Zero or not? Causes and consequences of zero-flow stream gage readings
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Environmental Sciences & Ecology↗
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Results for “point processes”
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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Semi‐Continuous Ex Situ Carbon Dioxide Mineralization in Produced Water for Calcite Production
ABSTRACT The mineralization of carbon dioxide (CO 2 ) to stable carbonate products is a desirable process for carbon capture utilization and storage (CCUS). However, improving the process economics through creative use of available reactants is necessary to develop a scalable mineralization process. This study details the development of a semi‐continuous CO 2 mineralization process that uses flue gas as a point CO 2 source, produced water (PW) as an alkaline source of Ca 2+ , and NaOH effluent (potentially sourced from integration with the chlor‐alkali process. Operating at a controlled pH allowed for both complete reaction of available Ca 2+ (100% carbonation potential or 9.9 g CO 2 .L –1 PW brine) and for reproducible control of the produced calcium carbonate (CaCO 3 ) product. A full factorial design of experiments was implemented to study the effects of reaction temperature, pH, and gaseous CO 2 concentration on the mineralization and CO 2 capture rates as well as product crystalline structure and morphology. A maximum CO 2 capture rate of 0.315 ± 0.007 kg.L –1 .d –1 was achieved at 25°C and 25% CO 2 . CO 2 gas to liquid phase mass transport is believed to be the rate limiting step. With improved reactor design and optimization, the proposed semi‐continuous mineralization process shows promise for scaling to a pilot scale CO 2 capture technology.
Bennett, Quinn [Institute for Sustainable Energy &↗
Advanced supercritical water-based process concepts for treatment and beneficial reuse of brine in oil/gas production
Produced water generated from oil/gas reservoirs is a cost burden for oil/gas producers, with over 21 billion barrels of the waste generated in 2019. Average disposal costs range from 4.00–8.00·bbl -1 , with up to 20·bbl -1 spent on transportation alone. Dissolved solids content is a contributing factor which limits economical treatment options for this waste stream. This work proposes a novel technique that can handle high salinity waste, employing favorable properties past the critical point of water. In this study, process simulation of two supercritical water desalination (SCWD) scenarios was completed. Zero liquid discharge (ZLD) and brine concentration operating scenarios were compared, weighing the associated economics and benefits for each case. The results were shown to be economically feasible for brines with a high dissolved solids content, ranging from 3.49 to 17.28·bbl -1 in an expanded sensitivity analysis.
42 ENGINEERING↗
Accelerated Nano-Optical Imaging through Sparse Sampling
The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, in this study, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl 3 ) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.
36 MATERIALS SCIENCE↗
Compact Absorber Technology Leads to Significant Reduction in the Cost of Point Source CO 2 Capture
The size of columns in traditional absorption-based processes for CO 2 capture contributes significantly to the overall capital cost. A demonstrated method to reduce the cost of point source CO 2 capture, focusing on reducing the absorber height by increasing the liquid-to-gas reaction contact area and decreasing the CO 2 diffusion resistance without increasing gas-side pressure drop is presented along with techno-economic analysis results. Bench-scale tests on the unique Compact Absorber showed overall CO 2 mass transfer enhancement of varying degrees compared to a traditional packed column for similar process conditions, demonstrating that a 60+% reduction in size of a typical post-combustion absorber with a packing height of 70-100 ft and total height of 150-180 ft can be achieved. The techno-economic analysis showed significant cost reductions when the Compact Absorber is combined with other transformative aspects of the University of Kentucky Institute for Decarbonization and Energy Advancement point source CO 2 capture process compared to the U.S. Department of Energy National Energy Technology Laboratory pertinent reference case for pulverized coal plants with CO 2 capture. Here, a levelized cost of electricity excluding CO 2 transportation and storage of $\$95.6$/MWh was estimated, which is a 9% reduction, with a total capital cost contribution of $45/MWh, which is a 12% reduction. Additionally, a breakeven CO 2 sales price also referred to as the cost of CO 2 capture, of $36.70/tonne was estimated when the UK hindered primary amine solvent is used, which is a 20% reduction compared to the reference case.
