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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

StressNet - Deep learning to predict stress with fracture propagation in brittle materials

Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Simple Data-Centric Methodology for Producible Geothermal Well Determinations: Preprint

The Bureau of Land Management (BLM) has traditionally lacked a standardized methodology for determining if a newly drilled geothermal well is "producible," a designation essential for deciding whether a lease should be "held by production." This is a straightforward problem to solve in oil and gas: Demonstrate that a well is economically viable, meaning it produces sufficient oil or gas to exceed direct operating costs and lease-related expenses, such as rentals or minimum royalties. In geothermal, the problem is more complex: Geothermal wells are tightly coupled with the downstream infrastructure - specifically, the power plant, which is often not designed until well after a lease is deemed as "held by production." Although this designation is critical for advancing geothermal power plant development on BLM-managed lands, current geothermal well assessments often rely on ad hoc approaches that can be complex, operator-biased, and heavy in assumptions related to economic viability. To address this, we have developed two complementary methodologies: a minimum power requirement-based approach and a productivity index (PI)-based approach. These methods leverage key flow test data - pressure, temperature, flow rate, and specific enthalpy - to provide reliable and standardized producible well determinations. The minimum power requirement-based approach evaluates wells against specific power output thresholds informed by reservoir experts and the associated temperature requirements. The PI-based approach assesses well productivity using widely accepted reservoir engineering metrics, proposing a threshold of 2.5 kg/s/bar. Both methods are data-driven and grounded in empirical production data from operational geothermal wells, avoiding uncertain economic assumptions while maintaining decision-making accuracy. Wells falling below key performance thresholds (i.e., PI, specific power) are deemed non-producible. These methodologies aim to streamline BLM's decision-making process, reduce nontechnical barriers to geothermal energy adoption, and enable regulatory expansion into states lacking geothermal expertise. Preliminary results indicate clear trends and thresholds in production data that provide actionable insights for evaluating well producibility. Validation using well completion report (WCR) data is ongoing, with promising results demonstrating the potential for these standardized methodologies to impact geothermal development significantly.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Multi-cyclone analysis and machine learning model implications of cyclone effects on forests

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R 2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗
DOE OSTI2023

Advective gas flow in bentonite: Development and comparison of enhanced multi-phase numerical approaches

Understanding the impact of repository gas, generated from degradation of waste and its interaction with the host rock, is essential when assessing the performance and safety function of long-term disposal systems for radioactive waste. Numerical models based on conventional multi-phase flow theory have historically been applied to predict the outcome and impact of gas flow on different repository components. However, they remain unable to describe the full complexity of the physical processes observed in water-saturated experiments (e.g., creation of dilatant pathways) and thus, the development of novel representations for their description is required when assessing fully saturated clay-based systems. This was the primary focus of Task A within the international cooperative project DECOVALEX-2019 (D-2019) and refinement of these approaches is the primary focus of this study (Task B in the current phase of DECOVALEX-2023). This paper summarises development of enhanced numerical representations of key processes and compares the performance of each model against high-quality laboratory test data. Experimental data reveals that gas percolation in water-saturated compacted bentonite is characterised by four key features: (i) a quiescence phase, followed by (ii) the gas breakthrough, which leads to a (iii) peak value, which is then followed by (iv) a negative decay. Three models based on the multiphase flow theory have been developed. These models can provide good initial values and reasonable responses for gas breakthrough (although some of them still predict a too-smooth response). Peak gas pressure values are in general reasonably well captured, although maximum radial stress differences are observed at 48 mm from the base of the sample. Here, numerical peak values of 12.8 MPa are predicted, whereas experimental values are about 11 MPa. These models are also capable of providing a reasonable representation of the negative pressure decay following peak pressure. However, other key specific features (such as the timing of gas breakthrough) still require a better representation. The model simulations and their comparison with experimental data show that these models need to be further improved with respect to model parameter calibration, the numerical representation of spatial heterogeneities in material properties and flow localisation, and the upscaling of the related physical processes and parameters. To further understand gas flow localisation, a new conceptual model has been developed, which shows that discrete channels can possibly be induced through the instability of gas-bentonite interface during gas injection, thus providing a new perspective for modeling gas percolation in low-permeability deformable media.

