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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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375 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Stress-controlled fatigue of HfNbTaTiZr high-entropy alloy and associated deformation and fracture mechanisms

The stress-controlled fatigue tests are carried out at a stress ratio of 0.1 and a frequency of 10 Hz, and span both low-cycle and high-cycle regimes by varying the applied stress amplitudes. The high-cycle fatigue regime gives a fatigue strength of 497 MPa and a fatigue ratio of 0.44. At equivalent conditions, the alloy's fatigue strength is greater than all other high-entropy alloys (HEAs) with reported high-cycle fatigue data, dilute body-centered cubic alloys, and many structural alloys such as steels, titanium alloys, and aluminum alloys. Through in-depth analyses of crack-propagation trajectories, fracture-surface morphologies and deformation plasticity by means of various microstructural analysis techniques and theoretical frameworks, the alloy's remarkable fatigue resistance is attributed to delayed crack initiation in the high-cycle regime, which is achieved by retarding the formation of localized persistent slip bands, and its good resistance to crack propagation in the low-cycle regime, which is accomplished by intrinsic toughening backed up by extrinsic toughening. Additionally, the stochastic nature of the fatigue data is neatly captured with a 2-parameter Weibull model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Radio AGN in nearby dwarf galaxies: the important role of AGN in dwarf galaxy evolution

ABSTRACT We combine deep optical and radio data, from the Hyper Suprime-Cam and the Low-Frequency Array (LOFAR), respectively, to study 78 radio active galactic nuclei (AGN) in nearby (z < 0.5) dwarf galaxies. Comparison to a control sample, matched in stellar mass and redshift, indicates that the AGN and controls reside in similar environments, show similar star formation rates (which trace gas availability) and exhibit a comparable incidence of tidal features (which indicate recent interactions). We explore the AGN properties by combining the predicted gas conditions in dwarfs from a cosmological hydrodynamical simulation with a Monte Carlo suite of simulated radio sources, based on a semi-analytical model for radio-galaxy evolution. In the subset of LOFAR-detectable simulated sources, which have a similar distribution of radio luminosities as our observed AGN, the median jet powers, ages, and accretion rates are ∼1035 W, ∼5 Myr, and ∼10−3.4 M⊙ yr−1, respectively. The median mechanical energy output of these sources is ∼100 times larger than the median binding energy expected in dwarf gas reservoirs, making AGN feedback plausible. Since special circumstances (in terms of environment, gas availability, and interactions) are not necessary for the presence of AGN, and the central gas masses are predicted to be an order of magnitude larger than that required to fuel the AGN, AGN triggering in dwarfs is likely to be stochastic and a common phenomenon. Together with the plausibility of energetic feedback, this suggests that AGN could be important drivers of dwarf galaxy evolution, as is the case in massive galaxies.

Davis, F. (ORCID:0000000285224980)↗
DOE OSTI2022

Volt-VAR Optimization in Distribution Networks Using Twin Delayed Deep Reinforcement Learning

Modern distribution grids are undergoing new challenges due to the stochastic nature of distributed energy resources (DERs). High penetration of DERs has a significant impact on Volt-VAR profile and system power losses. This work proposes a deep reinforcement learning (DRL)-based Volt-VAR optimization approach for improving voltage profile and reducing system power loss under high penetration of distributed energy resources, such as battery energy storage and solar photovoltaic units in distribution grids. The twin delayed deep deterministic policy gradient (TD3) method-based DRL agent is proposed to configure optimal set-points of reactive power outputs of fast responding smart inverters. The agent schedules the reactive power of inverters according to their physical capabilities, such as minimum allowed power factor, e.g., 0.9 leading/lagging. The reward function of the proposed DRL scheme is designed carefully to ensure a proper voltage profile of the grids with effective scheduling of reactive power outputs from inverters. The performance of the proposed model is verified on modified IEEE 34- and 123-bus systems and compared with base case with no reactive supply by inverters, and local droop Volt-VAR control approach. The results show that the proposed method performs better than the local droop control and deep deterministic policy gradient (DDPG)-based DRL method for reducing voltage fluctuation and minimizing power loss.

