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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 595 records · Page 33
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DOE OSTI2022

Dark Energy Survey year 3 results: Constraints on cosmological parameters and galaxy-bias models from galaxy clustering and galaxy-galaxy lensing using the redMaGiC sample

We constrain cosmological parameters and galaxy-bias parameters using the combination of galaxy clustering and galaxy-galaxy lensing measurements from the Dark Energy Survey (DES) year-3 data. We describe our modeling framework and choice of scales analyzed, validating their robustness to theoretical uncertainties in small-scale clustering by analyzing simulated data. Using a linear galaxy-bias model and redMaGiC galaxy sample, we obtain 10% constraints on the matter density of the Universe. Here, we also implement a nonlinear galaxy-bias model to probe smaller scales that includes parametrization based on hybrid perturbation theory and find that it leads to a 17% gain in cosmological constraining power. We perform robustness tests of our methodology pipeline and demonstrate stability of the constraints to changes in the theory model. Using the redMaGiC galaxy sample as foreground lens galaxies and adopting the best-fitting cosmological parameters from DES year-1 data, we find the galaxy clustering and galaxy-galaxy lensing measurements to exhibit significant signals akin to decorrelation between galaxies and mass on large scales, which is not expected in any current models. This likely systematic measurement error biases our constraints on galaxy bias and the S 8 parameter. We find that a scale-, redshift- and sky-area-independent phenomenological decorrelation parameter can effectively capture this inconsistency between the galaxy clustering and galaxy-galaxy lensing. We trace the source of this correlation to a color-dependent photometric issue and minimize its impact on our result by changing the selection criteria of redMaGiC galaxies. Using this new sample, our constraints on the S 8 parameter are consistent with previous studies and we find a small shift in the Ω m constraints compared to the fiducial redMaGiC sample. We infer the constraints on the mean host-halo mass of the redMaGiC galaxies in this new sample from the large-scale bias constraints, finding the galaxies occupy halos of mass approximately 1.6 × 10 13 M ⊙ /h.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

One-dimensional air temperature measurements by air resonance enhanced multiphoton Ionization thermometry (ART)

In this work, a detailed calibration study is performed to establish non-intrusive one-dimensional (1D) rovibrational temperature measurements in unseeded air, based on air resonance enhanced multiphoton ionization thermometry (ART). ART is generated by REMPI (resonance enhanced multi-photon ionization) of molecular oxygen and subsequent avalanche ionization of molecular nitrogen in a single laser pulse. ART signal, the fluorescence from the first negative band of molecular nitrogen, is directly proportional to the 2-photon transition of molecular oxygen C 3 Π (v = 2) ← X 3 Σ (v’=0), which is used to determine temperature. Experimentally, hyperfine structures of the O 2 rotational branches with high temperature sensitivity are selectively excited through a frequency-doubled dye laser. Electron-avalanche ionization of N 2 results in the fluorescence emissions from the first negative bands of N 2 + near 390, 425, and 430nm, which are captured as a 1D line by a gated intensified camera. Post processing of the N 2 + fluorescence yields a 1D thermometry line that is representative of the air temperature. It is demonstrated that the technique provides ART fluorescence of ∼5cm in length in the unseeded air, presenting an attractive thermometry solution for high-speed wind tunnels and other ground test facilities.

42 ENGINEERING↗
DOE OSTI2024

Scaleup and Site-Specific Engineering Design for Global Thermostat Direct Air Capture Technology

The overall goal of the project is completion of an initial design of a commercial-scale, Carbon Capture, Utilization, and Storage Direct Air Capture (CCUS-DAC) plant design at three different sites that captures a net of at least 100,000 tonnes per year (TPY) carbon dioxide (CO 2 ) from the atmosphere and considers compression and conditioning of the captured CO 2 for purpose of pipeline transportation to different geological formation sites for deep well injection and underground storage. In addition to the leading system consisting of a scaled-up Global Thermostat DAC unit, overall plant design includes a combined Heat and Power (CHP) unit integrated with a conventional liquid amine-based carbon capture system (90% capture) and compression facilities. These are considered balance of plant (BOP) systems and are required to provide low carbon-intensity steam and power to the DAC process. A second approach involving provision of DAC units modified to directly capture emissions from the CHP unit was initially assessed as an alternative and it was determined to be less mature than considered approach and not included in the scaled-up plant design efforts. Three geographically diverse continental United States locations were selected to better understand the effect of local/regional ambient conditions on scaled-up DAC system performance and project costs: Bucks, Alabama (hot wet climate), Odessa, Texas (dry hot climate), and Goose Creek, Illinois (mid continental climate). The team focused on initial engineering design activities, including the development of project design criteria, initiation of site-specific studies and investigations, completion of DAC system process and equipment design, and definition of balance of plant (BOP) engineering. The purpose of the activities were to develop Technoeconomic Analysis (TEA), Life-Cycle Analysis (LCA), and Environmental, Health, and Safety (EH&S) analysis, and Business Case Analysis (BCA) to validate that the project engineering and scale-up plans of the DAC systems, at each of the three distinct case studies considered, are technically, economically, and environmentally feasible for commercial-scale operation.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

