antivirus solution that mitigated the above drawbacks and allows control systems to cost-effectively apply malware protection while maintaining high reliability. The application of security patches can also be minimized since white listing maintains constant defense against unauthorized code execution. Security patches can instead be applied in less frequent intervals where system decommissioning can be scheduled and planned for. Since control systems are less dynamic than IT environments, the feasibility of maintaining a secure baselined state is more practical. Because upgrades are performed in infrequent, calculated intervals, it allows a new security baseline to be established before the system is returned to service. Exe-Guard built on the efforts of SNL under the Code Seal project. SNL demonstrated prototype Trust Anchors on the project which are independent monitoring and control devices that can be integrated into untrustworthy components. The exe-Guard team started with the lessons learned under this project then designed commercial solution for white list malware protection. Malware is a real threat, even on islanded or un-networked installations, since operators can unintentionally install infected files, plug in infected mass storage devices, or infect a piece of equipment on the islanded local area network that can then spread to other connected equipment. Protection at the device level is one of the last layers of defense in a security-in-depth defense model before an asset becomes compromised. This project provided non-destructive intrusion, isolation and automated response solution, achieving a goal of the Department of Energy (DOE) Roadmap to Secure Control Systems. It also addressed CIP-007-R4 which requires asset owners to employ malicious software prevention tools on assets within the electronic security perimeter. In addition, the CIP-007-R3 requirement for security patch management is minimized because white listing narrows the impact of vulnerabilities and patch releases. The exe-Guard Project completed all tasks identified in the statement of project objective and identified additional tasks within scope that were performed and completed within the original budget. The cost share was met and all deliverables were successfully completed and submitted on time. Most importantly the technology developed and commercialized under this project has been adopted by the Energy sector and thousands of devices with exe-Guard technology integrated in them have now been deployed and are protecting our power systems today

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Computationally Accelerated Discovery and Experimental Demonstration of High-Performance Materials for Advanced Solar Thermochemical Hydrogen Production

This project achieved its overarching goal of accelerating the discovery and validation of solar thermochemical hydrogen (STCH) materials through a tightly integrated approach that combined high-throughput computational screening, advanced machine learning (ML), and experimental testing. Guided by the objectives outlined in the Statement of Project Objectives (SOPO), our work fulfilled all major milestones across four technical tasks and delivered scientific breakthroughs and practical tools that significantly exceeded the original scope of the project. We began by addressing the challenge of predicting material phase stability through machine learning. A novel Python module was developed to generate thousands of meaningful features from composition, structure, and electronic properties, enabling rapid and reproducible ML model development. Using these tools, we trained a model to predict temperature-dependent Gibbs energies (G(T)) for inorganic crystalline materials with near-chemical accuracy—roughly 40 meV/atom—marking the first such descriptor of its kind. We also introduced a new machine-learned tolerance factor, τ, that accurately predicted perovskite formability with over 90% success, outperforming traditional heuristic models, such as the Goldschmidt tolerance factor. These capabilities allowed for rapid and accurate predictions of phase stability across a vast oxide composition space, setting the stage for high-throughput thermodynamic screening. Building on this foundation, we conducted an extensive computational screening of candidate STCH oxide materials. Over 1.1 million perovskite compositions were evaluated using the τ descriptor, leading to the identification of more than 27,000 predicted stable structures. Using density functional theory (DFT), we refined over 68,000 multinary perovskite structures and computed oxygen vacancy formation energies for over 1,300 ternary and double perovskites. These calculations enabled us to isolate compounds with redox behavior consistent with STCH requirements and resulted in a public dataset now hosted on the Materials Project. Recognizing that thermodynamic screening alone is insufficient, we addressed kinetic limitations by developing a suite of tools to estimate transition state (TS) energies for key redox reactions. We implemented a novel bounding approach that provides lower and upper estimates of TS energies with dramatically reduced computational cost, requiring less than 10% of the CPU time of a full nudged elastic band (NEB) calculation while maintaining high accuracy. This enabled rapid evaluation of over 200 reaction pathways across 90 materials. To further accelerate screening, we developed a SISSO-based ML model to predict diffusion barriers with a 96.7% success rate in classifying fast vs. slow materials, supporting a robust, data-driven framework for assessing redox kinetics. Experimental validation was critical to confirming the predictive power of our models. We synthesized and tested a wide array of candidate materials, including Mn-doped hercynite and several Gd- and La-based perovskites. Notably, Sr 0.4 Gd 0.6 Mn 0.6 Al 0.4 O 3 (SGMA) and Gd 0.5 La 0.5 Co 0.5 Fe 0.5 O 3 (GLCF) emerged as leading STCH materials, exhibiting robust redox cycling and high hydrogen yields exceeding 150 µmol H 2 /g per cycle. These materials also retained over 50% of their hydrogen productivity under high-conversion conditions (H 2 O:H 2 = 1333:1), demonstrating strong thermodynamic favorability and promising performance under industrially relevant scenarios. Additional candidates, such as La 2 MnNiO 6 (L2MN), were found to produce even higher yields than ceria under standard STCH conditions. Our collaborators at Sandia National Laboratories confirmed these findings using high-temperature X-ray diffraction and thermogravimetric analysis, observing stable phase evolution and reversible redox activity. In several respects, the project went beyond the goals initially outlined in the SOPO. We published 17 peer-reviewed articles, including a large dataset of over 66,000 theoretical perovskites and a new structure prediction method (SPuDS-DFT) that accurately identifies ground-state structures at a fraction of the cost of traditional DFT. We demonstrated that our machine-learned G(T) model offers accuracy rivaling quasiharmonic calculations while being orders of magnitude faster. In partnership with the Materials Project, we made our datasets openly available, providing a powerful new resource for the broader materials science community. The combined computational and experimental advances of this project represent a significant advance in STCH materials discovery. By creating a robust, generalizable, and open workflow for thermodynamic and kinetic screening, and validating key findings through synthesis and reactor testing, we have provided a practical and scalable pathway for the rapid identification of new redox-active materials. The tools, data, and materials developed under this project are already supporting ongoing research and have laid the groundwork for the next generation of solar fuel technologies.