Hossain, Rakib↗
DOE OSTI2024

Solid State Quantum Refrigeration Superconducting, Absorption and Measurement Based (Final Technical Report)

During this DOE grant, DE-SC0017890, in place for the past six years, all proposed research was carried out and published in peer-reviewed papers, as well as other projects that emerged during the research. In that effort the research team accomplished all proposed research, as well as many closely related research projects discovered and conceived of during the grant. These works included “Efficient Quantum Measurement Engines”, a work published in Physical Review Letters, giving a theory of quantum measurement-based engines, which uses quantum measurement as a resource. These engines are designed to efficiently convert energy from the stochastic quantum measurement process into useful work. Further publications include “Experimental Realization of a Quantum Dot Energy Harvester”, a joint theory and experimental work in collaboration with the group of Charles Smith in Cambridge, UK, as well as long time theoretical collaborators, Rafael Sánchez and Björn Sothmann. This work, featured as an Editor’s Suggestion in Physical Review Letters, realized an earlier theoretical proposal of ours, whereby two resonant tunneling quantum dots are connected to a central electronic cavity that is heated by a hot energy source. We also published “Superconducting Quantum Refrigerator: Breaking and Rejoining Cooper Pairs with Magnetic Field Cycles” a work done in collaboration with experimentalist Francesco Giazotto from ENS Pisa, Italy, which also resulted in a patent. This paper, published in Phys. Rev. Applied, advanced the concept of a cyclic fridge based on the normal/superconducting phase transition together with layered materials separated by tunnel junctions. We also completed the proposed research on a heat transistor, publishing “Thermal transistor and thermometer based on Coulomb-coupled conductors”, carried out as a collaboration between my group and theorists Splettstoesser (Lund U., Sweden), Sothmann (U. Duisburg-Essen, Germany), and Sánchez (U. Autónoma de Madrid, Spain). We carried out an analysis of a quantum coupled to a quantum point contact as a sensitive thermometer and heat transistor. We found the optimal statistical estimator for the temperature and compared it with experiments on the same type of devices. We also investigated autonomous quantum absorption refrigerators using quantum dots to cool by using a very hot thermal reservoir to drive heat between two other reservoirs. In the article “Quantifying the quantum heat contribution from a driven superconducting circuit”, we demonstrated that for a driven superconducting circuit, we showed heat flow provided by a hot source to the qubit can be switched on and off by varying external parameters, the frequency and the intensity of the driving. In the work “Stochastic thermodynamic cycles of a mesoscopic thermoelectric engine”, we reconsidered the autonomous thermoelectric heat engine in terms of underlying cycles. Rather than periodic behavior, the cycles were stochastic in nature. Nevertheless, by undertaking a graph theoretical analysis of the elementary transport processed, great quantitative and qualitative insight could be found. We also considered the quantum measurement process and showed that a quantum version of Maxwell’s demon could be related to the work extraction of a quantum system, closely related to arrow-of-time measures for quantum measurement, as described in our article “Thermodynamics of quantum measurement and Maxwell's demon's arrow of time”. This work was selected in Phys. Rev. A as an Editor’s Suggestion. A recent preprint titled “Cyclic Superconducting Quantum Refrigerators Using Guided Fluxon Propagation” accomplished an important piece of this grant: to propose a new kind of quantum refrigerator using the dynamics of fluxons in a type II superconductor. This invention envisioned a race-track type geometry where fluxons are confined. By applying a gradient of magnetic field together with electric current in a Corbino geometry, the circulating fluxons can actively cool a cold reservoir, realizing a new type of cyclic superconducting refrigerator. We also investigated the possibility of thermal control from different points of view. The application of quantum measurement to the system gives a new kind of control on the system of interest – we have pioneered this approach and shown that measurement can boost the thermal power of quantum engines as described in “Continuous measurement boosted adiabatic quantum thermal machines”. The ability to have heat flows on demand is an outstanding challenge, and we have provided new solutions to this problem in Thermal control across a chain of electronic nanocavities” for a chain of electron cavities using gating voltage control. The control methods using qubit/qubit coupling to create absorption fridges at their most fundamental level have also been developed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Linking classical PRA models to a dynamic PRA