Abstract Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Coarse-grained molecular dynamics simulation of solvent-dependent cellulose nanofiber interactions

Associations between cellulose are important both in biofuel production and in the use of cellulose for biomaterials. Cellulose nanofibers (CNFs) are sustainable, strong, light-weight alternatives to traditional materials in manufacturing, but are challenging to obtain due to irreversible aggregation in solution during preparative fibrillation. Therefore, it is imperative to understand the underlying factors driving aggregation with a view to designing solvents that can effectively compete with interfiber interactions, hence reducing aggregation. Molecular dynamics (MD) simulation at atomic detail can provide useful information on local interactions. However, the length and timescales accessible are too short to fully capture association processes. Here, we provide a method for accessing the longer length and timescales required using coarse-grained (CG) MD simulations with a MARTINI force field to calculate the interaction behavior of CNFs in three selected solvents: NaOH-urea-water, acetone, and neat water. The CG results are consistent with our prior all-atom MD and with previous experimental results. While acetone is found not to be an effective solvent, urea and ionic moieties in NaOH-urea-water not only solvate the fibrils but also improve the confinement of water molecules around them as shown by the solvent residence times and mean-square displacements. Overall, the presence of urea and ions reduces the likelihood of aggregation in multi-CNF systems relative to neat water irrespective of whether the hydrophobic or hydrophilic CNF surfaces are interacting. In conclusion, the CG method shows clear promise for selecting potential high-performance solvents for experimental prioritization in bioenergy and biomaterials research in a relatively fast manner as well as for understanding the aggregation and rheological behavior of CNF-solvent systems.

aggregation↗
DOE OSTI2022

Corrosion characteristics of Mo and TZM alloy for plasma facing components in molten lithium at 623 K

Here, in this study, the corrosion behaviours of molybdenum (Mo) and a Mo-based alloy (TZM) were investigated using a static immersion corrosion technique. Weight loss, surface microstructure, and corrosion depth of Mo and the TZM alloy were correlated with elements. The compatibility of Mo and the TZM alloy in static liquid Li was suitable. Mo demonstrated uniform corrosion with a homogenous dissolution of free C and Mo, and TZM alloy exhibited a nonuniform corrosion behaviour with a preferential grain boundary attack caused by the selective dissolution of free C, Ti, and Zr. When the surface oxidation layer of the samples was consumed, free C from Mo and TZM diffused into molten Li to form Li 2 C 2 and then was captured by Zr, Ti, and Mo to form thermodynamically stable carbides, which resulted in the enrichment of the C layer near the sample surfaces. In addition, Ti and Zr acted as N-trappers in liquid Li; the formation of Zr and Ti nitrides resulted in the enrichment of N, Ti, and Zr elements on the surface and led to TZM corrosion increase. Thus, Zr and Ti depletion, pitting, and grain boundary corrosion were problematic for TZM under long-term exposure to liquid Li. Reducing the content of free C and nonmetallic N and increasing the amount of Ti and Zr carbides on the surface of and inside the raw TZM alloy helped improve the corrosion resistance of TZM in liquid Li.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Single-Cell Metabolic Profiling: Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Plant Cell Populations

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. Small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 tesla Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Finally, ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells, and providing information on metabolic heterogeneity in the cell population.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing

Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high-quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, "graphene recipes for synthesis of high quality material" (Gr-ResQ: pronounced graphene rescue), which includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures similar to 300 parameters ranging from synthesis conditions such as a catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated, and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility, which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control and open opportunities for advancing not only graphene but also many other 1D and 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Time dependent supervisory control update with FARM using rolling window