08 HYDROGEN↗
DOE OSTI2017

Modeling and Analysis of Coupled Flow Through Packed Beds as a Diagnostic Tool for Packed Bed Reactor Health - 17172

The US Department of Energy, Office of River Protection, is building the Waste Treatment and Immobilization Plant (WTP) in Richland, Washington, to immobilize the hundreds of millions of liters of radioactive waste stored at the Hanford Site in Washington state. The offgas generated during vitrification of these wastes requires treatment to remove and/or destroy chemical and radioactive hazardous components before release to the atmosphere. In particular, volatile and semi-volatile hydrocarbon components are to be catalytically oxidized to carbon dioxide and water, whereas nitrogen oxides (NOx) are to be catalytically reduced to nitrogen and water. These tasks are accomplished in a series of packed bed reactors. Achieving the requisite Destruction/Removal Efficiency (DRE) for each of the tracked components is highly dependent on the flow characteristics of the offgas through the packed beds. The flow kinetics may be monitored and characterized during the initial warm-up of the system. The gases, initially at 76 deg. C (169 deg. F), (a) flow through one side of a countercurrent heat exchanger to recover heat from the exiting gases (which have already passed through the catalytic beds), (b) are then further heated electrically to approximately 400 deg. C (750 deg. F; the preferred operating temperature of the Thermo Catalytic Oxidizer (TCO)), (c) pass through the TCO where the reduced components of the offgas are oxidized over a supported Pt catalyst bed, (d) are partially cooled to approximately 315 deg. C (600 deg. F) by addition of approximately 25% (by weight) diluted ammonia/air mixture, (e) pass through the Selective Catalytic Reducer (SCR) where the NOx components are converted to N{sub 2} and H{sub 2}O over a supported V2O{sub 5}/TiO{sub 2} catalyst bed, and (f) exit through the hot side of the countercurrent heat exchanger. The electrical heater is not powerful enough to raise the temperature of the offgas to the desired 400 deg. C without the contribution of the heat recovered in the heat exchanger. Moreover, the thermal mass of the beds and housings of the TCO and SCR require a significant input of thermal energy to reach the desired operating temperatures; the better part of a day is required to bring the unit from ambient temperatures to operating temperatures. Therefore, the temperature of the stream during the initial warm-up of the system at each point in the system is dependent upon the thermal mass, flow dispersion, and permeability of the components, quantified as characteristic/representative volumes and residence times. By monitoring the temperature of the offgas as it enters and exits each portion of the system during this warm-up period one may model the system as a series of small Continuously Stirred Tank Reactors (CSTR)s (referred to as 'CSTRlets') to explain the flow kinetics through the catalyst beds. These component CSTRlets are adjustable in size and number, that one may model the flow profile of the gases through the catalyst beds. For example, a single large CSTR will present an effluent temperature profile with a simple exponential decay shape. Concatenating more CSTRlets generate more complex sigmoidal responses. Concatenating one CSTRlet significantly larger than the others in the train also alters the shape of the effluent temperature profile, in a fashion not replicable by a train of equal-sized CSTRlets. The ultimate goal is to accurately emulate the entire system as a representative CSTRlet train. Once accomplished, this characterization may be used in subsequent warm-ups (following shutdown for catalyst maintenance, for example) or by an intentional step change in inlet offgas temperature to detect/diagnose anomalies in flow through the beds (e.g., channeling or partial blockage.) This work presents development of this model through analytical, calculational, and empirical means. - The analytic model is accomplished by Laplace transform analysis of each component to recover component process transfer functions. - The calculational portion is performed via brute force finite element calculation. - Fitting empirical data from the warm-up of the TCO skid allows one to select the hypothetical CSTRlet trains which best fit the TCO and SCR beds for the analytical and finite element calculations. The first portion of this paper presents the development of the analytical (Laplace space) model of the coupled system. Estimated values for the initial temperature, mass, specific heat, electrical heat, and relative quantities of the offgas, catalyst beds, stainless steel housings, and ammonia dilution stream are used to generate expected temperature profiles at several selected junctures of the system. The number and size of the component hypothetical CSTRlets are calculated from the analytical model and are used to predict temperature profiles at the exit of the system and at intermediate junctions between system components (where thermocouples are present in the actual unit.) The finite element calculations employ a series of CSTRlet volumes to produce a similar suite of temperature profiles, for comparison with the analytical model results. Finally, empirical data from the Factory Acceptance Test system warm-up are used to select and compare the results from each modeling strategy to select the CSTRlet train(s) which best model the behavior of the actual system. This work presents a novel method of system component characterization, of value to the WTP plant and similar vitrification facilities as well as chemical process industries unrelated to waste management. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