58 GEOSCIENCES↗
DOE OSTI2020

In-Situ Stress Measurements at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) Site

A scientific injection campaign was conducted at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site in 2017 and 2019. The testing included pump-in/shut-in, pump-in/flowback, and step rate tests. Various methods have been employed to interpret the in-situ stress from the test dataset. This study focuses on methods to interpret the minimum in-situ stress from step rate, pump-in/extended shut-in tests data obtained during the stimulation of two zones in Well 58-32. This well was drilled in low-permeability granitoid. A temperature of 199 °C was recorded at the well’s total depth of 2297 m relative to the rotary Kelly bushing (RKB). The lower zone (Zone 1) consisted of 46 m of the openhole at the toe of the well. Fractures in the upper zone (Zone 2) were stimulated between 2123–2126 m measured depths (MD) behind the casing. The closure stress gradient variation depended on the depth and the injection chronology. The closure stress was found to increase with the pumping rate/volume. This stress variation could indicate that poroelastic effects (“back stress”) and the presence of adjacent natural fractures may play an important role in the interpretation of fracture closure stress. Further, progressively increasing local total stresses may, consequently, have practical applications when moderate volumes of fluid are injected in a naturally fractured or high-temperature reservoir. The alternative techniques that use pump-in/flowback tests and temperature signatures provide a valuable perspective view of the in-situ stress measurements.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

National energy savings potential of cellular shades: A measurement and simulation study

Windows are major contributors to energy demand in residential homes because of their inferior thermal resistance compared with the opaque envelope, and sometimes from unwanted solar heat gain. Window attachments can help mitigate the energy demand by controlling the solar heat gains and enhancing window thermal resistance. Cellular shades have the potential of superior thermal performance compared with generic shades because of its honeycomb structure. Here, in this study, the team analyzed the energy savings potential of cellular shades in residential homes via experimental testing for two heating seasons and energy simulations. Five shading devices—three single-cell and two double cellular/cell-in-cell shades—were used to compare the performance with generic horizontal venetian blinds using two nearly identical side-by-side rooms in a residential home. The experimental testing showed daily heating energy savings in the range of 17%–36% compared with the case without shades. The experimental testing data also exhibited improvements in thermal comfort when using cellular shades. Additionally, energy simulations were performed to evaluate the energy savings potential of the cellular shades using a residential prototype home, which demonstrated energy savings up to 9 kWh/m2/year in cold climates. The total site energy savings for heating and cooling from cellular shades was up to ~9% for the home with a heat pump and up to ~15% for a home with a gas furnace compared with cases without any shading devices. The energy savings at a national scale were up to 14.6 TWh assuming a 20% penetration rate in residential homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Large-scale parametric modeling of spent nuclear fuel dynamics in the 30 cm package drop scenario

Packages used to transport spent nuclear fuel (SNF) are required by the U.S. Code of Federal Regulations 10 CFR 71.71 to demonstrate satisfactory performance during a drop scenario. While the CFR is meant to ensure safe package function, it does not evaluate survival of the SNF within. The U.S. Department of Energy Spent Fuel and Waste Science and Technology program is working on closing the knowledge gap related to the response of SNF to external mechanical loads, including the hypothetical 30 cm package drop scenario in the CFR. In support of this effort, LS-DYNA finite element simulations were developed by Pacific Northwest National Laboratory (PNNL) to model generic drop scenarios at both the package and fuel assembly level. The models were validated against one-third scale package and full scale fuel assembly drop test data and were exercised to predict fuel cladding strains in a narrow range of model configurations. This work describes a large-scale parametric study conducted by PNNL using the previously developed and validated PWR finite element model, with the addition of a new generic BWR assembly model. The motivation for the parametric study was to characterize the broad range of SNF responses in the 30 cm package drop scenario. This was accomplished by varying the drop orientation, fuel assembly type (17x17 PWR and 10x10 BWR), burnup, cladding temperature, spacer grid buckling load, package mass, impact limiter stiffness, and mechanical gap conditions within the basket. A MATLAB framework was developed to automate LS-DYNA model generation and execution on PNNL institutional computing resources. In total, over 2000 simulations were performed. For each simulation, the SNF response was quantified in terms of permanent grid deformation, fuel rod contact pressure, and strains within the fuel rods, guide tubes, and water rods. The results provide valuable insight into the range of responses that could be reasonably expected from SNF in the hypothetical drop scenario, as well as the sensitivity to each input parameter. The results of this parametric study are a key component of the testing and modeling strategy the Spent Fuel and Waste Science and Technology program is using to close the external loads knowledge gap.