Here, this paper presents a series of methods designed to incorporate classical Probabilistic Risk Assessment (PRA) models such as Event Trees (ETs) and Fault Trees (FTs) into dynamic PRA. In contrast to classical PRA, dynamic PRA couples stochastic methods with system simulators to determine the risks associated with complex systems such as nuclear power plants. Compared with classical PRA methods, they can evaluate with higher resolution the safety impact of timing and sequencing of events on the progression of the accident. As part of a dynamic PRA analysis, it is not uncommon that parts of the system to be analyzed might not require a computationally expensive simulation model. These parts could be in fact modeled by employing classical PRA models (e.g., a FT). Here, we present a set of methods and tools that can be used to link the most common classical PRA models (ETs, FTs, reliability block diagrams and Markov models) to simulation codes such as RELAP5-3D: creating a “hybrid PRA.” In order to show the potential of such an hybrid PRA we employ this method to verify ET modeling assumptions (e.g., success criteria) using a large break loss of coolant accident initiating event as a test case. In this respect, we link a set of FTs from the original PRA to the RELAP5-3D code and perform a hybrid PRA. The FTs are employed to model the control logic of several safety systems and to propagate component failures throughout the system. Provided the generated dynamic PRA data, we show how conservative assumptions in the original PRA can be identified and how such original PRA can be modified by updating success criteria captured by the set of RELAP5-3D simulation runs.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Universal energy-speed-accuracy trade-offs in driven nonequilibrium systems

The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying nonequilibrium control costs, leading to a collection of thermodynamic speed limits, which rely on the assumption that the target probability distribution is perfectly realized. This is almost never the case in experiments or numerical simulations, so here we address the situation in which the external controller is imperfect. We obtain a lower bound for the dissipated work in generic nonequilibrium control problems that (1) is asymptotically tight and (2) matches the thermodynamic speed limit in the case of optimal driving. Along with analytically solvable examples, we refine this imperfect driving notion to systems in which the controlled degrees of freedom are slow relative to the nonequilibrium relaxation rate, and identify independent energy contributions from fast and slow degrees of freedom. Furthermore, we develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. Furthermore, this latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols we learn saturate the bound.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Seasonal controls on isolated convective storm drafts, precipitation intensity, and life cycle as observed during GoAmazon2014/5

Abstract. Isolated deep convective cloud life cycle and seasonal changes in storm properties are observed for daytime events during the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Green Ocean Amazon Experiment (GoAmazon2014/5) campaign to understand controls on storm behavior. Storm life cycles are documented using surveillance radar from initiation through maturity and dissipation. Vertical air velocity estimates are obtained from radar wind profiler overpasses, with the storm environment informed by radiosondes. Dry-season storm conditions favored reduced morning shallow cloud coverage and larger low-level convective available potential energy (CAPE) than wet-season counterparts. The typical dry-season storm reached its peak intensity and size earlier in its life cycle compared with wet-season cells. These cells exhibited updrafts in core precipitation regions (Z>35 dBZ) to above the melting level as well as persistent downdrafts aloft within precipitation adjacent to their cores. Moreover, dry-season cells recorded more intense updrafts to earlier life cycle stages as well as a higher incidence of strong updrafts (i.e., >5 m s−1) at low levels. In contrast, wet-season storms were longer-lived and featured a higher incidence of moderate (i.e., 2–5 m s−1) updrafts aloft. These storms also favored a shift in their most intense properties to later life cycle stages. Strong downdrafts were less frequent within wet-season cells aloft, indicating a potential systematic difference in draft behaviors, as linked to graupel loading and other factors between the seasons. Results from a stochastic parcel model suggest that dry-season cells may expect stronger updrafts at low levels because of larger low-level CAPE in the dry season. Wet-season cells anticipate strong updrafts aloft because of larger free-tropospheric relative humidity and reduced entrainment-driven dilution. Enhanced dry-season downdrafts are partially attributed to increased evaporation, dry-air entrainment mixing, and negative buoyancy in regions adjacent to sampled dry-season cores.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A REDUCED ORDER MODELING APPROACH TO PROBABILISTIC CREEP-DAMAGE PREDICTIONS IN FINITE ELEMENT ANALYSIS