NRAP-Open-IAM: NRAP Open Source Integrated Assessment Model

Note: This is the last version (a2.6.1) of NRAP-Open-IAM released during NRAP Phase II in 2022. The latest version of NRAP-Open-IAM is available here: https://edx.netl.doe.gov/dataset/phase-iii-nrap-open-iam NRAP-Open-IAM is an open-source software product that enables quantification of containment effectiveness and leakage risk at storage sites in the context of system uncertainties and variability. NRAP-Open-IAM represents the next-generation in a line of systems-based computational models developed for quantitative geological carbon storage (GCS) risk assessment. The model comprises a set of reduced-order and analytical models of various components of the GCS system, potential leakage pathways, receptors of concern including impact to groundwater resources and the atmosphere, a framework to support stochastic simulation, time stepping, uncertainty quantification, other analytical functionality for scenario and risk-performance evaluation, and a basic graphical user interface to support scenario development, data input simulation definition, and basic post-processing and results display. As the NRAP Open-IAM functionality continues to evolve, we continue to add to its capability to develop quantitative, probabilistic, and time-dependent profiles of the evolution of risk at a GCS site and evaluate the influence of uncertain parameters on uncertainty in predicted risk. It can be used to quantify the dynamics of reservoir saturation plume and pressure-affected area, for evaluation of the area of potential groundwater impact (i.e., Area of Review) and monitoring requirements to support cost and regulatory analysis, and for consideration of different post-injection site care and closure scenarios. This submission contains the current version of NRAP-Open-IAM available for evaluation and testing. To use the NRAP-Open-IAM, download the source code (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/4c24a3da-3b40-4ffe-9892-c807ae9f8760) then open the NRAP-Open-IAM user's guide (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669) to read more about the tool. Installation instructions for Windows, Mac, and Linux can be found in the "installers" folder of the extracted NRAP-Open-IAM folder and describe setup of environment (e.g., Python libraries) needed for proper work of the tool. Test of installation can be done by running "python openiam_setup_tests.py" in the "setup" folder. The installation test also runs a test suite to see if the NRAP-Open-IAM has been installed correctly. To run the test suite separately, run "python iam_test.py" in the "test" folder. User's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669 Developer's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/3bc6ee7d-609d-4eb6-80ba-fa6130ee0313 Reservoir simulation data used in some examples distributed with NRAP-Open-IAM: - Kimberlina: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/eb62cece-61b2-4037-9b6d-32407dde2ab8 - Kimberlina (compartmentalized): https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/366f9530-3b32-4b84-affe-ab2df1d9a8b5 - FutureGen 2.0: https://edx.netl.doe.gov/dataset/futuregen-2-0-1008-simulation-reservoir-lookup-table NRAP-Open-IAM GitLab repository: https://gitlab.com/NRAP/OpenIAM Related publications: - Bacon, D., Yonkofski, C., Brown, C., Demirkanli, D. and Whiting, J., 2019. Risk-based post injection site care and monitoring for commercial-scale carbon storage: Reevaluation of the FutureGen 2.0 site using NRAP-Open-IAM and DREAM. International Journal of Greenhouse Gas Control 90: 102784. - Bacon, D. Demirkanli, D., and White, S., 2020. Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site. International Journal of Greenhouse Gas Control 102: 103153. - Harp, D., Oldenburg, C., and Pawar, R., 2019. A metric for evaluating conformance robustness during geologic CO2 sequestration operations. International Journal of Greenhouse Gas Control 85: 100-108. - Lackey, G., Vasylkivska, V., Huerta, N., King, S., and Dilmore, R., 2019. Managing well leakage risks at a geologic carbon storage site with many wells, International Journal of Greenhouse Gas Control, 88 :182-194. - Vasylkivska, V., Dilmore, R., Lackey, G., Zhang, Y., King, S., Bacon, D., Chen, B., Mansoor, K., and Harp, D., 2021. NRAP-Open-IAM: A flexible open-source integrated assessment model for geologic carbon storage risk assessment and management, Environmental Modelling & Software, 143: 105114. Presentations: - Chen, B., Harp, D., and Pawar, R., A data assimilation approach (ES-MDA) coupling with NRAP-Open-IAM for quantifying uncertainty reduction in geological CO2 sequestration. AGUFM 2019: T44A-02. - Chen, B., and Harp, D., Improving risk analysis precision for geologic CO2 sequestration by quantifying the uncertainty reduction before and after acquiring monitoring data. 14th Greenhouse Gas Control Technologies Conference, Melbourne, Australia, 2018, pp. 21-26. - Harp, D., National Risk Assessment Partnership Task 2: Containment Assurance. No. LA-UR-19-28654, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2019. - Vasylkivska, V., King, S., Bacon, D., Harp, D., Chen, B., Mansoor, K., Onishi, T., Yang, Y., Zhang, Y., and Keating, E., NRAP-Open-IAM: An open-source integrated assessment model, poster, Mastering the Subsurface Through Technology Innovation, Partnerships and Collaboration: Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA, August 13-16, 2018. - Vasylkivska, V., Lackey, G., King, S., Wentworth, A., Huerta, N., Creason, C., DiGiulio, J., Yang, Y., and Dilmore, R., Long-term risk analysis of a geologic CO2 storage project during the post-injection period, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 25-March 1, 2019. - Vasylkivska, V., Overview of the NRAP-Open-IAM tool for carbon storage (beta release), 2019 Annual NRAP Tool Users Meeting, Pittsburgh, PA, August 27, 2019. - Vasylkivska, V., Bacon, D., Chen, B., Dilmore, R., Harp, D., King, S., Lackey, G., Lindner, E., Liu, G., Mansoor, K. and Zhang, Y., NRAP-Open-IAM: A new, open-source code for integrated assessment of geologic carbon storage containment effectiveness and leakage risk, poster, American Geophysical Union Fall Meeting 2020 (virtual meeting), December 2020. - Vasylkivska, V., NRAP open-source integrated assessment model and relevant application, oral presentation, NRAP workshop "NRAP Tools for Geologic Carbon Storage Risk-Based Decision Making" held in conjunction with Groundwater Protection Council (GWPC) 2021 Annual Forum (virtual meeting), Salt Lake City, UT, September 2021. - Vasylkivska, V., NRAP-Open-IAM: open-source integrated assessment model, digital poster/demonstration, software demonstration session, 2022 Carbon Management Project Review Meeting, August 16, 2022

AoR↗
DOE OSTI2019

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

Purpose: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. Methods: With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution’s pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes. Results: We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8–14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0–13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%. Conclusions: The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