Kadooka, Kevin↗
DOE OSTI2022

Conversion of METL Diagnostic Flowmeters to Permanent Magnet Based Electromagnetic Flowmeters

The Mechanisms Engineering Test Loop (METL) uses three flowmeters to monitor sodium circulation through the main loop, the Cold Trap leg, and the Plugging Meter leg. When constructed, the Cold Trap flowmeter (CTFM) and Plugging Meter flowmeter (PMFM) were equipped with permanent magnets and Hall Sensors. By October of 2019, during normal operations, both the CTFM and PMFM failed, producing either erroneous or zero signals. Repairs to the flowmeters were attempted in January of 2020 but failed to resolve the issues. Therefore, starting in March of 2021, a plan was developed to convert the installed flowmeters to more robust permanent magnet based electromagnetic flowmeters (EMFMs). The Cold Trap EMFM (CT EMFM) and Plugging Meter EMFM (PM EMFM) were fully installed by August of 2021. Initial testing and commissioning were completed in November of 2021. Finally, the first calibration runs were finished in March of 2022. This report discusses the design, conversion, and calibration of the as-built CTFM and PMFM to robust permanent magnet based EMFMs. During the design phase, theoretical models were used to estimate the signal output and required magnet sizes. Then, the CTFM and PMFM were disassembled and converted to EMFMs. After commissioning, the CT EMFM and PM EMFM were calibrated using a ‘Time Transfer Procedure’ carried out with METL Expansion Vessels and Test Vessel Two. The accuracy of the calibration was estimated through an uncertainty analysis. Finally, the report describes some lessons learned for future flowmeter conversions and follow-up work. In total, both METL EMFM’s met the desired performance targets. Testing data showed that the METL EMFMs provide a output signal of roughly 2 mV at 1 GPM and 250 °C. During calibration, flowrates between 0.7 GPM and 2.4 GPM were achieved. In this flow range, calibration coefficients of 0.485 and 0.935 were measured as with errors ranging from 15-5% and 16-5% for the CT EMFM and PM EMFM, respectively. Above 1.5 GPM, the error was assumed to be about 5% for both METL EMFMs. Several points will be considered for future follow-up work on the METL EMFM’s. First, the signal noise issues in the PM EMFM will be addressed. It is likely the source of the noise is due to some stray EMF from a heater. Secondly, the NdFeB magnets will be replaced with a grade that has a Curie temperature above 80 °C. For example, a few grades of NeFeB magnets have Curie temperatures which exceed 150 °C and some SmCo magnets have Curie temperatures as high as 500 °C. Third, more calibration runs will be completed in the range of 0.5-2.5 GPM. This will further improve the accuracy of the calibration coefficient. Fourth, calibration runs will be completed up to 5 GPM. This requires a larger pressure relief valve to be installed on the Expansion Vessel and Test Vessel 2, as well as a revised calibration procedure. Lastly, calibration runs will be completed to account for cold spots in the system, which reduced the inlet temperature of the METL EMFMs. Overall, these points will improve the calibration coefficients derived in this work and also improve the performance of the METL EMFMs.