CO2 capture↗
Predicting the influence of particle size on the glass transition temperature and viscosity of secondary organic material
Abstract Atmospheric aerosols can assume liquid, amorphous semi-solid or glassy, and crystalline phase states. Particle phase state plays a critical role in understanding and predicting aerosol impacts on human health, visibility, cloud formation, and climate. Melting point depression increases with decreasing particle diameter and is predicted by the Gibbs–Thompson relationship. This work reviews existing data on the melting point depression to constrain a simple parameterization of the process. The parameter $$\xi $$ ξ describes the degree to which particle size lowers the melting point and is found to vary between 300 and 1800 K nm for a wide range of particle compositions. The parameterization is used together with existing frameworks for modeling the temperature and RH dependence of viscosity to predict the influence of particle size on the glass transition temperature and viscosity of secondary organic aerosol formed from the oxidation of $$\alpha $$ α -pinene. Literature data are broadly consistent with the predictions. The model predicts a sharp decrease in viscosity for particles less than 100 nm in diameter. It is computationally efficient and suitable for inclusion in models to evaluate the potential influence of the phase change on atmospheric processes. New experimental data of the size-dependence of particle viscosity for atmospheric aerosol mimics are needed to thoroughly validate the predictions.
36 MATERIALS SCIENCE↗
Aerial Captured Data and Processed Models in Beaumont-Port Arthur Region in Feb and Oct, 2023
Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including:I. Beaumont Soccer ClubII. Corps’ Port Arthur Resident OfficeIII. Halbouty Pump Station comprises its vicinityIV. Lamar University (Including Exxon Power Plants close to Lamar Univ.)V. MLK Boulevard for aerial images of the industry and the ship channelVI. Salt Water Barrier (include some aerial images about the Big Thicket)Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location.The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.In October 2023, we had our follow-up data collection, including:I. Beaumont Soccer ClubII. Shipping and Receiving Center at Lamar UniversityAfter the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).
2D mapping↗
NSA Site Science: Use of ARM Observations from Northern Alaska to Evaluate and Improve Prediction Capabilities
The Arctic is warming at a rate nearly double that of the rest of the planet, leading to profound changes in atmospheric, oceanic, and ice processes. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility has played a significant role in Arctic research, operating observatories in Alaska's North Slope for over 25 years. These observatories provide a rich, and wide-reaching dataset that offers insight into atmospheric processes in northern Alaska. This report details the results of a nine-year research project (2015–2024) supported by the DOE Atmospheric Systems Research (ASR) program, that leverages data from ARM’s deployment of observing facilities at Utqiaġvik (known as the North Slope of Alaska, or NSA, site) and Oliktok Point, Alaska. The project was conducted in two phases: - Phase 1 (2015–2019): Focused on understanding key atmospheric processes at Oliktok Point, including cloud formation, high-latitude precipitation, aerosol-cloud interactions, and cloud properties. - Phase 2 (2019–2024): Extended the research to the broader North Slope region, using data from both Oliktok Point and Utqiaġvik. Topics explored included surface energy budgets, atmospheric stability, ice nucleation processes, and microphysics in Arctic clouds. The project resulted in numerous research products, including 50 peer-reviewed publications and dissertations, 169 presentations, and 10 data products. These products cover a variety of topics, including: - Cloud Macro- and Microphysical Properties: Arctic clouds play a crucial role in energy transfer, and accurate representation in models is critical. The study explored cloud transitions, ice crystal shapes, and dual-wavelength radar data to understand ice crystal habits and size distributions. - Aerosol Properties and Processes: The team examined aerosol sources in the Arctic, including industrial emissions and natural sources. Observations showed significant spatial gradients in aerosol concentrations due to human activities and wildfire smoke. The influence of aerosols on cloud formation and the surface energy budget was also assessed. - Aerosol-Cloud Interactions: Research revealed that aerosols might suppress cloud ice production, affecting cloud radiative forcing and precipitation. The impact of local industrial emissions on cloud properties was also investigated. - Contextualizing the North Slope of Alaska in the context of the broader Arctic: To understand broader trends, the project evaluated large-scale circulation patterns and the influence of weather systems on the Arctic. Studies indicated that large-scale processes play a significant role in temperature patterns and the timing of snowmelt. - Advancing ARM Observational and Modeling Capabilities: The project developed new radar data products and advanced measurement techniques, including clutter mitigation and drizzle detection. Uncrewed aerial systems (UAS) and tethered balloon systems (TBS) were deployed to gather detailed atmospheric data. Additionally, the project supported 10 early career scientists, providing training and mentorship to undergraduate interns, graduate students, postdoctoral researchers, and early career researchers. These efforts contributed to the advancement of ARM research capabilities and fostered a new generation of scientists skilled in Arctic atmospheric research. Ultimately, this ASR-supported project has provided valuable insights into Arctic atmospheric processes and their broader climate implications. Recommendations for future work include continuing support for long-term observing at Arctic locations to foster additional research, further exploration of aerosol-cloud interactions and the potential impacts of enhanced industrialization of the Arctic, and expanded use of uncrewed systems to gather data in this remote and harsh environment. Additionally, the data products developed by this work, and the data products developed through the ARM infrastructure, leave a treasure-trove of additional information that should be explored for many years to come to gain additional insight into physical processes in the Arctic atmosphere that drive the rapid changes occurring in at high latitudes and their global impact.
