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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical.

Wolfe, Matthew↗
NASA NTRS2002

Space-Time Controls on Carbon Sequestration Over Large-Scale Amazon Basin

A major research focus of the LBA Ecology Program is an assessment of the carbon budget and the carbon sequestering capacity of the large scale forest-pasture system that dominates the Amazonia landscape, and its time-space heterogeneity manifest in carbon fluxes across the large scale Amazon basin ecosystem. Quantification of these processes requires a combination of in situ measurements, remotely sensed measurements from space, and a realistically forced hydrometeorological model coupled to a carbon assimilation model, capable of simulating details within the surface energy and water budgets along with the principle modes of photosynthesis and respiration. Here we describe the results of an investigation concerning the space-time controls of carbon sources and sinks distributed over the large scale Amazon basin. The results are derived from a carbon-water-energy budget retrieval system for the large scale Amazon basin, which uses a coupled carbon assimilation-hydrometeorological model as an integrating system, forced by both in situ meteorological measurements and remotely sensed radiation fluxes and precipitation retrieval retrieved from a combination of GOES, SSM/I, TOMS, and TRMM satellite measurements. Brief discussion concerning validation of (a) retrieved surface radiation fluxes and precipitation based on 30-min averaged surface measurements taken at Ji-Parana in Rondonia and Manaus in Amazonas, and (b) modeled carbon fluxes based on tower CO2 flux measurements taken at Reserva Jaru, Manaus and Fazenda Nossa Senhora. The space-time controls on carbon sequestration are partitioned into sets of factors classified by: (1) above canopy meteorology, (2) incoming surface radiation, (3) precipitation interception, and (4) indigenous stomatal processes varied over the different land covers of pristine rainforest, partially, and fully logged rainforests, and pasture lands. These are the principle meteorological, thermodynamical, hydrological, and biophysical control paths which perturb net carbon fluxes and sequestration, produce time-space switching of carbon sources and sinks, undergo modulation through atmospheric boundary layer feedbacks, and respond to any discontinuous intervention on the landscape itself such as produced by human intervention in converting rainforest to pasture or conducting selective/clearcut logging operations.

Smith, Eric A.↗
DOE OSTI2020

Big Sky Regional Carbon Sequestration Partnership (Phase III Final Scientific/Technical Report)

The Big Sky Carbon Sequestration Partnership (BSCSP) pursued a Phase III demonstration project at Kevin Dome in north central Montana. Kevin Dome covers approximately 700 square miles and is a naturally occurring CO 2 reservoir that is flanked by oil and gas fields. The carbon dioxide (CO 2 ) is in the upper Devonian Duperow (carbonate) formation and does not reach the spill point of the dome; therefore, the dome has potential as a CO 2 sequestration reservoir, a CO 2 supply, or as both if anthropogenic sources and enhanced oil recovery (EOR) operations are associated with the dome. Kevin Dome could potentially act as a buffer to continue accepting anthropogenic CO 2 when EOR flooding operations are interrupted or completed. The project objective was to produce one million tonnes of CO 2 from the gas cap of Kevin Dome, pipe it laterally, inject, and re-store it in the brine leg of the same formation to test the hub / buffer storage concept. This was to be accomplished by drilling up to five production wells, building a short pipeline and compression facilities, and drilling an injection well and several monitoring wells. BSCSP commenced outreach and site characterization activities including acquiring baseline data for near-surface insurance monitoring, acquiring 3-dimensional, 9-component surface seismic over the project area, drilling two test wells (one in the production area and one in the injection area), coring key intervals, and performing comprehensive logging. Well tests of those wells revealed two barriers to the project. The production (Danielson 33-17) well was perforated in multiple zones but failed to produce any significant CO 2 . This was despite being drilled in the near vicinity of a historic well that had produced 3700 MCF per day in a drill stem test. Modeling indicated that this was likely due to a phase change during production causing a temperature drop resulting in hydrate and/or water ice formation that clogged the formation. Tests of the injection zone (Wallewein 22-1) well indicated total dissolved solids (TDS) slightly below the EPA required 10,000 parts per million (ppm) for a Class VI underground injection control permit. While the project was initiated before Class VI rules were promulgated, and this was an experimental project (seemingly qualified for a Class V permit), the Environmental Protection Agency (EPA) indicated that injection would require a Class VI permit. The low salinity result was unexpected as contours plotted based on regional formation water quality data indicated an expected TDS above 20,000 ppm, and wells between the recharge zone and the Wallewein well tested above 10,000 ppm. Faced with the inability to obtain an injection permit, the demonstration project could not proceed. However, the project had generated valuable samples and data on a large natural analog including 32 sq. mi. of 3-D, 9-C seismic, 430 ft. of carbonate core covering seven different depositional environments taken from areas with, and without the presence of CO 2 , 30 ft. of core of two caprocks, a tight carbonate and an anhydrite, a full set of modern logs on both wells, and well tests. DOE decided to re-scope the project around completing studies utilizing this data. This report covers both the initial scope and the re-scope (Task / Section numbers preceded with an R). While the report covers a wide range of project activities, highlights of this work include: Development of a geostatic model using neural nets to match well logs to facies and using multi-waveform seismic to inform reservoir heterogeneity; Unique mechanical testing of permeability – stress relationship in two caprock materials; Development of full waveform inversion to generate a high resolution velocity model; Model development for dual permeability (fracture and matrix) systems to better account for matrix-matrix interactions; Joint seismic wave inversion (including the first quadr-joint inversion) exhibiting better imaging of a challenging reservoir zone in stiff rock; Core flow and core flood results on a reactive carbonate; and Innovative laboratory measurements of seismic response of fractured core as a function of fluid fill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Spatial Engineering Direct Cooperativity between Binding Sites for Uranium Sequestration