42 ENGINEERING↗
DOE OSTI2022

Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps. All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions. Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive. To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to: 1. Reduce parking-seeking behavior in the study area by 20%; 2. Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%; 3. Increase curb space, alley space and private loading bay occupancy rates in the study area. The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighbourhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analysed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Emissions mitigation technology for advanced water-lean solvent-based CO 2 capture processes

This technical final report submitted to DOE/NETL presents all the research activities performed during the entirety of DE-FE0031660 project-Emissions Mitigation Technology for Advanced Water-Lean Solvent-Based CO 2 Capture Processes which spans from October 2018 through March 2022. RTI International has been conducting studies from fundamental and operational aspects to reduce the overall amine emissions from the advanced Water-Lean Solvent (WLS) systems, specifically RTI’s Non-Aqueous Solvent (NAS). This technical final report will highlight the key findings from project which align closely to the project objectives which are: Identify the contribution of vapor loss, entrainment, and aerosols to the overall emissions of water-lean systems; Determine the significance of CO 2 capture system operating parameters to the amine emissions; Develop an emissions model based on critical operating parameters; Evaluate the effectiveness of emissions mitigation devices to reduce the amine emissions to <1 ppm under flue coal-fired flue gas; and, Determine the contribution of the ECTs to the overall CO 2 capture cost. The following are the key findings based on numerous tests using both lab-scale setups and parametric testing performed at RTI’s Bench-scale Gas Absorption System (BsGAS). During the BP1, the aerosol generation system and monitoring equipment were installed at BsGAS to produce and determine the aerosol characteristics during the NAS CO 2 capture process. The aerosol produced by this setup produced aerosols with the peak diameter and concentration of 50 micron and 1.2E10 7 cm -3 , respectively. These particle sizes and concentrations are matched to those observed in the actual coal-fired power plant flue gases and expected to be found at the absorber inlet of the CO 2 capture system. Over 1,300 hours of parametric testing have been conducted to evaluate the impact of the aerosols and operating conditions during the CO 2 capture with NAS on the overall amine emissions in the treated flue gas. At the worse condition tested, the presence of the aerosols in the flue gas could increase the overall emissions by 10X compared to the baseline emissions from NAS’s vapor pressure. CO 2 capture rate was found to be a main factor impacting the overall emissions as well as aerosol size and concentrations in the absorber off-gas. The higher CO 2 capture rate, the higher amine emissions in the treated gas. The temperature difference between the temperature bulge seen in the absorber and the water wash temperature also impacts the particle growth where the larger the temperature difference, the more amine emissions from aerosols in the treated gas. The majority of the aerosols did not grow substantially in the system, and the particle concentrations remained nearly constant between the absorber inlet and wash outlet. Only a small portion of the particles were found to grow significantly. The high efficiency demister with mesh size of 5-10 micron can be installed to remove a portion of the aerosols from the gas stream leaving the water wash. Overall, these results from parametric testing have established the emission baseline and validate our assumption on the need of emission control technologies (ECT) in order to minimize the emissions from the baseline NAS CO 2 capture process. Over 2,000 of BsGAS operating hours was used to investigate a handful of process improvements which led to a selection of the vital few changes that effectively control the amine emissions. These process improvements are lime-coated-filters for absorber gas inlet, advanced demister at the top of the absorber, a second water wash with amine recovery unit were designed, installed, and tested at BsGAS at the end of BP1. The result showed that the NAS CO 2 capture process with these additional emission control devices could lower the amine emission in the treated gas to about 1 ppm using a simulated coal-fire flue gas stream. The main contributor in lowering the amine emission came from the second water wash with amine recovery unit where the amine concentration in the scrubbing water was kept below 2 wt% through a continuous amine removal via an adsorbent bed, resulting in a low amine vapor pressure. The adsorbent bed was regenerated via a direct steam regeneration and the recover amine was returned to the absorber to minimize wastewater and makeup amine. A flue gas generation system was designed and installed during the first half of BP2 to support the emission testing using a real coal-derived flue gas. The system is capable of generating both