58 GEOSCIENCES↗
Detection of photon statistics and multimode field correlations by Raman processes
Glauber’s g (2) -function provides a common measure of quantum field statistics through two-photon coincidence counting in Hanbury Brown–Twiss measurements. Here, we propose to use nonlinear optical signals as a tool for the characterization of quantum light. In particular, we show that Raman measurements provide an alternative direct probe for a different component of the four-point correlation function underlying the g (2) -function. We illustrate this capacity for a specific quantum state obtained from a frequency conversion process. Our work points out how the analysis of controlled optical nonlinear processes can provide an alternative window toward the analysis of quantum light sources.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
HERO WEC V1.0 - Electrical PTO Deployment Data and Analysis
This submission includes the processed and raw field electrical data from NREL's August 2022 HERO WEC (hydraulic and electric reverse osmosis wave energy converter) deployment at Jennette's Pier for the electrical PTO (power take-off).
16 TIDAL AND WAVE POWER↗
Hyperplane decision trees as piecewise linear surrogate models for chemical process design
Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.
Decision trees↗
Mathematical Optimization of NGCC Solvent-based Carbon Capture Processes to Explore High Capture Designs
For point-source capture, much of the work to date has focused on achieving 90% capture – a somewhat arbitrary target above which parasitic losses to drive carbon capture and compression systems were believed to be too great to be economically justifiable. The recent push for deep decarbonization has caused this target to be revisited, and existing literature is of limited usefulness as most published studies have focused on carbon capture percentages of 90%. In this work, numerous mathematical optimizations were performed between carbon capture targets between 90%-99.8% of a Natural Gas Combined Cycle (NGCC) plant to better understand incremental cost of these systems so they may be compared to other decarbonization technologies, and to inform technical risk associated with targeting higher capture performance for these types of systems.
carbon capture↗
Extraction of Vibration Data with Imaging
To date, the primary sensing technology used to measure the vibration response has been accelerometers and strain gages mounted directly to the structure and using either wired or, more recently, wireless telemetry. Cost issues with these sensors and the associated data acquisition systems typically limit the numbers that are deployed on in situ structures. Although there are a few structures with larger sensing counts that in some cases exceed over 1000 sensors, more typical numbers range from ten to one hundred sensors resulting in low spatial resolution when they are applied to physically large systems. When one considers that nuclear power plant structures usually have complex geometries, material properties, connectivity and boundary conditions, it is clear these current approaches to vibration measurements can only provide limited information about a system’s dynamics response characteristics. As an alternative, many non-contact measurement technologies have emerged, including point wise measurement methods such as Global Positioning System (GPS), microwave interferometry, and laser Doppler vibrometry (LDV), as well as simultaneous full-field measurement methods such as electronic speckle pattern interferometry, holography interferometry, and muon tomography, some of which can provide high spatial resolution measurements. Among these methods, digital video imaging techniques have emerged as a feasible solution for full-field vibration measurements that provide significantly more detailed dynamic response information because every pixel becomes a measurement point. Furthermore, recent advances in image processing and computer vision algorithms have been successfully used to process video data for experimental and operational modal analysis. Such full-field measurements have the potential to significantly improve many current structural assessment procedures including system identification (modal parameter estimation), structural health monitoring, load reconstruction, model validation, and model updating. Furthermore, more recent full-field imaging techniques can be accomplished with relatively low-cost, commercially-available off-the-shelf cameras. However, these measurement procedures have other limitations that must be considered such as the ability to only measure visibly accessible points on a structure and a more limited dynamic range and bandwidth than can be achieved with accelerometers or strain gages.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Doppler Lidar (DL) Instrument Handbook
The Doppler lidar (DL) is an active remote sensing instrument that provides range- and time-resolved measurements of radial velocity and attenuated backscatter. The principle of operation is similar to radar in that pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal. From the time delay between each outgoing transmitted pulse and the backscattered signal, the distance to the scatterer is inferred. The radial or line-of-sight velocity of the scatterers is determined from the Doppler frequency shift of the backscattered radiation. The DL uses a heterodyne detection technique in which the return signal is mixed with a reference laser beam (i.e., local oscillator) of known frequency. An onboard signal processing computer then determines the Doppler frequency shift from the spectra of the heterodyne signal. The energy content of the Doppler spectra can also be used to determine attenuated backscatter.
