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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗
DOE OSTI2021

Interior Athabascan Energy Capacity Building Network (Final Report)

Tanana Chiefs Conference (TCC) is the traditional tribal consortium for the 37 federally recognized Tribes and villages of Interior Alaska. TCC is based on a belief in tribal self-determination and the need for regional Native unity. TCC is a nonprofit organization that works toward meeting the needs and challenges for more than 10,000 Alaska Natives (mostly Alaskan Athabaskans) in Interior Alaska. With support from DOE-OIE’s Inter-Tribal Technical Assistance grant, TCC’s goal was to expand and build on the solid foundation of work that the existing TCC Energy Program established through the creation of the Interior Athabascan Energy Network (IAEN). More specifically, the IAEN was designed to create a network of village-based energy champions across the interior and hold quarterly teleconferences and annual inperson meetings. Among the greatest impacts of the IAEN were the creation and support of realistic, communitybased energy projects that produced tangible results such as fuel savings and skills and capacity development among local staff as well as establishment of an energy team that included TCC staff, Tribal participants, technical consultants, and support agencies. The DOE-TA funding allowed us to provide technical assistance to our Tribal communities to develop energy projects, and to foster a network consisting of Tribal community members and energy experts that has strengthened the ability in the region to troubleshoot energy issues, develop meaningful projects, envision a clean energy future, and to share project successes and challenges. The funding from DOE was a catalyst for growth, has created opportunities for learning and knowledge sharing, and has led to the development of cost saving energy projects. Most importantly, the DOE project allowed energy champions around the region to share their knowledge, experiences, and expertise to create place-based solutions and has forged inter-Tribal relationships that will continue to be an asset for years to come. Through information exchange that occurred at out IAEN annual meetings and other events, this technical assistance grant award has spurred a spinoff project that is focused on establishing a collaborative of independent utilities that work together to solve energy problems and share services to operate and maintain their electric utilities. We have indentified at least 10 and possibly 12 communities that are interested in collaborating in managing their utilities to reduce costs and increase reliability. As well, by identifying and supporting Community Energy Champions in the Interior villages and adding capacity to the TCC Energy Program, we were able to create actionable community energy plans, develop specific projects, and increase local capacity, skills, and literacy around clean energy initiatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Identification of drug repurposing candidates for amyotrophic lateral sclerosis using electronic health records: a retrospective cohort study

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with a life expectancy of only 3–5 years and few approved treatments. To identify drug repurposing candidates for the treatment of ALS, we analysed the electronic health records (EHRs) of a large cohort of military veterans with ALS. We analysed the EHRs of individuals in the US Veterans Health Administration (VHA) database who were diagnosed with ALS between Jan 1, 2009 and Dec 31, 2019 to assess medication effects. Individuals without recorded prescriptions after the date of diagnosis were excluded. Two sets of criteria were applied to ascertain exposure. Exposure criteria A were met if the dispense date or the end date of the medication was within 12 months of ALS diagnosis and the end date was at least 6 months after the dispense date. Exposure criteria B were met if there were at least two dispenses within 6 months before diagnosis and 12 months after diagnosis. Propensity score-matched control groups were generated on the basis of confounders included in the EHR, with methodology of potential outcomes used to infer treatment effects. The primary outcome was death. A standard Cox proportional hazards analysis was done to assess association with survival. Survival was defined as the time from diagnosis date recorded in the EHR to death reported in the Department for Veterans Affairs Vital Status File. Follow-up survival time was censored on Dec 31, 2020, for those alive on this date. Downstream protein targets of drugs with clinically significant effects were analysed using the protein–protein interaction networks-based algorithm PathFX. The EHRs of 11 003 individuals with ALS in the VHA database were appropriate for analysis. 162 medications with treatment groups of 30 or more individuals were identified. Among these 162 medications, 27 were associated with statistically significant changes (≥0·1) in the hazard ratio (HR) for death. 18 of the medications were associated with a reduced HR for death (prolonged survival), and nine were associated with an increased HR for death (reduced survival). Drugs associated with reduced HR included HMG-CoA reductase inhibitors (simvastatin, pravastatin, lovastatin, and atorvastatin), PDE5 inhibitors (vardenafil and sildenafil), and α-adrenergic antagonists (tamsulosin and terazosin). The medications associated with an increased HR were drugs used either in the management of clinical features of ALS associated with poor outcomes or in end-of-life care. PathFx analysis identified a complex of proteins interacting with several of the identified drugs. To our knowledge, this analysis is the largest EHR-based study for identifying drug repurposing candidates for ALS. We identified several drugs that warrant further assessment as therapeutic options in ALS, as well as a protein network complex that might serve as a therapeutic target for ALS.