California CO 2 Storage Assurance Facility Enterprise (C2SAFE): Final Technical Report

The California CO 2 Storage Assurance Facility Enterprise (C2SAFE) project conducted a preliminary study into the feasibility of a commercial-scale carbon dioxide (CO 2 ) storage complex located in California’s Southern San Joaquin Valley (SSJV). The work represents the first phase of a multi-phased development approach outlined by the United States Department of Energy’s (DOE’s) Carbon Storage Assurance and Facility Enterprise (CarbonSAFE) program meant to develop carbon storage sites capable of permanently storing 50 million metric tonnes of CO 2 , or greater, injected over a typical facility life of 20 to 30 years. This report represents the culmination of work performed under C2SAFE and outlines a phased implementation plan for large-scale, industrial carbon capture and permanent storage in California’s SSJV. The report is meant to be a standalone document and as such provides high-level summaries of all tasks of the project, but is also meant to complement the other three topical reports issued by the project – reports referenced herein. The study considered multiple scenarios including an east-side storage facility, a west-side storage facility, and potential multi-site storage facilities. The east-side storage facility is to be located at Clean Energy Systems, Inc.’s Kimberlina Power Plant, or nearby, and the west-side facility will be located at the California Resources Corporation’s (CRC’s) Elk Hills oil field. The majority of carbon emissions are planned to be captured from existing sources and transported to the selected and verified storage site(s). Capture from the single closest source to the eastern storage facility and the sources within the Elk Hills field would meet the CarbonSAFE storage goal of 50 million tonnes. The study has shown there is sufficient storage capacity to meet this requirement and more; thus, capture from any of the numerous additional large nearby sources would substantially leverage the CarbonSAFE investment. A comparison of preliminary CO 2 capture, transportation, and storage costs to economic value and incentives in the state shows current market conditions make it economically feasible to develop large-scale industrial storage sites in California’s SSJV. The business case for Carbon Capture and Storage (CCS) in California’s SSJV appears economically viable today for large CO 2 point sources involved in the production of transportation fuels, such as oilfield cogeneration units and steam boilers. The overall levelized CCS cost is estimated at roughly 125 per tonne of CO 2 , which includes CO 2 amine-solvent post-combustion capture, transportation, and storage. The levelized CO 2 capture cost is based on today’s technology, with no future cost reduction factored in for technology improvement. The sum of the value of incentives and revenue from California Low Carbon Fuel Standard (LCFS) credits (discounted roughly 20% from their current transaction value), sale of California cap-and-trade emission allowances, and Section 45Q tax credits—either for saline storage or the Enhanced Oil Recovery (EOR) credit plus CO 2 sales revenue—is about 165/tonne-CO 2 . The economic viability of CCS for Natural Gas Combined Cycle (NGCC) power plants in the SSJV is more site-specific. A plant providing power solely to the electric grid, if it added CCS, should be eligible for Section 45Q tax credits and could sell its excess cap-and-trade emission allowances, but it would not generate LCFS credits. Using the cost and incentive values detailed in this report, it would not be economical for such a plant to add CCS today, barring other considerations. An NGCC plant within an oilfield (e.g., CRC Elk Hills and NRG Sunrise) that provides power to the oilfield as well as to the grid should receive “partial credit” in terms of generating LCFS credits if it added CCS (pro-rated for the fraction of power going to the oilfield). Such a plant should also be eligible for Section 45Q tax credits and garner revenue from selling its excess cap-and-trade emission allowances. Using the cost and incentive values described in this report, the economic viability of adding CCS today would depend on the relative fractions of electricity supplied to the oilfield and the grid. As the value of cap-and-trade emission allowances grows over time (either by market forces or the escalating floor price), the relative fraction of power needing to remain in the oilfield to achieve “breakeven” would diminish. At some point, even plants providing all their power to the grid would be candidates for economical CCS application. The C2SAFE team has proposed to expand upon the knowledge gained during this study by conducting a detailed feasibility assessment under a follow-on phase of the program, Phase II – Feasibility. This would include detailed data collection at the two sites, analysis, reporting, stakeholder outreach, and risk mitigation activities to support the development of a commercial carbon storage complex in the SSJV.