Preorganization is a basic design principle used by nature that allows for synergistic pathways to be expressed. Herein, a full account of the conceptual and experimental development from randomly distributed functionalities to a convergent arrangement that facilitates cooperative binding is given, thus conferring exceptional affinity toward the analyte of interest. The resulting material with chelating groups populated adjacently in a spatially locked manner displays up to two orders of magnitude improvement compared to a random and isolated manner using uranium sequestration as a model application. This adsorbent shows exceptional extraction efficiencies, capable of reducing the uranium concentration from 5 ppm to less than 1 ppb within 10 min, even though the system is permeated with high concentrations of competing ions. The efficiency is further supported by its ability to extract uranium from seawater with an uptake capability of 5.01 mg g -1 , placing it among the highest-capacity seawater uranium extraction materials described to date. The concept presented here uncovers a new paradigm in the design of efficient sorbent materials by manipulating the spatial distribution to amplify the cooperation of functions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Feasibility of enhancing carbon sequestration and stock capacity in temperate and boreal European forests via changes to management regimes

Forest management practices might act as nature-based methods to remove CO 2 from the atmosphere and slow anthropogenic climate change and thus support an EU forest-based climate change mitigation strategy. However, the extent to which diversified management actions could lead to quantitatively important changes in carbon sequestration and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to simulate effects of multiple forest management scenarios on net primary productivity (NPP) and potential carbon woody stocks (pCWS) under twenty scenarios of climate change in a suite of observed and virtual forest stands in temperate and boreal European forests. Previous modelling experiments indicated that the capacity of forests to assimilate and store atmospheric CO 2 in woody biomass is already being attained under business-as-usual forest management practices across a range of climate change scenarios. Nevertheless, we find that on the long-term, with increasing atmospheric CO 2 concentration and warming, managed forests show both higher productivity capacity and a larger potential pool size of stored carbon than unmanaged forests as long as thinning and tree harvesting are of moderate intensity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Carbon sequestration of steel slag and carbonation for activating RO phase

Carbonation of Ca/Mg minerals in industrial alkaline residues is a technology to sequester CO{sub 2} and reduce its emissions to the atmosphere. In this work, BSE-EDS were used to determine the mineral phase in steel slag; compositions of RO phase were identified and simulated. The carbon sequestration of steel slag was studied, and RO phase was activated by carbonation. Result shows that the amount of CO{sub 2} sequestered in steel slag and RO phase increases as carbonation time increases. Under autoclaving condition, the hydration ratio of MgO in RO phase was 20.10%. Through carbonation, up to 58.83% of MgO in RO phase can be converted into MgCO{sub 3}, and the activation of RO phase by carbonation was manifested. The mechanical properties and volume stability of carbonated steel slag were improved, proving that the positive effect of carbonation on steel slag when applied in cement and cementitious materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dynamic risk assessment for geologic CO 2 sequestration

At a geologic CO 2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO 2 saturations and pressures in potentially leaky wellbores, CO 2 /brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the U.S. National Risk Assessment Partnership’s Open Source Integrated Assessment Model (NRAP-Open-IAM) coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.