Reimer, Richard J. [Stanford Univ., CA (United Sta↗
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning

Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of the Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

Jiang, Peishi↗
DOE OSTI2025

Complementarity-based complementarity: The choice of mutually unbiased observables shapes quantum uncertainty relations

Quantum uncertainty relations impose fundamental limits on the joint knowledge that can be acquired from complementary observables: Perfect knowledge of a quantum state in one basis implies maximal indetermination in all other mutually unbiased bases (MUBs). Uncertainty relations derived from joint properties of the MUBs are generally assumed to be uniform, irrespective of the specific observables chosen within a set. In this work, we demonstrate instead that the uncertainty relations can depend on the choice of observables. Through both experimental observation and numerical methods, we show that selecting different sets of three MUBs in a five-dimensional quantum system results in distinct uncertainty bounds, i.e., in varying degrees of complementarity, in terms of both entropy and variance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. In this perspective, we outline how the adoption of key Open Science principles—open data, open source and open methods—is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Evaluation of a New Chemical Mechanism for 2-Amino-2-methyl-1-propanol in a Reactive Environment from CSIRO Smog Chamber Experiments

Amines are considered as an emerging class of atmospheric pollutants that are of great importance to atmospheric chemistry and new particle formation. As a typical amine, 2-amino-2-methyl-1-propanol (AMP) is one of the proposed solvents for capturing CO 2 from flue gas streams in amine-based post-combustion CO 2 capture plants, and it is expected to result in AMP emission and secondary products formation in the atmosphere. However, the current knowledge of its atmospheric chemistry and kinetics is poorly understood, particularly in a reactive environment. As such, in this work we used the CSIRO smog chamber to study the photo-oxidation of AMP in the presence of VOCs–NOx surrogate mixtures over a range of initial amine concentrations. O 3 formation was significantly inhibited when AMP was added to the surrogate VOCs–NOx mixtures, implying that AMP could alter known atmospheric chemical reaction pathways and the prevailing reactivity. Simultaneously, a large amount of AMP-derived secondary aerosol was formed, with a considerably high aerosol mass yield (i.e. ratio of aerosol formed to reacted AMP) of 1.06±0.20. Based on updated knowledge of its kinetics, oxidation pathways and product yields, we have developed a new mechanism (designated as CSIAMP-19) and integrated into the Carbon Bond 6 (CB6) chemical mechanism, and evaluated it against available smog chamber data. Compared with the existing AMP mechanism (designated as CarterAMP-08), the modified CB6 with CSIAMP-19 mechanism improves prediction against AMP–VOCs–NOx experiments across a range of initial AMP concentrations, within ±10% model error for gross ozone production. Our results contribute to scientific understanding of AMP photochemistry and to the development of the chemical mechanism of other amines. The updated AMP chemical reactions scheme can be further embedded into the chemical transport model for regional modelling scenarios where AMP-related emissions are of concern.

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

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. Here, the novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial- temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad- GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

DEEPEN 3D PFA Weights for Exploration Datasets in Magmatic Environments

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This GDR submission includes those weights. The weighting was done using two different approaches: one based on expert opinions, and one based on statistical learning. The weights are intended to describe how useful a particular exploration method is for imaging each component of each play type. They may be adjusted based on the characteristics of the resource under investigation, knowledge of the quality of the dataset, or simply to reduce the impact a single dataset has on the resulting outputs. Within the DEEPEN PFA, separate sets of weights are produced for each component of each play type, since exploration methods hold different levels of importance for detecting each play component, within each play type. The weights for conventional hydrothermal systems were based on the average of the normalized weights used in the DOE-funded PFA projects that were focused on magmatic plays. This decision was made because conventional hydrothermal plays are already well-studied and understood, and therefore it is logical to use existing weights where possible. In contrast, a true PFA has never been applied to superhot EGS or supercritical plays, meaning that exploration methods have never been weighted in terms of their utility in imaging the components of these plays. To produce weights for superhot EGS and supercritical plays, two different approaches were used: one based on expert opinion and the analytical hierarchy process (AHP), and another using a statistical approach based on principal component analysis (PCA). The weights are intended to provide standardized sets of weights for each play type in all magmatic geothermal systems. Two different approaches were used to investigate whether a more data-centric approach might allow new insights into the datasets, and also to analyze how different weighting approaches impact the outcomes. The expert/AHP approach involved using an online tool (https://bpmsg.com/ahp/) with built-in forms to make pairwise comparisons which are used to rank exploration methods against one-another. The inputs are then combined in a quantitative way, ultimately producing a set of consensus-based weights. To minimize the burden on each individual participant, the forms were completed in group discussions. While the group setting means that there is potential for some opinions to outweigh others, it also provides a venue for conversation to take place, in theory leading the group to a more robust consensus then what can be achieved on an individual basis. This exercise was done with two separate groups: one consisting of U.S.-based experts, and one consisting of Iceland-based experts in magmatic geothermal systems. The two sets of weights were then averaged to produce what we will from here on refer to as the "expert opinion-based weights," or "expert weights" for short. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. More information on this approach along with the dataset used to produce the statistical weights may be found in the linked dataset below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Ultrasonic waveguide reflectometry for quench detection (CRADA Final Report)

