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At least 271 records · Page 15

Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models

Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the stellar initial mass function, the SN Ia delay time distribution, stellar yields, and stellar population mixing. Using flexCE, a flexible one-zone chemical evolution code, we investigate the effects of and trade-offs between parameters. Two critical parameters are SFE and the outflow mass-loading parameter, which shift the knee in [O/Fe]–[Fe/H] and the equilibrium abundances that the simulations asymptotically approach, respectively. One-zone models with simple star formation histories follow narrow tracks in [O/Fe]–[Fe/H] unlike the observed bimodality (separate high- α and low- α sequences) in this plane. A mix of one-zone models with inflow timescale and outflow mass-loading parameter variations, motivated by the inside-out galaxy formation scenario with radial mixing, reproduces the two sequences better than a one-zone model with two infall epochs. We present [X/Fe]–[Fe/H] tracks for 20 elements assuming three different supernova yield models and find some significant discrepancies with solar neighborhood observations, especially for elements with strongly metallicity-dependent yields. We apply principal component abundance analysis to the simulations and existing data to reveal the main correlations among abundances and quantify their contributions to variation in abundance space. For the stellar population mixing scenario, the abundances of α -elements and elements with metallicity-dependent yields dominate the first and second principal components, respectively, and collectively explain 99% of the variance in the model. flexCE is a python package available at https://github.com/bretthandrews/flexCE.

79 ASTRONOMY AND ASTROPHYSICS↗

On the general theory of thin airfoils for nonuniform motion

General thin-airfoil theory for a compressible fluid is formulated as boundary problem for the velocity potential, without recourse to the theory of vortex motion. On the basis of this formulation the integral equation of lifting-surface theory for an incompressible fluid is derived with the chordwise component of the fluid velocity at the airfoil as the function to be determined. It is shown how by integration by parts this integral equation can be transformed into the Biot-Savart theorem. A clarification is gained regarding the use of principal value definitions for the integral which occur. The integral equation of lifting-surface theory is used a s the starting point for the establishment of a theory for the nonstationary airfoil which is a generalization of lifting-line theory for the stationary airfoil and which might be called "lifting-strip" theory. Explicit expressions are given for section lift and section moment in terms of the circulation function, which for any given wing deflection is to be determined from an integral equation which is of the type of the equation of lifting-line theory. The results obtained are for airfoils of uniform chord. They can be extended to tapered airfoils. One of the main uses of the results should be that they furnish a practical means for the analysis of the aerodynamic span effect in the problem of wing flutter. The range of applicability of "lifting-strip" theory is the same as that of lifting-line theory so that its results may be applied to airfoils with aspect ratios as low as three.

BIBLIOGRAPHY-WINGS↗

Results of a Deep Chandra Observation of the Crab Nebula and Pulsar

The Crab Nebula and pulsar were observed for a total of 150 ksec with the LETG/HRC-S combination aboard the Chandra X-Ray Observatory in 2000, January. One of the principal aims of the experiment was to study the emission of from the pulsar as a function of pulse phase. Neutron stars are believed to be formed with core temperatures of T(sub c) approx. 10(sup 11) K. As the pulsar is the youngest known neutron star with an age of only 940 yrs, it should be possible to observe thermal emission from the hot stellar surface which in turn constrains equations of state. The pulsar, on the other hand, is a powerful non-thermal emitter, powering an X-ray bright synchrotron nebula which, in Einstein and ROSAT observations, overshadowed the fainter thermal surface emission. Making use of the high angular resolution provided by Chandra we were able to detect X-rays from the Crab-pulsar at all pulse phases. We discuss whether this detection is indeed of thermal emission or of a faint synchrotron component of the pulsed emission from the magnetosphere. We further report on dynamical effects observed in the pulsar-wind outflow and the analysis of the LETG spectral data, especially near the oxygen edge. The results of the spectral analysis has interesting implications for the composition of the interstellar medium.(c) 2000.: American Astronomical Society. All rights reserved

Weisskopf, M. C.↗

Diagnosis of Corrosion Process in Nuclear Power Plant Secondary Piping Structures