coal- and natural gas- derived flue gases with the composition of the gaseous species highly resemble to that of the power plant flue gases. The particulates detected in the coal-derived flue gas showed the mean diameter of 1 micron. The CO 2 capture operating was then proceed using the real coal-derived flue gas where the amine emission was controlled to be about 0-3 ppm for the total run time of about 200 hours. Similar testing was conducted with natural gas-derived flue gas and the result showed a highly amine emission of 30 ppm under the total run time of 200 hours. The Principal Component Analysis (PCA) and the Partial Least Squares Projection to Latent Structures (PLS) techniques were applied to the parametric testing data to derive a multivariate statistical model. The model was validated and trained with half of the data collected, and the predictive ability of the model was evaluated using the remaining half of the data. The resulting empirical model was capable of predicting the overall emissions from the NAS process without the ECTs with ±15% accuracy (average absolute deviation, AAD) in BP1. As more emission data were obtained under the real coal-flue gas in the BP2, the model incorporated these new set of data to reflect the final process configuration, operating parameters, and amine emission. This results in the updated empirical model predicting the amine emission from the NAS CO 2 capture process with 84% goodness-of-fit (R 2 ), 85% predictability (Q 2 ), and 15% AAD. The study evaluates the use of RTI’s Non-Aqueous Solvent technology for 90% CO 2 capture from a net 650 MWe pulverized coal power plant, downstream of the flue-gas desulfurization unit. The captured CO 2 has a purity of > 95% CO 2 , and is dried, compressed to 15.3 MPa (2,215 psia), ready for sequestration. The analysis uses Case B12B from the DOE Baseline study on Bituminous Coal, Revision 4 where the Cansolv CO 2 capture plant is replaced by the RTI CO 2 Capture plant. The CO 2 capture plant has been sized to capture >90% CO 2 from flue gas derived from a net 650 MWe supercritical pulverized coal power plant. The CO 2 capture plant is equipped with emission control technologies that limits the amine emissions to < 1 ppm. Two different cases were evaluated for the technoeconomic study. The key difference between the two cases is the regenerator pressure. In Case 1, the regenerator operates at 0.195 MPa (28.3 psia), whereas in Case 2, the regenerator pressure is 0.44 MPa (64 psia) thus removing the need for the first stage of compression of the eight-stage compression train. Results from the TEA are compared against the DOE reference cases for SCPC plant with and without CO 2 Capture (Case B12A and Case B12B of the DOE Baseline study, respectively). Case 2 with CO 2 regeneration at higher pressure results in the lower cost of CO 2 capture. The total capital cost of the capture process has been estimated using 2018 dollars in Aspen Process Economic Analyzer and was estimated to be $579 MM. The capture plant operation leads to a total parasitic power loss rate of 96 MWe, resulting in a decrease in pulverized coal power plant efficiency of 7.8% points. The resulting cost of electric power increases from 64.4 mills/kWh, for no capture, to 97.5 mills/kWh, with 90% capture, an increase of 51% in the COE. The cost of capturing 90% CO 2 was estimated to be $38.2/tonne-CO 2 , and meets the DOE target of $40/t-CO 2 . Emission control technologies (ECT) investigated in this project includes a second water wash with use of activated carbon beds for removal of amine from the wash water prior to recirculation in the water wash. These ECT allow operation of the CO 2 capture plant with < 1 ppm amine emissions with the treated flue gas and contributes to $2.4/t-CO 2 captured. Amine emissions derived from thermal and oxidative degradations were investigated under this project along with the emissions derived from aerosols for the NAS system. The thermally degraded of the lean NAS showed less than 4% decreased of the original total amine content in the NAS at 150 °C while the result obtained at 120 °C showed no drop in total amine content, suggesting that thermal degradation of the NAS is minimal. These results also suggested that the thermally degraded species are not likely formed and contributed to the emissions due to the low regeneration temperature of the NAS at 90-105 °C. The oxidative degradation, on the other hand, could become problematic as some of these oxidative degraded species were observed during the NAS-5 testing at National Carbon Capture Center (NCCC) and SINTEF in our previous project. The rapid screening of selected inhibitors suggested that oxidative degradation of NAS can be suppressed using thiol containing compounds in amounts of at least 1 mol%. The detailed mechanistic degradation pathway was conceived for a specific amine used in NAS formulation during BP2. he reduction of the nitrosamines caused by the NO x present in the flue gas was also examined. The study suggested that the thermo-chemical treatment of the NAS solvent would be a more effective and economically viable compared to removing NO x at the DCC.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Effect of thermal conditioning on the initiation threshold of secondary high‐explosives