Early detection of thermal runaway (quench) is critical for safely operating high-temperature superconductor (HTS) magnets. Voltage-based techniques provide late detection of quenching in HTS, which can lead to magnet burnout and system damage. Various non-voltage quench-detection approaches are therefore investigated nowadays that are based on magnetic, acoustic, and optical sensing. LBNL has been actively developing the quench detection principle based on diffuse ultrasonic wave propagation since 2017 and has demonstrated the approach's viability with several peer-reviewed publications and a patent application. Over the last decade, Etegent, LBNL partner for this proposal and effort, has been developing a sensing technology that utilizes the propagation of stress waves through the solid metallic and nonmetallic waveguides (WG). The method is based on the dependence of materials' elastic properties and the speed of sound on temperature. Compared with optical analog, acoustic WG operates at much lower frequencies, eliminating the need for expensive, complex, highly sensitive receivers and data processing. Etegent proved this technology in many harsh environments, mainly concentrated high-temperature measurements. The initial contact between LBNL and Etegent started in 2020, aiming at combining our knowledge and effort toward developing a viable ultrasound-based quench detection technique for use with HTS magnets. A preliminary study conducted jointly in 2022 found no principle restrictions for expanding the WG technology toward cryogenic temperature operation. The present CRADA work aimed at designing, building, and testing ultrasonic waveguide temperature sensors at relevant cryogenic conditions, and demonstrating this technology's applicability to detecting heating and quenching in High Temperature Superconductor (HTS)-based cables and magnets. The technical objectives of the project included: (1) Design and fabricate ultrasonic WG sensors suitable for cryogenic operation, (2) Test ultrasonic WGs under cryogenic conditions, (3) Determining a suitable way of insulating WGs to prevent ultrasonic leakage while maintaining good thermal contact with the surrounding medium, (4) WG integration and cryogenic test with HTS conductors and HTS magnets, and (5) Design and basic prototyping of a standalone WG-based quench detection system. This project adds value to the present LBNL magnet diagnostics portfolio and enables a breakthrough in addressing the HTS magnet quench detection challenge.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Persona of Transition Metal Ions in Solids: A Statistical Learning on Local Structures of Transition Metal Oxides

Abstract The local structure of a transition metal (TM) ion is a function of cation elements and valence states. More than that, in this work, by employing a trove of first‐principles data of TM oxides, the local structures of TM cations are statistically analyzed to extract detailed information about cation site preference, bond length, site structural distortion, and cation magnetization. It is found that cation radius alone poorly describes the local structure of a transition metal oxide, while the statistics of coordination number as well as the TMO bond length distribution, especially that of the 3d TMs, can provide comprehensive knowledge for understanding the behavior of TM elements. Based on these statistics, the interplay of site distortion due to the Jahn–Teller effect, cation site similarity, and a new set of ionic radii are all obtained to chart the “persona” of transition metal ions in solids.

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

Cluster expansion by transfer learning for phase stability predictions

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. Furthermore, this algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Shock tube/laser absorption measurement of the rate constant of the reaction: H 2 O 2 + CO 2 2OH + CO 2

We address the role of the linear mixing rule in the kinetics of the H 2 O 2 decomposition system by reporting the rate constant for H 2 O 2 + M = 2OH + M (M = Ar and CO 2 ) in the temperature range of 1087-1234 K at low pressures in a mixture of 20% CO 2 in Argon. The reaction rate constant was inferred from H 2 O concentrations monitored by using a laser-absorption spectroscopy-based water diagnostic. To the best of our knowledge, this is the first measurement of the rate constant of this reaction in a mixture to be reported in literature. A significant discrepancy was found between the rate constants derived using the traditional linear mixing rule and the reduced pressure linear mixing rule. Furthermore, this discrepancy can have serious implications on the predictive accuracy of these kinetic models, especially under conditions relevant to the operation of supercritical CO 2 (sCO 2 ) power cycles that rely on oxy-fuel combustion in a working fluid comprised almost entirely of CO 2 .

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Forest Canopy Density Effects on Snowpack Across the Climate Gradients of the Western United States Mountain Ranges

Limitations inherent to field studies such as limited spatial and temporal coverage has made it very challenging, if not impossible, to explicitly disentangle the effect of canopy density from climate on snowpack dynamics. Hence, our understanding of how variations in canopy density can affect snow processes under diverse climate conditions has been limited. To address this knowledge gap, this study uses a physics-based modeling approach to represent most key processes that drive snow accumulation and melt in the open and forest with varied forest density, across the climate gradients of the Western U.S. mountain ranges, as represented by 228 Snow Telemetry (SNOTEL) locations. Simulations suggest that, under most winter climate conditions, canopy density exerts a strong influence on under-canopy snowpack dynamics and their controlling processes. Changing canopy density can shift the direction of the modeled differential peak snow accumulation and snow disappearance date (SDD) between the open and forest. For example, while simulated snowpack lasts longer in the open than the high-density forest at most wet/warm and dry/cold locations by up to 55 days, about 90% of all locations show a longer snow duration in the low-density forest by up to 27 days as compared to the open. Interannual variability in climate can also change the magnitude and direction of canopy impact on snow processes. By comparing model simulations to published field observations, and we also identify conditions that require additional field observations to improve process-scale understanding of forest-snow interaction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection

Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.

47 OTHER INSTRUMENTATION↗