58 GEOSCIENCES↗
DOE OSTI2021

Skin factor and potential formation damage from chemical and mechanical processes in a naturally fractured carbonate aquifer with implications to CO 2 sequestration

Here, in this study, we investigate formation damage due to acidization and water injection tests into the naturally fractured carbonate Middle Duperow Formation at Kevin Dome, Montana, potentially diminishing the chance of a future successful Geological Carbon Sequestration (GCS) project. Multiple well-test analytical models, correlated with core description and lithology data, are used to determine flow behavior and communication between the water injection interval and surrounding formations. An improved three-dimensional (3D) geologic model with dual-continuum matrix and fracture properties is constructed based on most recent seismic, core, and water sample measurements. Brine injection is simulated to verify the interpretation from the analytical models, followed by CO 2 injection simulation. Geochemical calculations are performed to understand the in-situ processes that led to formation damage. Our findings suggest: (1) there are two possible scenarios that could lead to a positive total effective skin factor and permeability decline: partial penetration and formation damage; (2) analytical models indicate a positive total skin factor, contradicting results of a previous study suggesting that the well was mildly stimulated; (3) numerical simulation supports the formation damage hypothesis by matching the pressure buildup observed during the latter two brine injection tests; (4) several mechanical and chemical processes may have occurred during injection to clog the matrix/fracture system: anhydrite fines migration and/or calcite precipitation. We then make preventative suggestions for future GCS projects into carbonate reservoirs and remediation recommendations for GCS operation at the Kevin Dome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Carbon sequestration into lime-stabilized soils: Engineering and mineralogical characterization

Stabilizing weak clayey soils with lime is an effective method for improving the mechanical properties of soil. However, lime production is an energy-intensive process producing significant CO 2 emissions in lime-stabilized soils, which can be counteracted through accelerated carbonation that enhances its engineering performance. The present study evaluates accelerated carbonation of lime-treated soils by adding gaseous (CO 2 -rich gas), liquid (water-CO 2 mixture), and solid (sodium bicarbonate) CO 2 sources. Results indicated that samples carbonated with gaseous CO 2 exhibited 100% lime carbonation, while samples treated with solid and aqueous sources of CO 2 had a mean lime carbonation of 60% and 40%, respectively. Further, all lime-treated-carbonated samples exhibited a mean 50% increase in unconfined compressive strength compared to the untreated samples after a 7-day curing period. Durability evaluation through cyclic wetting and drying indicated that the carbonated samples had higher durability than the untreated samples. X-ray computed tomography showed that adding solid and liquid sources of CO 2 facilitated the flocculation of montmorillonite, reducing the porosity. However, a higher dosage of solid CO 2 induced clay dispersion, increasing the porosity. X-ray diffraction and thermogravimetric analysis verified CO 2 sequestration through the formation of calcite, a thermodynamically stable polymorph of calcium carbonate.

58 GEOSCIENCES↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
DOE OSTI2024

Carbon-sequestration gradient insulation composites

The massive use of carbon-sequestration building materials promises a potential global carbon sink in decarbonizing the building industry. Renewable biogenic materials from abundant agriculture waste for building practice have been around over thousands of years. However, in addition to their flammability and moisture problems, addressing their low thermal and structural performance is also becoming indispensable and urgent when it comes to environmentally sustainable and energy-efficient buildings. Here, we report a nature-inspired biogenic gradient insulation composite with an optimized silica concentration of 30 wt %, a density of 0.246 g/cm 3 , and a porosity of 86%. The gradient hybrid composite exhibits a thermal conductivity of 28.2 mW m -1 K -1 , which is the lowest achieved under optimal preparation conditions. Here, it also shows a flexural modulus of 590 MPa for the aerogel-rich layer without surface modification, and it demonstrates superior fire retardancy and superhydrophobicity after surface treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Selenite and Selenate Sequestration during Coprecipitation with Barite: Insights from Mineralization Processes of Adsorption, Nucleation, and Growth