While most performance metrics of high–explosive (HE) based devices like detonation velocity, detonation pressure, and energy output are expected to degrade over time, the evolution of initiation threshold appears less clear, with claims of both increasing and decreasing trends in threshold having been made in the literature. This work analyzes D–optimally designed sequential binary test data for a few thermally conditioned porous–powder and polymer–bonded HE initiator systems using a Bayesian likelihood method employing the probit regression model. Here we find that in most cases the initiation threshold decreases (i.e., sensitivity increases) upon accelerated thermal conditioning. However, such results are nuanced and influenced by factors like the contact area of initiating stimulus, HE characteristics like density and specific surface area, as well as possible thermally induced changes to other materials and interfaces involved.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate

Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

Accelerated aging↗
DOE OSTI2026

Gaussian processes for inferring parton distributions

The extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results. We study a framework based on Gaussian Process Regression (GPR) to reconstruct PDFs from lattice QCD matrix elements. Within a Bayesian framework, Gaussian processes serve as flexible priors that encode uncertainties, correlations, and constraints without imposing rigid functional forms. We investigate a wide range of kernel choices, mean functions, and hyperparameter treatments. We quantify information gained from the data using the Kullback-Leibler divergence. Synthetic data tests demonstrate the consistency and robustness of the method. Our study establishes GPR as a systematic and non-parametric approach to PDF reconstruction, offering controlled uncertainty estimates and reduced model bias in lattice QCD analyses.

hadronic spectroscopy↗
DOE OSTI2022

Bayesian time-varying occupancy model for West Nile virus in Ontario, Canada

Occupancy models determine the true presence or absence of a species by adjusting for imperfect detection in surveys. They often assume that species presences can be detected only if sites are occupied during a sampling season. We extended these models to estimate occupancy rates that vary throughout a sampling season as well as account for spatial dependence among sites. For these methods, we constructed a fast Gibbs sampler with the Pólya-Gamma augmentation strategy to conduct inference on covariate effects. We applied these methods to evaluate how environmental conditions and surveillance practices are associated with the presence of West Nile virus in mosquito traps across Ontario, Canada from 2002 to 2017. We found that urban land cover and warm temperatures drove viral occupancy, whereas viral testing on pools with higher proportions of Culex mosquitoes was more likely to result in a positive test for West Nile virus. Models with time-varying occupancy effects achieved much lower Watanabe-Akaike information criteria than models without such effects. Our final model had strong predictive performance on test data that included some of the most extreme seasons, demonstrating the promise of these methods in the study of pathogens spread by mosquito vectors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Discrete element model for powder grain interactions under high compressive stress

A reduced order, nonlocal model is proposed for the contact force between initially spherical particles under compression. The model in effect provides the normal component of the interaction force between elements in the discrete element method (DEM). It is applicable to high relative density and large stress in powder compaction. It takes into account the mutual interaction between multiple points of contact, in contrast to the usual assumption in DEM of pair interactions. The mathematical form of the model is derived from a variational formulation that leads to the momentum balance for the forces on each grain. The model is calibrated mainly using detailed three dimensional peridynamic simulations of single grains under compressive loading by rigid plates that move radially with prescribed velocity. This calibration takes into account the large deformation and fracture of the grains. The interaction model also includes terms for the unloading behavior and adhesion. Finally, as validation, the model is applied to test data on the compaction of microcrystalline cellulose bulk powder.

36 MATERIALS SCIENCE↗