Corrosion inside secondary pipe structures in Nuclear Power Plants is difficult to detect without intrusion. Statistical and machine learning techniques are used to predict corrosion using pipe vibration data. 3D accelerometers were used to record pipe vibration data in three directions using six sensors. Vibration data were processed using the Hilbert-Huang Transformation, which is known for its adaptable basis function to non-linear and non-stationary signals. Hilbert-Huang Transformation using empirical mode functions generates multiple intrinsic mode functions of different amplitude and frequency levels from raw vibration data. From intrinsic mode functions features are extracted which include phase, energy, entropy, mean, standard deviation, skewness, and kurtosis. Logistic regression using only phase information correctly distinguished mass additions from baseline data in roughly 83% and 85% of the cases. Phase information varied substantially among different baseline runs and within trials of the same run. Random Forest resulted in >95% testing accuracy for both mass addition and mass removal data. Support Vector Machine had ~98% testing accuracy for mass removal and ~92% testing accuracy for mass additions when using Permutation Importance feature selection. When using Principle Component Analysis with Support Vector Machine, testing accuracy was ~68% testing accuracy for mass additions and ~85% testing accuracy for mass.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A STUDY OF CHANGES IN DEFORMATION AND METABOLISM IN LEFT VENTRICLE AS A FUNCTION OF HYPERTROPHY IN SPONTANEOUS HYPERTENSIVE RATS USING MICROPET TECHNOLOGY

Problem: In the case of hypertrophy caused by pressure overload (hypertension) there is an increase in cardiac mass and modification cardiac metabolism. Aim: This study was designed to study the changes in glucose metabolism, ejection fraction, and deformation in the left ventricle with the progression of hypertrophy in spontaneous hypertensive rats (SHR). Methods: Dynamic PET data were acquired using the microPET II at UC Davis. Two rats were imaged at 10-week intervals for 18 months. Each time a dose of approximately 1- 1.5 mCi of F-18-FDG was injected into a normotensive Wistar Kyoto (WKY) rat and the same dose was injected into a SHR rat. Each rat was imaged using a gated dynamic acquisition for 80 minutes acquiring list mode data with cardiac gating of approximately 600-900 million total counts. For the analysis of glucose of metabolism, the list mode data were histogrammed into a dynamic sequence (42 frames over 80 mins). For each time frame, projection data of 1203 140x210 sinograms of 0.582 mm bins were formed by summing the last three gates before and one after the R-wave trigger to correspond to the diastolic phase of the cardiac cycle. Dynamic sequences of 128x128x83 matrices of 0.4x0.4x0.582 mm3 voxels in x, y, and z were reconstructed using an iterative MAP reconstruction which used a prior that penalized the high frequency components of the reconstruction using appropriate weighting between 26 nearest neighboring voxels. Time activity curves were generated from the dynamic reconstructed sequence for the blood and left ventricular tissue regions of interest which were fit to a 2-compartment model to obtain a least squares fit for the kinetic parameters. For the analysis of deformation, the list mode data were histogrammed into 8 gates of the cardiac cycle, each gate was the total sum of the later 60 mins of the 80 min acquisition. Images of 128x128x83 matrices for each gate were reconstructed using the same iterative MAP reconstruction used to reconstruct the dynamic sequence. The in-plane image dimensions were doubled to 256x256x83 in order to increase the resolution for the Warping analyses. These image data sets were then cropped to 128x128x83. The end-systolic image data sets were designated as the template images and the end-diastole image data sets were designated as the target images, thus providing an analysis of the diastolic relaxation and filling phases of the cardiac cycle. The template images were manually segmented to create surface definitions representing the epi- and endocardial surfaces. Finite element models of the left ventricles were created using the segmented surfaces and defining a transversely isotropic material with fiber angles varying from the epicardial surface to the endocardial surface. A Warping analyses was performed to obtain the LV strain tensor and fiber stretch distributions. Results: In one study, the average first principal Green-Lagrange strain, fiber stretch, ejection fraction, and metabolic rate of F-18-FDG was 0.22, 1.08, 80%, 0.1 for the WKY rat and 0.16, 1.06, 50%, 0.25 for the SHR rat, respectively. These same rats studied a year later presented with a metabolic rate of F-18-FDG of 0.11 and 0.25 for the WKY and SHR, respectively. A follow-up study the average strain (n=10) and ejection fraction (n=18) was 0.21, 72.7% for WKY and 0.17, 69.8% for the SHR, respectively. Conclusion: In the case of pressure overload there is an increased reliance on carbohydrate oxidation in an attempt to maintain contractile function.