Coprecipitation of selenium oxyanions with barite is a facile way to sequester Se in the environments. However, the chemical composition of Se-barite coprecipitates usually deviates from that predicted from thermodynamic calculations. This discrepancy was resolved by considering variations in nucleation and growth rates controlled by ion–mineral interactions, solubility, and interfacial energy. For homogeneous precipitation, ~10% of sulfate, higher than thermodynamic predictions (<0.3%), was substituted by Se(IV) or Se(VI) oxyanion, which was attributed to adsorption-induced entrapment during crystal growth. For heterogeneous precipitation, thiol- and carboxylic-based organic films, utilized as model interfaces to mimic the natural organic-abundant environments, further enhanced the sequestration of Se(VI) oxyanions (up to 41–92%) with barite. Such enhancement was kinetically driven by increased nucleation rates of selenate-rich barite having a lower interfacial energy than pure barite. In contrast, only small amounts of Se(IV) oxyanions (~1%) were detected in heterogeneous coprecipitates mainly due to a lower saturation index of BaSeO 3 and deprotonation degree of Se(IV) oxyanion at pH 5.6. Finally, these roles of nanoscale mineralization mechanisms observed during composition selection of Se-barite could mark important steps toward the remediation of contaminants through coprecipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Guanidinium-Based Ionic Covalent-Organic Nanosheets for Sequestration of Cr(VI) and As(V) Oxoanions in Water

Chromium- and arsenic-based oxoanions are among the major highly toxic and carcinogenic inorganic pollutants present in groundwater, demanding fast and selective sequestration. Efficient capturing and removal of these highly mobile oxometallates at neutral pH presents a great challenge in groundwater cleanup. In this report, a series of guanidinium-based ionic organic covalent nanosheets (iCONs) with varying hydrogen bonding, steric, and electronic properties was studied to examine the structure–activity relationship in the adsorption and removal of chromium- and arsenic-based oxoanions in water. Structural modulations in iCONs were found to alter the guanidinium acidity, thus regulating the oxoanion uptake limits via ion exchange. The hydrogen bonding, steric, and electrostatic interactions at/near the guanidinium-based anion binding site in iCONs exerted heavy influences on the uptake efficiency and selectivity of arsenate but not on those of chromate. Further analyses revealed that the parallel bidentate hydrogen bonding interactions play a key role in the weak binding of arsenate to the protonated/positively charged guanidine motifs, whereas the strong ion–ion interactions between chromate and guanidinium appear to be more tolerant to the geometric and structural perturbation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗
DOE OSTI2020

Coastal Wetland Carbon Sequestration in a Warmer Climate (Final Report)

Coastal wetlands are global hotspots of carbon storage and locations where carbon and nitrogen cycles have a disproportionately large impact on land, water, and air in comparison to the area they occupy. The extremely high rates of carbon sequestration in these systems are the result of complex feedbacks between vegetation and the physical environment. The plant and microbial interactions that drive these feedbacks are absent in ecosystem- and global-scale Earth System models. The goal of this proposal was to address the uncertain response of the coastal carbon sink to climate change, a critical knowledge gap in coastal carbon research and a significant barrier to incorporating coastal wetlands into Earth System Models. The Salt Marsh Accretion Response to Temperature eXperiment (SMARTX) was designed to understand the ecosystem-scale consequences of warming and elevated CO₂ (eCO₂) in tidal wetlands at the coastal terrestrial-aquatic interface. We successfully designed and built a novel whole-ecosystem warming and eCO₂ experiment in a coastal wetland. The gradient design of the warming treatments (+0, +1.7, +3.4, +5.1 ⁰C) allowed us to discover unexpected non-linear and non-additive responses arising from plant-microbe interactions. We concluded that these non-linear responses to warming were the result of plants and microbes responding to temperature at different thresholds, with important consequences for forecasting terrestrial ecosystem responses to climate change and understanding global trends.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