99 GENERAL AND MISCELLANEOUS↗
Machine learning pipeline to predict defect behavior in metallic alloy systems
The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
In this work, we present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our simplicial spacetime meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open sourced under the MIT license: https://github.com/hguo/ftk.
97 MATHEMATICS AND COMPUTING↗
Magnetically Driven Neutron-rich Ejecta Unleashed: Global 3D Neutrino–General Relativistic Magnetohydrodynamic Simulations of Collapsars Probe the Conditions for r -process Nucleosynthesis
Abstract Collapsars—rapidly rotating stellar cores that form black holes—can power gamma-ray bursts and are proposed to be key contributors to the production of heavy elements in the Universe via the rapid neutron capture process ( r -process). Previous neutrino-transport collapsar simulations have been unable to unbind neutron-rich material from the disk. However, these simulations have not included sufficiently strong magnetic fields and the black hole (BH), both of which are essential for launching mass outflows. We present ν h-amr , a novel neutrino-transport general relativistic magnetohydrodynamic ( ν GRMHD) code, which we use to perform the first 3D global ν GRMHD collapsar simulations. We find a self-consistent formation of a weakly magnetized dense accretion disk, which has sufficient time to neutronize. Eventually, substantial magnetic flux accumulates near the BH, becomes dynamically important, leads to a magnetically arrested disk (MAD), and unbinds some of the neutron-rich material. However, the strong flux also hinders accretion, lowers density, and increases neutrino-cooling timescale, which prevents further disk neutronization. Typical collapsar progenitors with mass accretion rates, M ̇ ∼ 0.1 − 1 M ⊙ s - 1 , do not produce significant neutron-rich ( Y e < 0.25) ejecta. However, we find that MADs at higher mass accretion rates, M ̇ ≳ few M ⊙ s - 1 (e.g., for more centrally concentrated progenitors), can unbind M ej ≲ M ⊙ of neutron-rich ejecta. The outflows inflate a shocked cocoon that mixes with the infalling neutron-poor stellar gas and raises the final outflow Y e ; however, the final r -process yield may be determined earlier at the point of neutron capture freeze-out. Future work will explore under what conditions more typical collapsar engines become r -process factories.
Issa, Danat (ORCID:0009000524787631)↗
Inferring the Dynamics of the State Evolution During Quantum Annealing
To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer, which then returns a user-specified number of low-energy solutions. It would be useful to know what happens in the quantum processor during the anneal process so that one could design better algorithms or suggest improvements to the hardware. However, existing quantum annealers are not able to directly extract such information from the processor. Hence, in this work we propose to use advanced features of D-Wave 2000Q to indirectly infer information about the dynamics of the state evolution during the anneal process. Specifically, D-Wave 2000Q allows the user to customize the anneal schedule, that is, the schedule with which the anneal fraction is changed from the start to the end of the anneal. Furthermore, using this feature, we design a set of modified anneal schedules whose outputs can be used to generate information about the states of the system at user-defined time points during a standard anneal. With this process, called "slicing", we obtain approximate distributions of lowest-energy anneal solutions as the anneal time evolves. We use our technique to obtain a variety of insights into the annealer, such as the state evolution during annealing, when individual bits in an evolving solution flip during the anneal process and when they stabilize, and we introduce a technique to estimate the freeze-out point of both the system as well as of individual qubits.
42 ENGINEERING↗