Sitek, Arkadiusz↗

DFTT report for Signal Analysis (2023)

The Detection Framework Testbed and Toolkit (DFTT) is a database and associated java programs intended to facilitate the development and testing of algorithms for operating suites of correlation and subspace detectors. This framework is a generalization of the system described in Harris and Dodge (2011) and Dodge and Harris (2016). DFTT allows retrospective processing of sequences of data using various system configurations. Results are saved in a database, so it is easy to compare the results obtained using different configurations of the system. The principal software components of DFTT are the ConfigCreator, the framework_runner and the Builder program. ConfigCreator assembles the files necessary to define a particular configuration used for processing. The framework_runner operates suites of detectors and saves the results in an Oracle™ database. Builder allows visual examination of templates and detected signals and allows editing and creation of detectors. In addition, there are tools for importing continuous data into the database. DFTT is licensed under the MIT license and has LLNL release number LLNL-CODE-801881. In the current fiscal year DFTT has been used in two different projects. The first (NTL-funded) project investigates the feasibility of screening nuisance detections using correlation detectors. The second (GNEM-funded) project seeks to extend the results of Harris and Dodge (2021) to 3- D sources and multiple stations. Each of these efforts required that additional functionality be added to DFTT. In this report I will summarize the enhancements to DFTT in the context of the relevant research effort.

58 GEOSCIENCES↗

Functional Response of a Near-Surface Soil Microbial Community to a Simulated Underground CO 2 Storage Leak

Understanding the impacts of leaks from geologic carbon sequestration, also known as carbon capture and storage, is key to developing effective strategies for carbon dioxide (CO 2 ) emissions management and mitigation of potential negative effects. Here, we provide the first report on the potential effects of leaks from carbon capture and storage sites on microbial functional groups in surface and near-surface soils. Using a simulated subsurface CO 2 storage leak scenario, we demonstrate how CO 2 flow upward through the soil column altered both the abundance (DNA) and activity (mRNA) of microbial functional groups mediating carbon and nitrogen transformations. These microbial responses were found to be seasonally dependent and correlated to shifts in atmospheric conditions. While both DNA and mRNA levels were affected by elevated CO 2 , they did not react equally, suggesting two separate mechanisms for soil microbial community response to high CO 2 levels. The results did not always agree with previous studies on elevated atmospheric (rather than subsurface) CO 2 using FACE (Free-Air CO 2 Enrichment) systems, suggesting that microbial community response to CO 2 seepage from the subsurface might differ from its response to atmospheric CO 2 increases.

54 ENVIRONMENTAL SCIENCES↗

Primary Modes of Global Drop Size Distributions

Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D 0 . Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w , LWC, and D 0 and are found to have a clustering of points (group 5) with high rain rates, large N w , and moderate D 0 , a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.

54 ENVIRONMENTAL SCIENCES↗

Polaron and Exciton Delocalization in Oligomers of High-Performance Polymer PTB7

A key characteristic of organic photovoltaic cells is the efficient charge separation in the active layer. Sufficient delocalization of the positive polaron in organic photovoltaics is considered essential for the effective separation of the opposite charges and the suppression of recombination. Here we use light-induced EPR and ENDOR spectroscopy combined with DFT calculations to determine the electronic structure of the positive polaron in PTB7-type oligomers. Utilizing the superior spectral resolution of high-frequency (130 GHz) D-band EPR, the principal components of the g tensors were determined. Pulsed ENDOR spectroscopy at X-band allowed the measurement of H-1 hyperfine coupling constants. A comparison of g tensors and H-1 hyperfine coupling constants of the PTB7-type oligomers with the high-performance PTB7 polymer revealed a delocalization of the positive polaron in the polymer over about four monomeric units, corresponding to about 45 angstrom in length. Our current study thus not only determines the polaron delocalization length in PTB7 but also validates the approach combining EPR/ENDOR spectroscopy with DFT-calculated magnetic resonance parameters. This is of importance in those cases where oligomers of defined length are not easily obtained. In addition, the delocalization of the neutral triplet exciton was also determined in the oligomers and compared with polymer PTB7. The analysis revealed that the neutral triplet exciton is substantially more delocalized than the positive polaron, exceeding 10 monomeric units.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Combined theoretical and experimental study of the valence, Rydberg, and ionic states of chlorobenzene