This report describes improvements to the Feasible Actuator Range Modifier (FARM) component of the RAVEN-based HYBRID framework for analysis of Integrated Energy Systems (IES). FARM supports the HERON plug-in that solves the power dispatch problem. The solution to the dispatch problem involves economically optimal dispatches that satisfy limits on production variables and their rates of variation (explicit constraints) as well as process variables tied to the service life of equipment (implicit constraints). FARM serves to validate or confirm that a HERON solution for explicit constraints also satisfies the implicit constraints. FARM-alpha was released by Argonne National Laboratory in January 2021 followed by FARM-Beta in January 2022 with the latter providing increased flexibility for the user. In this report, FARM-Gamma, the latest version of the code, is described. The major improvement is the implementation of a system identification algorithm based on the Dynamic Mode Decomposition with Control (DMDc) coupled with a “Rolling Window” scheme that allows obtaining linear time-varying state-space models. This feature equips FARM with the most accurate approximation of system dynamics, and it relieves the user from the burden of performing an exhaustive off-line characterization of the dynamics. FARM-Gamma capabilities are assessed by solving the power dispatch problem for a representative IES unit. The simulation times corresponding to the different releases are estimated and compared. These values capture the increasing computational burden of the successively higher fidelity state-space models adopted by FARM-Alpha, FARM-Beta and FARM-Gamma. The code implementation provides significant flexibility, i.e., the user can always select the most suitable version of FARM according to the problem to be solved and the available computational resources. It is anticipated that FARM will play a role in addressing several future IES applications. We outline how it can support the coordinated management and safe operation of a nuclear plant coupled to industrial processes to produce hydrogen and synfuels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Achieving Unprecedented CO 2 Utilization InCO 2 Concrete™: System Design, Product Development and Process Demonstration

Anthropogenic sources of carbon dioxide are generated from a number of sources, but the key among these are ordinary Portland cement (OPC) production and combustion of fossil fuels. Cement production is the largest global CO 2 source from the mineral decomposition of carbonates. This is due to the clinkering process whereby limestone (mainly consisting of CaCO 3 ) is decomposed into CaO and CO 2 , and combined with silica rich clays at high temperatures to form clinkers (i.e. the four key minerals that comprise cement). The high temperature range of 1400 – 1550°C required for this process accounts for up to 60% of the generated CO 2 from cement production. Combination of the limestone decomposition and thermal requirements of the clinkering process causes cement production to contribute 8-9% of annual global CO 2 emissions. Combustion of fossil fuels (coal, oil and gas) was shown to contribute a much larger portion of global CO 2 emissions. As of 2018, combustion of fossil fuels accounted for 65% of global CO 2 , where 41% was derived from stationary sources for electricity and heat generation and the other 24% was related to transport. To reduce these contributions, key steps forward in CO 2 utilization technologies are required. Therefore, a CO 2 mineralization technology (CO 2 mineralization concrete) to reduce the OPC content in concrete, while utilizing flue gas emissions from fossil fuel combustion has been developed to address both areas simultaneously. This Reversa™ technology utilizes low-carbon cementation agents produced by in situ CO 2 mineralization (“mineral carbonation reactions”) to offer a promising alternative to OPC. CO 2 mineralization relies upon the reaction of dissolved CO 2 with inorganic alkaline reactants to precipitate mineral carbonates (e.g., CaCO 3 ), which bind proximate particles and achieve cementation. Herein, a concrete green body, which is composed of a mixture of binder, water, and mineral aggregates, is exposed to CO 2 borne in industrial flue gas streams. This manner of CO 2 mineralization allows the production of construction components that feature equivalent engineering attributes as their OPC-based counterparts while featuring a much smaller embodied carbon intensity (eCI). The purpose of this project is to demonstrate the feasibility of the Reversa process evolving from a TRL-3 technology at the bench-scale up to TRL-6 technology at the pilot-scale. The reliability of the Reversa technology was tested to prove the effective production of three standard industrial concrete products selected during the course of the project. The results detailed herein will demonstrate the evolution of this technology to the industrial scale. The culmination of this work resulted in 9 production runs completed at the National Carbon Capture Center (NCCC), Wilsonville, AL, using natural gas (NG) flue gas as the CO 2 source. Over the course of the production runs at NCCC, the CO 2 utilization as a function of time, 24-h CO 2 uptake, electricity usage, and 28-d net area compressive strength recorded for each run. Collection of this data will be used to determine the success of the demonstration goals: (1) achieving in excess of 0.2gCO 2 /g reactant , (2) achieving greater than 50% reduction in global warming potential compared to standard produced units, and (3) ensuring compliance of carbonated concrete with industry standard specifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Vertically stacked photovoltaic and thermal solar cell