This paper introduces a computationally efficient Reduced Order Modeling (ROM) approach for the probabilistic prediction of creep-damage failure. Component-level probabilistic simulations are needed to assess the reliability and safety of high-temperature components. Full-scale probabilistic creep-damage modeling in finite element (FE) approach is computationally expensive requiring many hundreds of simulations to replicate the uncertainty of component failure. To that end, ROM is proposed to minimize the elevated computational cost while controlling the loss of accuracy. It is proposed that full-scale probabilistic simulations can be completed in 1D at a reduced cost, the extremum conditions extracted, and those conditions applied for lower-cost 2D/3D probabilistic simulations of components that capture the mean and uncertainty of failure. The probabilistic Sine-hyperbolic (Sinh) model is selected which in previous work was calibrated to alloy 304 stainless steel. The Sinh model includes probability density functions (pdfs) for test condition (stress and temperature), initial damage (i.e. microstructure), and material properties uncertainty. The Sinh model is programmed into ANSYS finite element software using the USERCREEP.F material subroutine. First, the Sinh model and FE code are subject to verification and validation to ensure the accuracy of the simulations. Numerous Monte Carlo simulations are executed in a 1D model to generate probabilistic creep deformation, damage, and rupture data. This data is analyzed and the probabilistic parameters corresponding to extreme creep response are extracted. The ROM concept is applied where only the extreme conditions are applied in the 2D probabilistic prediction of a component. The probabilistic predictions between the 1D and 2D geometry is compared to assess ROM for creep. The accuracy of the probabilistic prediction employing the ROM approach will potentially reduce the time and cost of simulating complex engineering systems. Future studies will introduce multi-stage Sinh, stochasticity, and spatial uncertainty for improved prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Wake steering optimization under uncertainty

Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertainties in many aspects of the wake steering problem. For example, infield sensors do not give perfect information, and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of independent uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and it is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. When maximizing expected power production, the OUU strategy also tends to favor smaller yaw angles, which have been shown in previous work to reduce turbine loading. Ultimately, the plant-level wake steering strategy formulated using an OUU approach yields 0.48?% more expected annual energy production for the 11-turbine wind plant than a strategy that neglects uncertainty when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, but it also reduces risk by prescribing strategies that call for less extreme yaw angles.

17 WIND ENERGY↗
DOE OSTI2026

Strong-field QED limitations on TeV-class plasma wakefield accelerators

We demonstrate that quantum and classical radiation effects can become non-negligible for TeV-class beams propagating through plasma channels typical of staged plasma accelerators. Although the quantum nonlinearity parameter χ e remains small under currently envisioned experimental conditions, the cumulative influence of radiation over long acceleration distances can lead to significant modifications to the beam’s energy spread, emittance, and polarization. Our analytic models, validated by particle-in-cell simulations, highlight that for standard Gaussian beams, the orbit-induced energy spread dominates over quantum stochastic effects but can be mitigated by tailoring the beam profile, for example, through ring-shaped transverse distributions. In regimes where the radiation reaction approaches the accelerating force, the emittance may be cooled, forming distinctive ring-shaped phase-space structures. Finally, we analyze the influence of the radiation effect on spin transport inside the wakefield. These findings underscore the importance of considering both classical and quantum radiation dynamics in the design and optimization of future high-intensity plasma accelerators.