New photoelectron (PE) and ultra violet (UV) and vacuum UV (VUV) spectra have been obtained for chlorobenzene by synchrotron study with higher sensitivity and resolution than previous work and are subjected to detailed analysis. In addition, we report on the mass-resolved (2 + 1) resonance enhanced multiphoton ionization (REMPI) spectra of a jet-cooled sample. Both the VUV and REMPI spectra have enabled identification of a considerable number of Rydberg states for the first time. The use of ab initio calculations, which include both multi-reference multi-root doubles and singles configuration interaction (MRD-CI) and time dependent density functional theoretical (TDDFT) methods, has led to major advances in interpretation of the vibrational structure of the ionic and electronically excited states. Franck-Condon (FC) analyses of the PE spectra, including both hot and cold bands, indicate much more complex envelopes than previously thought. The sequence of ionic states can be best interpreted by our multi-configuration self-consistent field computations and also by comparison of the calculated vibrational structure of the B and C ionic states with experiment; these conclusions suggest that the leading sequence is the same as that of iodobenzene and bromobenzene, namely: X{sup 2}B{sub 1}(3b{sub 1}{sup −1}) < A{sup 2}A{sub 2}(1a{sub 2}{sup −1}) < B{sup 2}B{sub 2}(6b{sub 2}{sup −1}) < C{sup 2}B{sub 1}(2b{sub 1}{sup −1}). The absorption onset near 4.6 eV has been investigated using MRD-CI and TDDFT calculations; the principal component of this band is {sup 1}B{sub 2} and an interpretation based on the superposition of FC and Herzberg-Teller contributions has been performed. The other low-lying absorption band near 5.8 eV is dominated by a {sup 1}A{sub 1} state, but an underlying weak {sup 1}B{sub 1} state (πσ{sup ∗}) is also found. The strongest band in the VUV spectrum near 6.7 eV is poorly resolved and is analyzed in terms of two ππ{sup ∗} states of {sup 1}A{sub 1} (higher oscillator strength) and {sup 1}B{sub 2} (lower oscillator strength) symmetries, respectively. The calculated vertical excitation energies of these two states are critically dependent upon the presence of Rydberg functions in the basis set, since both manifolds are strongly perturbed by the Rydberg states in this energy range. A number of equilibrium structures of the ionic and singlet excited states show that the molecular structure is less subject to variation than corresponding studies for iodobenzene and bromobenzene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Content Addition and Testing for 2017 NRC Amendment for the Versa-Pac Type A Fissile Packaging - 18120