According to some embodiments, the present invention provides a novel photovoltaic solar cell system from photovoltaic modules that are vertically arrayed in a stack format using thin film semiconductors selected from among organic and inorganic thin film semiconductors. The stack cells may be cells that are produced in a planar manner, then vertically oriented in an angular form, also termed herein tilted, to maximize the light capturing aspects. The use of a stack configuration system as described herein allows for the use of a variety of electrode materials, such as transparent materials or semitransparent metals. Light concentration can be achieved by using fresnel lens, parabolic mirrors or derivatives of such structures. The light capturing can be controlled by being reflected back and forth in the photovoltaic system until significant quantities of the resonant light is absorbed. Light that passes to the very end and can be reflected back through the device by beveling or capping the end of the device with a different refractive index material, or alternatively using a reflective surface. The contacting between stacked cells can be done in series or parallel. According to some embodiments, the present invention uses a concentrator architecture where the light is channeled into the cells that contain thermal fluid channels (using a transparent fluid such as water) to absorb and hence reduce the thermal energy generation.

14 SOLAR ENERGY↗
DOE OSTI2024

Modeling Cost of Offshore Carbon Storage in Saline Reservoirs

Poster presentation to the AAPG-SEG-SPE CCUS 2024 Conference. Offshore saline reservoirs provide a significant and accessible resource for geologic carbon storage (CS). The offshore environment requires distinct approaches to site selection, operations, monitoring, and risk that affect the technoeconomic assessment of offshore CS projects. The National Energy Technology Laboratory (NETL) has developed a CS cost model for offshore saline reservoirs known as CO2_S_COM_Offshore. Based on NETL’s widely used CO2_S_COM cost model for onshore saline CS, CO2_S_COM_Offshore enables technoeconomic analysis of CS in offshore areas. This model comprehensively incorporates multiple facets of offshore CS projects, from regional evaluation and site selection to permitting, transport, operations, monitoring, site closure, and decommissioning. Developed to model cost for offshore United States (US) Exclusive Economic Zones, aspects of this model can be adapted to international projects. Presented at the SPE/AAPG/SEG Carbon Capture Utilization and Storage Conference in Houston, TX, March 11-13, 2024.

Mark-Moser, Mackenzie K.↗
DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗
DOE OSTI2024

Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Enhanced Carbon Capture Behavior of Carbon Fibers via Ionic Liquid Modification

Carbon-based materials are widely deployed in carbon capture but are only limited to physisorption procedures. Further extending functionalized carbon materials to CO 2 chemisorption under low CO 2 concentration is highly desirable yet challenging. In this study, a carbon fiber composed of solely ultra-micropores (~0.45 nm) was deployed as the precursor to avoid the pore blocking effect, which was modified by superbase-derived ionic liquids (ILs) containing strong interaction sites with CO 2 . By forming a thin coating layer on the surface, the as-afforded surface-functionalized fiber materials demonstrated enhanced CO 2 uptake capacity and improved CO 2 sorption kinetics, as evaluated by both the volumetric method and thermogravimetric analysis, as well as the calculated energy distribution curves. The achievements made in this work provide guidance on the functionalization of carbon-based materials towards enhanced CO 2 chemisorption by forming a thin layer of selected IL coating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A phased workflow to define permit‐ready locations for large volume CO 2 injection and storage

Abstract To‐date, only two UIC Class VI permits have been issued by the US Environmental Protection Agency. We illustrate a four‐phase workflow to first identify regional storage resources and then down‐select sites to yield permit‐ready locations that can accept and store large volumes of CO 2 . Specific permit requirements should guide objectives and define deliverables of respective workflow phases. In the first phase we used available regional data and screened structure and injection zones to locate resources that match CO 2 volumes planned to be captured. Available data were also used to assess presence and depth of usable groundwater, the key resource being protected via permitting. We then used advanced, closed‐form, analytical solutions (EASiTool) to estimate CO 2 injectivity into each hydrologically connected injection compartment. In the second phase we acquired and conditioned additional wireline logs and leased available seismic datasets. We interpreted the depositional systems from wireline well‐log character and mapped sandbody geometry to interpolate injection and confining‐zone distribution. Using available data, we mapped faults and locations of freshwater and overpressure (or other capacity‐limiting geologic parameters) in more detail. In the third phase, we used the augmented geologic data to develop a static model for the selected area, extracted the areas of highest interest, and generated and ran dynamic (flow) models. In a fourth phase, we reduced major uncertainties identified in earlier phases. Our case study indicates that to complete preparation of a permit application requires (1) improved lithologic characterization information (thicknesses and horizontal and vertical connectivity) and (2) better definition of poorly defined local faults. © 2023 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

58 GEOSCIENCES↗
DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗
DOE OSTI2023

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

54 ENVIRONMENTAL SCIENCES↗