accelerator↗
DOE OSTI2023

A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching

To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level (bond type, non-uniform chain length, entanglement density, etc.) to macroscopic quantities that govern damage and failure must be established. Many of the most successful constitutive models for elastomers have been rooted in statistical mechanical treatments of polymer chains. Typically, such constitutive models have used variants of the freely jointed chain model with rigid links. However, since the free energy state of a polymer chain is dominated by enthalpic bond distortion effects as the chain approaches its rupture point, bond extensibility ought to be accounted for if the model is intended to capture chain rupture. To that end, a new bond potential is supplemented to the freely jointed chain model (as derived in the u FJC framework of Buche and Silberstein (2021) and Buche et al. (2022)), which we have extended to yield a tractable, closed-form model of single chain behavior that should be amenable to continuum-level constitutive model development. Inspired by the asymptotically matched u FJC model response in both the low/intermediate chain force and high chain force regimes, a simple, quasi-polynomial bond potential energy function is derived. This bond potential exhibits harmonic behavior near the equilibrium state and anharmonic behavior for large bond stretches tending to a characteristic energy plateau (akin to the Lennard-Jones and Morse bond potentials). Using this bond potential, approximate yet highly-accurate analytical functions for bond stretch and chain force dependent upon chain stretch are established. Then, using this polymer chain model, a stochastic thermal fluctuation-driven chain rupture framework is developed. This framework is based upon a force-modified tilted bond potential that accounts for distortional bond potential energy, allowing for the derivation and subsequent calculation of the dissipated chain scission energy. Here, the cases of rate-dependent and rate-independent scission are accounted for throughout the rupture framework. The impact of Kuhn segment number on chain rupture behavior is also investigated. The model is fit to single chain mechanical response data collected from atomic force microscopy tensile tests for validation and to glean deeper insight into the molecular physics taking place. Due to their analytical nature, this polymer chain model and the associated rupture framework can in the future be implemented in finite element models accounting for fracture and fatigue in polydisperse elastomer networks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Constraining the Reaction Rate of Criegee Intermediates with Carboxylic Acids during the Multiphase Ozonolysis of Aerosolized Alkenes

Criegee intermediates (CIs) are central to the ozonolysis of unsaturated organic compounds, controlling the formation of small fragmentation reaction products and higher molecular weight oligomeric compounds through competing unimolecular and bimolecular pathways. In particular, the reaction of thermalized CI with carboxylic acids (RCOOH) to produce oligomeric α-acyloxyalkyl hydroperoxides (AAHPs) is of interest to the chemical transformation of organic compounds in the atmosphere. In the gas phase, the CI + RCOOH reaction proceeds at supercollisional rates, consistent with a barrierless reaction mechanism. However, the rate of this reaction in condensed phases remains uncertain. Here, we report the results of aerosol flow tube studies of the ozonolysis of submicron organic aerosol containing mixtures of an internal n-alkene (Z-9-tricosene, tri) and a saturated acid (2-hexyldecanoic acid, HDA). The decay of the reactants and formation of reaction products are monitored using atmospheric pressure chemical ionization mass spectrometry (APCI–MS). A 6-fold decrease in the reactive uptake of ozone is observed with increasing acid concentration, consistent with a previous study using alcohol additives. The decay of HDA with ozone exposure shows that HDA scavenges CIs at rates comparable to competing unimolecular pathways involving CIs. The decay kinetics of HDA are analyzed using a simple kinetic model in order to constrain the ratio of the CI + RCOOH and unimolecular CI rate constants. Empirical fitting to this model, combined with theoretical estimates of the unimolecular loss rate, yields a CI + RCOOH rate constant of 1.85 ± 0.27 × 10 –19 cm 3 molec –1 s –1 , which is six orders of magnitude slower than the expected diffusion limit in a liquid organic matrix. Stochastic kinetic simulations are conducted with different values of the CI + RCOOH rate constant to validate this result and show that the diffusion-limited rate is indeed much too rapid to reproduce the experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
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

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