58 GEOSCIENCES↗
DOE OSTI2019

Cryogenic Carbon Capture Development (Final Report)

This project extends and leverages the work completed under funding from ARPA-E and other sources by exploring aspects of the Cryogenic Carbon Capture™ (CCC) process and issues discovered during those projects, but that were not within the scope or budget of previous projects or that became apparent during field tests. Our successful completion of the tasks outlined in this project has increased the reliability, efficiency, and scalability of the CCC process and we are better prepared for pilot-scale testing. The first phase of this project improved key areas of the process through iterative design and experiment, culminating with recommendations for improvements to the existing skid-scale system developed under non-federal funding. The second phase of this project implemented the improved unit operations into the Cryogenic Carbon Capture External Cooling Loop (CCC ECL™) skid and field tests the improved system at a coal-fired power plant. The specific areas of work in this project are: Budget Period 1: 1) Selection of the most energy-efficient drying system for ambient-pressure flue gas. Sustainable Energy Solutions, LLC (SES) has successfully demonstrated a dryer that removes bulk water using a low-temperature brine to remove about 99% of the water followed by a mol sieve 3A desiccant dryer to remove the remaining 1% water; 2) Selection and demonstration of efficient solution to fouling due to dissolved CO 2 . SES has tested several commercial heat exchangers that purport to be able to handle fouling issues similar to those experienced in the contact liquid chiller. Replicated tests using the CCC ECL™ skid and dimpled brazed plate heat exchangers demonstrate that quick throttling of refrigerant combined with a burst of clean gas mitigate the fouling caused by CO 2 solubility in contact liquid with only minor increases in parasitic load. SES models indicate that parasitic load would increase by approximately 0.0125 GJ/tonne, an order of magnitude less than what was determined to be the maximum allowable additional parasitic load caused by this system; 3) Selection and demonstration of efficient and scalable solid–liquid separation system. The screw press technology met or exceeded the 70% solids loading benchmark during several test runs exceeding 24 hours on the CCC ECL™ skid. The average solids loading during the entire 50-hour run was 67%. The energy demand for this system is less than 0.1 GJ e /tonne of CO 2 processed; 4) Optimize design and demonstration of patented desublimating heat exchangers. We designed and built a hybrid spray tower that was tested over many hundreds of hours and a 50-hour continuous test run was completed capturing over 0.5 tonne CO 2 /day at >90% capture; (5) Plan for safe dilution of oxygen-depleted, cleaned flue-gas stream. The AERSCREEN simulator developed by the EPA modeled the dispersion of oxygen-depleted flue gas from the CCC process at a commercial plant. The results indicate that this plan effectively mitigates the flue gas hazards; (6) Simulation of and experimental work related to multi-pollutant capture. SES programmed an upgrade to its solid/vapor/liquid equilibrium (SVLE) algorithm. This enables simultaneous modeling of multiple pollutants (i.e., CO 2 , SO x , NO x , Hg, HCl). Budget Period 2; (7) Modify the CCC ECL™ skid for field testing and complete shakedown testing. The plans for modifying the CCC ECL™ skid were finalized and put into place. Shakedown testing of the updated CCC ECL™ skid occurred at SES using closed-loop mode (i.e., using a synthetic flue gas consisting of only N 2 and CO 2 ) and at the Hunter Power Plant using flue gas from the coal-fired boiler. Shakedown testing took longer than expected due to delays in receiving equipment and unexpected equipment performance in the field; (8) Operate the CCC ECL™ skid in the field. Longer testing of the CCC ECL™ skid occurred at the Hunter Power Plant. Multiple tests exceeding 35 hours were completed, with a total of over 600 hours of CO 2 capture completed using flue gas at the Hunter Power Plant with an average CO 2 capture rate of over 91% during those hours. Extensive notes and documentation were acquired informing improved methods for controls, startup, operation, and shut down; (9) Determine figures of merit and quantify process improvement. SES defined figures of merit for unit operation performance and overall CCC ECL™ process performance. All figures of merit were achieved or surpassed, except for 500 continuous hours of demonstration at the Hunter Power Plant; (10) Complete an Environmental Health and Safety Risk assessment by a third party. Tri-State completed an Environmental Health and Safety Risk assessment; (11) Update techno-economic analysis with third-party validation. The techno-economic analysis has been completed. It confirms the potential large reduction in energy and cost of the CCC process compared with leading alternatives and that the estimates and estimation techniques are reliable.

20 FOSSIL-FUELED POWER PLANTS↗