The Versa-Pac is a certified Type A, fissile packaging (USA NRC Certificate no. USA/9342/AF-96), often used as a waste package for one time use. Continuous content additions have improved the versatility of the package resulting in expanded uses for the nuclear industry. Principal design of the Versa-Pac packaging maintains the use of an inner liner positioned inside a 55-gallon (VP-55) or 110-gallon drum (VP-110), as used for decades in the transport and disposal of Type A fissile materials. The Versa-Pac is used directly or in conjunction with pails, pipe containers, poly bottles, and a variety of smaller containers, inserts and vessels. The Versa-Pac content developments assist the industry with site shutdown, decommissioning, and cleanup operations, allowing for removal, transport, and storage options for the residual waste materials. A 2017 NRC license amendment application adds new capabilities to the Versa-Pac that include a low 1.25 wt% {sup 235}U enriched uranium loading, small quantities of UF{sub 6} in the form of 1S and 2S cylinders, and a payload mass increase from 250 to 350 pounds for the Versa-Pac 55-gallon version (VP-55). The lower uranium enrichment loading of 1.25 wt% {sup 235}U allows for a higher uranium mass limit. The inclusion of 1S and 2S UF{sub 6} cylinders fills an industry need to remove and dispose of long-stored material during site cleanup activities. The content payload mass limit is increased for low contaminated or radioactive heavy metal contents, such as cut plant components and ingots. Regulatory certification of new contents is demonstrated by test, analysis, and a combination of these techniques to show that the design function of the Versa-Pac as a Type A fissile package remains unchanged. Specific UF{sub 6} criticality and thermal analysis upgrades are added to the design documentation to accommodate 1S and 2S UF{sub 6} cylinders. The parameters for criticality analyses are simplified, ensuring no credit for cylinder containment is required for HAC thus reducing the reliance on a single specific shoring device. An upgrade of the Versa-Pac thermal analysis demonstrates compliance with ANSI N14.1 UF{sub 6} cylinder thermal limits. A new drop test sequence for the VP-55 was performed, documenting the addition of the 350-pound payload and demonstrating the VP-55 maintains structural integrity during the NCT and HAC drop, crush and puncture events as defined by 10 CFR 71. The testing and analyses show no loss of the material occurs and the package maintains the material in a subcritical state. This paper traces the engineering and licensing efforts involved in expanding the Versa-Pac's versatility for the 2017 NRC amendment application; summarizing the new testing and results for the payload increase, the content addition criticality and thermal evaluations, and content description for the Versa-Pac Safety Analysis Report. The NRC license amendment is scheduled for Winter 2017. Testing and supporting analyses reported in the Versa-Pac Safety Analysis Report demonstrates the content developments met the performance requirements of 10 CFR 71 for a Type A fissile package. A revision of the NRC Certificate of Compliance is expected in Spring 2018. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Thermally Induced Structural Transitions in Epoxy Thermoset Polymer Networks and Their Spectroscopic Responses

The polymer network structure of epoxy thermosets plays a significant role in its final material properties. However, the effects of mild thermal exposure on these network structures are poorly studied. In this work, wide-angle X-ray scattering was used to investigate the polymer network structure of two epoxy thermosets: homopolymerized bisphenol A (BPA) epoxy resin and BPA epoxy resin cured with a polyether amine hardener (BPA/T-403). Using density functional theory and wide-angle X-ray scattering, insights into the polymer network structure were obtained. Diffraction features were determined to originate from hardener-to-hardener molecular distance, perpendicular π–π stacking of aromatic p-phenylene rings, and the average carbon–carbon distance in the polymer. Thermal exposure was found to permanently alter these structural features for both thermosets, with an increase in the π–π stacking distance. Homopolymerized BPA had an additional decrease in the hardener-to-hardener distance. These structural alterations were found to be detectable using Fourier transform infrared spectroscopy and Raman spectroscopy, with changes in the hardener-to-hardener distance having the largest variations in the resulting spectra specifically at the aromatic and ether frequencies.

36 MATERIALS SCIENCE↗

Imaging via complete cantilever dynamic detection: General dynamic mode imaging and spectroscopy in scanning probe microscopy

We develop and implement a multifrequency spectroscopy and spectroscopic imaging mode, referred to as general dynamic mode (GDM), that captures the complete spatially- and stimulus dependent information on nonlinear cantilever dynamics in scanning probe microscopy (SPM). GDM acquires the cantilever response including harmonics and mode mixing products across the entire broadband cantilever spectrum as a function of excitation frequency. GDM spectra substitute the classical measurements in SPM, e.g. amplitude and phase in lock-in detection. Here, GDM is used to investigate the response of a purely capacitively driven cantilever. We use information theory techniques to mine the data and verify the findings with governing equations and classical lock-in based approaches. We explore the dependence of the cantilever dynamics on the tip–sample distance, AC and DC driving bias. This approach can be applied to investigate the dynamic behavior of other systems within and beyond dynamic SPM. In conclusion, GDM is expected to be useful for separating the contribution of different physical phenomena in the cantilever response and understanding the role of cantilever dynamics in dynamic AFM techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar-Radiometer Retrievals

Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR) TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm s database, a generally positive impact on latent heating estimation from passive microwave observations is expected.

Grecu, Mircea↗

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Materials Challenges and Opportunities for Energy Generation, Conversion, Delivery, and Storage (Applied Energy Tri-Laboratory Consortium Workshop Report)

This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.

36 MATERIALS SCIENCE↗

Objective models of compressed breast shapes undergoing mammography

Purpose: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes. Methods: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE). Results: The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material. Conclusions: Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

60 APPLIED LIFE SCIENCES↗

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